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  • Top 15 diseases featured in the most publications

Published Nov 10th, 2022

Published scientific articles can provide insight into the healthcare industry’s ongoing trends and upcoming developments . By analyzing publications across the industry, healthcare organizations can understand who are the leading and emerging experts within a therapeutic area, which issues are shaping the field, and which diseases are drawing the greatest attention.

The following table lists the top 15 diseases featured in the most publications in 2022. We analyzed more than 1 million publications from the Monocl product suite to obtain this data, including clinical trial articles, review articles, and guideline articles. We also utilized a keyword hierarchy to identify terms within the publications and tag them with the specific disease state.

Fig. 1 – Data is from the Monocl ExpertInsight product, accessed October 2022.

Which diseases were featured in the most publications?

In 2022 alone, 233,419 publications were published on the 15 leading disease states. With more than 1 million publications available in 2022, nearly 20% were focused on the top 15 diseases. COVID-19 ranked first, with 50,000 more publications then the second ranked disease, neoplasms.

COVID-19 is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. Given the prevalence of the COVID-19 pandemic, it’s no surprise that this disease tops the list of research areas.

Digging into the second most researched disease, neoplasms are a type of abnormal or excessive growth of tissue. Neoplasms have a significant number of related topics associated with this higher-level term, including cysts and precancerous conditions.

Neoplasms have been the subject of regular publications since 2000, with steady increases in the number of articles each year until 2020. Over the last two years, publications focused on neoplasms have declined.

Learn more

Healthcare Insights are developed with healthcare commercial intelligence from the Definitive Healthcare platform.

Monocl ExpertInsight enables you to view publications in a timeline view, based on disease state, proteins and chemicals, techniques, anatomy, or organisms. This view extends back as far as 1990, so you can identify emerging topics or trends on a broad scale. Knowing how many publications each year are focused on a specific topic supports biopharma organizations’ ongoing search for new potential markets or drug expansion opportunities. Want even more insights? Book a demo today to see the latest healthcare commercial intelligence on publications, facilities, disease states, and providers.

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Top 100 Disease Research Topics For Paper Writing

best disease for research paper

Students have many disease research topics to consider when writing research papers and essays. A disease occurs when the body undergoes some changes. Science philosophy has pointed at pathogens and the causes of illness. Today, medicine focus on biochemical factors, nutrition, immunology levels, and environmental toxins as the causes of diseases. Research papers on disease topics can focus on specific illnesses independently or in groups. You can also write about infectious diseases like COVID-19 and HIV or non-communicable diseases. Non-communicable diseases are also known as chronic illnesses. These are diseases that you can’t get from a sick person. They include heart disease, cancer, stroke, and lung disease. These diseases account for up to 70% of global deaths. Nevertheless, whether you opt to write about advanced topics in Lyme disease or something simple like flu, research will be paramount. You  can also buy research papers cheap, if you don’t have time for it. So, d on’t put your grade at risk and get research paper online help .  

Why Choose Our Disease Research Topics?

Educators want you to convince them that you have taken the time to think about your topic and research it extensively. What’s more, your research should make a meaningful contribution to your study field. Therefore, select a good topic and research it extensively before you start writing. Analyze your information to determine what will make it to your research paper. Here is a list of 100 disease research paper topics worth considering for your paper or essay.

Top Disease Research Topics

Maybe you want to research and write a research paper on a topic that anybody will find interesting to read. In that case, consider ideas in this list of disease research topics.

  • How NSAIDS lead to peptic ulcers
  • What are pandemic diseases?
  • What is the role of pandemic diseases in the mankind history?
  • What are the symptoms of acute lung disease?
  • Explain how Attention Deficit Hyperactivity Disorder affects children
  • Discuss the AIDS pandemic in third world countries
  • Describe the main causes of AIDS
  • Explain how AIDS affects children
  • Discuss the treatment of AIDS
  • Is alcohol addiction a disease?
  • Discuss the Alzheimer’s disease scope and how it affects the elderly persons’ brain
  • How can you help dementia or Alzheimer’s disease patients and caregivers?
  • What are the symptoms of Alzheimer’s disease?
  • What is autoimmune disease?
  • Explain how autoimmune thyroiditis begins
  • Examine acute protective membrane inflammation in bacterial meningitis
  • Compare pathology of AIDS and black death
  • Discuss the effects of cancer in today’s society
  • Autism and its causes
  • Different types of cancer and their prevalence

These are topics disease experts will recommend researching and writing about. And because students can write about these topics without getting complex, anybody will find them interesting. If you’re searching for research topics on Alzheimer’s disease, this list also has some ideas for you to consider.

Infectious Disease Topics for Research Papers

Are you interested in infectious disease research topics? If yes, you will find this list interesting. This category comprises hot topics in infectious disease fields. Consider some of these ideas for your research paper.

  • The virology, epidemiology, and prevention of COVID-19
  • The diagnosis of COVID-19
  • Prevention vaccines for SARS-CoV-2 infection
  • Questions people ask about COVID-19
  • Clinical features of COVID-19
  • COVID-19 management in a hospital setting
  • Infection control for COVID-19 in homes and healthcare settings
  • Skin abscess and cellulitis in adults
  • Clinical manifestation, diagnosis, and epidemiology of yellow fever
  • Transmission and epidemiology of measles
  • Role of untreated inflammation of genital tract in HIV transmission
  • Racial inequities of COVID-19 and HIV in black communities
  • Community-acquired pneumonia overview in adults
  • The use of procalcitonin in the infections of lower respiratory tract
  • Herpes simplex virus prevention and treatment
  • Uncomplicated Neisseria gonorrhea treatment
  • Society guidelines for COVID-19
  • Why public education is crucial in fighting COVID-19
  • Overview of Ebola over the last two decades
  • Investigations into the use of monoclonal antibody in treating Ebola

This category also has some of the best infectious disease presentation topics. Nevertheless, learners should prepare to research extensively before writing academic papers on these topics.

Interesting Disease Topics

Maybe you want to research and write a research paper on a topic that most people find interesting. In that case, consider these disease topics for research paper.

  • Discuss bulimia as a common eating disorder
  • Why are so many young people suffering from anorexia?
  • What causes most eating disorders
  • How serious are sleep disorders
  • Discuss rabies treatment- The Milwaukee protocol
  • Is assisted suicide a way to treat terminal diseases?
  • What are the effects of brain injuries?
  • What are professional diseases?
  • Is autism a norm variant or a disease?
  • The history of pandemics and epidemics
  • The role of antibiotics in treating diseases
  • What causes insomnia?
  • What are the effects of insomnia?
  • How to cope with insomnia
  • Can sleeping pills cure insomnia?
  • Explain what causes long-term insomnia
  • Using traditional medicine to fight insomnia
  • How to deal with bulimia and nervosa
  • How eating disorders affect self-harm behavior
  • How feminism affect anorexic women phenomenon

This is a list of easy disease topics for papers. What’s more, most people will find these research paper disease topics interesting to read about. Nevertheless, students should take time to research their preferred topics to come up with brilliant papers on any of these human disease research paper topics.

Cardiovascular Disease Research Topics

Maybe you’re interested in topic ideas on heart disease. Perhaps, you want to write about an illness of the respiratory system. In that case, consider these heart disease research topics.

  • An investigation of hypertrophic cardiomyopathy
  • A research of the causes of coronary artery disease
  • Antithrombotic therapy in surgical valve and prosthetic heart valve repair
  • Intervention choice for severe cases of calcific aortic stenosis
  • Prognosis and treatment of heart failure using preserved fraction of injection
  • Infective endocarditis management in adults
  • Risk assessment for cardiovascular disease for primary prevention
  • Prognosis and treatment of acute pericarditis
  • Treatment of reflex syncope in adolescents and adults
  • Anticoagulant therapy for preventing thromboembolism in atrial fibrillation
  • Cardiac manifestations of COVID-19 in adults
  • Acute decompensated heart failure treatment
  • What is hypertriglyceridemia?
  • How to manage elevated low-density lipoprotein-cholesterol in cardiovascular disease
  • Management and evaluation of cardiac disease
  • Conduction system and arrhythmias disease and COVID-19
  • Myocardial infarction in COVID-19
  • Can somebody inherit a cardiac disease?
  • How effective are treatments for irregular heartbeat?
  • How to determine the risk for a sudden cardiac death

This list comprises some of the best special disease topics. That’s because most people reading about these topics might not have heard about them before. Nevertheless, this category also has interesting research topics for disease control that may help individuals that want to avoid or manage some illnesses.

Research Topics for Chronic Disease

You probably know somebody living with a chronic illness. Unlike controversial topics in infectious disease, people don’t talk much about chronic illnesses. And for this reason, some people don’t know about these illnesses. When writing about non-communicable illnesses, you can settle for human genetic disease topics or even research topics for sickle cell disease. Here are some of the topics about non-communicable diseases that you can write about.

  • The risk of breast cancer after childbirth
  • Postpartum PTSD- Effective preventative measures
  • Experiences of females suffering from cardiac disease during pregnancy- A systematic review
  • Husbands attendance and knowledge of wives’ postpartum care in rural areas
  • Postpartum depression screening by perinatal nurses in hospitals
  • Gestational diabetes mellitus screening from the rural perspective
  • Maternal mortality- How to help cardiac and pregnant patients
  • Sex differences in cardio metabolic disorders and major depression- Effect of immune exposures and prenatal stress
  • Determinants and prevalence of anxiety and antepartum depressive symptoms in fathers and expectant mothers- Outcomes from perinatal psychiatric morbidity
  • Evaluating the effect of community health workers on non-communicable diseases, tuberculosis, malnutrition, antenatal care, and family planning
  • History of women with postpartum affective disorder and the risk of future pregnancies recurrence
  • New self-care guide package and its effect on neonatal and maternal results in gestational diabetes
  • Depressive symptoms and life events in pregnant women- Moderating the resilience role and social support
  • Gestational diabetes and ethnic disparities
  • Pregnancy and diabetes- Opportunities and risks
  • Cardiovascular disease maternal death reduction- A pragmatic investigation
  • Meta-analysis and systematic review of gestational diabetes mellitus diagnosis with a two-step or one-step associations and approaches with negative pregnancy outcomes
  • Gestational diabetes mellitus treatment in women- A Cochrane systematic overview
  • Research in non-communicable diseases in Africa- A strategic investment
  • How to finance the national response to non-communicable diseases

Whether you opt to write about research paper topics in Huntington’s disease or non-communicable liver disease topics, you have to engage in extensive research to come up with a brilliant paper. We have more health research topics for you, so don’t hesitate to check them. Therefore, select an idea you will be comfortable researching and writing about. That way, you will avoid enduring a boring process of investing your topic and writing the paper. If you want to hire someone to help you with your assignment, just c ontact us with a “ do my research paper now ” request and we’ll get your papers done. 

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To date, PLOS has published over 12,798 articles in Infectious Diseases, with more than 263,487 citations and with authors in 190 countries .

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In 2020, PLOS articles were referenced an estimated 107,840 times by media outlets around the world. Read Infectious Disease articles that made the news.

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As the world grappled with the effects of COVID-19 this year, the importance of accurate infectious disease modeling has become apparent. We invited a few authors to give their perspectives on their research during this global pandemic. 

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618 Disease Essay Topics & Examples

Need some disease essay examples to check out or ideas to look through? Good thing that our experts have prepared this list for you!

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👍 good disease topics to write about, ✅ easy diseases to write about, 💡 most interesting disease topics to write about, 📌 writing prompts about disease, 🔍 good research topics about disease, ❓ research questions about diseases.

After the Covid-19 pandemic, students are regularly assigned to explore health issues and precautions. Whether you’re interested in writing about risk factors, chronic illnesses, or lifestyles, we can help! Check our disease essay topics and get a perfect title for your paper.

