760 results on '"Eric J Topol"'
Search Results
2. Operation Nasal Vaccine-Lightning speed to counter COVID-19
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Eric J. Topol and Akiko Iwasaki
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COVID-19 Vaccines ,Immunology ,COVID-19 ,Humans ,General Medicine - Abstract
Given the poor ability of intramuscular mRNA COVID-19 vaccines to induce robust immunity in the respiratory mucosa, a push for a nasal vaccine strategy is needed.
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- 2022
3. More than meets the eye: Using AI to identify reduced heart function by electrocardiograms
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Evan D. Muse and Eric J. Topol
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medicine.medical_specialty ,business.industry ,media_common.quotation_subject ,Myocardial Infarction ,Arrhythmias, Cardiac ,Heart ,General Medicine ,law.invention ,Electrocardiography ,Randomized controlled trial ,law ,Artificial Intelligence ,Internal medicine ,Conduction system disease ,medicine ,Cardiology ,Suspected heart disease ,Humans ,cardiovascular diseases ,Function (engineering) ,business ,media_common - Abstract
Electrocardiographic (ECG) assessment of patients with suspected heart disease is a bedrock of cardiology for diagnosing conduction system disease, arrhythmias, and heart attack. Now, using AI-assisted interpretation of ECGs, the signals within these studies are able to tell us so much more. In their recent randomized trial published in Nature Medicine, Yao and colleagues illustrate the power of utilizing AI-enabled ECGs to identify individuals with reduced heart function using a scalable, pragmatic approach.
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- 2022
4. Conquering Atherosclerotic Cardiovascular Disease — 50 Years of Progress
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Christine E. Seidman, Eric J. Topol, and Gary H. Gibbons
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medicine.medical_specialty ,business.industry ,Atherosclerotic cardiovascular disease ,Hypercholesterolemia ,MEDLINE ,Cholesterol, LDL ,General Medicine ,History, 20th Century ,030204 cardiovascular system & hematology ,Atherosclerosis ,History, 21st Century ,United States ,03 medical and health sciences ,0302 clinical medicine ,Cardiovascular Diseases ,Humans ,Medicine ,030212 general & internal medicine ,business ,Intensive care medicine ,Cardiovascular mortality - Abstract
Conquering Atherosclerotic Cardiovascular Disease Substantial reductions in cardiovascular mortality in the United States reflect the power of a system that catalyzes the integration of basic scien...
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- 2021
5. Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study
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Jennifer M Radin, Giorgio Quer, Jay A Pandit, Matteo Gadaleta, Katie Baca-Motes, Edward Ramos, Erin Coughlin, Katie Quartuccio, Vik Kheterpal, Leo M Wolansky, Steven R Steinhubl, and Eric J Topol
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Adult ,Models, Statistical ,Health Information Management ,Adolescent ,SARS-CoV-2 ,Medicine (miscellaneous) ,Humans ,COVID-19 ,Decision Sciences (miscellaneous) ,Health Informatics ,United States - Abstract
Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing.This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (Hsub0/sub) or in combination with anomalous sensor data (Hsub1/sub). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort.Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The Hsub1/submodel significantly outperformed the Hsub0/submodel in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the Hsub0/submodel to the Hsub1/submodel. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future.Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes.The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.
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- 2022
6. Multimodal biomedical AI
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Julián N, Acosta, Guido J, Falcone, Pranav, Rajpurkar, and Eric J, Topol
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Artificial Intelligence ,Privacy ,Electronic Health Records ,Humans ,Pandemics - Abstract
The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.
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- 2022
7. 6 month serologic response to the Pfizer-BioNTech COVID-19 vaccine among healthcare workers
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Jason Cham, Amitabh C. Pandey, Jacob New, Tridu Huynh, Lee Hong, Natalia Orendain, Eric J. Topol, and Laura J. Nicholson
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Epitopes ,Multidisciplinary ,COVID-19 Vaccines ,Immunoglobulin M ,SARS-CoV-2 ,Health Personnel ,Immunoglobulin G ,COVID-19 ,Humans ,Antibodies, Viral ,BNT162 Vaccine - Abstract
Aim Healthcare workers (HCWs) were among the first group of people vaccinated with the Pfizer-BioNTech Covid-19 vaccine (BNT162b2). Characterization of the kinetics of antibody response to vaccination is important to devise future vaccination strategies. To better characterize the antibody response to BNT162b2, we analyzed the kinetics of IgG and IgM antibody response to 5 different SARS-CoV-2 epitopes over a period of 6 months. Methods and results An observational single-centered study was conducted to evaluate the temporal dynamics of anti-SARS-CoV-2 antibodies following immunization with two doses of BNT162b2. Anti-SARS-CoV-2 antibodies were assessed using the Maverick SARS-CoV-2 multi-antigen panel (Genalyte Inc.). Healthcare workers aged ≥18 receiving BNT162b2 vaccination who self-reported no prior symptoms of COVID-19 nor prior COVID-19 PCR test positivity, were included in this study. HCWs developed an IgG antibody response to SARS-CoV-2 Spike S1, Spike S1 receptor binding domain (RBD), Spike S1S2 and Spike S2 after vaccination. IgG response was observed at two weeks following immunization in most participant samples and continued to increase at week 4, but subsequently decreased significantly starting at 3 months and up to 6 months. In contrast, IgM response to respective epitopes was minimal. Conclusion Multiplex results demonstrate that, contrary to natural infection, immunization with BNT162b2 produces minimal anti-Spike IgM response. Polyclonal IgG response to Spike declined at 3 months and continued to do so up to 6 months.