  • Concepts of Alzheimer’s Disease The brain changes are the same in both men and women suffering from Alzheimer’s disease. There is also a significant increase in the death of the neurons leading to the shrinking of the affected regions.
  • Recommendations for Ensuring Food Safety & Reducing Disease-Causing Mosquitoes As such, the focus should be to introduce mandatory employee training especially in areas of food safety to guarantee that appropriate practices in hygiene, food handling and preparation, and sanitation are put in place in […]
  • Citrus Greening Disease in The United States The disease has led to decreased production of citrus fruits in the United States and this has greatly affected the citrus industry.
  • Living Environments and Its Correlation With Human Diseases For example, an examination of the spread of diseases within areas in India, particularly the city of Mumbai, show that the rate of catching disease is higher as compared to various suburbs located outside of […]
  • Control and Treatment of Communicable Diseases The goal of a good control program should be to prevent and reduce mortality and morbidity rates as well as spread of the infectious diseases.
  • Control of Communicable Diseases Hence, there is a need to prioritize the control and prevention levels for these diseases upon the occurrence of the calamities.
  • Health Risks and Prevention: Cardiovascular Disease and Cancers In addition, African Americans men have a higher risk of prostate cancer just as it is with men with a family history of the cancer. CRP test is recommended for persons who are at the […]
  • Communicable Diseases: HIV and AIDS When the virus has “blown out” and having affected the white blood cells to a point that they cannot protect the body any more, optimistic diseases take advantage and affect the person; these optimistic diseases […]
  • Countering to the Hepatitis Disease The state of affairs is worsened by the limited fiscal resources allotted by the exchequer. It is noted that others are known to cut off sections of the clitoris.
  • Understanding Alzheimer’s Disease Among Older Population After the 65 years, it has been found that the probability of developing Alzheimer’s disease doubles after every 5 years and as a result, by the age of 85 years, the risk of acquiring the […]
  • Classification of Water-Related Diseases One of the factors affecting the distribution of water-borne zoonoses is the presence of contaminated water sources that aid the movement of pathogens from one victim to another.
  • Alcoholism Disease or Self Will Alcoholism as a disease has serious physical effects to the body because it affects organs and systems such as the liver, the heart, and the nervous system amongst other critical organs in the body. Alcoholism […]
  • Psychological Disorders: Parkinson’s Disease The future research must focus on the analysis of the spiritual and emotional aspects of Parkinson’s disease and possible ways to improve psychological, emotional, and spiritual wellbeing of elderly people with PD.
  • How Age and Diseases Affect Memory However, in case of a disease such the Alzheimer’s disease, there is pervasive memory impairment to the extent that relationships and social activities are compromised. It is however not clear on the course of the […]
  • Huntington’s Disease: The Discovery of the Huntington’s Gene Since the sex chromosomes are not involved in the production of this disease, both men and women are equally susceptible to Huntington’s disease The gene that causes huninton’s disease is dominant which means that only […]
  • Environment, Disease and Crime in Egypt Similarly, the prevalence of diverse diseases in Egypt limits the citizens’ ability to attain sound health. Water erosion also occurring in the Northern regions of Egypt causes unprecedented degradations to land.
  • Diseases Caused by Alcohol Abuse and Its Preventions It is very important for the addicted person to feel all harmful consequences of the addiction and of alcohol in particular before giving up this bad habit.
  • The Spread of Diseases among Health Care Providers Communicable Diseases So as, to categorize communicable infections that pose a noteworthy threat to health care providers, it is crucial to identify the methods of spread of various forms of infectious agents.
  • Therapeutic Interventions for Parkinson’s Disease Over the years, Levodopa has become the preferred drug for the treatment of motor signs and symptoms of PD. To counter this effect, a combination therapy of levodopa and doperminergic agonists has been suggested in […]
  • The Disease of Autism Origin The disorder is one of the new diagnoses of the autism. Other effects of the disorder are constipation and growth failures that may be a problem to the lives of the individual.
  • AIDS: The Guilt and Failure of the West in a Spread of the Disease He contends that the genetic tests which identify the amount of mutation which has occurred to the virus suggest a date of entry into the human population that coincides with the imposition of forced labor […]
  • Disease: Analysis of the Article Preparing for the Next Pandemic by Michael T. Osterholm Culture: This article does not discuss cultural issues, for instance, the impact of the cultural background and national peculiarities on the pandemic prevention.
  • Diagnosis of Alzheimer’s Disease The most remarkable feature of the disease is the loss of ability to remember events in an individual’s life. According to the latter hypothetical medical study, it has been exemplified that the presence of deposits […]
  • Health Care for Elderly People With Alzheimer’s Disease C’s condition is not likely to affect the relationship between her and her relatives if they are sensible toward her. C is to take her to a nursing home for the elderly.
  • Connection Between Respiratory Diseases and Environmental Variables A study on the Asian sand dust found elements of quartz, which is reported to cause inflammatory reactions in the lungs due to the presence of cytokines.
  • Control of Infectious Diseases Vs Tobacco Use Despite the efforts made in regulating cigarette smoking, the habit is still practiced, and the number of smokers is ever increasing.
  • Treatment of Alzheimer’s Disease According to documented research, Alzheimer’s disease is the primary cause of dementia affecting close to half a million people in the United Kingdom and five million in the United States.
  • Sexually Transmitted Diseases In 1980s and early 1990s, the rate of nationwide gonorrhea infections had reduced due to the introduction of a program to control gonorrhea in the mid 1970s.
  • Breast Cancer: Disease Prevention The first indicator of breast cancer is the presence of a lump that feels like a swollen matter that is not tender like the rest of the breast tissues.
  • Water Pollution & Diseases (Undeveloped Nations) Restriction on movement and access to the affected area affects trade and the loss of human life and deteriorated health is a major blow on the economy and on the quality of human life.
  • Plague Disease Through the History The rich in the society managed to flee from the country, and as a result, the poor were the ones who were left vulnerable to the disease.
  • Heart Disease: Nutrition Assessment As such, it is important for the patients to increase their consumption of whole grains, vegetables, legumes and fruits that are rich in trans-fatty acids and saturated fatty acids.
  • Diagnosis a Treatment of Parkinson’s Disease This paper is a response meant to educate a patient diagnosed with Parkinson’s disease on the causes and treatment of the disease.
  • Gastroesophageal Reflux Disease (GERD) Treatment The proton pump inhibitor is in the class of drugs that permanently block gastric proton pump which is essential for the secretion of the gastric acid by the parietal cells of the stomach.
  • Huntington’s disease: Etiology and Symptoms, Diagnosis and Treatment According to the research, every child of an infected person has a 50 per cent probability of getting the mutated gene and thus being infected by the disease. There are medicines that are helpful in […]
  • Walking Economy in Parkinson’s Disease Patients However, there is need to perform a specific study to confirm the role of tremor, rigidity and reduced strides in poor walking economies among PD patients.
  • Disease in The News The article then focuses on the relationship between the experiences of the Cairo consensus and the population narrative and the response to AIDS.
  • The Development of Alzheimer’s Disease and It’s Effect on the Brain Research studies have revealed that prevalence of the Alzheimer’s disease is increasing exponentially due to change in lifestyles and the incurable nature of the disease.
  • Family Trend Change and Disease Factor The hastening of our customs and the organization of the family as the leading structure has led to a new family trend.
  • Medical, Social and Diet Changes and Heart Disease in Middle-Aged Men The questions seek to establish the relationship between the potential causes of heart disease and the occurrence of the disease in the surveyed population.
  • Medical Coverage for Smoking Related Diseases However, one of the most oblivious reasons is that it is a deterrent to this behavior, which is harmful to the life of the smoker.
  • Concept of Cardiovascular Diseases in UK Around 19 per cent of male and 10 per cent of female die prematurely due to the disease totaling the number of premature deaths in the UK to 31,000 as of 2006 according to the […]
  • The Centre for Disease Control and Prevention Socio-cultural factors Some of the most common socio-cultural factors influencing the recovery of TBI patients centre on the role of family and friends in the healing process, education and prevention programs, how the patients cope […]
  • Importance of Drug Therapy in Management of Alzheimer’s Disease The effects of Alzheimer’s disease can be controlled by early detection. Most studies are based on the effects of drug therapy mild Alzheimer’s patients.
  • Diagnosis and Treatment of Crohn’s Disease The research was primarily conducted to report the causes of crohn’s disease and the people who can be infected by the disease.
  • Diabetic Renal Disease The high blood sugar levels in the patient usually result in the incapacitation of the kidney hence hindering the normal functioning of this organ.
  • How Dysphagia Disorder (Swallowing Disorder) is Caused in Parkinson’s Disease The combination of the two increases the risk of a patient already suffering from Parkinson’s disease. In most cases, dysphagia is referred to as the presenting feature of the Parkinson’s disorder.
  • Core Functions of Public Health in the Context of Smoking and Heart Disease In the relation to our problem, heart attacks and smoking, it is important to gather the information devoted to the number of people who suffered from heart attacks and indicate the percentage rate of those […]
  • The Association Between Dust Incidents and Respiratory Diseases in Abu Dhabi In spite of the fact the main cause for the development of the chronic respiratory diseases is determined by the researchers as the climatic peculiarities and the frequent occurrence of dust and sand storms, the […]
  • Centre for Disease Control (CDC) Communication Plan The Department of Health and Human Services requires all the information from the ground, the circumstances leading to the event and what the CDC is doing to tackle the foregoing.
  • Concepts of Batten Disease In the course of their research, Fowler et al, further noted that the reason of a child getting the autosomal recessive is the inheritance of the gene that is defected.
  • Obesity, Diabetes and Heart Disease Chronic diseases such as obesity, diabetes and heart disease have become endemic and as such calls into question what processes can be implemented among members of the local population so as to prevent the spread […]
  • Chronic Disease-Racial Ethnic Disparity Evidently, it is crucial to understand critical racial/ethnic disparities in the context of their viability and contributions to health. This indicates the mentioned racial disparity in health relative to breast cancer.
  • Coronary Heart Diseases in African Americans: Intervention Plan The lack of patients and community involvement in the development of prevention strategies hinders the fight against coronary heart diseases in African Americans.
  • Communication Campaigns Enhancing the Understanding of the Epidemiology and Control of Chronic Diseases Through consistent communication, the health and medical department are able to educate the public on some of the various forms of diseases that are most dangerous.
  • The Barriers for Conducting Epidemiological Studies on Chronic Diseases In most of the third world countries, there is insufficient skilled manpower to carry out these studies. This has highly affected research activities in some of the countries.
  • Huntington’s Disease and Ethics In the case of predictive genetic testing for HD, clinicians and other health care team members must consider the benefit to the patient.
  • Association Between Respiratory Diseases and Dust Events in United Arab Emirates The scope of the study is to establish the relationship between respiratory diseases and dust events in the UAE. The aim of the study is to investigate the association between dust events in UAE and […]
  • Parkinson’s Disease Treatment Approaches This research paper compares and contrasts the effectiveness of pharmacological interventions such as the administration of Levodopa, Carbidopa, anticholinergics, and other drugs; the deep brain stimulation surgical therapy, and the cognitive-behavioral patient education programs in […]
  • Association Between Dust Events In United Arab Emirates And Respiratory Diseases It has also discussed some of the diseases associated with dust events in UAE and their potential effects to this society.
  • Public Awareness of Chronic Kidney Disease This will be important in the evaluation of the program activities. Lastly, the advocacy approach and the key messages to be passed to the public will be identified.
  • Budget for Chronic Kidney Disease With the increase in the prevalence rate of CKD the cost of administering the treatment of the diseases is also expected to increase.
  • Solid and Hazardous Waste and Vector-Borne Disease On the other hand, hazardous wastes are a category of solid wastes that are listed either specifically in the regulations or show perilous attributes of corrosiveness, flammability and reactivity.
  • Chronic Kidney Disease: Community and Public Issue CKD is regarded as both a community and public health issue as a result of four main reasons: firstly, the disease places a huge burden on the community, which continues to grow despite the numerous […]
  • Chronic Kidney Disease (CKD) This paper assesses the magnitude of CKD, develops a program, and sets objectives on how the program can be used to achieve the aim of the Healthy People 2020 in relation to CKD.
  • Food Borne Diseases of Intoxicants on MSG The most common symptoms of MSG food-borne illness include tightness in the chest, a burning sensation, dizziness, nausea, flushing, and headache.
  • Duties of Health Care Professionals During Pandemic of Highly Contagious Diseases Second, the shift should also be introduced to the employment environment with regard to the duties of health care professionals during pandemic.
  • Differences Between Ulcerative Colitis and Crohns Disease In Ulcerative Colitis and Crohns disease, the immune system, attacks the gastrointestinal system, which is the digestive tract or tube in the body that extends from the mouth to the anus.
  • Dengue Disease in Africa This is one of the challenges that should be considered. This is one of the hypotheses that should be tested.
  • Health Education and Disease Prevention The Unit’s core values are: Improved efficiency in service delivery and satisfaction of every shareholder To provide the best and most reliable counseling psychology and health coaching for the benefit of patients To ensure patients’ […]
  • Teaching Preventative Measures in Chronic Diseases Given that attention to preventive measures and changes in lifestyle can significantly cause all these functions, the future health care may have to increase on skills aimed at assisting the patients to capitalize on their […]
  • Missions of 3 Healthcare Centers: Disease Control and Prevention, Medicare and Medicaid Services, Health Resources and Services Administration The agency’s mission is to promote health of the nation and to identify and fight security and safety threats of foreign and domestic origins.
  • Tregs in Allergies and Autoimmune Diseases In the investigation of the role of Tregs in the autoimmunity, depletion of the Tregs resulted in increased sensitivity to LPS.
  • The Problem of Zoonotic Diseases There is a need to address the issue of zoonotic diseases in order to avoid the emergence of deadly diseases that may put lives of people in the society at risk.
  • Explanation of Cancer Disease This leads to numerous types of cancers depending on the part of the body where they form. Indicators of cancers depend on the location of the malignant cells.
  • Measures of Disease Frequency The critical rationale for diagnostic criteria is that it facilitates the establishment of the threshold for diagnosis of an ailment in those circumstances where the symptoms of the disease manifest themselves.
  • Youth With Autism Disorder: Education and Employment This includes the communication patterns of the teenager, the extent of social relations and the unusual behavioral characteristics of the teenager in the environment.
  • Medicine Issues: Emphysema’ Disease However noted as a cause, in comparison to smoking, the deficiency is said to be of negligible volume. Alpha-1 antitrypsin, as the University of Michigan Health System indicates, is a natural protein that circulates in […]
  • Psychology Issues: Alzheimer’s Disease Alzheimer’s disease is a psychological disorder that involves the progressive destruction of brain cells and reduction in the proper functioning of the brain.
  • Healthcare: Lyme Disease and Black-Legged Tick Ten to thirty percent of nymphs may be the carriers of the disease, among the adult ticks the rates are higher, twenty to seventy percent may be dangerous.
  • Disease Ecology Definition To investigate all factors which influence the development of disease and its treatment, disease ecology has a great number of different methods.
  • Medicine: Spatial Targeting Method in Disease Ecology Though being a very challenging task the process of disease control may be improved extensively with the help of the method known as spatial targeting, as it allows for creating a map of infectious disease […]
  • Lupus – Skin Disease (Discoid) Although most people choose to ignore diseases that affect the skin based on the notion that they do not have much of an effect on the body, it is important to point out that the […]
  • Ebola Virus Disease Etiology Ebola is a viral disease that attacks all the cells of the body in a systematic process starting with the white blood cells.
  • Diseases and Disasters: Where Is God in All This? Each stage of the plotline is characteristic of the freedom of God as evident in his progressive revelation of himself as a faithful God who keeps promises, but on the other hand declines to put […]
  • Lung Cancer Disease and Prevention Methods Lung cancer is a common and deadly form of cancer characterized by the development of cancerous cells in the lungs of the individual. Lung cancer is the type of cancer characterized by the development of […]
  • Diabetes and Cardiovascular Diseases in Medicine The aim is to enhance the impact of this intervention on individuals and on the society at large. General Concepts and Key Elements of the Program The planned strategy is a comprehensive undertaking in the […]
  • Diabetes: Symptoms, Treatment, and Prevention As a consequence, the amount of sugar in the blood is made to rise and this cause discomfort for the affected individuals.
  • The Role of Man in Environment Degradation and Diseases The link between environmental degradation and human beings explains the consequences of the same in relation to the emergence of modern-age diseases.
  • Age Ailment: Dementia and Alzheimer’s Disease It is a time for one to clean the mind and take time to do what matters most in life. With an increased level of technological advancements, a digital sabbatical is mandatory to lower the […]
  • The Black Death Disease’ History The disease is also believed to have come to Europe from the black mice that were often seen on the merchants’ boats.
  • Pregnancy’ and Sexually Diseases Prevention – Sex Education It is also common for students in college and universities to engage in an unsafe group sex where different STIs are passed to the participants.
  • The European Exploration: the Impact of Disease Spread Furthermore, various factors such as the political and economic relationship between the countries, diseases, development of the routes, and availability of the information have the influence on the ability to explore the different lands.
  • Athletic Trainers Role in Illnesses and Diseases Recognition Emergency services should be provided to a victim of the lightning strike. They should also ensure that they have the right equipment to offer protection and emergency treatment to victims of lightning strikes to avoid […]
  • Caffeine: Carriers, Addiction and Diseases When caffeine is taken in, the body absorbs and then gets rid of it fast. But, generally, it creates no threat to the physical and social aspects of health, like the addictive drugs do, though […]
  • The Molecular Basis of the Liposarcoma Disease This knowledge is important for the prevention and treatment of this disease. This is one of the main challenges that have not been overcome yet.
  • Medical Research: Mad Cow Disease in Human Form This implies that in every 2,000 people, one person has the protein that is associated with the disease. However, research has not revealed the number of carriers that are likely to develop the disease.
  • Lyme Disease in the “Under Your Skin” Film The online video evokes many thoughts because of the challenges people are going through with the disease. The other challenge with the disease relates to the inability of people to recognize its long-term dangers.
  • Pitting Edema Disease’ Analysis These situations may cause insufficient supply of the blood by the veins and the following raised back-pressure in the veins of the legs and stresses the fluid to remain in the ankles and feet or […]
  • The Polycystic Kidney Disease’ Etiology As stated earlier on this is the most common version of PKD and as suggested by the name, the disease is so serious such that in a family where one parent suffers from the disease […]
  • Disease Topic in the Docudrama “And the Band Played On” This docudrama describes the outbreak of AIDS epidemics in the United States in the eighties. Furthermore, this film depicts the efforts of medical workers and researchers to limit the spread of this disease.
  • Heart Disease in African Americans: Intervention According to the tests carried out among the target denizens of the population, 78% of the African Americans were in the risk area due to their unhealthy lifestyles, particularly improper dieting.
  • Typhoid Disease: Mary Mallon Quarantine Case Although the medical experts were able to establish that unhygienic conditions helped in the spread of the disease, it was not yet clear what the cause of the disease was.
  • Psychology: Disease Model Worksheet The case of William may show that traditional and positive psychology techniques contend on the best approaches to solve patients’ problems.
  • Tuberculosis and Infectious Disease Slogan The level of awareness about sexually transmitted diseases among people is higher compared to that of tuberculosis, owing to the fact the risk factors of the latter are hard to identify. The risk population of […]
  • Disease Harm Reduction Addiction Treatment Model The harm reduction model implies the ability of the addicted patient to control and govern the undesired personal behavior while the disease model excludes the self-control aspect from the treatment methods and offers to consider […]
  • Kawasaki Disease: Pathogenesis and Treatment Consequently, the rest of the literature will try to establish the syndromes, the vulnerable group, the causes, complications, and finally the treatment.
  • Cancer Disease and Its Impact The symptoms of the disease vary greatly, depending on the size of the tumor, location of the tumor, and the manner in which the tumor spreads.
  • Gastrointestinal Diseases: Dermatological Manifestations A gastrointestinal disease is a form of infection that affects the gastrointestinal tract, which is composed of the stomach, the liver, gallbladder, rectum, intestines, and the esophagus, among others.
  • Aging and Parkinson’s Disease Parkinson’s disease refers to a condition, where a portion of the brain is damaged progressively over a period of many years.
  • Disease Concept of Alcoholism The universal definition of a disease is anything that is capable of causing an imbalance in the body’s nervous system thus, going by this definition then it is a disease, but in this century whereby […]
  • Diabetes Disease in the USA Adults The disease has become a burden to the city of Baltimore in the state of Maryland. The city has to reallocate billions of money to control the disease.
  • Peripheral Vascular Disease in African American Women Peripheral vascular disease refers to any “disease or disorder of the circulatory system that takes blood to the brain and heart”. A majority of the African American women that suffer from peripheral vascular disease do […]
  • Heart Disease Prevention in Postmenopausal Women The article “Coronary Heart Disease Mortality and Hormone Therapy Before and After the Women’s Health Initiative” offers new insights that can be used to prevent cardiovascular diseases in postmenopausal women. The HRT approach can be […]
  • Kawasaki Disease: Molecular Basis Understanding Overall, many researchers have developed various techniques to understand and diagnose the molecular basis of Kawasaki disease because it lacks specific diagnostic tests.
  • Disease Model and Harm Reduction Model Comparison Both books are memoirs of a father and his son while the first book is written by father David, and the latter one reveals the point of view of his son.
  • Aspirin Use for Cardiovascular Disease Prevention There are many ways to prevent risk, and aspirin therapy is probably one of the safest for people between 45 and 80.
  • Parkinson’s Disease and Occupational Performance The mechanism of Parkinson’s disease affects the production of dopamine by the substantia nigra and, this tendency occurs due to the death or degeneration of the neurons that are situated in the basal ganglia.
  • Peptic Ulcer Disease and Stomach Cancer Diagnostics The main idea of this paper is to dwell upon peptic ulcer disease and stomach cancer, the causes of the diseases, signs and symptoms, and how the diseases are diagnosed.
  • Urinary Tract Diseases: Diagnostic Sonography Continence, in this case, is attributed to the reactions of the external sphincter, supported by the walls of the vagina and the reluctance of both the frontal and subsequent linings of the interior of the […]
  • Chronic Kidney Disease: Body Mass Index and Haemoglobin However, irrespective of the factors that initiate the condition, the onset of the disease triggers a chain of occurrences. The increase in the prevalence of CKD is attributed to different factors.
  • Chronic Kidney Disease Patients The clinic question is whether the consistent checkups, close monitoring, and nursing interventions improve the outcomes in the CKD patients compared to the patients who are just being evaluated regularly.
  • The Eustachian Tube Disease and Innovation Treatment This is why the researchers decided to investigate the issues that transpire during the process of the Eustachian tube treatment and come up with a method to mitigate the adverse consequences of this particular ailment.
  • Infectious Diseases in Children Measles diagnosis can be supported by the physical examination if the rash on other areas of the body is found and if there are small grey-white spots on the inside of his or her cheeks.
  • Kidney Disease: Advanced Practice Nurses Role and Barriers Meanwhile, the role of APNs would deepen and structure the research for the project. It might be reflected in the lack of experience and knowledge of core values and theories related to work performance.
  • Chronic Obstructive Pulmonary Disease: Diagnosing and Treatment COPD is also regularly misdiagnosed for several factors, such as misattribution of the fatigue and shortness of breath to aging, the low awareness of the symptoms of the disease, and the underestimation of its consequences.
  • Decreasing the Progress of Renal Disease In the context of the descriptive study, the instruments of a mixed design will be important. In the process of research, the initial hospital records data will be enlarged.
  • Cigarette Smoking and Parkinson’s Disease Risk Therefore, given the knowledge that cigarette smoking protects against the disease, it is necessary to determine the validity of these observations by finding the precise relationship between nicotine and PD.
  • Plasma Amyloid-Beta and Alzheimer’s Disease The impact of AD on public health includes increased rates of informal care and the direct charges of communal care. The aim of this study is to find the precise relationship between plasma amyloid beta […]
  • HIV/AIDS as a Communicable Disease Drawing from a study by Ngatchou, the choice of the word human is linked to the fact that the virus only causes disease in human beings.
  • Chronic Obstructive Pulmonary Disease In most of the cases, the early symptoms of the disease are often ignored because of the limited knowledge that people have about this disease.
  • Alzheimer’s Disease and Antisocial Personality Disorder Since there is currently no cure for Alzheimer’s disease, the future of the nursing care for the people that have the identified disorder concerns mostly maintaining the patient’s quality of life.
  • Graves’ Disease, Its Pathogenesis and Treatment However, several other physicians have made notes of the disease prior to that, and the first mentions of it could be traced to the Thesaurus of Shah of Khwarazm a 12th-century medical tractate. Maternal Graves’ […]
  • Chronic Disease Impact on Patient’s Family The evolution of the humanistic approach and reconsideration of the system of values resulted in the increased importance of any individual and his/her quality of life.
  • Alzheimer’s Disease in Medical Research The existing data proposes that if the illness is distinguished before the commencement of evident warning signs, it is probable that the treatments founded on the facts of fundamental pathogenesis will be of assistance in […]
  • Alzheimer’s Disease, Its Nature and Diagnostics According to the Alzheimer’s Association, this condition is the sixth leading cause of lethal outcomes in the United States. The most frequent symptoms of Alzheimer’s disease include problems with memory, reasoning, thinking processes, perception, and […]
  • Tetanus Disease Symptoms and Treatment The microorganism belongs to the genus Clostridium, and its form of a gram strain corresponds to the shape of a drumstick or the tennis rackets.
  • Glycogen Accumulation Disease’s Causes and Treatment The GSD III is considered to be a mild version of Glycogen Storage Disease I, and rarely leads to failure of the liver.
  • Dutch Elm Disease, Its Symptoms and Prevention The spreading of the produced hyphae enhances the tree to produce plugs that play a significant role in cutting off the xylem vessels which are responsible for movement of water within the plant.”The cutting of […]
  • International Classification of Diseases: 9 vs. 10 Revision Due to the ineffectiveness of the ICD-9-CM codes, many nations have adopted the use of ICD-10-CM. To begin with, ICD-10-CM code sets should be treated as an upgrade for the ICD-9-CM.
  • The Alzheimer’s Disease Concept In simple words, it is the condition caused by the negative changes in the human brain that, as the end result, leads to memory loss and some behavioral issues that worsen the quality of patient’s […]
  • Foodborne Disease Outbreak Investigation The quantity of instances that show that the occurrence of an outbreak depends on the present agent of an infection, the size of the population that has been affected by the infection, previous instances of […]
  • Mitochondrial Dysfunction in Cardiovascular Disease Given the explanation, the article is aimed to discuss the aspects of relating mitochondrial function and damage to the development of cardiovascular disease and the risk factors involved.
  • Disease Transmission, Pathogens, and Safety This category also includes the transmission from a woman to a fetus in her uterus and the transmission from one part of the body of an individual to another part.
  • Disease in Value or Dysfunction-Requiring Definition Therefore, this value-requiring definition of disease does not pass the test of the time and makes the definition rather confusing. However, in the frames of this value-requiring definition, pregnancy can be regarded as a malady […]
  • Allergic Patient Experiences and Disease Awareness The following section of results includes information presented by the interview and involves her experience regarding the course of the disease, its occurrence and treatment, and the limitations it set on her life.
  • “Cellular Metabolism and Disease” by DeBerardinis et al. In the article “Cellular Metabolism and Disease: What Do Metabolic Outliers Teach Us,” DeBerardinis and Thompson, provide a comprehensive overview of the role of three different types of metabolism in biological and physiological pathways in […]
  • The Sickle Cell Disease Concept The disorder arises when there is a transmutation in the order of nucleotides in the gene that ciphers the beta chain of the human hemoglobin.
  • Chikungunya, Its History, Prevention, Treatment: A Dangerous Viral Disease 1952 the first recorded outbreak of the disease in the region of southern Tanzania. 2013-2015 first reported cases in the Americas.
  • Hospital-Acquired Diseases and Infections Although the infection forms occur as a result of patients’ skin conditions, they are still considered hospital-acquired since they develop in a healthcare environment.
  • Cell Organelles, Their Functions, and Disease Mitochondria-associated membrane, a specialized sub-group of the ER has specific lipid and protein composition and is involved in cross-communication with mitochondria.
  • Foot and Mouth Disease Outbreak in Canada in 1952 This led to the formation of a commission that interviewed various stakeholders in the ministry of agriculture to investigate what happened that led to the indiscriminate spread of the foot and mouth disease.
  • How Vaccines Prevent Diseases Nevertheless, the origin of vaccines as an endeavor date later in the 1700s from the works of the farmer Benjamin Jesty and Doctor Edward Jenner on the appearances of milkmaids that demonstrated the capacity of […]
  • Devic’s Disease in Childhood NMO is a form of autoimmune disorder, and based on the nature of immune attacks, patients that suffered from the effects of Devic’s disease are affected by autoimmune attacks on the optic nerves and the […]
  • Women with Heart Disease: Risk of Depression The presence of heart disease can often lead to depression, as the person has to worry about his life and health every day, knowing that their heart is not as reliable as it used to […]
  • Physiology Concepts and Factors that Influence Disease The study of pathophysiology is crucial for all healthcare providers in general and for nurses in particular, as it helps recognize the progression and the state of a disease.
  • Cardiovascular Diseases in the UAE Apart from the establishment of the registry, the authorities have launched a few promotion initiatives that are to encourage more people in the United Arab Emirates to undergo necessary examination and increase the awareness of […]
  • Huntington’s Disease, Its Symptoms and Treatment Thus, the higher the number of repeats is, the more likely it is that a patient will reveal the symptoms of the condition at an earlier age.
  • Cardiovascular Disease Prevention Using Socio-Ecological Model In order for a public health promotion to have the maximum efficiency and outreach, it should follow a proper structure and socio-behavioral model. HAAD attempted to use health policy as a method of intervention to […]
  • Cardiovascular Diseases as a Public Health Challenge Different conditions of the heart known to affect the valves, rhythms, or muscles of the heart are categorized as cardiovascular diseases.
  • Ebola Virus Disease in Uganda and Sierra Leone
  • Phossy Jaw as an Occupational Disease
  • Obstructive Pulmonary Disease-Asthma Overlap
  • Heart Disease and Alzheimer’s in Adult Women
  • Alzheimer’s and Cardiovascular Diseases Progress
  • Heart Disease: Causal Effects of Cardiovascular Risk Factors
  • Alzheimer’s Disease in Newspaper Articles
  • Genitourinary System Diseases Diagnostics
  • Chronic Kidney Disease Morbidity Initiative
  • EHR Systems to Disease and Syndromic Surveillance
  • Alzheimer’s Disease: Managing Cognitive Dysfunction
  • Disease Control and Prevention Centers
  • Sexually Transmitted Diseases Peculiarities
  • Sexually Transmitted Disease: Syphilis
  • Alzheimer’s Disease Prevalence and Prevention
  • Sexually-Transmitted Diseases: The 21st Century Plague
  • Clostridium Perfringens Enterotoxin in Food-Borne Diseases
  • Pulmonary Rehabilitation for Chronic Respiratory Disease
  • Patient Examination & Assessment: Infectious Disease
  • Chronic Obstructive Pulmonary Disease’ Management
  • Chronic Kidney Disease and Health Disparities
  • Rheumatoid Arthritis: Disease’ Biology
  • Disease Outbreaks and Protective Measures
  • Genetic Factors of Huntington’s Disease Progression
  • Prevalence of Cardiovascular Disease and Associated Risks
  • Pelvic Inflammatory Disease: Diagnosis and Treatment
  • Heart Disease Reverse: Dr. Esselstyn’s Impact
  • Chronic Obstructive Pulmonary Disease in Florida
  • Chronic Obstructive Pulmonary Disease Definition
  • Gastroesophageal Reflux Disease
  • Pulmonary Function Testing in Chronic Obstructive Disease
  • Chronic and Infectious Disease Control
  • Cardiovascular Disease Risks and Preventive Education
  • The Center for Disease Control and Prevention
  • Infectious Diseases: Ebola and Poliovirus
  • Global Health Diseases in Africa
  • Workplace Accidents, Diseases and Safety Policies
  • Heart Disease, Risk Factors and Emotional Support
  • Malaria Disease and Drugs in Developing Economies
  • Environmental Health Burdens and Non-Communicable Disease
  • Diabetes: Disease Control and Investigation
  • Living with Chronic Obstructive Pulmonary Disease
  • Cooties Tag as a Children’s Fictional Disease
  • Spreading and Dying From AIDS and the Increasing Spread of the Disease
  • Parkinson’s Disease: Neurotransmitter Chart
  • Liver Disease Management and Treatment
  • Interstitial Lung Disease and Blurred Vision
  • Dancing and Risk of Alzheimer’s Disease
  • Food Choices: Diets and Diseases
  • Childhood Diseases and Vaccination Issues
  • Acute Disease: Metabolism – Hypothermia
  • Sexually Transmitted Diseases in Atlanta Community
  • Chronic Obstructive Pulmonary Disease and Tobacco Dependence
  • Community Teaching Work Plan: Diseases Prevention
  • Sickle Cell Disease and Scientific Inventions
  • Policy and Advocacy: Obesity Disease
  • Group Disease: Riley’s Case
  • Diseases in the Elderly: Informal Interview
  • Heart Disease from Medical and Social Viewpoint
  • Alzheimer’s Disease in Science Daily News Article
  • Thyroid Disease and the Stigma Surrounding It: Weight Has Nothing to Do With It
  • Multimorbidity and Chronic Diseases in Older Adults
  • Cardiovascular Disease in Adults in the Gulf Region
  • Chronic Kidney Disease Diagnosis and Treatment
  • Spirochaete: Syphilis T. Pallidum & Lyme Disease
  • Chinese Population’s Lifestyle and Diseases
  • Women’s Diseases: Cervical Ectropia
  • Physical Therapy and Occupational Therapy in Parkinson’s Disease
  • Ebola Virus Disease: Global Health at a Glance
  • “COMT Val158Met Polymorphism Modulates Huntington’s Disease Progression” by de Diego-Balaguer et al.
  • Huntington’s Disease: Diagnosis and Prevention
  • Disease Diagnosis & Treatment in Historical Contexts
  • Epidemiology of Cardiovascular Diseases in the Middle East
  • Chronic Obstructive Pulmonary Disease and Medication Treatment
  • Chronic Obstructive Pulmonary Disease’s Treatment
  • Beautifying Diseases in Modern Society
  • Cardiovascular Disease in African American Women: Reasons
  • Implementation of Clinical Decision Support Systems for Cardiovascular Diseases
  • How Is Disease Beautified in the Modern Society Under the Guise of Beauty?
  • Combating Heart Disease in the African American Community of Kings County, NY
  • Examining Pathophysiological Processes: Heart Failure & Chronic Kidney Disease
  • Heart and Lung Diseases: Health History and Assessment
  • Chronic Kidney Disease Etiology and Management
  • Medical Nutritional Therapy for Celiac Disease Patients
  • Women’s Health. Sexually Transmitted Diseases
  • Chronic Diseases: Heart Failure and Cancer
  • Heart Diseases and Their Pathophysiology
  • Kerataconus: Disease Development and Modern Treatment
  • Chronic Obstructive Pulmonary Disease Treatment
  • Multiple Sclerosis as a Neurological Disease
  • Adherence to Cardiac Therapy for Men with Coronary Artery Disease
  • Typhoid Fever as a Global Infectious Disease
  • Environmental Factors and Autoimmune Diseases Review
  • The Effects of Alzheimer’s Disease on Family Members
  • AIDS and Its Impact on Humankind: The Leading Killer Disease in the World
  • Lyme Disease: On Its History and Prevention
  • Polluted Water and Human Diseases
  • Radiation as a Diseases Cause
  • Infectious Diseases Caused by Insects
  • AIDS: Emergence Factors of Infectious Disease
  • Baclofen (Lioresal) and Cerebral Diseases
  • Cirrhosis: Non- and Alcoholic Fatty Liver Disease
  • Integumentary System Diseases. Skin Cancer and Eczema
  • Skeletal System. Osteoporosis and Paget’s Disease
  • Alzheimer’s Disease and Long Term Care
  • Beta Thalassemia: Disease Description
  • AIDS/HIV: Description of the Disease
  • Disease Prevention and Health Promotion Laws
  • Cigar Smoking and Relation to Disease
  • Cardiovascular Diseases: Statistics, Factors, Diets
  • Disease Prevention and Health Promotion Initiatives
  • Sexually Transmitted Diseases: Community Teaching Plan
  • Schizophrenia as a Common Mental Disorder
  • Sexually Transmitted Diseases: Teaching Plan
  • Infectious Diseases Analysis and Review
  • Parkinson’s Disease: Neuroscience of Aging
  • Comparing Alzheimer’s Disease and Parkinson’s Disease
  • Sexually Transmitted Diseases in the Mediterranean Region in the 15th-16th Century
  • Sex and Gender-Related Differences in Infectious Disease
  • Alzheimer’s Disease: Medical Analysis
  • Celiac Disease: Medical Analysis
  • Chronic Obstructive Pulmonary Disease. Medical Issues.
  • Congenital Diseases and Disorders
  • Conventional Angiography for Coronary Artery Diseases
  • Alcohol Consumption and Cardiovascular Diseases
  • Diabetes: Discussion of the Disease
  • Neuroscience. Huntington’s Disease Epidemiology
  • World AIDS Day Celebration: Increasing the Awareness of the People About the Disease
  • Congestive Heart Failure and Coronary Artery Disease
  • Center for Disease Control and HIV Prevention Goals
  • Chronic Obstructive Pulmonary Disease in a Male Adult
  • Depression and Alzheimer’s Disease
  • Chronic Disease That Affects Minority Populations
  • Clinical Heath Psychology and Cardiovascular Disease
  • Sexually Transmitted Diseases and Medical Issues
  • Phenylketonurea, Galactosemia, Tay-Sachs Disease
  • Diabetes: Encapsulation to Treat a Disease
  • Disease Risk Measures in Public Health
  • Genetics of Parkinson Disease-Associated PARK2 Gene
  • Biology: Coral Reef and Its Diseases
  • Biology. Coral Reef Disease as an Emerging Issue
  • Insects and Civilization: Vector-Borne Diseases
  • Health, Disease and Social Problems
  • Parkinson’s Disease Etiology
  • Recent Advances in Respiratory Care For Neuromuscular Disease
  • Disorder of Movements: Parkinson’s Disease
  • The Hot Zone: Making of A Global Disease
  • Alcoholism: The Disease Is Often Progressive and Fatal
  • Empirical Project: Social Networks and Lyme Disease
  • The Effect of Disease on Modern America
  • Neuropsychological Assessment of Patients With Parkinson Disease
  • Posttraumatic Stress Symptom Disease
  • Addiction: Is It a Disease or Moral Failing?
  • Nutrition for People With Hearth Disease
  • Parkinson Disease: Diagnosis and Treatment
  • Culture and Disease: Tuberculosis and African Americans
  • Heart Diseases: History, Risks and Prevention
  • World Health Organisation (WHO) And Infectious Diseases
  • Effect of Disease on Native Americans
  • The Mad Cow Disease in Britain
  • A Boxing Legend Muhammad Ali: Parkinson’s Disease
  • Disease in the News: “Bird Flu: If or When?” by Sellwood
  • Role of Alzheimer’s Disease Advanced in Our Understanding of the Aging Process
  • Lifestyle and Cardiovascular Disease
  • Sexually Transmitted Diseases: Causes and Treatment
  • Measles Disease Pathophysiology and Its Vaccination
  • Communicable Diseases: Tuberculosis
  • Gout Disease: Prevention and Treatment
  • Endocrine System and Diseases
  • Anthropology. Diseases and Their Impact on Humans
  • Non-invasive Ventilation in Non-Chronic Obstructive Pulmonary Disease Respiratory Failure
  • Anatomy Diagnosis: Cardiovascular Disease
  • Molecular Techniques Used in Hirschsprung Disease Study
  • “Disease of the Skin and Disease of the Heart”: China History
  • Cardiovascular Diseases and Saudi Male Patients Aged 40 – 65 Years
  • Culture & Disease: Malaria in Sub-Saharan Africa
  • Centers for Disease Control and Prevention Website Tools
  • Genomics, Prevention, and Control of Common Chronic Diseases
  • Disease Testing and Phenotype
  • “Field Epidemiology” by Gregg and “Infectious Diseases” by Tamarack
  • Hepatitis A: A Fatal Infectious Disease That Affects the Liver
  • Ongoing Gingivitis With Periodontal Disease: Symptoms and Prevention
  • Emerging Infectious Disease Preparedness and Response
  • Global Health Issue Analysis: HIV – A Relatively New Disease
  • Sexually Transmitted Diseases: Statistics in the New Jersey
  • The Problem of Food Safety and the Spread of Various Diseases
  • Tuberculosis: History and Current State of a Disease
  • Alcoholism as a Disease
  • Poverty and Diseases
  • Opioid Disease Prevention: Levels of Disease Prevention
  • Fatty Liver and Gastrointestinal Tract Disease in Dogs
  • Ebola Virus Disease Analysis
  • Aspects of Childhood Diseases
  • US Centers for Disease Control and Prevention Review
  • A Proband for the Pedigree: Hypertension as Hereditary Disease
  • Corona Virus Disease: Proposed Policy and Results
  • Parasitic Wedge and COVID-19 (Coronavirus Disease)
  • Dementia: Disease Analysis and Treatment Strategies
  • More People Die by Guillain-Barre Disease Than by Swine Flu
  • The Evaluation of the Website for the American Autoimmune Related Diseases Association
  • Typhoid Disease: Risk Factors, Symptoms and Prevention
  • Obstructive Sleep Apnea and Heart Diseases
  • COVID-19: Epidemiology of the Disease
  • Basic Information of Huntington’s Disease
  • Chronic Obstructive Pulmonary Disease Treatment Protocols
  • Strawberry Pest and Diseases Management
  • Nursing Legacy. Elderly Care Problems and Age-Related Health Diseases
  • Coronary Artery Disease
  • Connection Between Nutrition and Cardiovascular Diseases
  • Coronary Artery Disease, Parathyroid Adenoma, and 99mTc-SestaMIBI
  • Alzheimer’s Disease: Key Aspects
  • Bacteria Infectious Diseases: Strep Throat
  • Sexually Transmitted Diseases and Infections Education
  • Kinds and Methods of Treatment of Prion Diseases
  • Parthenogenesis of Celiac Disease
  • The Pathophysiology of Hashimoto’s Disease
  • Nervous System: Parkinson’s Disease
  • Plasminogen Activator Inhibitor -1 and Cardiovascular Diseases: The Connection
  • Systemic Lupus Erythematosus Disease
  • Health Promotion Program: Cardiovascular Disease Mortality Decrease
  • Organ Transplants and Communicable Diseases
  • Clearing the Air: An Examination of Modes of Disease Transmission on Airlines
  • Important Information of Parkinson’s Disease
  • Huntington’s Disease, Huntingtin Protein (Mhtt)
  • Cardiovascular Disease: Acute Coronary Syndrome in Women
  • Climate Change and the Occurrence of Infectious Diseases
  • Pathophysiology of Disease: High BP and NIDDM
  • Diabetes Type II Disease in the Community
  • Lifestyle Diseases and Reduce Productivity
  • Diabetes Mellitus Effects on Periodontal Disease
  • Infectious Diseases and Their Impact on History
  • Menkes Disease: Disorder of Copper Metabolism
  • Mercury Toxicity: Description of Disease
  • Ehlers-Danlos Syndrome: Description of Disease
  • Infectious Disease Control in Different Scenarios
  • Crohn’s Perianal Disease: A Comprehensive Review
  • Crohn’s Disease and Perianal Manifestations
  • Pediatric Gastroenterology and Infectious Diseases
  • Motor Neuron Disease: Types, Diagnostics, and Treatment
  • Disease Specific Program: Disease Management
  • Bleeding on Probing: Progression of Periodontal Disease
  • The Status of Hand Hygiene Practices and a Cause in Disease Outbreaks
  • The Role of Bacteria in Human Health and Disease Giving Specific Examples
  • Mapping the Neurofibrillary Degeneration From Alzheimer’s Disease Patient
  • My Path of Dealing With Limes Disease
  • Periodontal Disease: Medical Analysis
  • Periodontal Disease and the Gram Negative Bacteria
  • AIDS and Its Trends: An Infectious Disease That Causes the Vulnerability of the Human Internal System
  • Concepts of Culture and Disease Paper: AIDS
  • Application of Healthcare IT in Treatment of Cardiovascular Diseases
  • Race-Based Medicine: Diseases in Different Groups of the Population
  • Kawasaki Disease Analysis
  • World Health Organization, US Center of Disease Control and Individual Countries
  • Social Determinants of the Heart Disease
  • Infectious Diseases Overview and Analysis
  • Huntington’s Disease Analysis
  • Alzheimer’s Disease: Regarding Physiology
  • Graves Disease: Medical Case Assessment
  • Thalassemia – Inherited Autosomal Recessive Blood Disease
  • Melanocyte Disease and Its Treatment
  • Anemia Disease: Types and Causes, Treatment
  • Concepts of Pneumonia Disease
  • Concepts of the Ankylosis Disease
  • Chronic Disease Management Framework
  • Swine Flu H1N1: Populations Affected, Course of the Disease, Intervention
  • Therapeutic Properties of Fish Oil: Reduction of Heart Diseases
  • Osteoporosis and Periodontal Disease Relationship
  • Cardiovascular Disease and Caffeine Effects
  • Periodontal Disease and Contribution of Bacteria
  • Center for Disease Control and Prevention Program for Tanzania
  • Alzheimer’s Disease Article and Clinical Trial
  • Pain, Disease and Health Relationship
  • Disease Causing Organism: Salmonella Enterica Typhi
  • The Cystic Fibrosis Disease Analysis
  • Hemolytic Disease of the Newborn
  • Pelvic Inflammatory Disease: Managing Partners
  • Doxycycline in Periodontal Disease
  • Technostress: An Emerging Man-Made Modern-Day Disease
  • Cholera: A Waterborne Disease
  • Experimental Studies on Williams Syndrome Disease
  • Demographic Paper – Parkinson’s Disease
  • Gallstone Disease Pathology and Treatment
  • Policy Proposal in Regards to Sex Workers as a Site of Disease Spread
  • Periodontal and Cardiovascular Diseases: Research Development Project
  • Consumption of Caffeine Is Associated With Reduced Risk of Parkinson’s Disease
  • Health Behaviors That Impact Risk Factors for Diseases
  • Resistant Salmonella: Analysis and Cause of the Disease
  • Environmental Interview on a Patient With Alzheimer Disease
  • Screening for Diseases as Caution Against Potential Infections
  • Chronic Disease: Survey on Beliefs and Feelings
  • Human Disorders: Alzheimer’s Disease and Dementia
  • Effects of Whole Body Vibration in People with Parkinson Disease
  • Human Diseases: Exploring Malaria
  • Parkinson’s Disease and Its Nursing Management
  • Addison’s Disease: A Long-Term Endocrine Disorder
  • Acquired Immunodeficiency Syndrome: Thirty Years of a Disease
  • Leishmaniasis: Disease of the New World
  • Cardiovascular Disease Among Disorders of the Heart
  • Disease Surveillance and Monitoring
  • Causes & Preventing Proliferation of Cardiovascular Disease (CVD)
  • Social, Behavioral, and Psychosocial Causes of Diseases: Type 2 Diabetes
  • Current Challenges in Infectious Diseases
  • Epilepsy Disease Discussion
  • Bacterial Diseases of Marine Organisms
  • Researching the Giardiasis Disease
  • Understanding Emerging Diseases
  • Dietary Calcium Intake and Mortality From Cardiovascular Diseases
  • Genetically Identical Twins and Different Disease Risk
  • Quality of Life in African Americans With the End-Stage Renal Disease
  • The Nature and Control of Non-Communicable Disease – Asthma
  • Saturated Fatty Acids and Coronary or Cardiovascular Disease
  • Communicable Disease Control
  • Sexually Transmitted Diseases in Community
  • Polycystic Kidney Disease (PKD): Overview
  • Renewed Focus on Non-Communicable Diseases
  • Pathophysiology of Crohn’s Disease
  • Chronic Inflammation: Metabolic Syndrome and Cardiovascular Disease
  • Pharmacokinetics and Pharmacodynamics: Coronary Heart Disease
  • Acne: Disease Analysis
  • Arthritis: Disease Analysis
  • Acute Tonsillitis: Disease Analysis
  • Pediatrics: Kawasaki Disease
  • Maple Syrup Urine Disease Pathogenesis
  • Public Health Problems and Neglected Diseases
  • Prevention of Heart Disease and Stroke in Collier County
  • Heart Failure: Prevention of the Disease
  • Brain Reduction and Presence of Alzheimer’s Disease
  • Managing Sickle Cell Disease
  • Biological Basis of Asthma and Allergic Disease
  • Genetic Diseases: Sickle Cell Anemia
  • Alzheimer’s Disease and Naturopathic Medicine
  • Communicable Diseases and Precautionary Measures
  • Communicable Diseases: Tuberous Sclerosis-1
  • The Function of Kinase Inhibitor Staurosporine in Healthy and Disease States
  • Diet Therapy & Cardiovascular Disease
  • Oral Disease Prevention: Past and Present Practices
  • Synopsis of Research Studies of Individuals Afflicted by Mild Alzheimer’s Disease
  • Heart Disease Among Hispanic & Latino Population
  • Hypertension Disease Causation
  • Researching Cystic Fibrosis Disease
  • Epidemiology Discussions: Childhood Obesity Disease
  • Multiple Sclerosis. Disease Analysis
  • Hypertension. Disease Analysis
  • Identification and Assessment of Heart Disease
  • Intervention of Heart Diseases in Children
  • The Mechanisms That Auto Infectious Parasites Use in the Treatment of Autoimmune Diseases
  • Heart Disease: Cell Death During Myocardial Infarction
  • Heart Disease and Low Carbohydrate Diets
  • The Problems Associated With Cardiovascular Disease
  • Communicable Disease Control Strategies for AIDS
  • Psychiatric Genetics. Epigenetics and Disease Pathology
  • Disease Control Prevention & Epidemiology Concepts
  • Cardiovascular Diseases and Associated Risk Factors
  • Viral Skin Diseases: Plantar Warts and Hand, Foot and Mouth Disease
  • Chronic Obstructive Pulmonary Disease Physiology
  • Heart Disease in New York State
  • Legionnaires’ Disease: Causative Agents, Methods of Reproduction
  • Coronary Artery Disease: Normal Physiology and Pathology
  • Chronic Care For Alzheimer’s Disease
  • Osteomyelitis and the Differential Diagnosis of the Disease
  • Coronary Heart Disease Aggravated by Type 2 Diabetes and Age
  • Meningococcal Disease: Causes, Phases, Prevention
  • Genetic Counseling – Tay Sachs Disease
  • Identifying Lyme Disease Host Species
  • Lyme Disease and the Mystery Behind It
  • Lyme Disease: What Is the Mystery Behind It?
  • Depressive Symptoms and HIV Disease Relationship
  • Childhood Development and Cardiovascular Disease
  • Progeria: Disease Etiology, Symptoms, and Prognosis
  • The Impact of Chronic Disease in the Community
  • Nutrition: Preventing Food Born Diseases
  • Critical Analysis on Neurodegenerative Diseases
  • Congestive Heart Failure – One of the Most Devastating Diseases
  • The Burden of Alzheimer’s Disease
  • Challenges of Living With Alzheimer Disease
  • Blood Disorder: Disease Analysis
  • Inherited Mutant Gene Leading to Pompes Disease
  • Food Borne Diseases Associated With Chilled Ready to Eat Food
  • Emerging Infectious Disease: Epidemiology and Evolution of Influenza Viruses
  • Disease Trends and the Delivery of Health Care Services
  • Osteoarthritis Disease and Its Risk Factors
  • Risk Factors Involving People with Ischaemic Heart Disease: In-Depth Interview
  • End-Stage Renal Disease and Hemodialysis
  • Celiac Disease Description and Treatment
  • Hypoparathyroid Disease: Review
  • End Stage Renal Disease Prevalence in African American
  • Coronary Heart Disease: Review
  • Tasmanian Devil’s Facial Tumor Disease
  • Community Health: Alzheimer’s Disease
  • End Stage Renal Disease and Hemodialysis
  • Rabies in South Africa: Tropical Disease Control
  • Heart Disease and Stroke in Sarasota County
  • The Diagnosis and Prevention of Chronic Diseases
  • Chronic Obstructive Pulmonary Disease: 80-Year-Old Female Patient
  • Meningitis Disease: Symptoms and Treatment
  • Parkinson’s Disease: Aetiology, Risk Factors, and Symptoms
  • Researching Chlamydia Trachomatis Disease
  • Poliomyelitis: Disease Overview
  • Swine Flu Disease in Australia
  • Disease Control and Prevention: The Evaluation Process
  • Cardiology: Women and Heart Diseases
  • Epidemiological, Trends and Patterns of Norovirus Disease
  • Health Disparities & Chronic Kidney Disease
  • End-Stage Renal Disease: Creating Awareness Among Patients
  • Sickle Cell Disease Complications and Management
  • HIV and AIDS as a Chronic Disease: The Unique Contributions of Nursing Through Philosophical, Theoretical, and Historical Perspectives
  • Creutzfeldt – Jakob Disease: Diagnosis, Control, Treatment
  • Cardiovascular Diseases and Health Promotion in Women
  • Asthma: Culture and Disease Analysis
  • Chronic Kidney Disease Analysis
  • Emerging Infectious Diseases (EIDs)
  • Public Health and Chronic Disease – Obesity
  • The Relationship Between Vitamin D Deficiency and Asthma Disease in Children
  • Allergic Diseases and the Hygiene Hypothesis
  • Zoonotic Diseases: Leishmaniasis
  • The Global Burden of Disease
  • Detection of Newborn Disease by Liquid Chromatography-Mass Spectrometry
  • Nutrition Importance in Preventing Future Diseases
  • The Effect of Music on People With Alzheimer’s Disease
  • The Types of Sexually Transmitted Diseases
  • Cardiovascular Disease Profile in Female Patient
  • Primary Adrenocortical Insufficiency (Addison’s Disease)
  • Communicable Diseases: Rubeola and Pertussis
  • Frail Elderly: Geriatric Chronic Disease
  • Business Intelligence Systems: Coronavirus Disease
  • Werner Syndrome: Disease Process and Nursing Management
  • Pharmacologic Treatment for Gastroesophageal Reflux Disease
  • Heart Disease Among Hispanic and Latino Population
  • Are Hypometric Anticipatory Postural Adjustments Contributing to Freezing of Gait in Parkinson’s Disease?
  • Why Is Coronary Heart Disease an Important Health Concern?
  • What Types of Heart Disease Are Caused by Stress?
  • Does Cannabis Intake Protect Against Non-alcoholic Fatty Liver Disease?
  • What Is the Deadliest Disease in Human History?
  • How Does Cardiovascular Disease Affect the Lungs?
  • What Diseases Are Caused by Deforestation in the Amazon Rainforest?
  • How Can Genetic Technologies Be Used to Treat Specific Diseases?
  • Why Does Mediterranean Diet Reduce Heart Disease?
  • How Does Alzheimer’s Disease Affect the Entire Human Lifestyle?
  • Can Emotional Stress Cause Cardiovascular Disease?
  • What Were the Common Diseases Found in London During the 19th Century?
  • How Can Lifestyle Changes Help With the Management of the Diseases?
  • What Diseases Can Be Prevented by Vaccination?
  • How Is Coronary Heart Disease Affecting the World?
  • Does Diet Protect Against Parkinson’s Disease?
  • How Does Chronic Kidney Disease Affect the Body System?
  • What Lifestyle Factors Influence the Development of Coronary Heart Disease?
  • Is Smoking the Leading Cause of Disease?
  • How Does Flossing Prevent Periodontal Disease?
  • What Disease Was Killing Hundreds of People in London During 1854?
  • Does Heart Disease Cause Lung Infection?
  • What Are the Complications of Chronic Kidney Disease Dialysis?
  • How Does Heart Disease Affect Women Differently From Men?
  • What Are the Modern Ways of Genetic Diseases Treatment?
  • How Can Epigenetics Be Used to Treat Diseases?
  • Does Human Behavior Influence the Occurrence of Diseases?
  • What Is the Pathophysiology of Kawasaki Disease?
  • How Far Can Countries Be Prepared for Serious Outbreak of Disease?
  • What Is the Role of Probiotics in Preventing Allergic Disease?
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555 Disease Research Topics & Disease Project Ideas

Are you tired of disease essay topics lists simply enumerating common illnesses? Then this article is exactly what you need! Here, you will find an outstanding list of interesting diseases to write a report on or make a presentation about.

🏆 Best Disease Topics for Project

✍️ disease essay topics for college, 👍 good disease research topics & essay examples, 🌶️ hot disease project ideas, 🎓 interesting diseases to write a report on, 📌 easy disease essay topics, 💡 simple disease essay ideas, ❓ disease research questions.

  • Sexually Transmitted Diseases: Role of a Nurse
  • “The Bear Came Over the Mountain”: True Love and Alzheimer’s Disease
  • Levels of Disease Prevention
  • Gastroesophageal Reflux Disease
  • Alzheimer’s Disease in the “Away From Her” Movie
  • Research Proposal: Hypertension and Chronic Kidney Disease
  • Chronic Obstructive Pulmonary Disease Patient Case Study
  • Disease Management and Effects Regulation Disease management entails excellent healthcare interventions that aim at regulating the effects of a disease.
  • Risk Factors for Hypokinetic Disease This study gives a short definition of the diagnosed health condition of two family members including information on risk factors for hypokinetic disease.
  • Peru – Globalization, Environment, Crime and Disease The paper synthesizes a number of legitimate sources to focus on globalization and its effects on Peru with special relation to environmental issues, crime, and diseases.
  • Analysis of Alzheimer’s Disease This paper aims to discuss Alzheimer’s disease focusing on the problem, affected population, cultural, financial, legal, and ethical implications of the condition.
  • Cholera Infectious Disease. Disease Spread Pattern Cholera is an infectious disease that is caused by a bacterium. The disease is transmitted through the consumption of food or water that is contaminated with fecal material.
  • Disease Research: Breast Cancer Breast cancer is a multifactorial, complex illness that demands proper clinical understanding and a multidisciplinary way to determine diagnosis and treatment.
  • Chickenpox: Disease Control and Prevention This paper will present a list of epidemiologic information about Chickenpox, including its description, relation to the epidemiologic triangle, and other factors from a perspective of a nurse.
  • Alzheimer’s Disease: A Literature Review This paper focuses on the definition of Alzheimer’s disease, including the diagnosis, symptoms, prevalence, participating factors, and a description of more vulnerable groups.
  • Chronic Kidney Disease Patient’s Support Needs Chronic kidney disease (CKD) is a wide-spread illness among the US population. Patients suffering from CKD have different types of support needs to cope with their problems.
  • Real-Life Story of Post-traumatic Stress Disease Philips’s case is an actual representation of the manifestation and intervention of PTSD, a piece of evidence the condition is treatable.
  • Chronic Kidney Disease Patient Nursing Care Plan Patients with renal failure are the target population the needs of which will be addressed in the course of the study.
  • Heart Disease and Stroke: Project Proposal and Budgeting This paper is a project proposal for the management of heart disease and stroke in Minnesota, outlines a leadership and strategic plan for addressing the high incidences.
  • Chronic Kidney Disease: Preventive Measures There are many ways of helping patients with chronic kidney disease to manage their chronic condition and avoid worsening of the illness.
  • Cardiovascular Disease: General Information This paper focuses on cardiovascular diseases, drawing from evidence-based studies and scientific data to understand the causes and develop recommendations for lowering CVD prevalence.
  • Obesity as a Disease: Arguments For and Against Although some people consider that obesity is a disease caused by biological and psychological factors, others are confident that it should not be perceived as a disease.
  • Chronic Kidney Disease: Causes and Treatment Various studies have suggested a strong link between diabetes and kidney diseases. As described above, more than 44% of the incidences of kidney disease are associated with diabetes.
  • Sickle Cell Disease Concept Sickle cell anemia is an inherited health problem that medical researchers have been working tirelessly to find ways of managing.
  • ADHD: The Center’s for Disease Control and Prevention Webpage The Center’s for Disease Control and Prevention webpage on attention deficit hyperactivity disorder sufficiently employs ethos, pathos, and logos rhetoric allures.
  • Improving Performance in Disease-Specific Indicators Despite following the recommendations of the Medicare Hospital, the hospital performances for pneumonia, acute myocardial infarction, and congestive heart failure vary.
  • Cardiac Health and Disease Prevention The prevalence of cardiovascular conditions in today’s environment has become a pressing concern. Millions of people suffer from them.
  • Hypertension in Chronic Kidney Disease: Conditions An annotated bibliography is a summary and discussion of the journal articles reviewed about the conditions of patients with chronic kidney disease.
  • Healthy Lifestyle and Disease Prevention Representatives of the philosophical and sociological direction in medicine and prevention consider a healthy lifestyle as a global social problem.
  • Dealing with the Disease Outbreak A disease is a condition of the body in which some abnormality occurs, which may, in turn, cause pain, distress, or discomfort.
  • The Prevention of Sexually Transmitted Diseases: Community Teaching Plan The present paper will offer a reflection on a lesson with a focus on the evaluation of related teaching experience, community response, and the aspects that could be improved.
  • Chronic Obstructive Pulmonary Disease in Evidence-Based Practice In this paper, COPD, its pathophysiology, and etiology will be discussed as a part of evidence-based practice (EBP) to understand better the effects of its alternative therapies.
  • Diseases in the Elderly Population Over the recent years, there has been a significant increase of incidence rates for various diseases in the elderly population.
  • The Patients Suffering from Chronic Diseases The purpose of the given research is to determine the way a nurse might contribute to the improvement of the patients suffering from chronic diseases` health.
  • Peritoneal Dialysis and Hemodialysis as Treatment for End Stage Renal Diseases Recent studies in the field of the pathogenesis of chronic renal failure show that there is a wide multitude of factors influencing the outcomes of the treatment.
  • The Coronary Artery Disease: Nursing Intervention Nursing intervention has often been cited as a valuable component in preventing coronary artery disease through patient interaction and education.
  • Health Belief Model of Chronic Obstructive Pulmonary Disease Health Belief Model is important in prevention of illness and promotion of health. As a result, the nurse should apply it to prevent chronic obstructive pulmonary disease.
  • Chronic Renal Disease: Treatment and Management The treatment goals involve stopping or delaying the progression of the disease. The underlying causes and symptom management are usually the main focus of treatment.
  • Diabetes: Causes and Effects of Disease Diabetes is a common disease that is found in all parts of the world. Its defining feature is the accumulation of excessive sugar {glucose} in the bloodstream.
  • Diabetic Ketoacidosis Disease Diabetic ketoacidosis is a condition that can appear in patients suffering from diabetes mellitus and, in most severe cases, may turn out to be fatal if not addressed in due time.
  • Chronic Kidney Disease: Dimension and Perspectives Nowadays, there are a great number of people who suffer from various chronic illnesses, and it is extremely important for healthcare specialists to develop specific recommendations or plans of care.
  • Health Profile Assessment: Coronary Heart Disease Health Profile Assessment is a way of understanding the health conditions of individuals and families. It aids individuals to make decisions relating to their health.
  • The Spectrum of Infectious Diseases The spectrum of infectious diseases that can cause harm to human health is extensive. The detailed study of infectious diseases should become fundamental tasks of modern medicine.
  • Living With Chronic Obstructive Pulmonary Disease: Diagnosis and Management Plan This study focuses on issues surrounding the diagnosis and management of patients with Chronic Obstructive Pulmonary Disease.
  • Chronic Kidney Disease: Epidemiology Principals This paper considers the development of the disease in the modern society and focuses on the epidemiology principals applied currently to the chronic kidney disease.
  • Chronic Obstructive Pulmonary Disease: Symptoms, Treatment Chronic obstructive pulmonary disease (COPD) has varied pathophysiology, which provides for the need of assessment data while diagnosing and assessing the needs of patients.
  • Nutrition, Disease, and Malnutrition The paper remarks on the essence of nutrition as a critical component of an individual’s overall health, and stresses the malnutrition outcomes.
  • Human Disease Course Importance for Nursing Students For a nursing student, the information obtained during the Human Disease course provides a framework on top of which one may build further knowledge, skills, and competencies.
  • Sexually Transmitted Diseases Impacts STD is an infectious disease transmitted through unprotected sexual activities such as anal sex, vaginal penetration, intravenous drug use or IV.
  • Stereotyping Related to Parkinson’s Disease The current paper will focus on analyzing the stereotypes relating to Parkinson’s disease. Specifically, the patient group which will be the subject of the study is older men.
  • Gerontology Assignment: Aging and Chronic Diseases Most people on the planet long to have a long life, and many people make preparations on how to live their sunset years gracefully.
  • Final Care Coordination Plan: Alzheimer’s Disease Facts Care coordination serves as an essential practice in patient-centered care, targeting patients’ needs, preferences, and values and ensuring access to care and holistic treatment.
  • Epidemiologic Methods in Infectious Diseases Study This research focuses on epidemiologic concepts, theoretical frameworks, and study designs in the context of infectious diseases.
  • Drug Addiction: A Choice or a Medical Disease? This article examines two opposing points of view on the problem of drug addiction – does a person have a choice to be a drug addict or is drug addiction a medical disease?
  • Sexually Transmitted Diseases Awareness Campaigns Even though new problems have emerged, toning down the gravity of the HIV- and STD-related ones, there is still the necessity to educate young people about the threat of STD.
  • Common Lung Diseases Overview The human lung is a respiratory organ made up of secondary lobules and Broncho vascular bundles, alveoli and blood vessels, and an interstitial.
  • Chronic Obstructive Pulmonary Disease (COPD): Review Chronic Obstructive Pulmonary Disease (COPD) is nowadays, spreading widely across the globe. It’s anticipated to as the rank third cause of mortality by 2020.
  • Chronic Kidney Disease: Medical Analysis A special epidemiological strategy is needed to approach and treat chronic kidney disease in modern human society.
  • Health Promotion Project for Chronic Diabetic Kidney Disease The health promotion model designed and proposed by Nola Pender is one of the most effective nursing approaches in promoting health among communities.
  • Sexually Transmitted Diseases Rates in Prince George’s County This paper focuses on the rate of sexually transmitted diseases in county of Prince George, in Maryland. More specifically the community diagnosed is that of Upper Marlboro City.
  • The Disease of Seborrheic Dermatitis The article examines the relationship between diet and seborrheic dermatitis. The purpose of the study was to identify the effect of antioxidant capacity on the occurrence of diseases.
  • Treatment of Cardiovascular Diseases in the Case of Mrs. J In Mrs. J’s case, the disease history shows that her condition is related to the unhealthy lifestyle that she led, including ongoing smoking.
  • Chronic Kidney Disease: Prediction and Recognition The paper reviews studies on kidney disease, its primary recognition, prediction, examination, and report the state of chronic kidney disease in the United States.
  • Sickle Cell Disease and Healthcare Decisions The paper analyzes sickle cell disease, investigates the involvement of the family in making healthcare decisions and determines the role of grants and FDA regulations.
  • “Intensive Blood-Pressure Control in Hypertensive Chronic Kidney Disease”: Article Critique Chronic kidney disease (CKD) is a public health problem, and its prevalence has gradually increased over the years. This disease has a high prevalence among the blacks.
  • The Sexually Transmitted Diseases Screening, vaccinations, and awareness are required because of sexually transmitted diseases’ rising trends, negative effects on the public’s health, and escalating costs.
  • Tuberculosis: Family Medicine and Disease Prevention This essay focuses on tuberculosis infection, prevention and control, surveillance, epidemiology, and significant events.
  • Ethical Dilemma of Patient’s Disease Awareness The ethical dilemma in the case study may be defined as a conflict between a professional algorithm and an ambiguous request from the patient’s family members.
  • The Ebola Virus and Disease Prevention The Ebola virus belongs to the filioviridae family in the order of mononegaviruses. The virus is single-stranded and exhibits a distinct heterogenous threadlike structure.
  • Pelvic Inflammatory Disease Pathophysiology Pelvic inflammatory disease is a disease of pelvic organs in women. It is defined as “an infection of the upper genital tract occurring predominantly in young women”.
  • Cardiac Disease During Pregnancy Diagnosis of heart diseases during pregnancy can be done through checking patient’s medical history, physical examination, and chest examination.
  • Centers for Disease Control and Prevention (CDC) Seasonal Influenza Program Seasonal influenza is a program run by the Center for Disease Control and prevention to create awareness about influenza and curb its spread in the United States.
  • Lyme Disease: Causes and Treatment The purpose of this article is to consider Lyme Disease: pathogen, transmission routes, course of the disease, and treatment.
  • Mental Health Diseases: Diagnostic Assessment The patient, Zev, is a 45-year-old man who is obsessed with performing specific rituals many times each day, explaining this need as a possibility to prevent terrible things.
  • Sickle Cell Disease Analysis Sickle Cell Disease (SCD) also referred to as Sickle Cell Anemia (SCA) is an autosomal blood disorder that occurs in individuals who possess a pair of recessive Sickle Cell genes.
  • Oral Hygiene in Hospital Patients: Preventing Infectious Diseases Having an effective oral care program is a must for every hospital in order to protect patients from contracting infectious diseases.
  • Tarui’s Disease Due To Phosphofructokinase 1 Deficiency Tarui’s disease is a metabolic ailment that arises from the deficiency of the enzyme phosphofructokinase. The glycolytic pathway does not go to completion in the muscles.
  • Quality Metrics for Chronic Disease Management The management of chronic diseases is an essential healthcare approach that is aimed at reducing the negative impact of chronic diseases in patients.
  • The Cardiovascular Disease: Crucial Issues Cardiovascular disease is a significant global, national, and local health problem. Thousands of deaths that are witnessed every year are associated with the disease.
  • Cell Organelles in Health and Diseases Organelles are structures in a cell that have specific functions such as energy production and controlling growth.
  • Health Promotion Theory for Chronic Kidney Disease Effective prevention of chronic kidney disease (CKD), clearly, requires different approaches to lessen the number of deaths in the world.
  • Dementia – The Disease of the Older Generation The research paper explores the ways in which the quality of life of patients with dementia could be improved.
  • Myasthenia Gravis: An Autoimmune Disease Myasthenia Gravis is a neuromuscular autoimmune disease that causes varying degrees of muscle weakness. This paper seeks to review two articles done on the topic of MG.
  • A Pelvic Inflammatory Disease (PID) Pelvic inflammatory disease (PID) is defined as “an infection-induced inflammation of the female upper reproductive tract predominantly caused by sexually-transmitted pathogens
  • Alzheimer’s Disease and Dementia Description As time goes by, people are more likely to encounter memory problems. This can be both a natural sign of aging or illness.
  • Sexually Transmitted Diseases and Nursing Activities Public health nurses play a critical role in the prevention of STDs and HIV by providing specialized care and creating interventions aimed at educating the population.
  • Heart Disease Patients’ Education Promoting the active acquisition of knowledge and skills that are necessary for managing health-related issues is a crucial step toward meeting the needs of patients with heart disease.
  • Chronic Kidney Disease Identifying Chronic kidney disease is a critical illness that affects mainly the population older than 50 years. Its complications are rather severe, as they include disability and may even lead to lethal outcomes.
  • Chronic Kidney Disease: Progress Decreasing The paper evaluates clinical evidence on the value of different treatments of chronic kidney disease and their effectiveness in slowing down the progress of the disease.
  • Chronic Kidney Disease Patients: Pain Management The aim of this paper to analyze literature on oral or intravenous pharmacological treatments and adjunctive non-pharmacological interventions recommended for CKD patients.
  • Chronic Kidney Disease: Identification and Control The paper evaluates studies on the identification and control of chronic kidney disease, its examination in the early stages, and the prevention of this disease.
  • Tay-Sachs Disease, Its Signs and Symptoms The causal factor for Tay-Sachs disease is a genetic mutation occurring in the HEXA gene. Genetic mutations represent a lasting modification in the DNA chain that forms the gene.
  • Chronic Kidney Disease Patients and Supporting Resources Today, patients can find a sufficient number of medical centers and national resources that help cope with chronic kidney disease.
  • Effect of Bananas and Other Compounds on Lyme Disease Bananas contain many bioactive compounds that are beneficial for health. Antioxidants such as Vitamin C are present in bananas as well.
  • Aspects of Glycogen Storage Diseases Glycogen Storage Diseases refer to metabolic disorders that affect glycogen metabolism. The condition is genetic and passed down to children by their parents.
  • Sickle Cell Anemia as a Gene Mutation Disease This discussion post reviews sickle cell anemia, an autosomal recessive disorder that emanates from substitution mutations in the DNA.
  • Career Burnout in Nurses Serving Patients with Alzheimer’s Disease Career burnout in nurses, nannies, and caregivers serving patients with Alzheimer’s disease is an urgent problem.
  • Organelles Disease and Its Consequences on Human Health This paper discusses the disease that arises from the dysfunction of the endoplasmic reticulum, which is cystic fibrosis (CF).
  • An Analysis of the Health Conditions of the Patient with Alzheimer’s Disease This paper provides an analysis of the health conditions of the patient with Alzheimer’s disease, effects of health status on physical, psychological, and emotional aspects.
  • Peritonitis: Description of the Disease and Treatment Peritonitis is an acute peritoneum inflammation, which is a thin membrane covering the surface of the abdominal wall and the organs located in the abdominal cavity.
  • Caregiver Burden for Adult Children Whose Parent Has Alzheimer’s Disease The purpose of the proposed study is to critically explore the relationship between caregiver burden and social stigma for adult children whose parents have AD.
  • Alzheimer’s Disease as a Neurological Disease The deterioration of short-term memory and the inability to retain information for certain amounts of time can be seen as the main warning sign of progressing Alzheimer’s disease.
  • Hypertension and Chronic Kidney Disease: Correlation The evidence presented in studies provides a direct correlation between hypertension and chronic kidney disease.
  • Sickle Cell Disease: Symptoms, Treatment and Prevention The current report is aimed at producing an approximate plan of action for a definite patient who has been diagnosed with sickle cell anemia at birth.
  • Chronic Obstructive Pulmonary Disease: Symptoms, Risk Factors, and Treatment Chronic Pulmonary Obstructive Disease symptoms are normally occasioned by either chronic bronchitis or emphysema.
  • Health Promotion: Disease Control and Prevention Much attention from healthcare providers and healthcare agencies is dedicated to the problems of obesity. Still, not many professionals discuss the risk of being overweight.
  • An Outbreak of the Irrational: Refusion from Measles Disease Vaccination Today people do not face measles disease and its consequences, and for this reason, they are not afraid of it enough. The paper discusses the reasons for refuse from vaccination.
  • Heart Disease: Post-interventional Practice and Monitoring Patient education plays a key role, as the man must be aware of physical conditions characterized by negative dynamics.
  • Communicable Diseases: The Epidemiological Potential of HIV This study aims to investigate HIV from the perspective of epidemiology, addressing various issues related to the selected disease, including its causes and symptoms.
  • Chronic Obstructive Pulmonary Disease This paper includes educational lessons targeted at teaching how to overcome the burden of Chronic Obstructive Pulmonary Disease (COPD).
  • Research Utilization: Chronic Kidney Disease Chronic kidney disease has become one of the most threatening public health issues all over the globe and the aim of the text is to understand more about disease to prevent it.
  • Chronic Disease: Diabetes Mellitus To provide the best management and treatment of diabetes and DKD, a Diabetes Self-Management Education Program (DSME) has been implemented.
  • Residence and Genetic Predisposition to Diseases The study on the genetic predisposition of people to certain diseases based on their residence places emphasizes the influence of heredity.
  • Strategies for Empowerment and Communicable Diseases It is necessary to develop and try to introduce appropriate strategies to expand the rights and opportunities of junior medical personnel in clinics and hospitals.
  • Cardiovascular Disease: Issues and Research Cardiovascular (CVD) disease is one of the leading causes of mortality and morbidity in the United States as well as worldwide.
  • Sickle Cell Disease Gene Mutation This paper analyzes the gene mutation of the sickle cell disease, as well as whether it is acquired or inherited, and how the mutation occurs.
  • Education for Patients with Heart Disease Every hospital is obliged to conduct specialized training for people with heart diseases to make them aware of different actions that might have an adverse impact.
  • Female Patient With Hypotension and Alzheimer’s Disease The paper presents the case study analysis of a female patient with hypotension and Alzheimer’s disease who recently suffered a fall.
  • Quality Long-Term Care for Patients With Chronic Diseases The provision of quality long-term care for patients with chronic diseases can increase their life expectancy and improve its quality, although not cure the disease.
  • Kidney Stones Disease: Causes and Treatment The renal calculi, also known as kidney stones, is caused by an imbalance between the precipitation and solubility of the salts in the urinary bladder and the kidneys.
  • Family Planning: Human Reproductive Diseases Regular follow-up involves assessing satisfaction with the contraceptive method, changes in lifestyle and diet, or changes in medications.
  • Rheumatoid Arthritis: An Autoimmune Disease The paper states that rheumatoid arthritis is an autoimmune disease caused by inflammation in the body. It occurs when the body’s immunity targets healthy cells.
  • The Centers for Disease Control and Prevention The CDC can contribute to the enhancement of public health and safety, the promotion of equal opportunities, and the improvement of the quality of life.
  • Heart Disease Risk Profiles and Gender Differences There are gender differences in heart disease risk profiles and associated chronic conditions, despite the similarity of the classical risk factors.
  • Shifting Disease Burden and Age Discrimination The disease burden is exacerbated by social and economic disparities affecting healthcare access. Age discrimination and challenges caused by multimorbidity should be addressed.
  • Alzheimer’s Disease: Genetic Risk and Ethical Considerations Alzheimer’s disease is a neurodegenerative disease that causes brain shrinkage and the death of brain cells. It is the most prevalent form of dementia.
  • Dementia and Alzheimer’s Disease While Alzheimer’s disease can be found in every state, Texas’ statistics indicate the special prevalence of the condition, making dementia a permeating public health issue.
  • Feeding Patients With Dementia or Alzheimer’s Disease The effectiveness of probes in feeding persons with dementia/Alzheimer’s disease remains high. However, because of the risks, other less invasive methods are recommended.
  • Chronic Diseases’ Effects on Emotional & Social States Many people are diagnosed with various illnesses, and often those conditions are chronic. A significant number of people across the US face long-standing diseases.
  • Policy Changes to Control Disease Better Concerns and inquiries about the most effective ways to prevent and control viruses are raised by the increasing trend of disease transmission.
  • The COVID-19 Infectious Disease Analysis Talking about infectious diseases, modern people should remember the threat of COVID-19 and the necessity of taking certain precautionary steps.
  • Differences Between Illness and Disease If people do not realize they have it, the illness will not affect them, but someone may pass away from it. In a perfect world, the sickness is healed, and the ailment goes away.
  • Heart Disease in the Elderly: Risks, Pathological Changes, and Solutions Heart conditions or cardiovascular diseases are highly prevalent among elderly individuals and are considered a leading cause of mortality among individuals over 65 years.
  • Diabetes Disease, Its Prevention and Treatment This paper states that the critical element of achieving success in the precluding of diabetes and its complications appears to be the prevention of diabetes.
  • Discussion: Disease and Homeostasis Not all diseases can be cured, but some can be managed through treatments that help relieve symptoms and improve quality of life.
  • A Critical Review of Psychological and Behavioral Responses to Coronavirus Disease 2019 The critical review examines the psychological and behavioral responses of individuals to the COVID-19 pandemic, focusing on the influence of personality traits.
  • Akan Adults with Hypertension: Self-Disease Management This essay discusses the necessary change models applicable for the Akan adult population in West Africa living with hypertension.
  • The Ebola Virus Disease Outbreak in Nigeria Ebola is a highly infectious disease with elevated mortality and spread rates. The paper examines the Ebola virus disease outbreak in Nigeria.
  • Drug Addiction: A Disease or a Choice? Drug addiction remains a serious health concern for contemporary society. The problem of whether drug addiction can be viewed as a disease or a choice remains topical.
  • Analysis of Ebola Virus Disease This paper discusses the Ebola virus disease, including its transmission mode, symptoms, and prevention mechanism deeply.
  • Cholera as a Water-Related Disease This paper explores the relationship between water and global health problems, focusing on cholera as a specific water-related disease.
  • Aspects of Chronic Obstructive Pulmonary Disease COPD is the main nursing diagnosis. It is a chronic ailment that needs to be treated. Long-acting bronchodilators and steroid inhalation can be used to treat it.
  • Chronic Obstructive Pulmonary Diseases The chronic obstructive pulmonary disease poses a serious threat to public well-being. The population must be informed about the early symptoms of the disease.
  • Drug Addiction: The Brain Disease Drug addiction acts similarly to neurological diseases. Substances directly affected the brain, with addiction being the most acute phase of substance use disorder.
  • Graves’ Disease Symptoms and Treatment The purpose of this paper is to explore the symptoms, diagnostic methods, treatment, and possible prognosis of Graves’ disease.
  • Genetics in Diagnosis of Diseases Medical genetics aims to study the role of genetic factors in the etiology and pathogenesis of various human diseases.
  • Cancer: Disease Specifics and RNA-Based Detection The paper presents the analysis of cancer as one of the most common causes of death. It shows that there are many types of this disease.
  • Microbe-Human Interaction in Health and Disease One of the most critical topics in microbe-human interaction concerns microbial reservoirs since they inform one about the various places pathogens can be found.
  • The Syphilis Bacterial Infection and Disease By engaging in sexual activity with an infected person, an individual can contract the bacterial infection syphilis. This paper aims to analyze syphilis infectious disease.
  • The E-Cigarettes Impact on Respiratory Diseases Scientific studies prove that e-cigarettes impede the smoker’s immune system leading to increased levels of pneumonia and respiratory disease.
  • Healthy People 2030: Addressing Cardiovascular Diseases Healthy People 2030 develop programs and interventions to address the cardiovascular diseases issue from different angles simultaneously.
  • STDs Transmission Involving Drug Use This paper draws a hypothesis suggesting that preventive techniques need to specifically target drug addicts and sex workers to combat the STD epidemic.
  • Heart Disease and Its Causes: Stroke Statistics According to a study published in the American Journal of Nursing, the leading cause of heart disease is lifestyle choices.
  • Alzheimer’s Disease: Causes and Symptoms Alzheimer’s disease is considered to be one of the most common causes of the development of dementia. Currently, there is no treatment that can cure the disorder.
  • Heart Diseases in the United States Cardiovascular diseases lead as causes of death worldwide, and they are credited with 10% of overall deaths, with 85% of these occurring in low-income countries.
  • Non-Hodgkin’s Lymphoma Disease This paper discusses the most common type of lymphoma, Non-Hodgkin lymphoma, which affects the lymphatic system and can cause tumors in various parts of the body.
  • The Causes of the Spread of Disease in Hinckley Water contamination with hexavalent chlorine spread in local reservoirs due to unwise PG&E activities to protect gas pipes from corrosion.
  • Sex Chromosomes’ Impact on Metabolic Diseases Sex chromosomes should not only be associated with genetic illnesses because their impact is more profound, and metabolic diseases are responsible for a high percentage of deaths.
  • Sexually Transmitted Diseases Transmission Involving Drug Use Preventive techniques need to target drug addicts and sex workers to combat the STD epidemic and offer interventions to reduce risky sexual behaviors and drug use.
  • Osteopathic Manipulative Treatment in Patients With Gastroesophageal Reflux Disease Osteopathic interventions appear to be beneficial in decreasing GERD symptoms in the long term. OMT may be an additional or alternative treatment.
  • Care Services for Patients with Alzheimer’s Disease The growing number of Alzheimer’s cases requires the addition of new updated requirements to nursing care centers.
  • Epidemiology of Deadliest Diseases in History Deadly diseases continue to affect whole communities to this day. Such diseases as AIDS still do not have an effective cure, and some countries have many cases.
  • Alzheimer’s Disease: Mitochondrial Dysfunction Alzheimer’s is a mitochondrial dysfunction that affects the nervous system and the rest of the body. The disorder slowly and steadily destroys thinking and memory skills.
  • Guillain-Barré Disease and Therapy Options Guillain-Barre syndrome (GBS) is an uncommon condition in which the immune system targets the nerves in the body leading to muscle weakness, tingling in the extremities, etc.
  • The Impact of Alzheimer’s Disease on Relationships “Understanding how your relationship may change” provides a clear picture of the potential impact of Alzheimer’s disease on family and personal relationships.
  • Using Mobile Health to Manage Chronic Diseases and Empower Patients The utilization of m-health and electronic health information in CDM ensures more active and knowledgeable patients in the move towards a patient-centered healthcare delivery paradigm
  • Alzheimer’s Disease: The Challenges Imposed on Family Members Specific strategies can be implemented to reduce the negative impact of Alzheimer’s dementia on families and relatives.
  • Patient Navigation Initiative in Care Coordination for Chronic Diseases
  • Nursing Assessment of Patient With Respiratory Disease
  • Parkinson’s Disease: Description, Causes, and Symptoms
  • Lewy Body Disease in Aging Patients With Dementia
  • Dementia Disease and Its Physiological Effects
  • Heart Failure as Dangerous Heart Disease
  • Tuberculosis as an Infectious Disease
  • Drugs for Neglected Disease Initiative
  • Vitamin 12 and Its Deficiency and Excess Diseases
  • Chronic Pulmonary Disease (COPD) in Rural America
  • Researching the Disease Prevention
  • Acromegaly: Disease Prognosis and Treatment
  • Centers for Disease Control and Prevention’s Tasks and Aims
  • Sickle Cell Disease: Background, Issues & Effects
  • Association Between the Dietary Inflammatory Index and Small Vessel Disease
  • Obesity and How It Can Cause Chronic Diseases
  • Physical Wellness to Prevent Obesity Heart Diseases
  • Literature Review: COVID-19 and Eye Diseases
  • Lymphedema as a Lymphatic System Disease
  • Mobile Health Technologies for Diagnosing Diseases and Their Treatment
  • Effect of Weight on Cardiovascular Disease Risk in Each BMI Range
  • Infectious Disease Prevention in the Orlando, FL
  • Cardiovascular Disease: Study Analysis
  • Osteoporosis: The Metabolic Bone Disease
  • Thyroid Disease as Chronic Complex Endocrine Condition
  • Strategies to Control Disease Incidence
  • Screening Tools for Sexually Transmitted Diseases
  • Alzheimer’s Disease and Dementia
  • Marketing to Promote Parkinson’s Disease Studies
  • Traveling With Congestive Heart Failure Disease
  • Disease in Vaccinated Populations
  • Nutritional Therapy and the Management of Cardiovascular Disease
  • Measures of Disease Frequency: Zika Virus
  • The Treatment of the Patient with Exacerbation of Chronic Obstructive Pulmonary Disease
  • Multiple Myeloma. Disease Analysis
  • Chronic Obstructive Pulmonary Disease Recovery Plan
  • Overview of African Americans’ Genetic Diseases
  • Child Health and Communicable Disease
  • Genes and Epigenetic Regulation of Learning and Memory, Addiction, and Parkinson’s Disease
  • Diseases and Their Risk Factors
  • Women and Heart Disease: Knowledge, Worry, and Motivation
  • Schizophrenia as Dangerous Mental Disease
  • Tuberculosis: Control of Non-Endemic Communicable Diseases
  • HIV Disease’s and Kaposi Sarcoma’s Relationship
  • Communication Between Sadness and Disease in the Elderly
  • Public Health Nursing: Alzheimer’s Disease
  • The Incidence of End-Stage Renal Disease
  • Theories in Epidemiology. Stress and Heart Disease
  • Epidemiologic Methods in the Study of Infectious Diseases
  • Disease Outbreaks: Toxicity of Fatty Acid Profiles
  • Patients With Chronic Obstructive Pulmonary Disease: Self-Management Behaviors
  • Disease Emergence in Multi-Patch Stochastic Epidemic Models
  • Evaluating Electronic Disease Surveillance Systems
  • Epistemology of Ebola Virus Disease (EVD)
  • Cardiovascular Disease Etiology and Prevention
  • Cocaine Addiction and Parkinson Disease
  • Disease and Circulatory System Correlation Analysis
  • Epidemiology and Prevention Policy for Non-Communicable Diseases
  • Health Promotion Among Australian Aborigines with Respiratory Diseases
  • Covid-19 as an Emerging Infectious Disease
  • Heart Disease Is a Silent Killer
  • Role of Nurses in Preventing the Spread of Diseases and COVID-19
  • Analyzing Disease Frequency What Impact on Mortality
  • Hispanic or Latino Populations of the USA: Health Status and Promotion and Disease Prevention
  • Sexually Transmitted Disease: Chlamydia Trachomatis
  • Distribution of Oral Diseases
  • DNP: Sexually Transmitted Diseases Prevention
  • Lysosomes and Krabbe Disease: Overview
  • Effects of Parkinson’s Disease on Victims and Family
  • Cardiovascular Disease Prevention
  • Sickle Cell Disease: Symptoms
  • Infectious Disease and Public Health Focus
  • Management and Treatment of Chronic Obstructive Pulmonary Disease: Change of Lifestyle
  • Autoimmune Disease: Sarcoidosis
  • Heart Disease: An Epidemiological Problem in the U.S.
  • Analysis of a Preventing Chronic Disease
  • Addison’s Disease: Symptoms and Effects
  • A Preliminary Care Coordination Plan: Alzheimer’s Disease
  • Preventing Iodine Deficiency Disease in China
  • Communicable Disease Control in Emergencies
  • Chronic Obstructive Pulmonary Disease Overview: Diagnosis, Treatment, Care, and Condition
  • Life Stories of Older Adults With Sickle Cell Disease
  • Health Promotion and Disease Prevention Strategies for Access to Care
  • Lyme Disease in Children. The Lyme Disease Bacterium
  • Managing Communicable Disease in the Complexities of a Humanitarian Emergency
  • Disease Management and Its Relevance to the Managed Care System
  • Parkinson’s Disease and Toxoplasma Gondii Correlation
  • Sexually Transmitted Disease: Public Health Campaign
  • Genetics and Public Health: Disease Control and Prevention
  • Leprosy (Hansen’s) Disease: Diagnosis and Treatment
  • Identification, Control and Prevention of Mesothelioma Disease in the UK
  • The Prevalence and Risk Factor for Cardiovascular Diseases Among Hispanics
  • Pelvic Inflammatory Disease: Key Points
  • Renin-Angiotensin-Aldosterone Mechanism in Terms of Disease Prevention
  • Infectious Disease: The CDC’s Malaria Program
  • Disease Processes. Traumatic Brain Injury
  • Effectiveness of Pharmacotherapeutics for Patients With Psychosocial Diseases
  • Heart Disease Risk Factors and Assessment Approach
  • Specific Disease Condition of Women Life Span
  • Brain Disease: Bipolar Disorder
  • Congenital Heart Disease in Children
  • Predicting Disease Occurrence With Statistical Model
  • The Spread of Preventable Diseases
  • Improving Disease Surveillance in Developing Countries
  • Type II Diabetes: Disease Analysis
  • Integrated Concepts of Disease Management
  • Child’s Auto Immunological Diseases
  • Reproductive Diseases and Disorders
  • Degenerative Diseases: Alzheimer’s Disease – Causes and Treatment
  • Crohn’s Disease Pathophysiology and Treatment
  • Alzheimer’s Disease: Overview and Analysis
  • An Algorithm for Coronary Artery Disease
  • Sexually Transmitted Disease Overview
  • The Importance of Understanding Alzheimer’s Disease
  • Mental Diseases and Violent Offenders
  • Cardiovascular Disease Prevalence in South Florida
  • Researching the Alzheimer’s Disease: Causes and Symptoms
  • Gene Modification: Means of Disease Prevention
  • Alzheimer Disease: Causes and Treatment
  • The Link Between Hypertension and Chronic Kidney Disease
  • Public Health Campaign on Sexually Transmitted Diseases Among Teenagers
  • The Cardiovascular Disease: Risk Factors
  • Chronic Kidney Disease: Evaluating Intervention Plan
  • Alzheimer’s Disease: Study Instruments
  • Universal Healthcare for Chronic Respiratory Diseases from Economic Perspective
  • Heart Disease: Types, Risk Factors, Symptoms and Treatment
  • Fever, Cold, or Pfeiffer’s Disease: Diagnosis and Treatment
  • Cardiovascular Diseases: Effects of Diet and Exercise
  • Heart Disease’s Fundamental Pathophysiologic Mechanism
  • Economics of End-Stage Renal Disease
  • Effects of Nutrition on Cancer and Cardiovascular Disease Control
  • Pathophysiology of Creutzfeldt–Jakob Disease
  • Defining The Harm of Alcoholism Disease
  • Universal Healthcare for Chronic Respiratory Diseases: Barriers and Supports
  • Crohn’s Disease and Ulcerative Colitis
  • Blood Sickle Cell Disease: Etiology and Treatment
  • Cesarean Section and Immune Function Disease
  • Chronic Disease Prevention: Program’s Effectiveness
  • Crohn’s Disease: Symptoms and Treatment
  • Coronary Artery Disease: Prevalence of Risk Factors in African American Society
  • Alzheimer’s Disease – Diagnostic Picture and Treatment
  • Arteriosclerosis in the Development of Cardiovascular Diseases
  • The Herniated Disk Disease: Causes and Treatment
  • Limitations and Solutions Related to Diseases
  • Vitamins E and C in the Prevention of Cardiovascular Disease in Men
  • The Prevalence of Fiber-Implicated Diseases
  • Lupus Disease: The Causes, Symptoms, and Types
  • Cholera: Overview of the Affected Population and Description of the Disease
  • Ulcerative Colitis and Crohn’s Disease: Symptoms and Treatment
  • Epidemiology and Communicable Diseases: Tuberculosis
  • Radiation Effect and Human Disease Correlation
  • Huntington’s Chorea Disease: Genetics, Symptoms, and Treatment
  • A List of Blood Diseases and Their Overview
  • Chronic Kidney Disease: Program Planning Project
  • Epidemiology. Tuberculosis as Communicable Disease
  • Registries as the Tools in Improving Disease Treatment: An Overview
  • A Connection Between Chronic Degenerative Diseases
  • Genetic Diseases: Hemophilia
  • Alzheimer’s Disease and Recent Study Findings
  • Transcranial Doppler in Sickle Cell Disease
  • Genetics: Gaucher Disease Type 1
  • Chronic Disease: Occupational Health Promotion Interventions for Individuals at Risk
  • New Technology in Diagnosing Respiratory Diseases
  • Measles Disease Among Children Comprehensive Study
  • Analysis of Disease and Caring for the Nguyens
  • Methamphetamine, the Root Cause of Trauma Disease
  • Medical History Patient With Chronic Obstructive Pulmonary Disease
  • Healthcare IT in Treatment of Cardiovascular Diseases
  • Chronic Kidney Disease: the Evaluation Plan
  • Coronavirus Disease 2019 (COVID-19): Symptoms and Causes
  • About International Classification of Diseases
  • Alzheimer’s Disease Diagnostics: Mr. M.’s Case Evaluation
  • The Alzheimer’s Disease: Key Issues
  • The Use of Medical Marijuana in the Fight Against Various Diseases
  • The Alzheimer’s Disease: Basic Facts
  • Aspects of Sickle Cell Disease
  • The Disease of Breast Cancer: Definition and Treatment
  • Risk of Heart Disease in Obese Individuals
  • Health. Centers for Disease Control and Prevention
  • Binge Drinking May Cause Alzheimer’s Disease
  • Overview of Alzheimer’s Disease Patient Education
  • Alzheimer’s Disease Through the Lens of a Relationship
  • Epidemiology. Disease Burden in Miami-Dade County
  • Crohn’s Disease: A Patient Education Plan
  • Health Promotion: Coronary Heart Disease Prevention
  • The Parkinson’s Disease Process of Diagnosis
  • Survey on Non-Infectious and Blood-Borne Diseases
  • Stigma Associated With Disease
  • Universal Healthcare: Chronic Respiratory Diseases Management
  • Researching the Issue of Tuberculosis Disease in the World
  • Stem Cell Therapy in the Treatment of Heart Disease
  • Multifactorial Disease: Diagnosis and Treatment
  • Disease Management: Therapeutic Education Research
  • Encouraging Alternative Medicinal Solutions to Diseases Before Attempting Conventional Treatments
  • Heart Disease and Stroke (HDS): Pathophysiology and Treatment
  • On the Influence of the Disease on the Personality
  • Alzheimer’s Disease: Symptoms, Prevalence, Treatment
  • Pathophysiology of Gastroesophageal Reflux Disease
  • Discussion Board Post on the Huntington’s Disease
  • Assignment on Cardiovascular Disease
  • Mental Disorders in the US: Alzheimer’s Disease
  • Cardiovascular Disease and Framingham Global Risk Model
  • Disease Pathology, Management, and Pharmacological Impact for Tularemia and Hantavirus
  • Alzheimer’s Disease and Its Global Prevalence
  • Anemia of Chronic Diseases, The Review of Literature
  • Donepezil for Dementia Due to Alzheimer’s Disease by Govind
  • The Impact of Climate Change on Inflectional Diseases
  • Manifestations of Lyme Disease
  • Management of Kawasaki Disease
  • Vaccination Challenges and New Disease Outbreaks
  • Dupuytren’s Disease: A Unified Treatment Protocol
  • Obesity Disease: Symptoms and Causes
  • Center for Disease Control and Prevention’s Concussion Initiative Evaluation
  • The Chronic Obstructive Pulmonary Disease and its Burden on Society
  • Sexually Transmitted Diseases in the US Society
  • Single Children Caregivers vs. Married Couple Caregivers: Alzheimer’s Disease
  • Skin Diseases: Pseudomonas Dermatitis, Propionibacterium Acnes
  • Women’s Disease: Breast Cancer and Its Consequence
  • Tar Sands Pipelines: Source of Dutch Disease in Canada?
  • Delirium Disease and Older Adult Patients
  • The Dissemination of Infectious Diseases
  • Alzheimer’s Disease: Diagnosis and Evaluation
  • Project and Study Design of Sexually Transmitted Diseases Teeneagers
  • Epidemiology: Waterborne Diseases Development
  • Obesity: Is It a Disease?
  • Truths and Myths About Alzheimer’s Disease
  • Lou Gehrig’s Disease, Symptoms and Treatment
  • Polycystic Kidney Diseases: Types, Symptoms, and Complications
  • Chronic Obstructive Pulmonary Disease Treatment Protocols
  • Antigens, Cure, and Disease in Immunology
  • Communicable Disease Control. Medical Issues.
  • Parkinson’s Disease: Evaluation of Nursing Care
  • Pulmonary Diseases’ Diagnostic and Assessment
  • Diseases Prevention and Management. Nursing Research
  • Drug Addiction Is a Chronic Disease
  • Gangs, a Social Causation, Societies Disease
  • STD: Proportion of Females Aged 15-44 Years Who Required Treatment for Pelvic Inflammatory Disease
  • Reducing Readmission in Patients with Lung Disease
  • Alzheimer’s Disease and Healthy People 2020
  • Identifying Alzheimer’s Disease in Elderly Patients
  • Infectious Disease Trends and Nursing Epidemiology
  • Sexually Transmitted Diseases in Miami Community
  • Understanding Ebola: Epidemiology of Virus Disease
  • Contagious Diseases in the United States in 21st Century
  • Communicable Diseases: Measles and Its Impact on the Population
  • Communicable Diseases: Influenza Analysis Through the Lenses of Determinants of Health and the Epidemiological Triangle
  • An Elderly Patient’s Diseases and Interventions
  • Alzheimer’s Disease Effects: Public Policy Meeting
  • Incurable Disease in Christianity and Buddhism
  • Sexually Transmitted Diseases: Community Teaching Plan
  • Health Statistics and Populations With Coronary Heart Disease
  • Preventing Occupational Respiratory Disease
  • Public Health Policies for Disease Prevention
  • Communicable Disease Health Education in Uganda
  • Respiratory Diseases Caused by Climate Change
  • Prevention of Chronic Disease in the Modern Society
  • Hospital-Acquired Diseases & Hand Hygiene Studies
  • Frontotemporal Dementia and Alzheimer Diseases
  • Chronic Obstructive Pulmonary Disease Symptoms
  • Readmissions in Patients With Lung or Heart Diseases: Methodology
  • Patients with Frontotemporal Dementia and Alzheimer Diseases
  • Personal Genetics and Risks of Diseases
  • Genetic Predisposition to Alcohol Dependence and Alcohol-Related Diseases
  • Anthropology: Infectious Disease Education for Indian People
  • Effectiveness of Acupuncture on the Treatment of Nervous System Diseases
  • Psychology: Prejudice as Disease Protection
  • Patient Medication Education for Chronic Diseases
  • Health Informatics: Centers for Disease Control and Prevention
  • Alzheimer’s Disease and Family Counseling Services
  • Communicable Diseases: Empowerment and Management
  • The Advocacy Model in the Diseases Problem Addressing
  • Acute Pancreatitis as a Life-Threatening Disease
  • Heart Disease Prevention and Patient Teaching Plan
  • Chronic Obstructive Pulmonary Disease Readmission
  • The Center for Disease Control and Prevention
  • Public Health Programs and Their Role in Disease Prevention
  • The Centers for Disease Control: Regulatory Agency
  • Alzheimer’s Disease Care: Project Implementation
  • Transition in Terms of Chronic Diseases
  • Diseases and Health Promotion in African Americans
  • Prostatitis Disease Treatment: Principles and Practice
  • Communicable Diseases in Miami
  • Patient History with Respiratory Disease
  • Physiology of Parkinson’s Disease
  • Disease Control and Prevention Centers
  • The Physiology of Parkinson’s Disease
  • Alzheimer’s Disease in Pinecrest Community
  • Communicable Disease Reporting Systems in the World
  • Pelvic Inflammatory Disease and Its Prevention
  • Miami Sexually Transmitted Diseases and Action Plan
  • Pelvic Inflammatory Disease and Treatment
  • School-Based Interventions for Preventing Diseases
  • Xeroderma Pigmentosum: Analysis and Symptoms of the Disease
  • Parkinson’s Disease and Primary Headache Disorder
  • Abortion in Case of Down Disease in Fetus
  • Dialysis Patient Population: Chronic Kidney Disease
  • Clinical Question: Sexually Transmitted Diseases
  • Fight Against Infectious Diseases in Humans
  • Smoking and Heart Disease Rates in African-Americans
  • Heart Disease Patients’ Education and Barriers
  • Chronic Kidney Disease: Locating Resources
  • Human Immunodeficiency Virus as Infectious Disease
  • Sexually Transmitted Diseases in High School Students
  • Sexually Transmitted Diseases in Adolescents
  • Self-Management Skills in Chronic Disease Management
  • Alzheimer’s Disease and Its Affecting Factors
  • “Zika Virus Disease: A Public Health Emergency of International Concern”: Article Summary and Significance
  • Heart Disease and Stroke in Miami-Dade County
  • Chronic Obstructive Pulmonary Disease Therapy
  • Cardiovascular Disease Prevention Programs in the US
  • Chronic Obstructive Pulmonary Disease Description
  • Chronic Obstructive Pulmonary Disease Treatment
  • US Geriatric Population and Its Chronic Diseases
  • Alzheimer’s Disease: Symptoms and Treatment
  • Heart Disease in American Women: Raising Awareness
  • Lupus, Lyme Disease, Mononucleosis Diagnostics
  • Climate Change: Changing Patterns of Malaria Disease
  • Alzheimer’s Disease as One of the Mortality Causes
  • Alzheimer’s Disease and Memory Dysfunction
  • Patients With Diabetes and Concomitant Diseases’ Risk
  • Elderly With Alzheimer’s Disease: Functions and Falls
  • Heart Disease and Stroke Prevention Strategies
  • Gastroesophageal Reflux Disease: Treatment and Care
  • Sickle Cell Disease and Family Involvement
  • Venereal Diseases and Sex Education in Adolescents
  • Healthy People 2020 Program and Infectious Diseases
  • Sexually Transmitted Diseases’ Prevention and Management
  • Chronic Obstructive Pulmonary Disease: Behavioral Risk
  • Miami Infectious Diseases and Healthy People 2020
  • Descriptive Epidemiology: Alzheimer’s Disease
  • Miami-Dade County’s Communicable Diseases
  • Patient Teaching Plan: Hypertension as a Modifiable Risk Factor for Kidney Disease
  • Sexually Transmitted Diseases: Patient Education
  • Ebola Virus Disease and Global Health Risk
  • Child Disease in “First Look: U.S. Youth and Seizures”
  • Miami Communicable Diseases and Action Plan
  • Sexually Transmitted Diseases Impact and Prevention
  • Alzheimer’s Disease, Statistics and Disparities
  • Parkinson’s Disease Case: Patient’s History, Diagnosis, and Treatment
  • Chronic Diseases in Elderly People and Education
  • Communicable Diseases in Miami-Dade County
  • Cardiovascular Diseases in African Americans
  • Brucellosis, Gonorrhea, Lyme Disease in Miami
  • Type 2 Diabetes: Disease Process and Screening
  • Breast Cancer: Disease Screening and Diagnosis
  • Infectious Diseases Caused by Infectious Agents
  • Herpes Zoster: Disease Features and Prevention Strategies
  • Air Ventilation Effectiveness for Advanced Disease Patients
  • Cardiovascular Disease in African American Population
  • Cardiovascular Diseases: Causes and Risk Factors
  • Health Promotion and Disease Prevention
  • Understanding Alzheimer’s Disease: The Most Frequent Illness in the Elderly
  • Seven Stages of Alzheimer’s Disease
  • Alzheimer’s Disease Stages and Risk Factors
  • Physical Activity Role in Cardiovascular Diseases
  • Ulcerative Colitis and Crohn’s Disease
  • Parkinson’s Disease: Categories, Causes, Symptoms, and Treatment
  • Psychology: Amyloid Deposition and Alzheimer’s Disease
  • How Does Healthy Eating Prevent Disease?
  • Can Neurostimulation Prevent the Risk of Alzheimer’s Disease in Elderly Individuals With Schizophrenia?
  • Does Better Disease Management in Primary Care Reduce Hospital Costs?
  • Can Embryonic Stem Cells Be Used in the Treatment of Parkinson’s Disease?
  • Are Quality-Adjusted Medical Prices Declining for Chronic Disease?
  • Does Chronic Kidney Disease Result in High Risk of Atrial Fibrillation?
  • Can Flaxseed Prevent Heart Disease?
  • How Does Alzheimer’s Disease Affect the Brain?
  • Does Cognitive Impairment Affect Rehabilitation Outcome in Parkinson’s Disease?
  • Can Disease-Specific Funding Harm Health?
  • How Does Anxiety Affect the Chronic Obstructive Pulmonary Disease?
  • Does Forest Loss Increase Human Disease?
  • Can Non-human Primates Serve as Models for Investigating Dengue Disease Pathogenesis?
  • How Does Bioarchaeology Reveal the Evolution of Disease?
  • Does Global Drug Innovation Correspond to Burden of Disease?
  • Can Stress Cause Coronary Heart Disease?
  • How Can Lifestyle Changes Affect Chronic Disease Management?
  • Does Lifestyle Affect the Development of Coronary Heart Disease?
  • How Did Disease Shape the History of London Between 1500 and 1900?
  • Can the Mediterranean Diet Reduce Heart Disease?
  • What Are Pathogenic, Deficiency, Hereditary, and Physiological Diseases?
  • How Does Chronic Kidney Disease Affect the Level of Organization?
  • What Causes Acid Reflux Disease?
  • How Has Human Disease Impacted Our Evolution?
  • Why Is Alcohol and Drug Addiction Considered a Disease?
  • How Are Stem Cells Changing the Way We Think About Disease?
  • Will Long-Term Periodontal Disease Causes Alzheimer’s Disease?
  • How Can Technology Cure Disease?
  • Why Has the Disease Been an Enemy of a Human Ever Since It Appeared?
  • How Will Climate Change Affect the Rates of Disease?