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- 2021
8. Three year clinical outcomes in a nationwide, observational, siteless clinical trial of atrial fibrillation screening—mHealth Screening to Prevent Strokes (mSToPS)
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Eric J. Topol, Alison M. Edwards, Troy C. Sarich, Gail S. Ebner, Lauren Ariniello, Steven R. Steinhubl, Katie Baca-Motes, Robert A. Zambon, Jill Waalen, and Anirudh Sanyal
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Male ,Health Screening ,Epidemiology ,Myocardial Infarction ,law.invention ,Electrocardiography ,Randomized controlled trial ,law ,Atrial Fibrillation ,Clinical endpoint ,Medicine and Health Sciences ,Mass Screening ,Public and Occupational Health ,Myocardial infarction ,Stroke ,Multidisciplinary ,Atrial fibrillation ,Middle Aged ,Telemedicine ,Bioassays and Physiological Analysis ,Treatment Outcome ,Cohort ,Medicine ,Female ,Arrhythmia ,Research Article ,medicine.medical_specialty ,Endpoint Determination ,Science ,Cardiology ,Research and Analysis Methods ,Internal medicine ,medicine ,Humans ,Risk factor ,Aged ,Heart Failure ,Health Care Policy ,business.industry ,Electrophysiological Techniques ,medicine.disease ,Clinical trial ,Health Care ,Medical Risk Factors ,Cardiac Electrophysiology ,business ,Screening Guidelines - Abstract
Background Atrial fibrillation (AF) is common, often without symptoms, and is an independent risk factor for mortality, stroke and heart failure. It is unknown if screening asymptomatic individuals for AF can improve clinical outcomes. Methods mSToPS was a pragmatic, direct-to-participant trial that randomized individuals from a single US-wide health plan to either immediate or delayed screening using a continuous-recording ECG patch to be worn for two weeks and 2 occasions, ~3 months apart, to potentially detect undiagnosed AF. The 3-year outcomes component of the trial was designed to compare clinical outcomes in the combined cohort of 1718 individuals who underwent monitoring and 3371 matched observational controls. The prespecified primary outcome was the time to first event of the combined endpoint of death, stroke, systemic embolism, or myocardial infarction among individuals with a new AF diagnosis, which was hypothesized to be the same in the two cohorts but was not realized. Results Over the 3 years following the initiation of screening (mean follow-up 29 months), AF was newly diagnosed in 11.4% (n = 196) of screened participants versus 7.7% (n = 261) of observational controls (p Conclusions At 3 years, screening for AF was associated with a lower rate of clinical events and improved outcomes relative to a matched cohort, although the influence of earlier diagnosis of AF via screening on this finding is unclear. These observational data, including the high event rate surrounding a new clinical diagnosis of AF, support the need for randomized trials to determine whether screening for AF will yield a meaningful protection from strokes and other clinical events. Trail registration The mHealth Screening To Prevent Strokes (mSToPS) Trial is registered on ClinicalTrials.gov with the identifier NCT02506244.
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- 2021
9. Smartphone apps in the COVID-19 pandemic
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Jay A, Pandit, Jennifer M, Radin, Giorgio, Quer, and Eric J, Topol
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SARS-CoV-2 ,COVID-19 ,Humans ,Contact Tracing ,Mobile Applications ,Pandemics - Abstract
At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics.
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- 2021
10. AI in health and medicine
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Pranav Rajpurkar, Emma Chen, Oishi Banerjee, and Eric J. Topol
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Artificial Intelligence ,Humans ,Medicine ,General Medicine ,Prospective Studies ,Delivery of Health Care ,General Biochemistry, Genetics and Molecular Biology ,Algorithms - Abstract
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.