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These essay examples and topics on Disease were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

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How to Create a Structured Research Paper Outline | Example

Published on August 7, 2022 by Courtney Gahan . Revised on August 15, 2023.

How to Create a Structured Research Paper Outline

A research paper outline is a useful tool to aid in the writing process , providing a structure to follow with all information to be included in the paper clearly organized.

A quality outline can make writing your research paper more efficient by helping to:

  • Organize your thoughts
  • Understand the flow of information and how ideas are related
  • Ensure nothing is forgotten

A research paper outline can also give your teacher an early idea of the final product.

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Table of contents

Research paper outline example, how to write a research paper outline, formatting your research paper outline, language in research paper outlines.

  • Definition of measles
  • Rise in cases in recent years in places the disease was previously eliminated or had very low rates of infection
  • Figures: Number of cases per year on average, number in recent years. Relate to immunization
  • Symptoms and timeframes of disease
  • Risk of fatality, including statistics
  • How measles is spread
  • Immunization procedures in different regions
  • Different regions, focusing on the arguments from those against immunization
  • Immunization figures in affected regions
  • High number of cases in non-immunizing regions
  • Illnesses that can result from measles virus
  • Fatal cases of other illnesses after patient contracted measles
  • Summary of arguments of different groups
  • Summary of figures and relationship with recent immunization debate
  • Which side of the argument appears to be correct?

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Follow these steps to start your research paper outline:

  • Decide on the subject of the paper
  • Write down all the ideas you want to include or discuss
  • Organize related ideas into sub-groups
  • Arrange your ideas into a hierarchy: What should the reader learn first? What is most important? Which idea will help end your paper most effectively?
  • Create headings and subheadings that are effective
  • Format the outline in either alphanumeric, full-sentence or decimal format

There are three different kinds of research paper outline: alphanumeric, full-sentence and decimal outlines. The differences relate to formatting and style of writing.

  • Alphanumeric
  • Full-sentence

An alphanumeric outline is most commonly used. It uses Roman numerals, capitalized letters, arabic numerals, lowercase letters to organize the flow of information. Text is written with short notes rather than full sentences.

  • Sub-point of sub-point 1

Essentially the same as the alphanumeric outline, but with the text written in full sentences rather than short points.

  • Additional sub-point to conclude discussion of point of evidence introduced in point A

A decimal outline is similar in format to the alphanumeric outline, but with a different numbering system: 1, 1.1, 1.2, etc. Text is written as short notes rather than full sentences.

  • 1.1.1 Sub-point of first point
  • 1.1.2 Sub-point of first point
  • 1.2 Second point

To write an effective research paper outline, it is important to pay attention to language. This is especially important if it is one you will show to your teacher or be assessed on.

There are four main considerations: parallelism, coordination, subordination and division.

Parallelism: Be consistent with grammatical form

Parallel structure or parallelism is the repetition of a particular grammatical form within a sentence, or in this case, between points and sub-points. This simply means that if the first point is a verb , the sub-point should also be a verb.

Example of parallelism:

  • Include different regions, focusing on the different arguments from those against immunization

Coordination: Be aware of each point’s weight

Your chosen subheadings should hold the same significance as each other, as should all first sub-points, secondary sub-points, and so on.

Example of coordination:

  • Include immunization figures in affected regions
  • Illnesses that can result from the measles virus

Subordination: Work from general to specific

Subordination refers to the separation of general points from specific. Your main headings should be quite general, and each level of sub-point should become more specific.

Example of subordination:

Division: break information into sub-points.

Your headings should be divided into two or more subsections. There is no limit to how many subsections you can include under each heading, but keep in mind that the information will be structured into a paragraph during the writing stage, so you should not go overboard with the number of sub-points.

Ready to start writing or looking for guidance on a different step in the process? Read our step-by-step guide on how to write a research paper .

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Coronary Heart Disease Research

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For almost 75 years, the NHLBI has been at the forefront of improving the nation’s health and reducing the burden of  heart and vascular diseases . Heart disease, including coronary heart disease, remains the leading cause of death in the United States. However, the rate of heart disease deaths has declined by 70% over the past 50 years, thanks in part to NHLBI-funded research. Many current studies funded by the NHLBI focus on discovering genetic associations and finding new ways to prevent and treat the onset of coronary heart disease and associated medical conditions.

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NHLBI research that really made a difference

The NHLBI supports a wide range of long-term studies to understand the risk factors of coronary heart disease. These ongoing studies, among others, have led to many discoveries that have increased our understanding of the causes of cardiovascular disease among different populations, helping to shape evidence-based clinical practice guidelines.

  • Risk factors that can be changed:  The NHLBI  Framingham Heart Study (FHS)  revealed that cardiovascular disease is caused by modifiable risk factors such as smoking,  high blood pressure ,  obesity , high  cholesterol  levels, and physical inactivity. It is why, in routine physicals, healthcare providers check for high blood pressure, high cholesterol, unhealthy eating patterns, smoking, physical inactivity, and unhealthy weight. The FHS found that cigarette smoking increases the risk of heart disease. Researchers also showed that cardiovascular disease can affect people differently depending on sex or race, underscoring the need to address health disparities. 
  • Risk factors for Hispanic/Latino adults:  The  Hispanic Community Health Study/Study of Latinos (HCHS/SOL)  found that heart disease risk factors are widespread among Hispanic/Latino adults in the United States , with 80% of men and 71% of women having at least one risk factor. Researchers also used HCHS/SOL genetic data to explore genes linked with central adiposity (the tendency to have excess body fat around the waist) in Hispanic/Latino adults. Before this study, genes linked with central adiposity, a risk factor for coronary heart disease, had been identified in people of European ancestry. These results showed that those genes also predict central adiposity for Hispanic/Latino communities. Some of the genes identified were more common among people with Mexican or Central/South American ancestry, while others were more common among people of Caribbean ancestry.
  • Risk factors for African Americans:  The  Jackson Heart Study (JHS) began in 1997 and includes more than 5,300 African American men and women in Jackson, Mississippi. It has studied genetic and environmental factors that raise the risk of heart problems, especially high blood pressure, coronary heart disease,  heart failure ,  stroke , and  peripheral artery disease (PAD) . Researchers discovered a gene variant in African American individuals that doubles the risk of heart disease. They also found that even small spikes in blood pressure can lead to a higher risk of death. A community engagement component of the JHS is putting 20 years of the study’s findings into action by turning traditional gathering places, such as barbershops and churches, into health information hubs.
  • Risk factors for American Indians:  The NHLBI actively supports the  Strong Heart Study , a long-term study that began in 1988 to examine cardiovascular disease and its risk factors among American Indian men and women. The Strong Heart Study is one of the largest epidemiological studies of American Indian people ever undertaken. It involves a partnership with 12 Tribal Nations and has followed more than 8,000 participants, many of whom live in low-income rural areas of Arizona, Oklahoma, and the Dakotas. Cardiovascular disease remains the leading cause of death for American Indian people. Yet the prevalence and severity of cardiovascular disease among American Indian people has been challenging to study because of the small sizes of the communities, as well as the relatively young age, cultural diversity, and wide geographic distribution of the population. In 2019, the NHLBI renewed its commitment to the Strong Heart Study with a new study phase that includes more funding for community-driven pilot projects and a continued emphasis on training and development. Read more about the  goals and key findings  of the Strong Heart Study.

Current research funded by the NHLBI

Within our  Division of Cardiovascular Sciences , the Atherothrombosis and Coronary Artery Disease Branch of its  Adult and Pediatric Cardiac Research Program and the  Center for Translation Research and Implementation Science  oversee much of our funded research on coronary heart disease.

Research funding  

Find  funding opportunities  and  program contacts for research on coronary heart disease. 

Current research on preventing coronary heart disease

  • Blood cholesterol and coronary heart disease: The NHLBI supports new research into lowering the risk of coronary heart disease by reducing levels of cholesterol in the blood. High levels of blood cholesterol, especially a type called low-density lipoprotein (LDL) cholesterol, raise the risk of coronary heart disease. However, even with medicine that lowers LDL cholesterol, there is still a risk of coronary heart disease due to other proteins, called triglyceride-rich ApoB-containing lipoproteins (ApoBCLs), that circulate in the blood. Researchers are working to find innovative ways to reduce the levels of ApoBCLs, which may help prevent coronary heart disease and other cardiovascular conditions.
  • Pregnancy, preeclampsia, and coronary heart disease risk: NHLBI-supported researchers are investigating the link between developing preeclampsia during pregnancy and an increased risk for heart disease over the lifespan . This project uses “omics” data – such as genomics, proteomics, and other research areas – from three different cohorts of women to define and assess preeclampsia biomarkers associated with cardiovascular health outcomes. Researchers have determined that high blood pressure during pregnancy and low birth weight are predictors of atherosclerotic cardiovascular disease in women . Ultimately, these findings can inform new preventive strategies to lower the risk of coronary heart disease.
  • Community-level efforts to lower heart disease risk among African American people: The NHLBI is funding initiatives to partner with churches in order to engage with African American communities and lower disparities in heart health . Studies have found that church-led interventions reduce risk factors for coronary heart disease and other cardiovascular conditions. NHLBI-supported researchers assessed data from more than 17,000 participants across multiple studies and determined that these community-based approaches are effective in lowering heart disease risk factors .

Find more NHLBI-funded studies on  preventing coronary heart disease  on the NIH RePORTER.

plaque

Learn about the impact of COVID-19 on your risk of coronary heart disease.

Current research on understanding the causes of coronary heart disease

  • Pregnancy and long-term heart disease:  NHLBI researchers are continuing the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b)   study to understand the relationship between pregnancy-related problems, such as gestational hypertension, and heart problems. The study also looks at how problems during pregnancy may increase risk factors for heart disease later in life. NuMoM2b launched in 2010, and long-term studies are ongoing, with the goal of collecting high-quality data and understanding how heart disease develops in women after pregnancy.
  • How coronary artery disease affects heart attack risk: NHLBI-funded researchers are investigating why some people with coronary artery disease are more at risk for heart attacks than others. Researchers have found that people with coronary artery disease who have high-risk coronary plaques are more likely to have serious cardiac events, including heart attacks. However, we do not know why some people develop high-risk coronary plaques and others do not. Researchers hope that this study will help providers better identify which people are most at risk of heart attacks before they occur.
  • Genetics of coronary heart disease:  The NHLBI supports studies to identify genetic variants associated with coronary heart disease . Researchers are investigating how genes affect important molecular cascades involved in the development of coronary heart disease . This deeper understanding of the underlying causes for plaque buildup and damage to the blood vessels can inform prevention strategies and help healthcare providers develop personalized treatment for people with coronary heart disease caused by specific genetic mutations.

Find more NHLBI-funded studies on understanding the  causes of coronary heart disease  on the NIH RePORTER.

statin tablets

Recent findings suggest that cholesterol-lowering treatment can lower the risk of heart disease complications in people with HIV.

Current research on treatments for coronary heart disease

  • Insight into new molecular targets for treatment: NHLBI-supported researchers are investigating the role of high-density lipoprotein (HDL) cholesterol in coronary heart disease and other medical conditions . Understanding how the molecular pathways of cholesterol affect the disease mechanism for atherosclerosis and plaque buildup in the blood vessels of the heart can lead to new therapeutic approaches for the treatment of coronary heart disease. Researchers have found evidence that treatments that boost HDL function can lower systemic inflammation and slow down plaque buildup . This mechanism could be targeted to develop a new treatment approach for coronary heart disease.
  • Long-term studies of treatment effectiveness: The NHLBI is supporting the International Study of Comparative Health Effectiveness with Medical and Invasive Approaches (ISCHEMIA) trial EXTENDed Follow-up (EXTEND) , which compares the long-term outcomes of an initial invasive versus conservative strategy for more than 5,000 surviving participants of the original ISCHEMIA trial. Researchers have found no difference in mortality outcomes between invasive and conservative management strategies for patients with chronic coronary heart disease after more than 3 years. They will continue to follow up with participants for up to 10 years. Researchers are also assessing the impact of nonfatal events on long-term heart disease and mortality. A more accurate heart disease risk score will be constructed to help healthcare providers deliver more precise care for their patients.
  • Evaluating a new therapy for protecting new mothers: The NHLBI is supporting the Randomized Evaluation of Bromocriptine In Myocardial Recovery Therapy for Peripartum Cardiomyopathy (REBIRTH) , for determining the role of bromocriptine as a treatment for peripartum cardiomyopathy (PPCM). Previous research suggests that prolactin, a hormone that stimulates the production of milk for breastfeeding, may contribute to the development of cardiomyopathy late in pregnancy or the first several months postpartum. Bromocriptine, once commonly used in the United States to stop milk production, has shown promising results in studies conducted in South Africa and Germany. Researchers will enroll approximately 200 women across North America who have been diagnosed with PPCM and assess their heart function after 6 months. 
  • Impact of mental health on response to treatment:  NHLBI-supported researchers are investigating how mental health conditions can affect treatment effectiveness for people with coronary heart disease. Studies show that depression is linked to a higher risk for negative outcomes from coronary heart disease. Researchers found that having depression is associated with poor adherence to medical treatment for coronary heart disease . This means that people with depression are less likely to follow through with their heart disease treatment plans, possibly contributing to their chances of experiencing worse outcomes. Researchers are also studying new ways to treat depression in patients with coronary heart disease .

Find more NHLBI-funded studies on  treating coronary heart disease  on the NIH RePORTER.  

lungs

Researchers have found no clear difference in patient survival or heart attack risk between managing heart disease through medication and lifestyle changes compared with invasive procedures. 

Coronary heart disease research labs at the NHLBI

  • Laboratory of Cardiac Physiology
  • Laboratory of Cardiovascular Biology
  • Minority Health and Health Disparities Population Laboratory
  • Social Determinants of Obesity and Cardiovascular Risk Laboratory
  • Laboratory for Cardiovascular Epidemiology and Genomics
  • Laboratory for Hemostasis and Platelet Biology

Related coronary heart disease programs

  • In 2002, the NHLBI launched  The Heart Truth® ,  the first federally sponsored national health education program designed to raise awareness about heart disease as the leading cause of death in women. The NHLBI and  The Heart Truth®  supported the creation of the Red Dress® as the national symbol for awareness about women and heart disease, and also coordinate  National Wear Red Day ® and  American Heart Month  each February. 
  • The  Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC)  facilitates access to and maximizes the scientific value of NHLBI biospecimen and data collections. A main goal is to promote the use of these scientific resources by the broader research community. BioLINCC serves to coordinate searches across data and biospecimen collections and provide an electronic means for requesting additional information and submitting requests for collections. Researchers wanting to submit biospecimen collections to the NHLBI Biorepository to share with qualified investigators may also use the website to initiate the application process. 
  • Our  Trans-Omics for Precision Medicine (TOPMed) Program  studies the ways genetic information, along with information about health status, lifestyle, and the environment, can be used to predict the best ways to prevent and treat heart, lung, blood, and sleep disorders. TOPMed specifically supports NHLBI’s  Precision Medicine Activities. 
  • NHLBI  population and epidemiology studies  in different groups of people, including the  Atherosclerosis Risk in Communities (ARIC) Study , the  Multi-Ethnic Study of Atherosclerosis (MESA) , and the  Cardiovascular Health Study (CHS) , have made major contributions to understanding the causes and prevention of heart and vascular diseases, including coronary heart disease.
  • The  Cardiothoracic Surgical Trials Network (CTSN)  is an international clinical research enterprise that studies  heart valve disease ,  arrhythmias , heart failure, coronary heart disease, and surgical complications. The trials span all phases of development, from early translation to completion, and have more than 14,000 participants. The trials include six completed randomized clinical trials, three large observational studies, and many other smaller studies.

The Truth About Women and Heart Disease Fact Sheet

Learn how heart disease may be different for women than for men.

Explore more NHLBI research on coronary heart disease

The sections above provide you with the highlights of NHLBI-supported research on coronary heart disease. You can explore the full list of NHLBI-funded studies on the NIH RePORTER .

To find more studies:

  • Type your search words into the  Quick Search  box and press enter. 
  • Check  Active Projects  if you want current research.
  • Select the  Agencies  arrow, then the  NIH  arrow, then check  NHLBI .

If you want to sort the projects by budget size — from the biggest to the smallest — click on the  FY Total Cost by IC  column heading.

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February 28, 2024

Communicating About Rare Diseases

By Joni L. Rutter, Ph.D., Director, NIH’s National Center for Advancing Translational Sciences

 Joni L. Rutter, Ph.D. Director, National Center for Advancing Translational Sciences

The rare diseases community represents millions of people, and communicating about rare diseases research can make a real difference. It gives patients and their families a sense of the new advances that are here or coming. It keeps researchers informed of cutting-edge approaches and can lead to productive collaborations. It offers powerful stories that advocates and others can use to promote the need for policy changes and continued funding for treatments and cures.

More than 10,000 rare diseases affect nearly 30 million people in the United States. When you add up all the related direct and indirect costs, rare diseases carry a U.S. economic burden of nearly $1 trillion every year. What’s even starker is that burden is likely larger because many rare diseases are essentially invisible in our health care system.

I’m more than aware of the impact rare diseases can have. My mother, Dorothy, had a rare disease called primary myelofibrosis, a condition characterized by the buildup of scar tissue in the bone marrow that ultimately stops the body’s ability to make blood cells. Her 15-year journey to diagnosis ended only to see the beginning of another one: finding treatments. But there were none for her disease. Clinical trial options eventually became available, but they required her to travel 800 miles away from her rural Kansas home.  

My mom’s story is not unique. Those with rare diseases struggle for about six years on average before they receive an accurate diagnosis. Unfortunately, only about 5% of rare diseases have treatments that are approved by the U.S. Food and Drug Administration.

I believe right now is an important turning point for rare diseases. There has been tremendous progress in rare diseases research and treatment development. Just last year, a medication for primary myelofibrosis was approved. Although it was too late for my mom, there is hope for others with a similar disease.

Much more progress is on the horizon. Exciting areas include the use of data-driven approaches like machine learning to diagnose rare diseases sooner, and new therapeutic development approaches that build upon the understanding that about 80% of rare diseases are caused by single gene mutations. NIH is investing heavily in this research.

In the cancer field, the term “cancers” became an umbrella term for all cancers, and this approach led to nationwide research and advocacy programs that are bringing hope to all impacted by a cancer diagnosis. NIH is taking a similar approach for rare diseases—that is, individually they are rare, but collectively they are common. By studying what is common across rare diseases, we can maximize research approaches for more than one disease at a time.

So how can you help? Here are three tips to keep in mind when you’re communicating about rare diseases research.  

Tailor your story for who will be most interested in it. The rare diseases community is broad. It includes researchers, advocacy groups, federal agencies, policymakers, patients and their caregivers, parents who started companies or advocacy organizations to help their children living with a rare disease, and other companies and investors. Researchers are most interested in important scientific advances. Federal agencies and industry want to know about new technologies or approaches that could work for many diseases. Clinical trial findings and drug approvals grab the attention of policymakers as well as patients, caregivers and advocacy organizations affected by those diseases or similar diseases. Stories about rare diseases research can attract interest from pharmaceutical companies and investment firms. Broader audiences also could be interested in these stories because a better understanding of rare diseases can offer insights into the biology of common diseases, such as cancer and Parkinson’s disease.

Put the research in context , so your audience better understands how it fits into the bigger picture. It can take a long time for a promising finding in the lab to ever make it to the medicine cabinet. Odds are it never will—9 out of 10 potential treatments fail in clinical trials. When you’re communicating about rare diseases, you may need to educate your audiences about the drug discovery and development process, especially what the promise of the finding actually is at this stage. It’s also important to avoid using misleading headlines. In addition, stories of progress can show the process unfolding in real-time and can convey that there is still a lot left to do.

Include the patient perspective. Stories that capture the voice of people directly impacted by a rare disease can be incredibly compelling. In another post I recently wrote , I shared drawings made by a young girl with a rare disease before and after treatment as a way to demonstrate the impact of an NIH-funded research tool. Finding people to share their stories can require some effort and, when you do, you must be sensitive to their needs and availability.

Below are some resources to help you tell stories of rare disease progress. One of the best places to discover helpful resources, learn about the latest research, and hear personal stories is Rare Disease Day at NIH . The goal of Rare Disease Day is to raise awareness about rare diseases and their impact on our society. Rare Disease Day always occurs on the last day of February. This year, it falls on the rarest day – Feb. 29.

Helpful Resources on Rare Diseases and Research

Advocacy organizations for individual rare diseases as well as those representing groups of rare diseases (e.g., Global Genes , EveryLife Foundation ) offer information about the diseases, current research, and challenges. They can be a potential source for identifying people with rare diseases.

Federal agencies , including NIH and the U.S. Food and Drug Administration, offer educational resources and disease information (e.g., NIH's Genetic and Rare Diseases Information Center ) and highlight research priorities and explain drug development processes (e.g., NCATS Toolkit for Patient-Focused Therapy Development ).

Scientific organizations offer resources to learn about research and drug development approaches in a less technical way (e.g., American Society of Gene + Cell Therapy’s Gene & Cell Therapy 101 ).

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Popular deep learning algorithms for disease prediction: a review

  • Published: 13 September 2022
  • Volume 26 , pages 1231–1251, ( 2023 )

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  • Zengchen Yu   ORCID: orcid.org/0000-0003-1931-0810 1 ,
  • Ke Wang 2 ,
  • Zhibo Wan 1 ,
  • Shuxuan Xie 1 &
  • Zhihan Lv 3  

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Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.

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1 Introduction

In recent years, with the development of medical detection technology, a large amount of health data has been generated, which requires corresponding big data analysis methods to process these data and generate valuable information, which is helpful for disease diagnosis, personalized medicine and other medicine activities. Artificial intelligence (AI) and machine learning can be used to identify, analyze, predict and classify medical data [ 1 ], so in the past 10 years various AI algorithms have been effectively applied to process data generated in healthcare [ 2 , 3 ], such as applying logistic regression to heart disease prediction to achieve early detection of heart disease [ 4 ]. However, when the data reaches a certain level, the efficiency of traditional Machine Learning algorithms will be significantly reduced, that is, these Machine Learning algorithms lack certain big data analysis capabilities. And deep learning algorithms, namely deep neural networks (DNNs), can solve this problem. The DNN simulates the conduction of the human brain neural network (NN), and defines the input and output through complex layers composition. Each layer composition includes corresponding neurons and nonlinear functions (activation functions) [ 5 ]. Compared with traditional machine learning, the advantage of deep learning is that it can learn from the original data and has multiple hidden layers. It can learn abstract information based on input, process massive data and obtain high accuracy and performance. Therefore, it has been applied to the medical field by many scholars.

This article will divide deep learning into two types according to data types: structured data algorithms and unstructured data algorithms. Structured data algorithms include Artificial Neural Network (ANN) and Factorization Machine-Deep Learning (FM-Deep Learning), which can play a better role in processing structured medical record data. After the combination of FM and DNN, it can solve many problems that ordinary DNN cannot solve. FM is developed from the matrix factorization algorithm. Singular Value Decomposition (SVD), non-negative matrix decomposition and probability matrix decomposition are traditional matrix decomposition methods. They can decompose high-dimensional matrices into two or more low-dimensional matrices, which is convenient to study the properties of high-dimensional data in a low-dimensional space. These matrix factorization methods are widely used in prediction, recommendation and other fields because of their high scalability and good performance. However, traditional matrix factorization methods lack the effective use of context information. In this context, the FM model was proposed and popularized. FM was proposed by Rendle [ 6 ]. It is a supervised learning model [ 7 ], which combines the advantages of matrix decomposition and Support Vector Machine (SVM). Similar to SVM, the difference is that FM models pairwise feature interaction as the inner product of hidden vectors between features through matrix decomposition, so as to better mine feature interaction information, to reduce complexity, to solve sparsity and improve performance. FM was first applied to the Click-through Rate (CTR) predicton information behind the user’s click behavior. But in real-life data are often highly non-linear, so capture high-order feature interaction information can significantly improve performance. Although FM can theoretically model high-order feature interaction, it will cause parameter explosion and huge amount of calculation, resulting in significant increase in time complexity and storage space consumption. Therefore, only second-order feature interaction modeling is usually considered. If the high-order feature combination is performed manually, there are the following disadvantages: (1) experts in related fields need to spend a lot of time to study the correlation between features, which is time-consuming and laborious; (2) for large-scale prediction system, the amount of data is huge, and it is unrealistic to extract features manually; (3) it is impossible to generalize feature interactions that are not in the training set. Deep learning can automatically perform various combinations and nonlinear transformations on the input features, so as to learn high-order feature interaction information. Therefore, the combination of deep learning and FM can capture low-order to high-order features, and can better predict whether patients have diseases and disease types.

Unstructured data algorithms include Convolutional NNs (CNN) and Recurrent NNs (RNN), etc. This article will only explore the development of CNN and RNN and their applications in the medical field. CNN [ 8 ] is a DNN structure including convolutional computation, which has the ability of representation learning and can realize translation-invariant classification of input information according to hierarchical structure. CNNs generally include convolutional layers, batch-normalization layers, pooling layers, fully connected layers, etc. The core of which is the convolutional layer. The function of the convolution layer is to perform feature extraction on the input image. The convolution layer contains multiple convolution kernels. Each element that constitutes the convolution kernel has a corresponding weight coefficient and bias value, similar to the neurons of a feed-forward NN. Convolution calculation means that the convolution kernel slides on the image, and its corresponding elements are multiplied and summed with the covered image features. This process can achieve the effect of extracting local features and reducing parameters. Because the CNN can extract local features and reduce parameters (through weight sharing), it is particularly suitable for the field of image processing. Because there are a lot of image data in the medical field, the application range of CNN in the medical field exceeds that of other models. CNN can solve the problem of spatial dimension, but cannot process data in time dimension. The RNN [ 9 ] came into being, which consists of neurons and feedback loops. RNN has unique advantages for scenarios where the previous input and the next input have dependencies. Specifically, the network will remember the previous information and apply it to the current output calculation, that is, the nodes between the hidden layers are connected, and the input of the hidden layer includes not only the output of the input layer, but also the output of the hidden layer at the previous time. RNN can process time series data well, and is widely used in natural language processing, machine translation, speech recognition, image description generation, text similarity calculation and other fields.

This paper will explore the theories, development and disease application cases of these algorithms. Specifically, the contributions and characteristics of this paper are as follows:

According to the type of main processing data, the algorithm is divided into structured data algorithm and unstructured data algorithm.

CNN and RNN papers account for a high proportion in the field of in-depth learning, and papers on structured data processing methods are rare. Therefore, readers can understand the processing algorithms of structured data in detail through this article.

Different from the summary of classification according to disease types, this paper is classified according to the characteristics of algorithms. For example, in CNN’s disease application section, some paragraphs focus on transfer learning, some paragraphs focus on combinatorial algorithms, and some paragraphs focus on combining attention mechanism.

This paper probes into the problems existing in the current research of disease prediction, such as poor interpretability, unbalanced data, poor data quality and few samples in some cases, and gives the current feasible solutions.

The two major trends in future medical care, integrating Digital Twins and promoting precision medicine, are analyzed, indicating that deep learning disease prediction has a bright future.

This paper will help relevant researchers to understand the characteristics and development trends of related disease prediction algorithms, and ensure that they can purposefully select the most appropriate algorithm in the process of doing research.

Section  2 of this paper will introduce the theories, development and disease application cases of two kinds of structured data algorithms, ANN and FM-Deep Learning. Section  3 will introduce the theories, development and disease application cases of CNN and RNN. Section  4 will respectively introduce the current defects in the field of disease prediction algorithms and the coping strategies. Section  5 analyzes the two major trends of medical treatment in the future, that is, integrating Digital Twins and promoting precise medical treatment. Section  6 summarizes the full text.

2 Structured data algorithms

2.1 artificial neural network, 2.1.1 theory.

ANN consists of multiple layers, each layer has one or more artificial neurons. Each neuron receives one or more inputs. First, each input is multiplied by a network weight (network parameter), which is generally randomly initialized. Calculate the sum of all weighted inputs and deviation values of each neuron, and then input this value into the activation function (nonlinear variation function). Activation function is the core of NN. It introduces non-linearity into the network and makes it possible for the network to learn more complex functions. The output of the activation function is the output of neurons, and the output of each layer of neurons is used as the input of the next layer of neurons. In the iterative training process, the whole network will find the optimal weight distribution, and the loss function is used to measure whether the network weight is optimal. Figure 1 is a schematic diagram of a three-layer ANN. The whole network has an input layer, hidden layers (generally multiple) and an output layer. In practical application, the number of layers of the network will reach dozens or even hundreds of layers.

figure 1

Artificial neural network diagram

2.1.2 Disease application

Because the structure of ANN is relatively simple, it does not have the excellent characteristics of CNN and RNN, so there are few researches in this area [ 10 , 11 ]. Khanam and Foo [ 12 ] implemented a NN model for diabetes prediction, using 1, 2, and 3 hidden layers in the NN model and changing their epochs to 200, 400, and 800, respectively. Hidden layer 2 has 400 epochs and provides 88.6% accuracy, surpassing machine learning models such as Decision Tree, K-Nearest Neighbor (KNN), Random Forest, Logistic Regression, SVM, etc. In 2021, Soundarya et al. [ 13 ] used ANN to compare with machine learning models to detect Alzheimer’s Disease (AD) and found that ANN achieved the highest accuracy with sufficient data. Pasha et al. [ 14 ] used ANN to improve the prediction accuracy of cardiovascular disease. When dealing with large datasets, traditional machine learning models do not perform well, while ANN can play an advantage. These all indicate that ANN is one of the future trends, and deep learning represented by ANN will become the mainstream algorithm for disease prediction.

2.2 FM-deep learning

2.2.1 theory.

To capture second-order interactions between features, a second-order cross term is usually added to the linear regression formula:

There are \({\text {n}}({\text {n}}-1)/2\) parameters in the second-order intersection part, but when finding \(w_{ij}\) , it is necessary that the features \(x_{i}\) and \(x_{j}\) are not 0 at the same time, and the sparse data (especially after one-hot code) satisfies that \(x_{i}\) and \(x_{j}\) are not 0 at the same time There are few cases, so there are fewer samples of corresponding feature interactions in the training set, resulting in inaccurate learned \(w_{ij}\) and over-fitting. In order to solve this problem, FM decomposes \(w_{ij}\) into hidden vectors vi and \(v_{j}\) , that is, \(w_{ij}=\langle v_{i}, v_{j}\rangle\) , where \(v_{i}\) =( \(v_{i1}\) , \(v_{i2},\ldots ,v_{ik}\) ) (k is a hyper-parameter, indicating the length of the hidden vector). The matrix W composed of \(w_{ij}\) can be expressed as follows:

Now there are n * k binomial parameters, far less than the original number of \(w_{ij}\) .

Why do we say that hidden vectors can solve data sparsity? Because all samples containing non-zero feature combinations of \(x_{h}\) can be used to learn \(v_{h}\) . For example, the parameters of \(x_{h} x_{i}\) and \(x_{h} x_{j}\) are \(\langle v_{h}, v_{i}\rangle\) and \(\langle v_{h}, v_{j}\rangle\) , respectively. They have a common item \(v_{h}\) , so the value of \(v_{h}\) can be estimated reasonably. This can greatly reduce the impact of data sparsity.

The implicit vector mechanism can also increase the generalization of the model. According to the principle that FM can solve sparsity, when FM learns the embedded hidden vector weight of a single feature, it does not depend on whether a specific feature combination has occurred. For the feature combination \(x_{i} x_{j}\) that has never appeared before, as long as FM learns the hidden vectors corresponding to \(x_{i}\) and \(x_{j}\) , the weight of this feature combination can be calculated through the inner product, so FM has strong generalization ability. The formula of FM is as follows [ 15 ]:

It can be seen that the complexity of FM is O( \(n^{2}k)\) , and its complexity can be reduced to O(n * k) by the following steps:

The final FM equation is:

In fact, the essence of FM is embedding plus interaction, by assigning each feature \(x_{i}\) (discrete features will be one-hot encoded before) a implicit vector \(v_{i}=(v_{i1}, v_{i2}, v_{i3}\) , \(v_{i4}\) ) (assuming here k = 4), change the original high-dimensional data into a low-dimensional dense vector e through the embedding layer, that is, multiply \(x_{i}\) by the corresponding hidden vector \(v_{i}\) to obtain \(e_{i}\) , as shown in Fig. 2 .

figure 2

Embedding of feature \(x_{i}\)

The entire Embedding layer is shown in Fig. 3 :

figure 3

Embedding layer of FM

In summary, the overall structure of FM can be drawn, as shown in Fig. 4 , where \(y_{Linear} = w_{0} + \sum _{i = 1} ^{n} w_{i} x_{i}\) , \(y_{FM2} = \frac{1}{2} \sum _{f = 1} ^{k} \left( \left( \sum _{i = 1} ^{n} v_{if} x_{i}\right) ^{2} - \sum _{i = 1} ^{n} v_{if} ^{2} x_{i} ^{2} \right)\) .

figure 4

Overall structure diagram of FM

2.2.2 Development history

In 2016, Zhang et al. [ 16 ] proposed a FM Supported NN (FNN). The model uses a DNN with embedded layers to complete the CTR prediction, which obtains the dense vector of each feature through pre training the FM model. Then all embedded vectors of the sample are spliced and input to DNN for training. The feature of FNN is that the embedding vector of each feature is trained by FM model in advance. Therefore, when training DNN model, the overhead is reduced and the model can converge faster. However, the performance of the whole network is limited by the performance of FM. In the same year, Qu et al. [ 17 ] introduced a product layer between the embedding layer and the fully connected layer to propose Product-based Neural Network (PNN). PNN finds the relationship between features through inner product or outer product between features, but it lacks low-order feature interaction, so it may ignore the valuable information contained in the original vector. He et al. studied the recommendation problem in the case of sparse input data, and proposed Neural FM (NFM) [ 18 ]. NFM adopts a framework similar to Wide&Deep [ 19 ], and it uses Bi-Interaction Layer (Bi-linear interaction) structure to process the second-order cross information, so that the information of the cross features can be better learned by the DNN structure, reducing the difficulty of the DNN learning higher-order cross feature information. In order to learn low-level feature interaction, Guo et al. [ 20 ] proposed DeepFM, which combined Deep and FM, used FM for low-level interaction of features, and DNN for high-level feature interaction, combining the two methods in parallel. And both parts share the same input. The final first-order features and second-order and higher-order feature interactions are simultaneously input to the output layer, and the whole process does not require pre-training and feature engineering. He et al. proposed Attention FM (AFM) [ 21 ] by extend NFM. They introduce the attention mechanism into the Bi-Linear interactive pooling operation, which further improved the representation ability and interpretability of NFM. AFM only adds an attention mechanism on the basis of FM, and the quadratic term does not enter the deeper network, so AFM does not take advantage of DNN. Zhang et al. [ 22 ] combined DeepFM and AFM, and proposed Deep AFM (DeepAFM), which combined the AFM and deep learning in a new NN structure for learning. Compared with existing deep learning models, this method can effectively learn the weighted interaction between features without feature engineering by introducing the feature domain structure. There are also many explorations of attention mechanism. Zhang et al. [ 23 ] proposed a new model FAT-DeepFFM, which dynamically captures the importance of each feature before the explicit feature interaction process by introducing CENet domain attention, thus enhancing the DeepFFM. Tao et al. [ 24 ] proposed Higher-order AFM (HoAFM), by explicitly considering the interaction of high-order sparse features, they designed a cross interaction layer, updated the representation of features by aggregating the representation of other co-occurrence features, and implemented a bit by bit attention mechanism to determine the different importance of co-occurrence features in dimensional granularity. Yu et al. [ 25 ] proposed Gated AFM (GAFM) based on dual factors of accuracy and speed, using the structure of gates to control speed and accuracy. Wen et al. [ 26 ] proposed Neural Attention Model (NAM), which deepens the FM by adding fully connected layers. Through the attention mechanism, NAM can learn the different importance of low-order feature interactions. By adding fully connected layers on top of the attention component, NAM can model higher-order feature interactions in a non-linear fashion. In 2019, Yang and colleagues [ 27 ] proposed Empirical Mode Decomposition and FM based NN (EMD2FNN). Empirical mode decomposition helps to overcome the non stationarity of data, and the FM helps to master the nonlinear interaction between inputs. Zhang et al. [ 28 ] proposed High-order Cross-Factor FM (HCFM). They designed Cross-Weight Network (CWN) to achieve high-order display interactions. The cross and compression layers of CWN are designed to effectively learn important feature combinations, and the weight pooling layer aims to learn the weights of different interaction orders to balance the different weights between high-order and low-order feature interactions. Lu et al. [ 29 ] proposed Dual-Input FMs (DIFM), which can efficiently and adaptively learn different representations of a given feature according to different input instances, and can efficiently learn input-aware factors simultaneously at the bit-wise and vector levels (using for re-weighting the original feature representation). The DIFM strategically integrates various components including multi-head self-attention, residual networks, and DNN into a unified end-to-end model. Deng et al. [ 30 ] proposed a new Deep Field-weighted FM (DeepFwFM), which itself combines FwFM components and ordinary DNN components, shows unique advantages in structure pruning, using this combination can greatly reduce inference time. Yu et al. [ 31 ] proposed Neural Pairwise Ranking FM (NPRFM), which integrates a multilayer perceptual NN into Pairwise Ranking Factorization Machine model. Specifically, to capture higher-order and nonlinear interactions between features, a multi-layer perceptual neural network is superimposed on a double-interaction layer to encode the second-order interactions between features. Pande [ 32 ] proposed Field Embedding FM (FEFM) and Deep FEFM (DeepFEFM). FEFM learns the symmetric matrix embedding of each field pair and the single vector embedding of each feature. DeepFEFM combines the FEFM interaction vector learned by FEFM components with DNN to learn high-order feature interaction. Qi and Li [ 33 ] proposed Deep Field-Aware Interaction Machine (DeepFIM) to solve the “short expression” problem and better capture multi-density feature interactions. They proposed a new feature interaction expression based on field identifier, namely “hierarchy expression”. On this basis, they designed a cross interaction layer to identify field and field interaction, and used attention mechanism to distinguish the importance of different features. A dynamic bi pool layer is introduced to enhance the acquisition of high-order features.

There is also a combination of FM and CNN. Zhang et al. [ 34 ] proposed Deep Generalized Field-aware FM (DGFFM), which uses a wide-deep framework to jointly train Generalized Field-aware FM (GFFM) and DenseNet. It aims to combine the advantages of traditional machine learning methods, including their faster learning speed for low-rank features and the ability to extract high-dimensional features, where GFFM can significantly reduce computation time by exploiting the corresponding positional relationship between field indices and feature indices. Chanaa and El Faddouli [ 35 ] proposed Latent Graph Predictor FM (LGPFM), which utilizes CNN to capture interaction weights for each pair of features. LGPFM combines the advantages of FM and CNN, and CNN can work efficiently in the grid topology, which will significantly improve the accuracy of the results.

Metric learning can also be combined with the FM algorithm. Guo et al. [ 36 ] proposed an FM framework based on generalized metric learning technology. The metric method based on Mahalanobis distance uses semi positive definite matrix to project features into a new space, so that the features obey certain linear constraints. The distance function based on DNN is designed to capture the nonlinear feature correlation, which can benefit from the strong representation ability of metric learning method and NN. At the same time, a learnable weight is introduced for the interaction of each attribute pair, which can greatly improve the performance of the distance function.

2.2.3 Disease application

Chen and Qian [ 37 ] proposed NN and FM for the diagnosis of children’s sepsis. NN can better process the test index result value of patients, and FM can better process the test index state data of patients with sparse structure. Ronge et al. [ 38 ] developed a deep FM model for AD diagnosis, which consists of three parts: an embedding layer that handles sparse categorical data, Factorization Machine that efficiently learns pairwise interactions, DNN that implicitly model higher-order interactions. The above are simple combinations of NN and FM, and the FM-Deep Learning algorithms with better performance mentioned in Section 2.2.2 are not used. While Fan et al. [ 39 ] applied DeepFM to predict the recurrence of Cushing’s disease after transsphenoidal surgery, predicted the recurrence of 354 patients with initial postoperative remission in Peking Union Medical College Hospital, and obtained the highest AUC value (0.869) and the lowest logistic loss value (0.256), which exceeded other models.

3 Unstructured data algorithm

3.1 convolutional neural network, 3.1.1 theory.

CNN is particularly suitable for learning image features. Before CNN was proposed, the fully-connected network was generally used to extract image features, but the entire fully-connected network often had a particularly large number of connections, which would lead to an explosive increase in the number of parameters and training time. It can be noted that it is not necessary for each neuron to perceive the entire image, the image has a strong 2D local structure, that is, spatially adjacent variables (or pixels) are highly correlated. So people put forward the concept of CNN, which combines three ideas: local receptive field, shared weight and down sampling. The size of convolution kernel is called receptive field. The convolution kernel slides on the image and extracts the features of its coverage area, which can achieve the purpose of forcibly extracting local features, and extract visual features such as edges and corners. Because each region of the image is scanned by a convolution kernel with the same weight, the weight sharing is realized and the number of parameters is greatly reduced. Therefore, the convolution layer of CNN can extract local features well and reduce the number of parameters.

CNN also includes batch-normalization layers, activation layers, and pooling layers. The batch-normalization layer standardizes the small batch data to make it conform to the standard normal distribution, and performs scaling and migration operations, which effectively avoids the disappearance of the gradient, speeds up the decline of the gradient and accelerates the convergence. The activation layer non-linearly processes the input through the activation function, which enables the whole NN to fit any function. The formula is as follows:

Here a is the activation function, x is the input, and both w and b are weight parameters.

Figure 5 is a simple schematic diagram of CNN.

figure 5

Convolutional neural network diagram

3.1.2 Development history

In 1989, LeCun et al. [ 40 ] designed CNN with two convolutional layers (with convolution kernel size of \(5\times 5\) ), trained on the handwritten zip code dataset of the United States Post Office, and the generalization performance of the model reached best at the time. This network is actually the prototype of LeNet, but the whole network only has convolution layer and full connection layer. In 1998, LeCun et al. [ 41 ] formally put forward LeNet5, which includes convolution layer, pooling layer and full connection layer. There are seven layers in total. The convolution layer uses \(5\times 5\) convolution kernels, and the activation function uses sigmoid. LeNet-5 has a total of 340,908 connections, but the number of trainable parameters is reduced to 60,000 due to weight sharing. After LeNet-5 was proposed, the research of CNN in speech recognition, object detection, face recognition and other application fields has gradually been carried out. After 2012, the CNN entered the stage of large-scale application and in-depth research. The sign was that Krizhevsky et al. [ 42 ] proposed AlexNet-8, and its ImageNet Top5 error rate reached 15.3% in the 2012 ILSVRC competition. AlexNet-8 consists of five convolutional layers, which are filled with all zeros and use ReLU as the activation function. Some convolutional layers are followed by a maximum pooling layer, which can better extract feature textures. AlexNet also uses Dropout to prevent over-fitting. Simonyan and Zisserman [ 43 ] proposed VGGNet-16 and VGGNet-19, which used a small convolution kernel ( \(3\times 3\) receptive field), which improved the recognition accuracy while reducing parameters. VGGNet also adds a batch-normalization layer to speed up the training process, and its number of layers exceeds the previous network, reaching 16–19 layers, which can better learn sample features. The entire network structure is regular and suitable for parallel acceleration. In the 2014 ILSVRC competition, VGGNet reduced the ImageNet Top5 error rate to 7.3%. In the same year, InceptionNet, that is, GoogleNet, was proposed [ 44 ], with a depth of 22 layers, and using convolution kernels of different sizes in one layer to improve the perception of the model. InceptionNet uses a \(1\times 1\) convolution kernel to change l output features. The number of channels of the graph (can reduce network parameters). Its ImageNet Top5 error rate was reduced to 6.7%.

Although the depth increase is the development trend of CNN, the gradient will disappear as the number of layers increases to a certain extent. At this time, the accuracy of the depth learning model reaches saturation, and then the training error and test error will decrease significantly, resulting in the inability of the model to converge. So in 2015, the Kaiming He team [ 45 ] proposed Residual NN (ResNet), which is connected by residual skip connections between layers, which is mainly to add several identity mapping layers (input equal to output) after some layers, In this way, the forward information can be introduced, which can suppress the disappearance of the gradient, which enables the number of layers of the NN to exceed the previous constraints, reaching hundreds of layers and improving the accuracy. ResNet evaluated on the ImageNet dataset are 152 layers deep-8 times deeper than VGGNet, but still less complex. In addition, the model also uses a global pooling layer to replace the fully connected layers, which can also achieve the purpose of reducing parameters.

3.1.3 Disease application

Acharya et al. [ 46 ] were the first to use CNN for Electroencephalogram (EEG) signal analysis. In this work, the authors implement a 13-layer CNN to detect normal, preictal and epileptic seizure categories without separate feature extraction and feature selection steps. Muhammad et al. [ 47 ] proposed CNN-based fusion model for EEG pathology detection. Hossain et al. [ 48 ] uses Deep Learning techniques for Epilepsy Seizure Detection. Chanu and Thongam [ 49 ] proposed a computer-aided 2D cellular neural network classification technique to classify MR images into two categories: normal and tumor. This method is suitable for inclusion in clinical decision support systems for the initial diagnosis of brain tumors by clinical experts. In 2022, Seven et al. [ 50 ] used the deep learning of Endoscopic Ultrasonography (EUS) images to predict whether the malignant potential of gastrointestinal stromal tumors. First let the EUS image be resized in \(28 \times 28 \times 1\) format through Lanczos interpolation. The deep learning part uses 20 CNN kernels for the first layer and 50 for the second layer. After each kernel layer, the image resolution is halved. After these convolutional processes, the feature image information is put into the ANN model to train the AI system. The results show that the AI of deep learning based on EUS images can predict the malignant potential of gastric stromal tumors with high accuracy. Yin [ 51 ] constructed two 50-layer ResNets based on different building blocks to classify skin lesion images. Although these studies have no major innovations, they have exerted the unique image feature extraction ability of CNN and achieved good results. Rahman et al. uses CNN with relevant adversarial examples (AEs) for COVID-19 diagnosis [ 52 ].

Transfer learning refers to transferring the parameters of the trained model (pre training model) to a new model to help train the new model. Because transfer learning can ensure that the model has a higher starting point (before fine tuning, the initial performance of the model is higher), a higher slope (during the training process, the promotion rate of the model is faster) Higher asymptotic (the model converges better after training), so it often plays a role in the field of disease prediction in combination with CNN. In 2019, Amin et al. [ 53 ] proposed a new method to classify tumor/non-tumor Magnetic Resonance Images (MRI), where the segmented images are fed to a pre-trained CNN model where feature learning is performed by AlexNet and GoogleNet. Fully connected layer are used for feature mapping and score vectors are obtained from each trained model. In addition, the score vector is provided to the softmax layer and multiple classifiers. In 2020, Wang et al. [ 54 ] proposed two CNN models, which can automatically distinguish benign and malignant masses, lipomas, benign schwannomas and vascular malformations by learning image features. The author chose VGGNet-16 architecture pre-trained on ImageNet dataset to build two CNN models, so as to improve performance by using transfer learning and DNN architecture. Chelghoum et al. [ 55 ] used nine pre-trained deep networks, including AlexNet, GoogleNet, VGG-16, VGG-19, ResNet-18, ResNet-50, ResNet-101, ResNet-Inception-V2, and SENET to solve the problem of brain tumor classification by using transfer learning method. The results show that when the number of training samples is small and the number of iterations is small, the performance of the model is still good and the time consumption can be reduced. Similar to the research of Chelghoum et al., Kaur and Gandhi [ 56 ] also explored different pre trained classical CNN models to explore the transfer learning ability in pathological brain image classification. The author uses various pre trained DCNN, namely AlexNet, ResNet-50, GoogleNet, VGGNet-16, ResNet-101, VGGNet-19, Inception V3 and Inception ResNet V2. The last layers of these models are replaced to adapt to the training set. Compared with other models, AlexNet shows the best performance in a shorter time. Rehman et al. [ 57 ] also aimed at the problem of brain tumors, combined with the traditional machine learning model, adopted three classical CNNs (AlexNet, GoogleNet and VGGNet) to classify brain tumors such as meningioma, glioma and pituitary tumor. The author took these three CNNs as pre-training models and used their different freezing layers respectively. Finally, SVM is used for classification. The results show that the fine tuned VGGNet-16 architecture achieves the highest accuracy in classification and detection, reaching 98.69%. Kumar and Nandhini [ 58 ] adopted the entropy image slicing method to select the most informative MRI slices during the training phase. Transfer learning training was performed on the ADNI dataset, and the VGGNet-16 network was used to classify AD of normal individuals. By introducing the MRI slice method, the model can effectively reduce the preprocessing complexity, and use the VGG-16 network transfer learning technique to solve the unreliability problem. Extracting the parameters of the pre-training model for processing is also one of the methods of transfer learning. Tsai and Tao [ 59 ] trained the deep Convolution NN model, and extracted the modified parameters in the network layer to identify the abundant different tissue types in the histological images of colorectal cancer. Eweje et al. [ 60 ] utilized a deep learning approach combining conventional MRI images and clinical features to develop a model to classify the malignancy of bone lesions. The method consists of three parts: (1) Imaging data model: By adopting the EfficientNet deep learning architecture, an image classification model is developed. EfficientNet models initialized with weights pre-trained on the ImageNet database can extract features from imaging data. (2) Clinical data model: logistic regression model using clinical variables. Inputs are patient age, gender, and lesion location. For 21 locations (clavicular, skull, proximal femur, distal femur, foot, proximal radius, distal radius, proximal ulna, distal ulna, hand, hip, proximal humerus, distal humerus, proximal tibia end, distal tibia, proximal fibula, distal fibula, mandible, rib/chest wall, scapula, or spine) were one-hot encoded so that the model received 23 different input variables for data quantification. (3) Ensemble model: (1) and (2) are combined using a stacking ensemble approach, where the voting ensemble receives as input the malignancy probability from the imaging and clinical feature models and creates an output based on the sum of the predicted probabilities.

Previously, Rehman et al. combined AlexNet, GoogleNet, and VGGNet with traditional machine learning models, and achieved good results, but if two different deep learning models can be combined, better results can be achieved. In 2021, Kokkalla et al. [ 61 ] proposed a deep dense initial residual network model for the three-class classification of brain tumors, which customized the output layer of inception ResNet V2 with fully connected networks and softmax layer. In the same year, Ning et al. [ 62 ] proposed an automatic Congestive Heart Failure (CHF) detection model based on a hybrid deep learning algorithm of CNN and Recursive Neural Network. Normal sinus heart rate signals and CHF signals were classified according to ECG and time spectrum. The author carries out feature extraction of ECG signal, mainly extracts RR interval sequence, calculates the time spectrum of ECG signal, and uses CNN to automatically identify the spectrum and related features crossed with time domain. Srinivasu et al. [ 63 ] introduced MobileNet V2 with LSTM components to accurately classify skin diseases from images captured from mobile devices. MobileNet V2 is used to classify skin disease types, and LSTM is used to enhance the performance of the model by maintaining state information of features encountered in previous generation image classification.

The attention mechanism can assign different weights to the input features, so that the model can focus on more important features and information. Therefore, some scholars combine the attention mechanism with CNN for disease prediction. Toğaçar et al. [ 64 ] proposed a deep learning model BrainMRNet for brain cancer detection. BrainMRNet is a feedforward end-to-end convolution model, including super column technology, attention module and residual block. Using the super column technology, the features of the input image extracted through the convolution layer of each pixel are combined through the super vector, and the most effective features in the vector are selected and transferred to the next layer; Through the attention module, BrainMRNet attracts attention to the important areas of input data, while unnecessary areas are ignored, which can increase the verification success rate of BrainMRNet; the whole model is composed of residual blocks, which can improve the performance of the model by updating the weight parameters of back propagation. Metric learning is also called similarity learning, which is to classify by comparing the similarity between samples. Some scholars combine CNN with metric learning. Jiao et al. [ 65 ] adopted a deep distance metric to learn breast mass classification. The model contains convolutional layers and metric layers. Firstly, the model trains and fine tunes the level of CNN. The CNN structure can provide a good depth feature extraction network and a baseline for breast mass classification. Then, the large edge metric learning method with hinge loss is used to initialize the ensemble learning layer, and the ensemble learning layer is trained to make the characteristics of different breast masses more separable. The metric layers benefits from the representative characteristics of the convolutional layers, and the data flow between them is limited by one-way transmission. The relationship between the two layers is similar to the parasitic relationship in biology/ecology. Therefore, the proposed method is called parasitic metric learning network.

Shallow CNNs can reduce spatial and temporal constraints. Tripathi and Singh [ 66 ] proposed a hybrid, flexible deep learning architecture, OLConvNet, which combines the interpretability and depth of traditional object-level features by using a shallower CNN named CNN3L. Extract DL features from the original input image. Then the two sets of features are fused together to generate the final feature set. Multilayer perceptron uses the final fused feature set as input to classify the histopathological nuclei into one of four categories.

Although CNN is mainly used in the image field, some scholars also apply it to structured medical record data and speech data. In 2016, Cheng et al. [ 67 ] proposed a deep learning method for phenotypic analysis from patients’ Electronic Medical Records (EHR). Firstly, the EHR of each patient is expressed as a time matrix, with time in one dimension and events in another dimension. Then a four layer Convolution NN model is established for phenotypic extraction and prediction. The first layer consists of these EHR matrices. The second layer is a unilateral convolution layer from which the phenotype can be extracted. The third layer is the largest aggregation layer that introduces sparsity to the detected phenotypes, so as to retain only those significant phenotypes. The fourth layer is the fully connected softmax prediction layer. In order to integrate the temporal smoothness of patients’ EHR, the author also studied three different temporal fusion mechanisms in the model: early fusion, late fusion and slow fusion.

In 2019, Gunduz [ 68 ] proposed two frameworks based on CNNs to classify Parkinson’s Disease (PD) using sound (speech) feature sets. Although the two frameworks are used to combine various feature sets, they are different in combining feature sets. The first framework combines different feature sets and provides them as input to 9-layer CNN, while the second framework transfers the feature sets to the parallel convolution layer. The second framework can learn deep features from each feature set through parallel convolution layer. The extracted deep features can not only successfully distinguish patients with PD from healthy people, but also effectively enhance the discrimination ability of the classifier.

In 2020, Sajja and Kalluri [ 69 ] proposed a CNN to predict whether a patient has heart disease. The convolutional architecture adopted by the authors consists of two convolutional layers, two Dropout layers, and an output layer. The model predicts disease with 94.78% accuracy on the UCI-ML Cleveland dataset, outperforming logistic regression, KNN, Naive Bayes, SVMs, and NNs. This is also an application of CNN to structured data.

3.2 Recurrent neural network

3.2.1 theory and development.

RNN [ 70 ] is used for pattern recognition of streaming or sequential data such as speech, handwriting and text. There is a circular connection in the hidden layer of RNN. The RNN performs cyclic calculation in the cyclic connection of these hidden units to process the input data in sequence. Each previous input data is stored in a state vector in the hidden unit, and these state vectors are used to compute the output. In summary, RNN calculates a new output considering the current input and the previous input. Although RNN has good performance, in the back-propagation of RNN, when calculating the gradient adjustment weight matrix, due to many partial derivatives multiplied continuously, the gradient in the network will become very small and gradually disappear, or become too large, which makes it difficult for RNN to learn long-distance information. In order to solve this problem, some scholars proposed long short-term memory (LSTM) network [ 71 ], which can store sequence data for a long time and solve the problem of gradient disappearance. As shown in the upper part of Fig. 6 , LSTM uses a gating mechanism and introduces an input gate, a forget gate and an output gate. When the gate is closed, it will prevent changes to the current information, so that the previous dependency information will be learned; when the door is open, it does not completely replace the previous information, but makes a weighted average between the previous information and the current information. Therefore, no matter how deep the network is and how long the input sequence is, as long as the door is open, the network will remember these input information. The input gate controls the information of the current word to be integrated into the cell state. The current cell state integrates the information of the current word and the cell state of the previous moment, and represents the long-term memory. The input gate determines how much information about the current word will be stored in the current cell state. The forget gate controls the information of the cell state at the previous moment to be integrated into the current cell state. When understanding a sentence, the current word may continue to describe the meaning above, or it may start to describe new content from the current word, which has nothing to do with the above, so it is necessary to do the corresponding forgetting operation. The forget gate is responsible for selectively forgetting the information of the cell state. The output gate is responsible for selectively outputting the cell state information. Gated Recurrent Unit, GRU [ 72 ] is a simplified version of LSTM. As shown in the lower part of Fig. 6 , GRU changes the original three gates into two gates—update gate and reset gate. The reset gate is used to control the influence of the hidden layer state at the previous moment (representing the past information) on the current word. The update gate is a merger of the forget gate and the input gate in LSTM, and is responsible for assigning the importance of past and present information. In this way, the structure of GRU is simpler and matrix operations are less in calculation. Therefore, GRU can save more time than LSTM in the case of large training data.

figure 6

LSTM and GRU structure diagram. upper: LSTM; lower: GRU

3.2.2 Disease application

RNNs with LSTM hidden units, pooling, and word embeddings are used in DeepCare [ 73 ], an end-to-end deep dynamic network that infers current disease states and predicts future medical outcomes, the authors also conditioned LSTM cell with decay effect to handle irregularly timed events. In 2018, Chu et al. [ 74 ] proposed a new context-aware attention mechanism for detecting Adverse Medical Events (AME) of cardiovascular diseases to learn the local context information of words in medical texts. The attention mechanism enables the keywords related to the target AME to get more attention signals, and then drives the model to locate prominent parts of medical texts. The proposed neural attention network is combined with the standard Bi-LSTM model to detect AMEs from a large number of EHR data. The combination of global order-dependent signals of words captured by standard Bi-LSTM and local context signals of words captured by context attention mechanism can significantly improve the performance of AME detection in medical texts.

Some scholars use LSTM for Electrocardiogram (ECG) signal processing. In 2018, Tran et al. [ 75 ] proposed a feature extraction-based method to process ECG signals from Internet of Things (IoTs)-specific devices, employing an Auto-Encoder (AE) model to reduce data dimensionality, by combining LSTM extracts top ECG features. Finally, the full connection layers were used to distinguish normal ECG from abnormal ECG.

Some medical record data with time characteristics (i.e. serialized data) can also be analyzed by LSTM. In 2018, Reddy and Delen [ 76 ] used RNN–LSTM method to predict the readmission probability of lupus patients within 30 days by extracting the time relationship from longitudinal EHR clinical data. RNN–LSTM method can make use of the relationship between patients’ disease state and time, which makes the model have higher performance. In 2019, Wang et al. [ 77 ] used LSTM to predict 6-month, 1-year and 2-year mortality in dementia patients. The deep learning model proposed by the authors consists of two stacked LSTM layers and two attention layers: one between the input layer and the LSTM layer, and the other between the LSTM layer and the output layer. Stacked LSTM layers support hierarchical abstraction of the input data. Attention layers are used to improve model performance as well as keep track of the importance of temporal inputs as the model makes predictions.

There are also several examples of GRU applications. There are also several application cases of GRU. In 2017, Choi et al. [ 78 ] used GRU for heart failure diagnosis. Compared with popular methods such as logistic regression, Multi-Layer Perception (MLP), SVM and KNN, GRU performed well in heart failure diagnosis. The results show that the deep learning model suitable for using time relationship improves the performance of the model for detecting sudden heart failure in a short observation window of 12–18 months. Choi et al. [ 79 ] used RNN with GRU to develop doctor AI, an end-to-end model that uses patient history to predict subsequent diagnosis and drug treatment.

Some scholars have proposed that RNN is lighter than CNN and it can also be used for image processing. In 2020, Amin et al. [ 80 ] proposed an automatic classification method for brain tumors based on LSTM of MRI. First, N4ITK of size 595 and Gaussian filter are used to improve the quality of multi-sequence MRI. The classification is performed using the proposed four-layer deep LSTM model. In each layer, 200, 225, 200 and 225 are selected as the optimal number of hidden units, respectively. The lightweight four layer LSTM model proposed by the author has achieved better results in temporal data processing, which is conducive to the learning of multi sequence MRI.

4 Existing defects and solutions

Here we list several problems in current disease research, which will correspondingly affect the diagnosis rate of disease prediction algorithms. These problems are: Poor Interpretability, Data Imbalance, Data Quality Issues, Too Little Data. Among them, Poor Interpretability is about deep learning algorithms. Poor interpretability leads to low reliability of deep learning disease prediction algorithms, which is not good for helping doctors analyze pathological causes. The remaining three problems are related to the data. Data Imbalance will cause the classifier to lose its classification ability. Data Quality Issues, poor quality datasets will lower the performance limit of deep learning algorithms on specific problems. Too Little Data, a small amount of data will lead to over-fitting and seriously reduce the quality of deep learning algorithms. In addition to enumerating these problems, this section also presents the current corresponding solutions.

4.1 Poor interpretability

Traditional statistical methods are usually based on manual feature engineering of medical related domain knowledge. These methods are closely combined with medical knowledge. Although the effect is not very outstanding, they give doctors reliable interpretability. Deep learning algorithms are like a black box and are driven by data which we cannot see the feature extraction and screening process. Therefore, although deep learning improves the feature extraction ability and classification ability of the model, its interpretability is very poor, which is easy to lead to the unreliability of the results and bring risks. Only by solving the interpretability problem of the model, deep learning can be more widely used in the actual disease prediction, better serve doctors and patients, and make them have confidence in the diagnosis results of the model.

The general solution is to add attention mechanism, which is suitable for both structured and unstructured data. Attention mechanism was first applied to the field of natural language processing, which can better find the relationship between words in sentences and better predict the next words. AFM and DeepAFM are the application of attention mechanism in FM algorithm; Woo et al. [ 81 ] proposed the Convolutional Block Attention Module (CBAM) in 2018. Woo et al. [ 81 ] proposed convolutional attention module (CBAM) in 2018. Given an intermediate feature map, CBAM module will infer the attention map along two independent dimensions (channel and space), and then multiply the attention map with the input feature map. CBAM is a lightweight general module, which can be seamlessly integrated into any CNN architecture for end-to-end training with basic CNN without excessive additional over-head.

The Local Interpretable Model Agnostic Explanations (LIME) can also be adopted to solve the problem of poor interpretability. LIME establishes a linear separable model locally in the model through local disturbance sampling and linear approximation, and estimates the importance of each feature through the feature weight of the linear model [ 82 , 83 ].

For images, interpretability methods based on activation mapping can be adopted, such as Class Activation Mapping (CAM) [ 84 ], Grad-CAM [ 85 ], Grad-CAM++ [ 86 ], and Score-CAM [ 87 ], etc. This method generates saliency map by linear weighted combination of activation mapping to highlight important areas in image space. The saliency map is used to highlight the features in the input considered to be related to the prediction of the learning model, which does not need training data or modify the model.

4.2 Data imbalance

There is always an imbalance in medical data because there are fewer people who are sick than those who are not. When the data is severely unbalanced, the model always classifies the samples into the majority class, for example if a model is trained to predict whether a patient has a tumor, when the number of negative samples (patients without a tumor) in the training set is much higher than When the number of positive samples is positive, when predicting whether a new patient has a tumor, the model always diagnoses the patient as not having a tumor, which is obviously not what we want.

For image data, Generative Adversarial Networks (GAN) [ 88 ] can be used. GAN can generate minority class samples that are close to real samples and solve the problem of data imbalance. For binary classification problems, the method of Synthetic Minority Oversampling Technique, SMOTE [ 89 ] can also be used. SMOTE can up-sample or down-sample the training set, so that the proportion of positive and negative samples reaches a balanced state.

Structured data can also use the SMOTE method, but up-sampling will destroy the discreteness of the data, making discrete features into continuous features, resulting in inconsistent data types in the training and test sets, which is not conducive to the learning of FM algorithms. If the number of minority class samples is too small, using down-sampling will lead to a serious shortage of training samples. These are questions to be studied in the future.

4.3 Data quality issues

Data quality remains the biggest challenge in model training. The excellent performance of deep learning models in disease prediction relies on high-quality medical data. While medical data is readily available under existing conditions, the quality of the data remains low. Moreover, there may be problems such as the mismatch between the training samples and the real samples and the existence of some abnormal features, which will affect the model effect. There is also a lot of medical data that requires experienced medical experts to give sample labels.

For image, speech and other types of data, the quality can be improved by using GAN, up-sampling, Fourier transform and other methods. For structured data, methods such as filling in missing values, deleting duplicate values, and outliers are often used for data cleaning, and methods such as discretization, filter, wrapper, and Principal Component Analysis (PCA) are used for feature selection to obtain higher-quality samples. Since we are talking about deep learning algorithms, it is possible to build end-to-end deep learning algorithms like DeepFM, without feature engineering, and let deep learning exert automatic feature learning capabilities to overcome data quality issues. The automatic learning ability of deep learning can also be applied to sample label processing, which involves unsupervised learning and is beyond the scope of this article.

4.4 Too little data

Although a large amount of health data has been generated at present, many medical data sets involve privacy issues, which are stored in independent institutions and are not made public. Therefore, a large number of data sets can’t be used for practical research, so the model can’t be fully trained, and it’s hard to exert its real effect. Here we only discuss how to solve the problem from the aspect of algorithms.

For images, the method of Few-shot Learning [ 90 , 91 , 92 ] can be used, that is, the model is trained through a large number of tasks to improve the generalization ability of the model. When faced with similar new tasks, the model can be trained after a small number of iterations. achieve better results. Few-shot Learning includes the following methods in total: (1) model fine-tuning [ 93 , 94 ], obtaining a pre-trained model on a source dataset with a large number of samples, and then fine-tuning the pre-trained model on a target dataset with a small number of samples. This method is more suitable for scenarios where the source dataset and target dataset are similar, but in practical scenarios, the two datasets are usually dissimilar, which often leads to over-fitting. (2) Data augmentation refers to the use of some additional datasets or information to expand the target data set or enhance the characteristics of the samples in the target data set [ 95 , 96 ]. In the early stage, the data set was expanded through spatial transformation, but this could not expand the types of samples. Later, people used methods such as GANs for data augmentation. Meta learning refers to letting the model learn meta-knowledge from a large number of tasks, and use this meta-knowledge to quickly adapt to different new tasks. Meta learning includes Memory NN [ 97 , 98 ], Meta Network [ 99 ], Model-Agnostic Meta-Learning (MAML) [ 100 ] and other algorithms. Metric learning, also known as similarity learning, calculates the distance between two samples through a distance function, so as to measure the similarity between them and determine whether they belong to the same category. The metric learning algorithm consists of an embedding module and a measurement module. The embedding module converts the samples into vectors in a low-dimensional vector space, and the measurement module gives the similarity between samples. Metric learning is divided into fixed distance based metric learning [ 101 ] and learnable network based metric learning [ 102 ].

However, few-shot learning is mainly applied to images, and it is often ineffective in structured data. Because the idea of Few-shot Learning is similar to that of a child distinguishing animals, after seeing a lot of animal pictures, give him a picture of a rhino, and he can find a rhino among many animals. Images have certain similarity and have a general large data set, so they can meet the requirements of a large number of similar tasks. However, different diseases have different features, and these features have different characteristics. Therefore, there is no general large data set, which is difficult to meet the requirements of a large number of similar tasks. At present, there are traditional machine learning algorithms (low complexity), Boosting sampling algorithms, and feature selection to solve the problem of small amount of structured data. Among them, traditional machine learning algorithms and feature selection make up for the overfitting problem caused by the small amount of data by reducing the complexity (model complexity or feature complexity). There is no more effective way to solve this problem.

5 Future works and prospects

5.1 incorporating digital twins.

Digital Twins refers to building the same entity in the digital world through digital means to realize the understanding, analysis and optimization of the physical entity. With the development of technologies such as AI, Big Data, Virtual reality, IoT, and cloud computing [ 103 , 104 ]. Digital Twins have begun to shine in industrial, medical and other fields. The application of Digital Twins in medical care is usually to create a model based on real medical data in the virtual world, and then observe and analyze the stimulus changes of the model to various conditions, such as the feedback generated by the intervention of new drugs or new treatment regimens. These real medical data come from EHRs, daily behavior databases, medical wearable devices, and more. Therefore, through Digital Twins, medical activities such as health detection, telemedicine, early disease diagnosis, and disease treatment can be realized [ 105 , 106 ], providing revolutionary solutions in the field of healthcare [ 107 ]. Health monitoring is an important means in modern medicine. The use of various wearable sensors in the Digital Twins can realize ubiquitous monitoring of the health status of patients [ 108 ], and can also reduce medical costs, reduce the number of hospitalizations, and improve the quality of life of patients [ 109 , 110 ].

Digital Twins can be combined with deep learning algorithm of disease prediction to realize faster and more developed electronic medical treatment and automated medical treatment. The general realization methods are as follows: firstly, collect data, and use various sensors, especially various convenient wearable sensors to collect various health information [ 111 , 112 ], and transmit these data to the cloud. It can also collect various electronic medical record data and daily behavior database data. Then, using these collected medical data, a digital model of disease prediction is established in the cloud by deep learning algorithm. Finally, the digital model is used to process and analyze the health data, so as to predict the patient’s physical condition, whether he is ill or not, the probability of illness, etc. In the process of analysis, new knowledge and new information will be generated [ 113 ], which will help to adjust and upgrade the model, and help related researchers to better understand the mechanism behind the disease, so as to find a better treatment.

Many scholars have proposed a combination of Digital Twins and deep learning. For example, Chakshu et al. [ 114 ] proposed a method to achieve cardiovascular Digital Twins using reverse analysis, which uses a virtual patient database. By inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral, and brachial), the blood pressure waveforms in various blood vessels of the body are calculated backwards with the help of LSTM cells. The reverse analysis system established by this method is mainly used for the detection of abdominal aortic aneurysm and its severity. Quilodrán-Casas et al. [ 115 ] created two Digital Twins systems of SEIRS models and applied them to simulate the spatial and temporal propagation of COVID-19, and compared their prediction results with real data. They compared the performance of the two digital twin models [also known as Non-invasive Reduced Order Model (NIROM)]. The first method is to use PCA for dimensionality reduction and Bi-LSTM with data correction (through optimal interpolation) for prediction. The second NIROM uses PCA for dimensionality reduction again and GAN for prediction. In addition, there are many related studies.

In the future, we should realize a more intelligent processing mode through Digital Twins and deep learning model, realize a truly automatic and intelligent medical system, and greatly reduce the workload of doctors. At the same time, more Digital Twins medical system platforms need to be developed to achieve a wider range of intelligent medical treatment. Intelligent medical treatment is one of the important links of smart city, and intelligent medical treatment is indispensable to the realization of smart city. Therefore, on the basis of ensuring the security of Digital Twins medical platform, we should further broaden the scope of application and serve the user group more comprehensively. Intelligence is one of the core elements of future medical and urban development. To truly realize comprehensive medical intelligence, we must better integrate medical Digital Twins and deep learning algorithm technology.

5.2 Promoting precision medicine

Precision medicine is the principle and practice of integrating modern medical technology and traditional medical methods, scientifically understanding human body functions and the nature of diseases, systematically optimizing the principles and practices of human disease prevention and control, and maximizing individual and social health benefits with efficient, safe and economical health care services. In clinical practice, precision medicine pursues accurate and reasonable diagnosis and treatment methods for each patient in order to minimize iatrogenic damage, minimize medical costs and maximize patient benefits. Compared with traditional medicine, it can provide patients with more effective, cheaper and more timely medical services. Since it was proposed in 2015, it has been the key to global healthcare and one of the important goals of many sustainable development plans around the world [ 116 , 117 ]. The concept of precision medicine opens up new ideas for human health and healthcare [ 118 , 119 ].

Like personalized medicine, precision medicine focuses on individual differences [ 120 ], exploring the impact of individual factors on disease [ 121 ]. Assessment of personal health from genomics, living environment, etc., coupled with clinical data analysis, will have higher performance. For example, Panayides et al. [ 122 ] proposed that starting from the methods of radiomics and radiogenomics, combined with precision medicine, some abnormal diseases can be found more quickly when dealing with disease problems. Precision medicine also has good performance in preventing malignant diseases, such as cancer [ 123 , 124 ], tumor [ 125 ] and so on. It can be said that disease prediction and disease treatment are moving towards the era of precision medicine [ 126 ].

At present, there are many researches on precision medicine in Western countries, but the research on precision medicine in the Asia–Pacific region is still in the initial stage. On the one hand, it is necessary to ensure the diversity and high quality of gene collection. On the other hand, it is necessary to extract the genetic characteristics consistent with the population of the Asia–Pacific region. These two are both urgent problems to be solved at present, and they are also the reasons that hinder development.

In the next era, precision medicine will be combined with multi-field applications. Realize the systematic operation of medical diagnosis and promote the development of medical care in a more intelligent direction. For example, Lu and Harrison [ 127 ] pointed out that CNN can realize large-scale medical image analysis and labeling, and can accurately obtain pathological information of different patients. Laplante and Akhloufi [ 128 ] proposed a deep NN classifier to identify the anatomical location of tumors. Using the 27 TCGA miRNA stem cell ring cohort, tumors at 20 anatomical sites were classified with 96.9% accuracy. Therefore, deep learning can be combined with precision medicine [ 129 ] to better process big data and fundamentally promote the development of precision medicine [ 130 ]. As part of precision medicine, accurate prediction of disease embodies enormous advantages and value, and can advance the development of modern medical technology. However, the current precision medicine is still in the stage of exploration and development [ 131 , 132 , 133 ], the research situation of different diseases is very different, and the application of deep learning technology is still in the development stage. In the future, AI-related researchers should focus more on precision medicine and build deep learning models that better meet the requirements of precision medicine in combination with the research on radiomics and genomics in the medical field. While promoting the progress of precision medicine, it also drives the multi-faceted development of deep learning, which is more in line with social needs.

6 Conclusion

This paper reviews the deep learning algorithms in the field of disease prediction. According to the type of data processed, the algorithms are divided into structured data algorithms and unstructured data algorithms. Structured data algorithms include ANN and FM-Deep Learning algorithms. Unstructured data algorithms include CNN, RNN, etc. This paper expounds the principle, development history and application of these algorithms in disease prediction. In the application part of disease prediction of each algorithm, try to analyze the literature according to the characteristics of the algorithm. Although these algorithms are the mainstream algorithms at present and in the future, there will be some problems in the current research, such as poor interpretability, sample imbalance, data quality, few samples in some cases, etc. This paper gives some temporary solutions, hoping to have better solutions in the future. At the end of the article, we elaborate and analyze the two development trends of disease prediction in the future. The future medical technology should be combined with Digital Twins to realize real intelligent medical treatment, pay more attention to personalized medical treatment, integrate with precision medical treatment, and serve individuals more conveniently. This paper can enlighten relevant researchers, help them understand the current development, existing problems and future development trend of disease prediction algorithms, and let them focus on hot spot algorithms, combine current advanced technologies and concepts, and make more efficient, effective and reasonable research with the goal of medical development trend.

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Yu, Z., Wang, K., Wan, Z. et al. Popular deep learning algorithms for disease prediction: a review. Cluster Comput 26 , 1231–1251 (2023). https://doi.org/10.1007/s10586-022-03707-y

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  • Josh F. Peterson 5 ,
  • Cynthia A. Prows 2 ,
  • Megan J. Puckelwartz 13 ,
  • Heidi L. Rehm   ORCID: orcid.org/0000-0002-6025-0015 1 ,
  • Dan M. Roden   ORCID: orcid.org/0000-0002-6302-0389 5 ,
  • Elisabeth A. Rosenthal   ORCID: orcid.org/0000-0001-6042-4487 10 ,
  • Robb Rowley 4 ,
  • Konrad Teodor Sawicki 13 ,
  • Daniel J. Schaid 11 ,
  • Roelof A. J. Smit 3 ,
  • Johanna L. Smith   ORCID: orcid.org/0000-0002-5861-0413 11 ,
  • Jordan W. Smoller   ORCID: orcid.org/0000-0002-0381-6334 12 ,
  • Minta Thomas 15 ,
  • Hemant Tiwari 7 ,
  • Diana M. Toledo 1 ,
  • Nataraja Sarma Vaitinadin 5 ,
  • David Veenstra 10 ,
  • Theresa L. Walunas   ORCID: orcid.org/0000-0002-7653-3650 13 ,
  • Zhe Wang   ORCID: orcid.org/0000-0002-8046-4969 3 ,
  • Wei-Qi Wei   ORCID: orcid.org/0000-0003-4985-056X 5 ,
  • Chunhua Weng 6 ,
  • Georgia L. Wiesner 5 ,
  • Xianyong Yin   ORCID: orcid.org/0000-0001-6454-2384 19 &
  • Eimear E. Kenny 3  

Nature Medicine volume  30 ,  pages 480–487 ( 2024 ) Cite this article

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  • Clinical genetics
  • Risk factors

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Polygenic risk scores (PRSs) aggregate the effects of many genetic risk variants and can be used to predict an individual’s genetic predisposition to a disease or phenotype 1 . PRSs are being calculated and disseminated at a prodigious rate 1 , 2 , but their development and application to clinical care, particularly among ancestrally diverse individuals, present substantial challenges 3 , 4 , 5 . Incorporation of genomic risk information has the potential to improve risk estimation and management 4 , 6 , particularly at younger ages 7 . Clinical use of PRSs may ultimately prevent disease or enable its detection at earlier, more treatable stages 7 , 8 , 9 , 10 . Improved estimation of risk may also enable targeting of preventive or therapeutic interventions to those most likely to benefit from them while avoiding unnecessary testing or overtreatment 10 , 11 . However, clinical use of Eurocentric PRSs in diverse patient samples risks exacerbating existing health disparities 12 , 13 , 14 .

PRSs for individual conditions are typically generated from summary statistics derived from genome-wide association studies (GWASs), which are themselves derived from populations that are heavily overrepresented by individuals of European ancestry 12 . Such scores have been shown to have limited prediction accuracy with increasing genetic distance from European populations 12 , 15 . PRSs can be improved if developed and validated using multiancestry cohorts 16 . Clinical and environmental data combined with monogenic and polygenic risk measurements can improve risk prediction, as demonstrated in ref. 17 and other studies 18 . Approaches for combining genomic and nongenomic information, optimizing models for populations of diverse genetic ancestry and across age groups, and conveying this information to clinicians and patients have yet to be developed and applied in clinical care. Various forms of PRSs are available to consumers through commercial platforms such as 23andMe, Myriad Genetics (riskScore), Allelica, Ambry Genetics, and others, and several noncommercial studies have explored the clinical use of PRSs in direct-to-participant models 19 , 20 , 21 ; however, there is limited information on the clinical implementation considerations of returning PRSs across multiple phenotypes in a primary care setting 20 . Even before assessing the ability of PRSs to improve health outcomes, reduce risk and enhance clinical care, large multicenter prospective pragmatic studies are needed to assess how patients and care providers interact with and respond to PRSs in a primary care setting 22 .

The Electronic Medical Records and Genomics (eMERGE) Network is a multicenter consortium established in 2007 to conduct genomic research in biobanks with electronic medical records 23 , 24 . In 2020, eMERGE embarked on a study of genomic risk assessment and management in 5,000 children and 20,000 adults of diverse ancestry, beginning with efforts to identify and validate published PRSs across multiple race-ethnic groups (and inferred genetic ancestries) in ten common diseases with complex genetic etiologies. The study plans for 25,000 individuals (aged 3–75 years) to be recruited from general healthcare system populations. Six of the ten recruitment sites are committed to recruiting an ‘enhanced diversity cohort’, meaning that their enrollment will target 75% of enrolled individuals belonging to a racial or ethnic minority or medically underserved population, whereas the remainder of clinical sites will target 35% minority participants 22 . Enrollment is not targeted to individuals with specific conditions, although individuals with prevalent conditions can be included. For this prospective, pragmatic study, the primary outcome being measured is the number of new healthcare actions after return of the genome-informed risk assessment. This paper describes (1) identification, selection and optimization of the PRSs that are included in the study; (2) calibration of ancestry for PRS estimation using a modified method developed for eMERGE; (3) development and launch of clinical reporting tools; and (4) an overview of the first 2,500 samples processed as part of the study.

PRS auditing and evaluation

To select the PRSs for clinical implementation, the Network conducted a multistage process to evaluate proposed scores (Fig. 1 ). An initial set of 23 conditions was selected based on considerations including relevance to population health (condition prevalence and heritability), strength of evidence for PRS performance, clinical expertise in the eMERGE Network, and data availability that would facilitate validation of the PRS in diverse populations. These conditions were abdominal aortic aneurysm, age-related macular degeneration, asthma, atopic dermatitis, atrial fibrillation, bone mineral density, breast cancer, Crohn’s disease, chronic kidney disease, colorectal cancer, coronary heart disease, depression, hypercholesterolemia, hypertension, ischemic stroke, lupus, nonalcoholic fatty liver disease, obesity, primary open angle glaucoma, prostate cancer, rheumatoid arthritis, type 1 diabetes and type 2 diabetes.

figure 1

a ,Timeline and process for selection, evaluation, optimization, transfer, validation and implementation of the clinical PRS test pipeline. Dashed lines represent pivotal moments in the progression of the project with duration between these events indicated in months (mo) above the blue arrow. Numbers in white represent the number of conditions being examined at each stage and their fates. List of ten conditions on the right-hand side indicates the conditions that were implemented in the clinical pipeline for this study. b , Overview of the eMERGE PRS process. Participant DNA is genotyped using the Illumina Global Diversity Array, which assesses 1.8 million sites. Genotyping data are phased and imputed with a reference panel derived from the 1,000 Genomes Project. For each participant, raw PRSs are calculated for each condition ( PRS raw ). Each participant’s genetic ancestry is algorithmically determined in the projection step. For each condition, an ancestry calibration model is applied to each participant’s z- scores based on model parameters derived from the All of Us Research Program (Calibration) and an adjusted z -score is calculated ( PRS adjusted ). Participants whose adjusted scores cross the predefined threshold for high PRS are identified and a pdf report is generated. The report is electronically signed after data review by a clinical laboratory director and delivered to the study portal for return to the clinical sites.

Network sites completed a comprehensive literature review on 23 proposed conditions and the corresponding PRSs. A summary of the features of the PRS for each of the final conditions chosen is shown in Supplementary Table 1 . The collated information included analytic viability—a description of covariates, the age, and ancestry effects of the original PRS model; feasibility—access to sufficiently diverse validation datasets (genetic ancestry and age) as well as condition prevalence and relevance to preventative care; potential clinical actionability—existing screening or treatment strategies, and magnitude (odds ratio) of risk in the high-risk group; and translatability—expected public health impact across diverse populations. Candidate PRSs were restricted to those that were either previously validated and published (journal or preprint) or for which there was sufficient access to information to develop and/or optimize new PRSs, which could then be validated.

In auditing and evaluating evidence of PRS performance, the eMERGE steering committee considered PRSs for conditions that could be implemented in pediatric and/or adult populations, and for diseases with a range of age of onset (0 to >65 years of age). We considered published single nucleotide polymorphism (SNP)-based heritability estimates available for ten of the 23 conditions, ranging from 3% to 58%. The majority of PRSs under consideration aimed to identify individuals at high risk for disease; however, PRSs to predict disease severity and drug response were also considered. Two of the conditions, breast cancer and prostate cancer, were only considered for implementation in individuals whose biological sex was female or male, respectively. As the eMERGE Network plans to enroll >50% participants from underrepresented groups (including racial and ethnic minority groups; people with lower socioeconomic status; underserved rural communities; sexual and gender minority groups) 25 , emphasis was placed on PRSs that were already available for, or could be developed and validated in, diverse population groups.

To define population groups, study-level population descriptors were first extracted from published literature, preprints or information shared directly by collaborators on data used to develop and/or optimize and/or validate PRSs. Methods for defining population groups across studies ranged from self-reporting, extraction from health system data and/or analysis of genetic ancestry. We designated four population groups: European ancestry (that is, study population descriptors included European, European-American or other European descent diaspora groups), African (African, African American or other African descent diaspora groups), Hispanic (that is, Hispanic, Latina/o/x or those who have origins in countries in the Caribbean and Latin America) and Asian (that is, South Asian, East Asian, South-East Asian, Asian-American or other diaspora Asian groups).

Thirteen conditions were considered and not selected for clinical implementation (Fig. 1 ). Of the six conditions dropped from consideration in August 2020, low disease prevalence across ancestral groups (age-related macular degeneration), availability of diverse genetic datasets for validation (primary open angle glaucoma, rheumatoid arthritis and Crohn’s disease) and the lack of a validated algorithm to identify patients and controls based upon electronic health record (EHR) (bone mineral density) were the driving factors. In March 2021, five additional conditions were dropped from consideration for clinical implementation based upon the progress of the development and validation of a multiancestral PRSs (depression, ischemic stroke), the low predictive value of candidate PRSs (hypertension, nonalcoholic fatty liver disease) and ethical considerations around returning results to a condition with low population prevalence (lupus).

Conditions not prioritized for implementation continued on a ‘developmental’ pathway for further refinement. Each of the 12 conditions that were selected to move forward from the March 2021 review was assigned a ‘lead’ and ‘co-lead’ site, which worked together to develop, validate and transfer the score to the clinical laboratory for instantiation and Clinical Laboratory Improvement Amendments (CLIA) validation. Assignment of leads was based on site preference, expertise and distribution of workload.

Selection, optimization and validation

A systematic framework was developed to evaluate the performance for the remaining 12 PRSs, in accordance with best practices outlined in ref. 26 . An in-depth evaluation matrix of the 12 chosen conditions can be found in Supplementary Table 2 . The Network carefully considered a variety of strategies to optimize PRS generalizability and portability. The Network prioritized validation across four ancestries with an emphasis on African and Hispanic ancestry due to their underrepresentation in genetic research and projected representation within the study cohort. We determined that a PRS was validated if the odds ratios were statistically significant in a minimum of two and up to four ancestral populations: African/African American, Asian, European ancestry, and Hispanic/Latino. The PRS Working Group members conducted an extensive scoping exercise to identify suitable datasets of multiple ancestries for disease-specific PRS validation. These included datasets from early phases of eMERGE (2007–2019) as well as external datasets such as the UK Biobank and Million Veteran Program. These larger population-level databases had the advantage of large sample sizes and less case–control ascertainment bias (though other sources of bias can still be an issue; ‘Discussion’). A standardized set of questions was addressed by the disease leads that included the source of discovery and validation datasets, the availability of multiancestry validation datasets, the availability of cross-ancestry PRSs (that is, PRS models that were developed and validated in more than one genetic ancestry), proposed percentile thresholds for identifying high-risk status, model discrimination (AUC) and effect sizes (odds ratios) associated with high-risk versus not high-risk status (Supplementary Table 2 ). For seven out of the 12 candidate scores, no further optimization of the original model was performed. For five scores, an additional optimization effort was undertaken to further refine the score performance in multiple ancestries. Details of the optimization can be found in Supplementary Table 3 . A specific score optimization was applied for chronic kidney disease. This optimization consisted of adding the effect of APOL1 risk genotypes to a polygenic component, which has been found to improve risk predictions in African ancestry cohorts 27 .

For the final selection of PRSs to be included in the prospective clinical study, the steering committee considered the score performance summaries (presented by condition leads) in addition to the actionable and measurable recommendations relevant for return, for each condition, in the prospective cohort. Abdominal aortic aneurysm was removed from the clinical pathway in June 2021 based on inability to pull a critical risk factor from the EHR (smoking) and a relatively low disease prevalence in Asian and Hispanic populations. Colorectal cancer was removed in June 2021 because the development and validation of the PRS was not complete for all the ancestral groups (Fig. 1 ). For the ten remaining phenotypes, the prospective pragmatic study required a small number of measurable primary clinical recommendations per phenotype so that the utility of the PRS to change physician and patient behavior can be measured. These recommendations can be found in Supplementary Tables 2 and 4 of ref. 22 .

Population-based z -score calibration

In this study, the focus is on integration and implementation of validated PRSs in clinical practice rather than novel PRS development. Ultimately, the Network opted to balance generalizability and feasibility by validating and returning cross-ancestry PRSs. However, even with cross-ancestry scores, differences remain in the distribution of z -scores for the PRSs across genetic ancestries that can result in inconsistent categorization of individuals into ‘high’ or ‘not high’ polygenic risk categories for a given condition 28 . To that end, the Network chose to develop methods to genetically infer each participant’s ancestry and calibrate the distribution of resulting z -scores through a population-based calibration model 28 , 29 (see below). An alternative would have been to apply existing PRSs in available samples of different ancestries and derive ancestry-specific effect estimates. However, returning ancestry-specific risk estimates is challenging in real-world implementations as it would require self-reporting of ancestry by patients (who may not be able to provide this with accuracy) and developing multiple ancestry-specific reports for each health condition. In addition, such PRSs would be problematic to return to patients of mixed ancestry.

PRSs often have different means and standard deviations for individuals from different genetic ancestries. While some of these differences could be due to true biological differences in risk, they also result from allele frequency and linkage disequilibrium structure differences between populations 30 . This problem is more acute when a PRS is calculated for an individual whose ancestry does not match the ancestries used to develop the PRS. A clinically implemented PRS test to return disease risk estimates, therefore, must be adjusted to account for these differences due to ancestral background. A calibration method based on principal component analysis (PCA), which was initially described in ref. 28 , was modified to model both the variance and means of scores as ancestry dependent, as compared to the previous method ( Methods ), which modeled only the means as dependent on ancestry. This modification was found to be necessary because some conditions were found to exhibit highly ancestry-dependent variance, which would have led to many more or fewer participants of certain ancestries receiving a ‘high PRS’ determination than was intended. One option considered to create and train the calibration model was to enroll and process a representative number of participants then pause on the return of results while the model was trained and the older data reprocessed. This stop–start approach was deemed suboptimal. Instead, the model was fit, with permission, to a portion of the All of Us (AoU) Research Program ( https://www.researchallofus.org/ ) cohort genotyping data, which allowed for continuous return of results to eMERGE participants once the study began. Of note, the All of Us Research Program cohorts selected for both training and testing the calibration model exhibited high degrees of genetic admixture, which would be expected to accurately reflect the study enrollment population. Importantly, because no ancestry group is homogenous, when individuals are compared directly to other individuals in their assigned population group, a dependence between admixture fraction and PRS can result. This dependence is removed by the described PCA calibration method, and the resulting calibrated PRSs are independent of admixture fraction. More details about the ancestry calibration can be found in Methods .

Transfer and implementation

Once the final ten conditions had been selected, condition leads worked with computational scientists at the clinical laboratory (Clinical Research Sequencing Platform, LLC at the Broad Institute) to transfer the PRS models and create the sample and data-processing workflow (Fig. 2 ). Condition-specific models were run with outputs from the lab’s genotyping (Illumina Global Diversity Array (GDA)), phasing (Eagle2 (ref. 31 ) https://github.com/poruloh/Eagle ) and imputation (Minimac4 (ref. 32 ) https://genome.sph.umich.edu/wiki/Minimac4 ) pipelines to assess genomic site representation (see Methods for more information on the architecture and components of the pipeline). Several rounds of iteration between the clinical laboratory and condition leads followed in which any issues with the pipeline were resolved and the effect of genomic site missingness was assessed (Table 1 ). The final version of the implemented models was returned to the condition leads to recalculate effect sizes in the validation cohorts.

figure 2

‘High-PRS threshold’ represents the percentile that is deemed to be the cutoff for a specific condition above which a high-PRS result is reported for that condition. Odds ratios are reported as the mean odds ratios (square dot) associated with having a score above the specified threshold, compared to having a score below the specified threshold, along with 95% confidence intervals (CIs), shown in the whiskers. The number of case and control samples used to derive these odds ratios and CIs for each condition can be found in Supplementary Table 2 . Note that the odds ratio for obesity is not reported here, as it will be published by the Genetic Investigation of ANthropometric Traits consortium (Smit et al., manuscript in preparation). ‘Number of SNPs’ represents the range of numbers or sites included in each score. ‘Age ranges for return’ indicates the participant ages at which a PRS is calculated for a given condition. AFIB, atrial fibrillation; BC, breast cancer; CKD, chronic kidney disease; CHD, coronary heart disease; HC, hypercholesterolemia; PC, prostate cancer; T1D, type 1 diabetes; T2D, type 2 diabetes.

Finally, as part of the implementation of the PRS pipelines as a clinical test in a CLIA laboratory, a validation study was performed (see Methods for a detailed description; Table 1 summarizes some of the results). Briefly, this study leveraged 70 reference cell lines from diverse ancestry groups (Coriell) where 30X whole genome sequencing data were generated to form a variant truth set from which the technical accuracy and reproducibility of imputation and PRS calling was assessed. A second sample set of 20 matched donor blood and saliva specimens was procured to assess the performance of the pipeline with different input materials. A set of three samples, each with six replicates, was run end-to-end through the wet lab and analytical pipelines as an assessment of reproducibility. As a verification of the clinical validity of the scores, cohorts of cases for eight of the ten conditions were created using the eMERGE phase III imputed dataset (available on https://anvil.terra.bio/#workspaces/anvil-datastorage/AnVIL_eMERGE_GWAS/data (registration required)). PRS performance measures were calculated to confirm associations between scores and conditions. Due to limitations in the eMERGE phase III imputation (no chromosome X, different imputation pipeline), the odds ratios from this analysis were not included in the final reports; rather, the odds ratios calculated in the condition-specific validation cohorts (using the final clinical lab pipeline) were used (Fig. 2 and Table 1 ). A validation report was created for each condition. This report was reviewed and approved by the Laboratory Director in compliance with CLIA regulations for the development of a laboratory-developed test. Personnel were trained on laboratory and analytical procedures, and standard operating procedures were implemented. Data review metrics were established, sample pass/fail criteria were defined, and order and report data-transfer pipelines were built as described in ref. 22 .

Creation of pipeline for report creation, review, sign-out and release

A software pipeline was built to facilitate the data review and clinical report generation. Reports were created both as documents (in pdf format) and structured data (in JSON format; a sample report is included in the Supplementary Information ). Automated rules for case triage were built into the PRS calculation and reporting pipeline to account for differences in return based on age and sex at birth for certain conditions. For instance, the PRS for breast cancer is only calculated for participants who report sex at birth as female; similarly, prostate cancer scores are only generated for participants who report sex at birth as male. Age-related restrictions were similarly coded into the pipeline to account for study policies on return. Data review by an appropriately qualified, trained individual is required for high complexity clinical testing. In the PRS clinical pipeline, this review takes the form of a set of metrics that are exposed by the pipeline to the reviewer. These include a z -score range for each condition (passing samples will have a score −5 <  z  < +5), a PCA plot per batch against a reference sample set (visual representation of outlier samples), monitoring the z -score range for each control per condition (one control on each plate; NA12878) and flagging any samples with multiple ‘high risk’ results for further review.

Each participant’s sample is also run on an orthogonal fingerprinting assay (Fluidigm biomark) that creates a genotype-based fingerprint for that DNA aliquot. Infinium genotyping data are compared to this fingerprint as a primary check of sample chain-of-custody fidelity and to preclude sample or plate swaps during lab processing. Reviewed and approved data for a participant are processed into a clinical report. The text and format of this report were created during an iterative review process by consortium work groups. For this pragmatic clinical implementation study, two results are returned to participants: ‘high risk’ or ‘not high risk’ based on the PRS 22 . In the clinical report, a qualitative framework has been developed to indicate for which condition(s) a participant has been determined to have a high PRS (if any). Quantitative values ( z- scores) are not included for any condition in the main results panel. For breast cancer and CHD, the z -score is presented in another section of the report for inclusion in integrated score models for those conditions. For breast cancer specifically, the provided z -score is used with the BOADICEA 33 model to generate an integrated risk that is included in the genome-informed risk assessment (GIRA), as described in ref. 22 .

Overview of the first 2,500 clinical samples processed

Between the launch in July 2022 and May 2023, 2,500 participants were processed through the clinical PRS pipeline (representing ∼ 10% of the proposed cohort). Of the first 2,500 participants processed, 64.5% (1,612) indicated sex at birth as female, while 35.5% (886) indicated male. Median age at sample collection was 51 years (range: 3 years to 75 years). Participants self-reported race/ancestry, with 32.8% (820) identifying as ‘White (for example, English, European, French, German, Irish, Italian, Polish, etc.)’; 32.8% (820) identified as ‘Black, African American or African (for example, African American, Ethiopian, Haitian, Jamaican, Nigerian, Somali, etc.)’; 25.4% (636) identified as ‘Hispanic, Latino or Spanish (for example, Colombian, Cuban, Dominican, Mexican or Mexican American, Puerto Rican, Salvadoran, etc.)’; 5% (124) identified as ‘Asian (for example, Asian, Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, etc.)’; 1.5% (38) identified as American Indian or Alaska Native (for example, Aztec, Blackfeet Tribe, Mayan, Navajo Nation, Native Village of Barrow (Utqiagvik) Inupiat Traditional Government, Nome Eskimo Community, etc.); 0.9% (22) identified as Middle Eastern or North African (for example, Algerian, Egyptian, Iranian, Lebanese, Moroccan, Syrian, etc.); 0.8% (21) selected ‘None of these fully describe [me_or_my_child]’; 0.7% (17) selected ‘Prefer not to answer’; 0.1% (2) participants had incomplete data. A summary of the performance of the first 2,500 samples and resulting high-PRS metrics are shown in Fig. 3 . In the first 2,500 participants, we identified 515 participants (20.6%) with a high PRS for one of the ten conditions, 64 participants (2.6%) had a high PRS for two conditions, and two participants (0.08%) had a high PRS for three conditions. The remaining 1,919 participants had no high PRS found. High-PRS participants spanned the spectrum of genetic ancestry when projected onto principal component space (Fig. 3 ). Observed numbers of high-PRS assessments were largely consistent with the corresponding expected numbers. The P values in Fig. 3c are two-sided P values, which are calculated taking into account both the finite size of the eMERGE cohort and the finite size of the training data used to estimate the ancestry adjustment parameters. The P values are further adjusted for multiple hypothesis testing using the Holm–Šidák procedure 34 .

figure 3

a , PCA of ancestry indicating participants with a result of ‘high PRS’ for any condition (red dots) compared to participants who did not have a high PRS identified (gray dots). b , Summary of number of high-risk conditions found per participant. c , Observed numbers of high PRS called per condition compared to the expected numbers of high PRS per condition. P values are two-sided P values calculated by simulation to account for the uncertainty in the All of Us (AoU) derived ancestry calibration parameters due to the finite size of the AoU training cohort, and further adjusted for multiple hypothesis testing using the Holm–Šidák procedure. Note not all participants get scored for every condition based on age and sex at birth filters.

While the predictive performance of PRSs has improved substantially in recent years, challenges remain in ensuring that PRSs are applicable and effective in diverse populations. In particular, the vast majority of GWASs have focused on individuals of European ancestry, and the predictive accuracy of PRSs declines with increasing genetic distance from the discovery population 5 , 30 , 35 . This risks exacerbating existing health disparities, as clinical use of Eurocentric PRSs in diverse patient samples may not accurately reflect disease risk in non-European populations. To address these challenges, the eMERGE Network has conducted a multistage process to evaluate and optimize PRS selection, development and validation. The Network has prioritized conditions with high prevalence and heritability, existing literature, clinical actionability and the potential for health disparities, and has developed strategies to optimize PRS generalizability and portability across diverse populations. In particular, the Network has emphasized performance across four major ancestry groups (African, Asian, European, Hispanic, as reflected by self-identified race/ethnicity) and has developed a pipeline for clinical PRS implementation, a framework for regulatory compliance and a PRS clinical report.

The potential impact of PRS-based risk assessment in clinical practice is substantial. By enabling targeted interventions and preventative measures, PRS-based risk assessment has the potential to reduce the burden of a range of conditions 22 . Moreover, the development of PRS-based risk assessment in diverse populations has the potential to reduce health disparities by ensuring that clinical use of PRSs accurately reflects disease risk in diverse populations.

However, challenges remain in the successful implementation of PRS-based risk assessment in clinical practice. Participation bias in training or validation datasets that do not accurately represent the broader populations, for example the United Kingdom BioBank, can lead to skewed results and reduced generalizability in PRS test development 36 . Other challenges include concerns about genetic determinism, the potential for stigmatization and the need for robust regulatory frameworks to ensure that PRS-based risk assessment is deployed safely and effectively. Furthermore, to have more clinical utility, an individual’s PRS-based risk would be calculated as age-based absolute risk. Challenges also remain in healthcare provider and patient understanding and interpretation of PRS results and how to effectively communicate these results. Additionally, one of the biggest challenges is the implementation of effective disease prevention strategies after the return of the results. Return of the results will not result in a benefit without effective disease prevention or early detection strategies. The eMERGE Network’s work provides a blueprint for addressing these challenges, but ongoing research and evaluation will be necessary to ensure that PRS-based risk assessment is implemented in a responsible and effective manner. While this study will not answer all of the unanswered challenges and questions, the results from the 25,000 subjects from the eMERGE study will provide additional data to existing risk stratification to model harms and benefits over patient lifetimes.

Future groups developing, transferring and implementing PRSs into a clinical setting could build upon the eMERGE experience. Slightly less than half of the phenotypes originally considered for PRS development were able to be continued through clinical implementation based on varying considerations, suggesting that a moderately high number of phenotypes with measurable genetic contributions will be appropriate for PRS-based clinical tools. Thresholds for returning ‘high risk’ PRS were identified by each phenotype working group based in part upon the statistical significance between the ‘high-risk’ and ‘not high-risk’ groups. Future studies might consider standardizing the analyses and methods used to define these thresholds. Additionally, to have more clinical utility, an individual’s PRS-based risk would be calculated as an age-based absolute risk. While data for these risk assessments are available for some phenotypes (for example, cardiovascular and cancer), age of onset data are lacking for many clinically important phenotypes. Finally, the standards, guidance and the development of best practices for the integration of PRSs into clinical processes are yet to be developed. Future studies can learn from eMERGE and other groups' experiences will be a foundation for ongoing opportunities for the integration of polygenic risk predictions in clinical care settings.

In conclusion, the eMERGE Network’s work in PRS development represents an important step forward in the implementation of PRS-based risk assessment (in combination with other risk estimates from monogenic testing and family history) in clinical practice.

Consent and ethical approval

The study was conducted in accordance with the Declaration of Helsinki, and the central institutional regulatory board protocol was approved by the Ethics Committee of Vanderbilt University. All participants for eMERGE are consented, using a global primary consent and a site-specific consent. Minors acknowledge study participation by signing an assent (if local policy dictates) and the child’s parent/guardian signs a parental permission form. The Vanderbilt University Medical Center Co-ordinating Center is the institutional review board of record (no. 211043) for the Network’s single institutional review board, approved in July 2021.

For the All of Us Research Program, informed consent for all participants is conducted in person or through an eConsent platform that includes primary consent, Health Insurance Portability and Accountability Act authorization for research EHRs and consent for return of genomic results. The protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program. The All of Us Institutional Review Board follows the regulations and guidance of the National Institutes of Health Office for Human Research Protections for all studies, ensuring that the rights and welfare of research participants are overseen and protected uniformly.

Clinical trials registration

The eMERGE genomic risk assessment study is a registered, prospective, interventional clinical trial registered with clinicaltrials.gov (Identifier: NCT05277116 ). The purpose of the study is to determine if providing a GIRA will impact clinical actions taken by providers and patients to manage disease risk and the propensity of participants to develop a disease reported in the GIRA. For this prospective, pragmatic study, the primary outcome being measured is the number of new healthcare actions after return of the genome-informed risk assessment. Number of new healthcare actions will be measured by electronic health record data and participant-reported outcomes through a REDCap survey. Prespecified actions will include a condition-specific composite of new encounters, clinical orders or specialty referrals for clinical evaluation associated with the condition(s), placed by a provider within six months of result disclosure.

Secondary outcomes are the number of newly diagnosed conditions after return of the genome-informed risk assessment and the number of risk-reducing interventions after return of the genome-informed risk assessment (time frame: six months and 12 months post return of results to participant).

Population group definition

In the score auditing and evaluation phase, condition leads cataloged population groups used in the development or validation of given scores from available publications, preprints or information shared directly from collaborators. Across the initial list of evaluated scores, methods for defining population groups included self-reporting, extraction from health system data and/or analysis of genetic ancestry. In the optimization phase, populations were defined using either computational analysis alone or both computational analysis and self-reported ancestry, as indicated in Supplementary Table 3 . For creation of the training model for PRS ancestry calibration, populations were computationally determined as described in ‘PRS ancestry calibration overview’ below.

Populations with that are underserved and more frequently experience health disparities include racial and ethnic minority groups; people with lower socioeconomic status; underserved rural communities; sexual and gender minority groups; and people with disabilities 25 .

Analytical and technical validation studies

Broad imputation pipeline overview.

An imputation pipeline that takes as an input a variant call format (VCF) file generated from a genotyping microarray and imputes the genotypes at additional sites across the genome was developed. The pipeline architecture and composition was based on the widely used University of Michigan Imputation Server, which uses a software called Eagle ( https://github.com/poruloh/Eagle ) for phasing and Minimac4 ( https://genome.sph.umich.edu/wiki/Minimac4 ) for the imputation. The pipeline uses a curated version of the 1,000 Genomes Project (1KG, www.internationalgenome.org ) as the reference panel. Additional details on the imputation pipeline can be found at https://broadinstitute.github.io/warp/docs/Pipelines/Imputation_Pipeline/README .

Broad curated 1KG reference panel

During the validation process, we determined that some sites in the 1KG reference panel were incorrectly genotyped compared to the sites in matching whole genome sequencing data. To increase accuracy of the imputation and PRS scoring, we curated the original panel by removing sites that were likely incorrectly genotyped based on comparing allele frequencies to those reported in gnomAD v.2 ( https://gnomad.broadinstitute.org/ ). Documentation of this curation can be found at https://broadinstitute.github.io/warp/docs/Pipelines/Imputation_Pipeline/references_overview and a publicly available version of the panel at the following Google Cloud location (accessible via the gsutil utility): gs://broad-gotc-test-storage/imputation/1000G_reference_panel/.

Selection of a reference panel for imputation as an input to a PRS is an important consideration. Some reference panels (for example, Trans-Omics for Precision Medicine (TOPMed)) have more samples than the default used in our pipeline (that is, 1KG). This leads to more variants being imputed. The question is whether this would materially change the PRSs calculated from samples imputed with the TOPMed panel. Access to this panel computationally is restricted (and local download prohibited) so it was deemed infeasible to implement in our clinical production environment. The performance of a non-eMERGE PRS (for CHD; ref. 28 ) using the two different reference panels was determined for 20 GDA saliva specimens and for 42 AoU array v.1 specimens. The cohort was imputed both by the Broad imputation pipeline with curated 1KG as the reference panel as well as on the TOPMed imputation server with TOPMed as the reference panel. Imputed arrays were scored by the PRS pipeline.

The PRS percentiles computed with each method are highly concordant for both cohorts. The Pearson correlation coefficient is 0.996 for both cohorts, the P value of the Welch two-sample t- test is equal to 0.93 and 0.85 (indicating no statistical difference between the methods) for GDA and AoU v.1 cohorts, respectively.

Performance verification of the imputation pipeline

Imputation accuracy was determined for 42 specimens that were processed through a genotyping microarray (AoU v.1 array—the precursor to the commercial Global Diversity Array) and imputed with curated 1KG as the reference panel where corresponding deep-coverage (>30X) PCR-free whole genome sequencing data were used as a truth call set to calculate sensitivity and specificity. The arrays were also imputed on the Michigan Imputation Server with 1KG as the reference panel.

Within the cohort, four different ancestries were represented: non-Finnish Europeans, East Asians, South Asian (SAS), African (AFR). Broad imputation pipeline sensitivity for SNPs is >97% and insertions/deletions (INDELs) >95% for all ancestries. Similarly, specificity for SNPs from the Broad imputation pipeline is above 99% and the specificity for INDELs is >98%. See Extended Data Table 1 for a table of results. Results were highly concordant with those returned by the remote server at Michigan.

Performance evaluation of different input material types

To assess the performance of specimens derived from both saliva and whole blood, a set of 20 matched blood and saliva pairs were run through the GDA genotyping process and the resulting VCFs were imputed using the Broad pipeline to be compared against results for matched blood-derived whole genome data. The Pearson correlation between sensitivity and specificity of blood- and saliva-derived samples are equal to 100% and 100%, respectively. For the same pairs, the Welch two-sample t- test statistic is 0.997 and 0.987, respectively. There is no significant difference between the different input sample types.

Imputation repeatability and reproducibility

Imputation pipeline repeatability was assessed by repeating imputation of a cohort of 1,000 Global Screening Array arrays ten times over the course of two weeks and was found to be 100% concordant. Imputation pipeline precision (reproducibility) was also tested on technical replicates. Three individual samples derived from saliva were each genotyped six times, followed by an imputation in a cohort of all saliva-derived samples. In each set of technical replicates, all pairs and variants in each pair were compared (making a total of 45 pairs for which genotypes were compared). Reproducibility is measured using Jaccard scores. ‘Reproducibility over variants’ was calculated only over sites where at least one of the two replicates in a pair calls a non hom-ref genotype and was found to be 99.91% (95% CI 99.89–99.93) for SNPs and 99.87% (95% CI 99.85–99.90) for INDELs. ‘Reproducibility over all sites’ was calculated over all genotyped sites, including sites genotyped as hom-ref in both replicates and was found to be 100% (95% CI 100–100) for both SNPs and INDELs.

Imputation performance as a function of variant frequency

Because we expect accuracy to be impacted by the frequency of a variant in the population (rare variants are less likely to be in the reference panel and therefore less accurately imputed), we further subdivided the performance assessment by allele frequencies on two cohorts: 42 AoU v.1 arrays and 20 blood–saliva pairs of GDA arrays. Accuracy of imputation of variants as a function of population allele frequency performed as expected, with rare variants in the population not being as accurately represented. Imputation is more accurate for variants that are more frequently observed in the population (≥0.1 allele frequency (AF)).

Impact of genotyping array call rate on imputation performance

The impact of call rate on the imputation was assessed by generating a downsampled series of 42 arrays, each with call rates of 90%, 95%, 97% and 98%. Pearson correlation values for SNPs and INDELs were calculated across bins of allele frequencies, assessed against gnomAD common variants (AF > 0.1), for the cohorts with downsampled call rates. Call rates below 95% were found to produce suboptimal results. At this rate the mean R 2 dosage score for sites with AF ≥ 0.1 was found to be 0.98% (95% CI 0.98–0.98) for both SNPs and INDELs compared to 0.99% for call rates of 97% and 98%.

Impact of imputation batch size on performance

Batch size effect of the imputation pipeline was assessed by imputing and analyzing arrays in a cohort of size 1,000 (randomly chosen), ten cohorts of size 100 (nonoverlapping subsets of the 1,000 cohort) and ten cohorts of size ten (nonoverlapping subsets of one of the 100 cohorts). Pearson correlations of dosage scores were calculated across bins for allele frequencies (assessed against gnomAD) for smaller cohorts versus larger cohorts. The data show that imputation is highly correlated across batch sizes with batches down to as few as ten samples, producing acceptable performance. The mean R 2 correlation of dosage scores for sites with allele frequency greater or equal to 0.1 is above 0.97 in all cases both for SNPs and INDELs and increases to 0.98 for the larger studied cohorts. Increasing batch sizes produces very slight improvements in imputation but these are not significant and the choice of imputation batch size (above or equal to ten samples) can be made on practical and operational grounds.

Broad PRS pipeline overview

The PRS pipeline begins by calculating a raw score using plink2 ( https://www.cog-genomics.org/plink/2.0/ ). For each condition, effect alleles and weights are defined for a set of genomic sites stored in a weights file. At each site, the effect allele dosage observed in the imputed VCF is multiplied by the effect weight in the weights file. The raw score is the sum of these products over all the specified sites.

Validation of technical and analytical performance of the PRS pipeline

For each of the ten conditions chosen by the consortium for clinical return, a validation study was performed to assess the technical and analytical performance as well as to verify the association between score and disease risk. See Extended Data Table 2 for a summary of the validation measures.

PRS pipeline accuracy

Accuracy of the pipeline was determined by calculating the Pearson correlation between PRSs calculated from 70 specimens imputed from GDA array data and PRSs of corresponding deep-coverage PCR-free whole genome sequencing data (used as a truth call set).

Input material performance

Accuracy of PRS scoring when different sample types (blood or saliva) are used as inputs was determined by comparing the PRSs from matched blood and saliva pairs collected from 20 individuals.

PRS pipeline repeatability

PRS pipeline repeatability was assessed by running the pipeline on the same dataset of 70 imputed GDA arrays ten times over the course of two weeks (without call caching). Scores generated from the different processing runs were compared to determine if there are any differences observed for a given PRS when the pipeline is run at different times.

PRS pipeline reproducibility

PRS pipeline precision (reproducibility) was assessed using three samples each run six times end-to-end and then compared in a pairwise manner. The z -score standard deviation is used as a measure of variability.

PRS site representation

The SNP weight sites that are not called during genotyping or imputation were determined. These are sites not present in the intersection of an imputed GDA array and the reference panel. Ideally, all sites required for PRS calculation are present either as genotyped or imputed sites; however, in practice, a small number of sites are not present due to differences in the data used to create the score and the specific array and imputation reference panel used in this study.

Performance verification using eMERGE I–III cohort

A cohort of samples with known phenotypic information was used to verify the relationship between PRS as determined by our pipeline and disease risk. For conditions where cases and controls could be identified in the eMERGE I–III cohort, we determined performance using metrics outlined in the ClinGen working group recommendations 26 . Specifically, we determined the PRS distributions for cases and controls, we examined the impact of ancestry adjustment on the distributions and we examined the relationship between observed and predicted risk. An example of this analysis (for T2D is shown below).

The T2D weight file used for PRSs in this validation report comes from a GWAS by Ge et al. 29 where they reported that individuals in the top 2% of the PRSs in the population have an increased risk of developing T2D.

The T2D cohort in the eMERGE I–III dataset consisted of 19,145 cases and 68,823 control samples. The mean adjusted PRS for case samples was 0.435, while the mean for control samples was −0.042. Individuals with higher adjusted PRS scores tend to be more likely to develop disease (see Extended Data Fig. 1 for a histogram of T2D PRSs in cases and controls).

There are some limitations to this analysis: (1) the eMERGE I–III dataset being used for this analysis was generated from different array platforms and was imputed with a different pipeline including a different version of the 1KG reference panel than the one currently implemented; (2) the eMERGE I–III imputed dataset does not include variants from chromosomes X or Y. For these reasons, the PRS disease association analysis represents a verification of the clinical validation performed by eMERGE condition leads rather than the quantitative measure of the impact of the score on risk. The clinical associations (odds ratios) that are reported on the clinical report for each condition were independently determined by eMERGE disease-specific expert teams.

Validation of pipeline and ancestry adjustment in original case–control cohorts

The final pipeline was made available to computational scientists at each of the eMERGE disease-specific expert teams who had access to appropriate case–control cohorts. These groups confirmed the performance of the final pipeline on their cohorts. The odds ratios for each condition that are reported on the clinical reports come from these cohorts rather than the eMERGE cohort for the reasons described above.

PRS ancestry calibration overview

Pca method description.

For a PRS, which is a sum of SNP effects (linear weights), the central limit theorem states that the distribution of scores in a homogenous population will tend towards a normal distribution as the number of SNPs becomes large. When two different homogenous populations are randomly mixed, the additive property of the PRS leads the resulting distribution to be similarly normally distributed, with mean and variance depending on the mean and variance of the original homogenous populations 37 , 38 . We can therefore model the distribution of the PRS as being normally distributed, with mean and variance being functions of genetic ancestry. Practically, we implement this as

with genetic ancestry being represented by projection into principal component (PC) space 39 . The α and β parameters are found by jointly fitting them to a cohort of training data. This fit is performed by minimizing the negative log likelihood:

where i runs over the individuals in the training cohort, prs i is the i th individual’s raw PRS, and μ i and σ i are calculated using equations ( 2 ) and ( 3 ) above by projecting the i th individual into PC space. Note that, due to the simplicity of the model, overfitting is unlikely to be a problem, and so no regularization or other overfitting avoidance technique is implemented. An individual’s PRS z -score can then be calculated as

where μ and σ have again been calculated based on the specific individual’s projection into PC space. In this way, once the model has been trained, the z -score calculation is fully defined by the fitted model parameters, and z -scores can be calculated without needing additional access to the original training cohort.

Generating trained models from All of Us data

Generating the trained models consisted of three steps: (1) selecting the training cohort; (2) imputation of the training cohort; and (3) training the models on the training cohort. A test cohort was also generated to test the performance of the training.

Ancestry-balanced training and test cohorts were generated by subsampling from an initial cohort of around 100,000 All of Us samples. For the purposes of balancing the cohort, each sample was assigned to one of the five 1KG super populations. Principal component analysis was first performed on a random subset of 20,000 samples. The 1KG samples were projected onto these principal components, and a support vector machine was trained on 1KG to predict ancestry. The support vector machine was then used to assign 108,000 AoU samples to one of the five 1KG super populations. A balanced training cohort was selected based on these predicted ancestries, and principal components were recalculated using this balanced training cohort. A similarly balanced test cohort was selected based on ancestries estimated from projection on the training set PCs. The resulting breakdown of the cohorts by estimated ancestry is shown in Extended Data Table 3 .

Both the training and testing cohorts include a number of individuals with highly admixed ancestry. Admixture was quantified using the tool Admixture 40 with five ancestral populations. The resulting admixtures of each cohort are shown in Extended Data Fig. 2 , and the most common admixed ancestries in each cohort are summarized in Extended Data Table 4 .

Each cohort was imputed using the imputation pipeline described above, with 1KG as the reference panel. By keeping the imputation pipeline identical to the pipeline used for the eMERGE dataset, and because the AoU dataset uses the same GDA array as the eMERGE dataset, any potential biases resulting from differing data production and processing methods were removed. The training cohort was scored for each of the ten conditions, and model parameters were fit by minimizing the negative log likelihood as described. The test cohort was then used to evaluate the generalizability of these model parameters.

Performance on test cohort

Extended Data Fig. 3 illustrates the distribution of calibrated z -scores in the test cohort using the parameters fit in the training cohort. As can be seen, all ancestries show the intended standard normal distribution of calibrated scores.

One of the main improvements of this method over previous methods is the inclusion of an ancestry-dependent variance in addition to the ancestry-dependent mean. The importance of this is shown for the hypercholesterolemia PRS in Extended Data Fig. 4 . The variance of this score differs significantly across ancestries, so that a method that only fits the mean of the distribution as ancestry dependent can result in z -score distributions that have been attenuated towards zero or expanded away from zero for some ancestries. By also treating variance as ancestry dependent, this method results in z -score distributions that are more standardized across ancestries.

In addition to improving calibration across ancestries, this method can improve calibration within ancestries, particularly for highly admixed individuals. An example of this can be seen in Extended Data Fig. 5 . Because no ancestry group is homogenous, when individuals are compared directly to other individuals in their assigned population group, a dependence between admixture fraction and PRS can result. This dependence is removed by the described PCA calibration method, and the resulting calibrated PRSs are independent of admixture fraction.

Reporting summary

Further information on the research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Underlying data used to verify the performance of the PRS pipeline are available in dbGaP https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001584.v1.p1 . De-identified data relating to trial participants will be available through dbGaP ( https://www.ncbi.nlm.nih.gov/gap/ ) access and the AnVIL platform ( https://anvil.terra.bio/ ) as an interim analysis in 2024 and final dataset at the end of the study, expected in 2026. Information (sites and weights) on the implemented scores can be found at https://github.com/broadinstitute/eMERGE-implemented-PRS-models-Lennon-et-al and also on the UCSC browser https://genome.ucsc.edu/s/Max/emerge . Additionally, PGS Catalog IDs for most of the implemented scores are indicated in Supplementary Table 3 .

Code availability

Codes used in this work to create and operate the imputation and PRS pipelines are hosted at https://github.com/broadinstitute/palantir-workflows/tree/main/ImputationPipeline . Code for the PRS ancestry calibration can also be found in the AoU demonstration workspace https://workbench.researchallofus.org/workspaces/aou-rw-bef5bf62/demopolygenicriskscoregeneticancestrycalibration/data (open access but researcher registration required).

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Acknowledgements

We thank the past and future participants of the eMERGE Network projects. We thank M. O’Reilly for help with figure creation. We thank our All of Us Research Program colleagues, A. Ramirez, S. Lim, B. Mapes, A. Green and A. Musick, for providing their support and input throughout the ancestry calibration demonstration project lifecycle. We also thank the All of Us Science Committee and All of Us Steering Committee for their efforts evaluating and finalizing the approved demonstration projects. The All of Us Research Program would not be possible without the partnership of contributions made by its participants. To learn more about the All of Us Research Program’s research data repository, please visit https://www.researchallofus.org/ . This phase of the eMERGE Network was initiated and funded by the National Human Genome Research Institute through the following grants: U01HG011172 (Cincinnati Children’s Hospital Medical Center); U01HG011175 (Children’s Hospital of Philadelphia); U01HG008680 (Columbia University); U01HG011176 (Icahn School of Medicine at Mount Sinai); U01HG008685 (Mass General Brigham); U01HG006379 (Mayo Clinic); U01HG011169 (Northwestern University); U01HG011167 (University of Alabama at Birmingham); U01HG008657 (University of Washington); U01HG011181 (Vanderbilt University Medical Center); U01HG011166 (Vanderbilt University Medical Center serving as the Coordinating Center). The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA#: AOD 16037; Federally Qualified Health Centers: 75N98019F01202; Data and Research Center: 1 OT2 OD35404; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 OT2 OD030043; Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276.

Author information

These authors contributed equally: Niall J. Lennon, Leah C. Kottyan.

Full lists of members and their affiliations appear in the Supplementary Information.

Authors and Affiliations

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Niall J. Lennon, Christopher Kachulis, Marissa Fisher, Joel Hirschhorn, Candace Patterson, Maegan Harden, Joel N. Hirschhorn, Amit Khera, Katie Larkin, Edyta Malolepsza, Li McCarthy, Nihal Pai, Heidi L. Rehm & Diana M. Toledo

Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA

Leah C. Kottyan, Lisa J. Martin, Tesfaye B. Mersha, Bahram Namjou & Cynthia A. Prows

Icahn School of Medicine at Mount Sinai, New York, NY, USA

Noura S. Abul-Husn, Gillian Belbin, Clive Hoggart, Roelof A. J. Smit, Zhe Wang & Eimear E. Kenny

National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

Josh Arias, Sonja I. Berndt, Sonja Berndt, Teri A. Manolio & Robb Rowley

Vanderbilt University Medical Center, Nashville, TN, USA

Jennifer E. Below, Ellen Wright Clayton, Digna R. Velez Edwards, QiPing Feng, Jodell E. Linder, Jonathan D. Mosley, Josh F. Peterson, Dan M. Roden, Nataraja Sarma Vaitinadin, Wei-Qi Wei & Georgia L. Wiesner

Columbia University, New York, NY, USA

Wendy K. Chung, Atlas Khan, Krzysztof Kiryluk & Chunhua Weng

University of Alabama at Birmingham, Birmingham, AL, USA

James J. Cimino, Marguerite R. Irvin, Nita Limdi & Hemant Tiwari

Children’s Hospital of Philadelphia, Philadelphia, PA, USA

John J. Connolly, Joseph T. Glessner, Hakon Hakonarson & Margaret Harr

Tulane University, New Orleans, LA, USA

David R. Crosslin

University of Washington, Seattle, WA, USA

David R. Crosslin, Gail P. Jarvik, Elisabeth A. Rosenthal & David Veenstra

Mayo Clinic, Rochester, MI, USA

Ozan Dikilitas, Robert R. Freimuth, Iftikhar Kullo, Daniel J. Schaid & Johanna L. Smith

Mass General Brigham, Boston, MA, USA

Tian Ge, Elizabeth W. Karlson, James B. Meigs & Jordan W. Smoller

Northwestern University, Evanston, IL, USA

Adam S. Gordon, Yuan Luo, Elizabeth M. McNally, Lorenzo L. Pesce, Megan J. Puckelwartz, Konrad Teodor Sawicki & Theresa L. Walunas

Boston Children’s Hospital, Boston, MA, USA

Joel Hirschhorn & Joel N. Hirschhorn

Fred Hutchinson Cancer Center, Seattle, WA, USA

Li Hsu, Ulrike Peters & Minta Thomas

Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Ruth Loos & Ruth J. F. Loos

The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

National Institutes of Health, Bethesda, MD, USA

  • Anjene Musick

Nanjing Medical University, Nanjing, China

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The All of Us Research Program

Contributions.

N.J.L. and L.C.K. contributed equally. N.J.L., L.C.K., D.R.C., O.D., T.G., J.T.G., H.H., L.H., E.W.K., R.L., E.M.M., J.B.M., B.N., R.A.J.S. and E.E.K. were responsible for PRS development. N.J.L., L.C.K., D.R.C., O.D., T.G., J.T.G., A.S.G., H.H., L.H., E.W.K., J.E.L., R.L., Y.L., E.M., L.M., J.B.M., B.N., L.L.P., J.F.P., M.J.P., R.R., K.T.S., R.A.J.S., J.L.S., C.W., W.-Q.W. and E.E.K. conducted PRS evaluation. PRS selection, optimization and validation was done by N.J.L., L.C.K., D.R.C., Q.F., O.D., T.G., J.T.G., A.S.G., H.H., L.H., E.W.K., J.E.L., R.L., Y.L., E.M., L.M., B.N., L.L.P., M.J.P., R.R., K.T.S., R.A.J.S., J.L.S., G.L.W., C.W., W.-Q.W. and E.E.K. Population-based z- score calibration was done by N.J.L., C.K., T.G., B.N. and E.E.K. N.J.L., L.C.K., C.K., J. J. Connolly., D.R.C., T.G., E.M., B.N., M.J.P., R.A.J.S. and E.E.K. were responsible for PRS transfer and implementation. Assessment of the first 2,500 participants was done by N.J.L., C.K., Q.F., B.N. and E.E.K. N.J.L., L.C.K., C.K., J.E.L., T.A.M., J.W.S., J.F.P. and E.E.K. wrote the first draft of the paper. All authors reviewed the paper.

Corresponding author

Correspondence to Niall J. Lennon .

Ethics declarations

Competing interests.

N.S.A.-H. is an employee and equity holder of 23andMe; serves as a scientific advisory board member for Allelica, Inc; received personal fees from Genentech Inc, Allelica Inc, and 23andMe; received research funding from Akcea Therapeutics; and was previously employed by Regeneron Pharmaceuticals. E.E.K. received personal fees from Illumina Inc, 23andMe and Regeneron Pharmaceuticals and serves as a scientific advisory board member for Encompass Bioscience, Foresite Labs and Galateo Bio. J.N.H. has equity in Camp4 Therapeutics and has been a consultant to Amgen, AstraZeneca, Cytokinetics, PepGen, Pfizer and Tenaya Therapeutics and is the founder of Ikaika Therapeutics. J.F.P. is a paid consultant for Natera Inc. A. Khera. is an employee of Verve Therapeutics. N.L. received personal fees from Illumina Inc and is a scientific advisory board member for FYR Diagnostics. J.F.P. is a consultant for Myome. D.V. is a consultant for Illumina and has grant support from GeneDx. T.L.W. has grant funding from Gilead Sciences, Inc. The other authors declare no competing interests.

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Nature Medicine thanks Cathryn Lewis, Lili Milani, Bjarni Vilhjálmsson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Anna Maria Ranzoni, in collaboration with the Nature Medicine team.

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Extended data

Extended data fig. 1 case-control prs histograms..

Histograms of T2D PRS scores for case and control samples in the eMERGE I-III dataset.

Extended Data Fig. 2 Representation of the genetic ancestry admixture composition of both the Test and Training cohorts.

The x-axis represents individuals within the cohorts and the color-coding highlights the proportion of genetic admixture observed.

Extended Data Fig. 3 Calibrated z-scores.

The distributions of calibrated z-scores in the test cohort when the training cohort parameters are applied.

Extended Data Fig. 4 Hypercholesterolemia PRS calibrated z-scores of training cohort.

Note the improvement when an ancestry dependent variance is used over a constant variance method.

Extended Data Fig. 5 PRS z-score as a function of African Admixture Fraction, for individuals of African ancestry.

In the ‘Bucketing’ method, a z-score is calculated by comparing to the mean and variance of all individuals of African ancestry in the cohort. The ‘PCA Calibrated’ method is the method described above. Note the dependence on admixture fraction in the ‘Bucketing’ method, which has been removed in the ‘PCA Calibrated’ method.

Supplementary information

Supplementary information.

Sample clinical report and list of consortia members.

Reporting Summary

Supplementary table 1.

Supplementary Tables 1–3 (tabs in a single worksheet).

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Lennon, N.J., Kottyan, L.C., Kachulis, C. et al. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat Med 30 , 480–487 (2024). https://doi.org/10.1038/s41591-024-02796-z

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Genetic risk prediction for 10 chronic diseases moves closer to the clinic

As part of a nationwide collaboration, Broad Clinical Labs researchers have optimized 10 polygenic scores for use in clinical research as part of a study on how to implement genetic risk prediction for patients.

Graphic highlighting different spots in a genetic sequence

Related News

Polygenic scores for heart disease

By analyzing millions of small genetic differences across a person’s genome, researchers can calculate a polygenic risk score to estimate someone’s lifetime odds of developing a certain disease. Over the past decade, scientists have developed these risk scores for dozens of diseases, including heart disease, kidney disease, diabetes, and cancer, with the hope that patients could one day use this information to lower any heightened risk of disease. But determining whether such tests work effectively across all populations, and how they can guide clinical decision-making, has been a challenge.

Now, a team of researchers at the Broad Institute of MIT and Harvard, in collaboration with 10 academic medical centers across the US, has implemented 10 such tests for use in clinical research. In a study published today in Nature Medicine , the team outlined how they selected, optimized, and validated the tests for 10 common diseases, including heart disease, breast cancer, and type 2 diabetes. They also calibrated the tests for use in people with non-European ancestries.

The scientists worked in collaboration with the national Electronic Medical Records and Genomics (eMERGE) network , which is funded by the National Human Genome Research Institute to study how patients’ genetic data can be integrated with their electronic medical records to improve clinical care and health outcomes. The 10 collaborating medical centers are part of the project and enrolling 25,000 participants for it, while researchers at Broad Clinical Labs , a subsidiary of the Broad Institute, carry out the polygenic risk score testing for those participants.

“There have been a lot of ongoing conversations and debates about polygenic risk scores and their utility and applicability in the clinical setting,” said Niall Lennon , chief scientific officer of Broad Clinical Labs, an institute scientist at Broad, and first author of the new paper. “With this work, we’ve taken the first steps toward showing the potential strength and power of these scores across a diverse population. We hope in the future this kind of information can be used in preventive medicine to help people take actions that lower their risk of disease.”

What’s the score?

Most polygenic risk scores have been developed based on genetic data largely from people of European ancestry, raising questions about whether the scores are applicable to people of other ancestries.

To optimize polygenic risk scores for a diversity of people, Lennon and his colleagues first combed the literature looking for polygenic risk scores that had been tested in people from at least two different genetic ancestries. They also searched for scores that indicate a disease risk that patients could reduce with medical treatments, screening, and/or lifestyle changes.  

“It was important that we weren’t giving people results that they couldn’t do anything about,” said Lennon.

The team selected 10 conditions to focus on for polygenic risk scores: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. 

For each condition, the researchers identified the exact spots in the genome that they would analyze to calculate the risk score. They verified that all those spots could be accurately genotyped, by comparing the results of their tests with whole genome sequences from each patient’s blood sample.

Finally, the researchers wanted to make polygenic risk scores work across different genetic ancestries. They studied how genetic variants differ across populations by analyzing data from the National Institutes of Health’s All of Us research program, which is collecting health information from one million people from diverse backgrounds across the U.S. The team used that information to create a model to calibrate a person’s polygenic risk score according to that individual’s genetic ancestry.

“We can’t fix all biases in the risk scores, but we can make sure that if a person is in a high-risk group for a disease, they’ll get identified as high risk regardless of what their genetic ancestry is,” explained Lennon.

With that optimization complete, Lennon’s team at Broad Clinical Labs ended up with 10 tests that they are now using to calculate risk scores for the 25,000 people enrolled in the eMERGE study. With their eMERGE collaborators, they are also planning detailed follow-up studies to analyze how polygenic risk scores might influence patients’ health care.

“Ultimately, the network wants to know what it means for a person to receive information that says they’re at high risk for one of these diseases,” Lennon said.  

This phase of the eMERGE Network was initiated and funded by the National Human Genome Research Institute.

Paper cited:

Lennon, NJ. et al. Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations . Nature Medicine . Online February 19, 2024. DOI: 10.1038/s41591-024-02796-z

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Hot topics and trends in cardiovascular research

1 Department of Cardiovascular Sciences, KU Leuven, Campus Gasthuisberg O/N1 704, Herestraat 49, Leuven, Belgium

2 ECOOM, Department of Managerial Economics, Strategy and Innovation, KU Leuven, Naamsestraat 61, Leuven, Belgium

Wolfgang Glänzel

3 Department Science Policy & Scientometrics, Library of the Hungarian Academy of Sciences, Arany János u. 1, Budapest, Hungary

Karin R Sipido

Associated data.

Comprehensive data on research undertaken in cardiovascular medicine can inform the scientific community and can support policy building. We used the publication output from 2004 to 2013 and the 2014 references to these documents, to identify research topics and trends in the field of cardiovascular disease.

Methods and results

Text fragments were extracted from the titles and abstracts of 478 000 publications using natural language processing. Through machine-learning algorithms, these text fragments combined to identify specific topics across all publications. A second method, which included cross-references, assigned each publication document to a specific cluster. Experts named the topics and document clusters based on various outputs from these semi-automatic methods. We identified and labelled 175 cardiovascular topics and 20 large document clusters, with concordance between the approaches. Overarching, strongly growing topics in clinical and population sciences are evidence-based guidance for treatment, research on outcomes, prognosis, and risk factors. ‘Hot’ topics include novel treatments in valve disease and in coronary artery disease, and imaging. Basic research decreases its share over time but sees substantial growth of research on stem cells and tissue engineering, as well as in translational research. Inflammation, biomarkers, metabolic syndrome, obesity, and lipids are hot topics across population, clinical and basic research, supporting integration across the cardiovascular field.

Growth in clinical and population research emphasizes improving patient outcomes through novel treatments, risk stratification, and prevention. Translation and innovation redefine basic research in cardiovascular disease. Medical need, funding and publishing policies, and scientific opportunities are potential drivers for these evolutions.

Introduction

Current policies for public funding of health research increasingly focus on innovation, with a final goal to improve health outcomes. 1 To support policies, roadmaps are established, for example for diabetes 2 and respiratory 3 diseases. In the USA, the joint Academies developed a document to guide national policy in health 4 with a dedicated document for cardiovascular medicine 5 that includes general directions for research. In Europe, building a roadmap for cardiovascular research is one of the tasks of the ERA-CVD network. 6 Expert opinion guides the exercise but a macro and global-level overview of past cardiovascular research can enrich the debate and strengthen the basis for recommendations. The breadth of cardiovascular research is astounding, 7 with research undertaken across a variety of institutions and with each piece of research having its own scope/focus or topic. It is thus challenging to review and summarize all the research that has been undertaken.

Identifying all the relevant research is the first hurdle to overcome, then classifying or identifying topics of research is the next significant hurdle. Journal classification systems offer little assistance, as they are not granular enough to identify more specific topics within broader fields. Thesauri or medical dictionaries, such as PubMed or the International Classification of Diseases (ICD), do not offer an overview of time-dependent changes in topics or changing concepts.

Identifying key topics using semi-automatic approaches based on text analysis is an alternative solution that takes advantage of recent developments in high-level informatics. As this is not reliant on a predefined classification, it may result in different outcomes. Various methods use natural language processing (NLP) to extract topics or clusters from text. For example, the bibliometric community has compared the results when varying methods are applied to a set of astronomy publications, focusing on the importance having topic expert input throughout the process. 8 The recent CardioScape project analysed abstracts of 2476 research projects awarded 2010–12 as published by funding bodies. The authors assigned research project to topics, based on the abstract text, using a semi-automatic process that tested and trained the data to more quickly allocate abstracts to a topic than depending solely on expert review. They produced a detailed taxonomy or classification of cardiovascular research based on the list of topics of the European Society of Cardiology, creating a hierarchical list of over 600 topics. 9

Here, we aim to identify topics in published cardiovascular research and their evolution between 2004 and 2013, assessing whether they have appeared, disappeared, or changed over time. In a comprehensive approach, we use a combination of existing methods for text mining, network analysis, and clustering, and further develop these tools to handle a large dataset of >400 000 publications.

In our study, we use two different and complementary approaches. A first one detects topics across the collection of publications, counting number of documents, and relations between topics. A second one maps document networks into clusters with an identifiable subject of research. These approaches are described here in brief, with more detail provided in the Supplementary material online .

Data sources

The dataset includes the reference, abstract, address, and citation data for 478 006 cardiovascular publications from 2004 to 2013, including 2014 references to these documents, using an expert informed search strategy and references to core cardiovascular journals, as previously published. 7 The documents span across >5000 journals, and include cardiovascular publications in leading general journals in medical and life sciences ( Supplementary material online , Table S1 ). We obtained the data from Clarivate Analytics Web of Science Core Collection (WoS) through a custom data license held by ECOOM, KU Leuven.

Text pre-processing

We took all titles and abstracts of the above publications, and extracted the noun phrases (text fragments of various lengths) using the NLP framework developed at Stanford. 10 Supplementary material online , Figure S1 illustrates the subsequent data flow for the analysis.

Topic modelling

For this approach, we applied latent Dirichlet allocation (LDA) 11 to the above-mentioned text fragments from the titles and abstracts of all publications. This LDA approach groups the text fragments to identify topics and allocates documents to topics. In this approach, a document contributes to several topics. Of note, general terms or terms that are used frequently across the majority of documents are filtered out as part of the methodology, resulting in groups of highly specific text fragments and, consequently, topics, as illustrated in Supplementary material online , Figure S2 .

At least three cardiovascular experts (listed in the Acknowledgements section) named each topic based on a set of the top 40 text fragments representing a topic. Further rounds of cross-review validated and consolidated the naming process. A final review of all topics ensured naming consistency across the topics and allowed for additional expert-based classification as clinical, basic, or population research.

We then calculated the number of documents that contributed to a topic, using probability analysis in LDA. Furthermore, we calculated the co-occurrence of topics in the publications, and visualized the outcome of this network analysis using VOSViewer ( www.vosviewer.com ). 12

Document clustering

For this second approach, the dataset was reduced to two periods, and we analysed the cardiovascular publications from 2006 to 2008 and those from 2011 to 2013, separately. For each time period, we then calculated the similarities between documents based on the noun phrase text fragments from the titles and abstracts of all publications and based on the references in these publications, using adapted cosine calculations and a hybrid document clustering algorithm, as previously described. 13 We then applied the Louvain 14 community detection algorithm to identify clusters of similar documents. For this method, each document is only located in one cluster. Subsequently, we applied the DrL/OpenOrd algorithm 15 to map and visualize the documents and clusters. We used R 16 in a high-powered cloud-based parallelized computing environment for all operations.

We identified and described the core documents, 13 the most common text fragments, as well as, the most highly cited documents and the most productive authors in each cluster, to name the clusters. For each document cluster, we identified the most highly representative topics from the LDA topic model.

Evolution of cardiovascular topics—trends and ‘hot’ topics

We identified 175 topics, listed alphabetically in Supplementary material online , Table S2 . This list groups specific topics within areas such as atherosclerosis, coronary artery disease, arrhythmias, heart failure, and their evolution over time.

For a visual and comprehensive overview, we prepared a map of the topics and their interrelation, based on co-occurrence within publications using a network analysis ( Figure ​ Figure1 1 A ). This map identifies different categories of research: population (at the top, blue), clinical (left, green/yellow), and basic research (right, red). Large topics in each category define overarching interests such as Evidence-guided-treatment and Outcomes and prognosis in clinical research, and Epidemiology of CVD and risk factors in population research, topics that have seen large growth in numbers of publications since 2004 ( Figure ​ Figure1 1 B ). Cell signalling and gene transcription is a central topic for basic research, with modest growth ( Figure ​ Figure1 1 B ).

An external file that holds a picture, illustration, etc.
Object name is ehz282f1.jpg

Main areas and organization of research focus. ( A ) Visual presentation of the topics in 2013 and how they relate to each other, based on how often the topics are included in the same publication. Each circle represents one topic and each group of topics is highlighted in a separate colour; the most similar documents and clusters are located closer to each other based on VOSviewer mapping. ( B ) Evolution of overarching topics.

More focused ‘hot’ topics that experienced a large growth in number of publications are presented in Figure  2 .

An external file that holds a picture, illustration, etc.
Object name is ehz282f2.jpg

Topics with large growth. For population research, the eight topics that increased more than two-fold in volume are shown; for clinical research, 27 topics increased more than two-fold and 10 of these are presented; for basic research only two topics had more than a two-fold increase, and the top 8 growers are presented. Overarching topics are shown in Figure ​ Figure1 1 B .

In population research, risk factors with research on metabolic syndrome, lipids, diabetes, physical activity, and mental health are prominent. In clinical research, patient management after myocardial infarction (MI) and outside the hospital are leading topics, but the true ‘hot’ topic was aortic valve disease that saw a surge of interest, related to transaortic valve repair, starting 2008. Though still small in numbers, heart failure research and stem cells saw substantial growth. This last clinical topic complements the major hot topics in basic research, on stem cells and cardiac repair and tissue engineering. In basic research, increasing translational output in metabolic syndrome and diabetes use mostly mouse models. Focused topics are organelle studies on mitochondria and endoplasmic reticulum.

Table  1 complements the fast growing topics of Figure  2 with additional leading 2013 topics. Most of these also have grown since 2004, but two topics, even if large, seem to have lost momentum, i.e. longitudinal studies on blood pressure, and basic research in cardiac electrophysiology.

Large topics in 2013

PCOS, polycystic ovary syndrome.

Only four topics in clinical, and none in population research, saw a decrease, whereas seven topics in basic research saw a decline in output ( Figure ​ Figure3 3 A ). Across all topics, the growth in publication output, measured as the number of documents in 2013 divided by the number of documents in 2004, was significantly larger in clinical and population research topics than in basic research topics ( Figure ​ Figure3 3 B ).

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Unequal growth of research output across categories. ( A ) Topics that saw a decrease of >5%, i.e. 4/102 clinical and 7/50 basic research topics. ( B ) Average growth in each category. Each dot presents a topic; the values are the fractional growth, i.e. the number of documents in 2013 divided by the number of documents in 2004. Kruskal–Wallis followed by Dunn’s test for multiple comparisons; *** P < 0.0001 basic vs. clinical and vs. population.

When considering the overall output and growth of publications across the categories of population, clinical and basic research, the data suggest that the share of basic research publications is declining.

Document clusters define large research areas and trends

The size of topics represents the activity within each of these—documents contribute to more than one topic. In a complementary approach, we examined how documents group together based on the similarity of their text and of their references, whereby each document can belong to one cluster only, effectively dividing the total publication output into different areas. The hybrid clustering algorithm was applied to two datasets, i.e. the publications from 2006 to 2008 and 2011 to 2013.

In each period, 10 large clusters emerged, accounting for >90% of all documents.

To identify trends, we compare the two periods and examine the evolution over time ( Figure  4 ). In the graph legends, emerging areas are marked by green triangle, decreasing ones with a red triangle. Risk scoring in the population and related patient management are the leading areas, growing over time (top position). In 2011–13, a large cluster emerges that relates to gene and stem-cell therapy, including research on inducible pluripotent stem cells. Documents within this cluster include research on ischaemic heart disease and arrhythmias. Haemodynamics and biomechanics are another emerging area that includes documents on atherosclerosis and vascular diseases such as aneurysms, but also heart failure and assist devices. Aortic valve disease is a newly defined area in 2011–13. Imaging also becomes very prominent as an area in its own right. Whereas in 2006–08, hypertension was a defined area, this is no longer identifiable in 2011–13.

An external file that holds a picture, illustration, etc.
Object name is ehz282f4.jpg

Distribution of document clusters in 2006–08 and in 2011–13. ( A ) In 2006–08, the 10 largest clusters represent 93% of the total publication output in this period. ( B ) In 2011–13, the 10 largest clusters represent 92% of the total publication output in this period. The colour codes for similar clusters are maintained across the periods. However, some clusters are present in only one period. The clusters are arranged by size, reading clockwise from the top, and the legends arranged accordingly. Red triangles mark clusters that disappeared and green triangles emerging clusters.

For the last period, we also examined the structure and interrelation of clusters, using a graphical rendering, giving insight in the size, composition, and presence of subclusters ( Figure  5 ).

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Document clusters’ map 2011–13. A visual presentation of documents in clusters and subclusters: the most similar documents and clusters are located closer to each other, based on the DrL two-dimensional mapping layout technique.

In this force-directed DrL graph layout, the documents and clusters are mapped to minimize the distance between the most similar documents and maximize the distance between non-linked documents. This produces a two-dimensional co-ordinate layout where the documents closest to each other share the most similarities since they share common text fragments and references. Conversely, documents and clusters on the edges of the graph have the least similarity to other documents or clusters.

Cluster 2 on gene and stem cells is dense and separate, yet touches and interacts with Cluster 5 [acute coronary syndrome (ACS) and MI]. Cluster 9 on imaging is spread out in subclusters at different locations, including one near Cluster 5 (ACS and MI), and one near Cluster 4 (heart failure). Cluster 8 (arrhythmias) is also split with one part closer to heart failure, another to anticoagulation and atrial fibrillation.

Further naming the subclusters is presently beyond reach, as it would require a lot of expert input and resources. However, linking the clusters and the topics adds granularity to the larger research areas and provides internal methodological validation of the cluster naming.

Table  2 presents the most highly associated topics in the ten largest document clusters in each period. Overall, agreement with the LDA topics is high and provides more detail on the research contained in the clusters. E.g., the cluster ‘Haemodynamics’ is now showing different areas of focus, i.e. in congenital disease, aortic, and valvular diseases; the topic ‘Arrhythmias’ is more populated with device research in the second time period compared to the first.

Cluster names and topics present within clusters

AF, atrial fibrillation; ANS, autonomic nervous system; BP, blood pressure; CABG, coronary artery bypass grafting; CRT, cardiac resynchronization therapy; CT, computed tomography; CV, cardiovascular; DES, drug-eluting stent; ECG, electrocardiogram; HF, heart failure; LV, left ventricular; NOAC, new oral anticoagulant; PTCI, percutaneous transluminal coronary intervention; RV, right ventricle; STEMI, ST elevated myocardial infarction.

The method for identification of topics in cardiovascular publication output allowed the visualization and evaluation of trends in cardiovascular research. Over a 10-year period significant shifts occur.

Identification of cardiovascular research topics through natural language processing

In cardiovascular research, topics are generally predefined in a taxonomy that can be hierarchical and/or matrix structured. The CardioScape project approach (see Introduction section) was well suited to its purpose of the analysis of 2476 project abstracts in a single time period and using an existing taxonomy has the advantage of recognizable areas of research. The bottom-up approach used here lent itself well to analysis of much larger numbers of documents and generated a topic list that represents the interests from the community during the period under study.

A recent study by the WHO working to identify cardiovascular disease research output from random sets of publications from PubMed required a significant amount of expert-based review of only a small proportion of the published articles. 17 The current approach was more comprehensive in coverage of the field, but despite reliance on advanced automated analysis, experts still had an important role in interpreting and linking concepts to validate the results.

In the current naming of topics and clusters, experts frequently used terms that connect to a classic hierarchical list in the field, including major diseases, and recognizing clinical, population, and basic discovery research. Nevertheless, the approach uncovered specific emerging areas of research such as transcatheter aortic valve implantation (TAVI), topics consistent with broad trends, such as risk stratification and evidence-based guidance, and innovation (gene and stem cell research). Some of these terms would not appear in a classic taxonomy and thus the NLP approach offers novel insights.

The present study was not attempting to classify all research but to capture and identify the most common and evolving topics over time in the cardiovascular field by using a comprehensive set of cardiovascular publications across some 5000 journals.

Emphasis on improving clinical care and risk assessment

The most represented and fast growing topics across the documents are evidence-based guidance for treatment and research on outcomes and prognosis. These result underscore the attention given to guidelines and evidence based medicine (EBM). 18–23 Part of this research is likely to represent the large number of clinical trials taking place in the cardiovascular field, 24 which over time have had a significant effect on the reduction of mortality from CVD due to establishing the effectiveness and safety of a number of drugs and medical interventions in cardiovascular disease. 25 The presence of policy related topics, such as the topics on quality of care and health economics likewise supports the focus on implementation research and a shift of focus from reducing acute mortality to care in chronic disease.

Growth of research on risk factors emphasizes the importance of preventative medicine, evident in both the topics analysis and the document cluster analysis. However, some specific blood pressure studies declined over time, perhaps reflecting the change in focus on the single risk factor of ‘blood pressure’ to a multivariable spectrum and newly identified risk factors. We have also previously shown that hypertension has moved more closely to clinical cardiovascular research over time. 26

Smaller topics illustrate crosstalk with non-cardiovascular diseases, because of shared risk factors or common methods used in research or occurrence of cardiovascular complications. The latter is particularly evident in two topics that focus on cardiovascular complications in pregnancy and in cancer.

Innovation and translation in clinical and basic science

Major diseases such as ischaemic heart disease and arrhythmias, remain present over time but shifts can be seen. There is for example, a larger focus on atrial fibrillation, in particular embolic risk, on novel treatments, such as stem cells in heart failure, and transcatheter aortic valve interventions as a dominant element within the topic of valvular heart disease. 19 Imaging is present in several topics but emerges as a cluster in its own right in the document analysis. Many of these changes are driven by technological innovation and translation.

Basic research as a whole saw its share decline, but with interesting shifts in content. Although the topic analysis and mapping identifies basic research topics as a category, there are complementarities across categories. Stem cell research, tissue engineering, and biomechanical factors saw rapid growth and are also present in clinical topics. This also applies to inflammation and diabetes. Animal models for disease are rapidly growing topics consistent with growth of translational research.

An analysis of the countries of authorship of the publications in the emerging clusters of discovery research shows that the USA leads in the number and share of publications (30%+), followed mostly by Germany, or the UK or Italy. However, for the large document cluster on genes and stem cells in 2011–13, the second most productive country is China, contributing 17.5% of the publications in this cluster (Supplementary material online, Figure S3 ).

Interestingly, inflammation, biomarkers, metabolic syndrome, obesity, and lipids are hot topics with growing research output in population, clinical and basic research, indicating integration and crosstalk across the spectrum of cardiovascular research.

Drivers of change

Technology and opportunity-driven scientific interest, but also strategic choices and funding policies are likely to influence trends in research. CardioScape studied public and charity funding in the years 2010–12 and describes major investments in clinical research. Yet the share of publication output globally for clinical research appears to be substantially larger than the share of funding for clinical research reported in CardioScape. This could be explained by clinical research funded by other sources, such as industry or local funding, which are not included in the CardioScape analysis. Also, the present data represent global output. Major research investments in China, and the emphasis on clinical research in the USA, can contribute to some of the global trends.

The slower growth in basic science could reflect a slower growth in investment. This can be absolute or relative towards the increasing costs of advanced research methodology. Another reason could be editorial pressure for more comprehensive papers that may reduce quantity to the benefit of rich content in individual papers.

Finally, growing translational research may blur the boundaries between basic and clinical research and lead to an apparent slower growth in discovery research.

Policy perspectives

Policy development is a forward looking exercise. In health research, medical needs identified by health data and expert opinion, are an important consideration. 27 Past research output helps to identify areas that may need more investment. Research funders also use input from society. 28 When assessing current priorities in cardiovascular research for the Dutch 28 and British 29 Heart Foundations we can see that research into heart failure and arrhythmias are common across their top priorities. Focus on healthy lifestyles is a top priority in the Dutch Heart Foundation as well as in the US vision and strategic agenda. 4 , 5 At the macro-level, the data presented here indicate that some of the main issues presented in these research agendas are actively pursued but others less so.

Study limitations

Limitations of studying research topics have been addressed in the bibliometric field. 8 The reliance of expert input is a limitation and potential source of bias that we tried to minimize by using mixed panels.

The current approach was not sufficiently granular to extract recent emerging topics that contain a limited number of documents. In addition, publication output is somewhat delayed vs. actual research and experts may be aware of ongoing research with still limited output. In this case, the method and dataset can be used to interrogate about specific developments (see Supplementary material online , Table S3 for data on micro-RNA and personalized medicine).

As the data set ends in 2013, very recent developments are not covered. This relates to the methodological complexity. Web of Science data including 2014 references were available mid-2015, the cardiovascular publications dataset was complete in 2016 and algorithms for analysis including re-iterative expert review required another 18 months. A similar time lag is seen in other studies that rely on data mining and processing. 9 Congress abstracts could be considered as a source to identify emerging topics but have several limitations. They are of a different nature than papers and the scope of a congress shapes content of selected abstracts. We provide a complementary survey of 3000 abstracts from the 2018 congress of the European Society of Cardiology, illustrating the strong presence of clinical research at this event, within the topics of Clusters 1 and 3–7 of Table  2 ( Supplementary material online , Figure S4 ). Two emerging topics were cardio-oncology and digital health, each representing however <25 abstracts.

In the present analysis, quality and impact of studies in a particular domain were not evaluated, though highly cited papers were part of the cluster identification. In their analysis of poorly cited papers covering 165 000 papers in 1997–2008, Ranasinghe et al . 30 noted the highest percentage of poorly cited papers in the clinical and population research category. Nevertheless, as they and others 31 have noted, citations are not the only parameter to assess impact, in particular in clinical medicine.

Conclusions

Identification of leading research topics and trends illustrates the emphasis on improving clinical medicine, and the growing interest in risk stratification and preventive medicine. Translation and innovation redefine cardiovascular research. Linking the present data with the insights of the professional community and of funders and society, may contribute to the building of a future research roadmap.

Supplementary Material

Ehz282_supplementary_data, acknowledgements.

The authors thank to the following experts for their review of the text fragments and input into the names of the topics: Dr Matthew Amoni, Dr Peter Haemers, Prof Sian Harding, Dr Frederik Helsen, Prof Gerd Heusch, Prof Tatiana Kuznetsova, Prof Tobias Op‘t Hof, Prof Frank Rademakers, Dr Sander Trenson, Dr Bert Vandenberk, and Dr Maarten Vanhaverbeke.

D.G. had a PhD Fellowship through KU Leuven.

Conflict of interest: K.R.S. is Past Editor-in-Chief of Cardiovascular Research (2013–17). W.G. is Editor-in-Chief of Scientometrics .

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A dose of the new Moderna spikevax autumn Covid booster vaccination

Two very rare Covid vaccine side-effects detected in global study of 99 million

Results confirm how uncommon known complications are as researchers confirm benefits from vaccines still ‘vastly outweigh the risks’

Two new but exceptionally rare Covid-19 vaccine side effects – a neurological disorder and inflammation of the spinal cord – have been detected by researchers in the largest vaccine safety study to date.

The study of more than 99 million people from Australia, Argentina, Canada, Denmark, Finland, France, New Zealand and Scotland also confirmed how rare known vaccine complications are, with researchers confirming that the benefits of Covid-19 vaccines still “vastly outweigh the risks”.

Researchers working as part of the Global Vaccine Data Network used deidentified electronic healthcare data to compare the rates of 13 brain, blood and heart conditions in people after they received the Pfizer, Moderna or AstraZeneca vaccine with the rate that would be expected of those conditions in the population before the pandemic.

The study confirmed with a high level of accuracy known links between mRNA (Pfizer and Moderna) vaccines and the rare side-effects of myocarditis (inflammation of the heart muscle) and pericarditis (swelling of the thin sac covering the heart). It also confirmed Guillain-Barré syndrome (where the immune system attacks the nerves) and cerebral venous sinus thrombosis (a type of blood clot in the brain) as rare side effects linked to the AstraZeneca vaccine.

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But a new rare side-effect, acute disseminated encephalomyelitis – an inflammation and swelling in the brain and spinal cord – was also identified in the data analysis as being linked to the AstraZeneca vaccine.

The findings were published in the international journal Vaccine on Friday.

Prof Jim Buttery, co-director of the Global Vaccine Data Network, said the finding prompted researchers to independently confirm the side-effect by completing a second study, this time analysing a separate dataset of 6.8 million Australians who received the AstraZeneca vaccine.

Not only did the Australian study confirm acute disseminated encephalomyelitis as a rare side-effect, but the large amount of AstraZeneca-specific data also allowed them to detect a second new rare side-effect, known as transverse myelitis, or spinal cord inflammation.

Also published in Vaccine on Friday, the Australian study found the data translated to an extremely small risk of acute disseminated encephalomyelitis of 0.78 cases for every million doses, and 1.82 cases per million doses for transverse myelitis.

Buttery, who is also a senior research analyst with the Murdoch Children’s Research Institute in Australia, said “for rare side effects, we don’t learn about them until the vaccine has been used in millions of people”.

“No clinical trial can ever have the size to answer those questions and so we only find out those questions after a vaccine has been introduced.”

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Buttery said the risk of myocarditis is even higher with natural Covid infection than it is following vaccination.

Both conditions are serious but patients usually recover from them, he said.

Prof Julie Leask, a vaccine expert at the University of Sydney, said it’s important to keep these findings in perspective and that a Covid infection increases the risk of some of these rare conditions “much more than a vaccine” does.

She said the studies also confirmed that “our vaccine experts are paying attention to when vaccines lead to serious side-effects, and they’re acting on it”.

“Being confident in a system that will detect problems and address them, is a very important part of a robust vaccination program.”

  • Coronavirus
  • Infectious diseases
  • Vaccines and immunisation

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