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- 2021
11. Has SARS-CoV-2 reached peak fitness?
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Roberto, Burioni and Eric J, Topol
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SARS-CoV-2 ,Host-Pathogen Interactions ,Mutation ,COVID-19 ,Humans ,Biological Evolution ,Immune Evasion - Published
- 2021
12. COVID-19 vaccine breakthrough infections
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Ravindra K. Gupta and Eric J. Topol
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COVID-19 Vaccines ,Time Factors ,Multidisciplinary ,SARS-CoV-2 ,fungi ,Immunization, Secondary ,COVID-19 ,Vaccine Efficacy ,food and beverages ,Viral Load ,complex mixtures ,Immunogenicity, Vaccine ,Mutation ,Spike Glycoprotein, Coronavirus ,Humans ,Immune Evasion - Abstract
Vaccine efficacy wanes over time but can be fully restored with a booster dose
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- 2021
13. Digitising heart transplant rejection
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Faisal, Mahmood and Eric J, Topol
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Postoperative Complications ,Heart Diseases ,Heart Transplantation ,Humans ,General Medicine - Published
- 2022
14. Deep learning a person's risk of sudden cardiac death
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Natalia A, Trayanova and Eric J, Topol
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Death, Sudden, Cardiac ,Deep Learning ,Risk Factors ,Humans ,General Medicine - Published
- 2022
15. AI-facilitated health care requires education of clinicians
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Eric J. Topol and Pearse A. Keane
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Deep Learning ,Nursing ,Education, Medical ,business.industry ,Computer science ,Health Personnel ,Health care ,MEDLINE ,Humans ,General Medicine ,Curriculum ,business ,Delivery of Health Care - Published
- 2021
16. Assessing the human immune response to SARS-CoV-2 variants
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Roberto, Burioni and Eric J, Topol
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COVID-19 Vaccines ,Neutralization Tests ,SARS-CoV-2 ,COVID-19 ,Humans ,Virus Replication ,Immune Evasion - Published
- 2021
17. Messenger RNA vaccines against SARS-CoV-2
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Eric J. Topol
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2019-20 coronavirus outbreak ,COVID-19 Vaccines ,Coronavirus disease 2019 (COVID-19) ,T-Lymphocytes ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Treatment outcome ,Antigen-Presenting Cells ,Biology ,Placebo ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Drug Delivery Systems ,0302 clinical medicine ,Humans ,030304 developmental biology ,B-Lymphocytes ,Vaccines, Synthetic ,0303 health sciences ,Messenger RNA ,SARS-CoV-2 ,COVID-19 ,Bench to Bedside ,Virology ,Bench to bedside ,Treatment Outcome ,Liposomes ,Spike Glycoprotein, Coronavirus ,Nanoparticles ,Synthetic immunology ,030217 neurology & neurosurgery - Abstract
The first two vaccines proven to be effective for inhibiting COVID-19 illness were both mRNA, achieving 95% efficacy (and safety) among 74,000 participants (half receiving placebo) after intramuscular delivery of two shots, 3-4 weeks apart. To view this Bench to Bedside, open or download the PDF.
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- 2021
- Full Text
- View/download PDF
18. Variant-proof vaccines - invest now for the next pandemic
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Dennis R, Burton and Eric J, Topol
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AIDS Vaccines ,Vaccines ,COVID-19 Vaccines ,Time Factors ,SARS-CoV-2 ,COVID-19 ,Disaster Planning ,Influenza Vaccines ,Drug Design ,Mutation ,Spike Glycoprotein, Coronavirus ,Humans ,Investments ,Pandemics ,Broadly Neutralizing Antibodies ,Immune Evasion - Published
- 2021
19. The Proportion of SARS-CoV-2 Infections That Are Asymptomatic : A Systematic Review
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Eric J. Topol and Daniel P Oran
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medicine.medical_specialty ,2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,viruses ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,MEDLINE ,Reviews ,medicine.disease_cause ,01 natural sciences ,Asymptomatic ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,Pandemic ,Internal Medicine ,medicine ,Seroprevalence ,Humans ,030212 general & internal medicine ,0101 mathematics ,skin and connective tissue diseases ,Asymptomatic Infections ,Pandemics ,Mass screening ,Coronavirus ,Transmission (medicine) ,business.industry ,SARS-CoV-2 ,010102 general mathematics ,fungi ,virus diseases ,COVID-19 ,General Medicine ,Virology ,body regions ,Observational study ,medicine.symptom ,business - Abstract
Asymptomatic infection seems to be a notable feature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the prevalence is uncertain. This review summarizes available evidence to estimate the proportion of persons infected with SARS-CoV-2 who never develop symptoms., Background: Asymptomatic infection seems to be a notable feature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), but the prevalence is uncertain. Purpose: To estimate the proportion of persons infected with SARS-CoV-2 who never develop symptoms. Data Sources: Searches of Google News, Google Scholar, medRxiv, and PubMed using the keywords antibodies, asymptomatic, coronavirus, COVID-19, PCR, seroprevalence, and SARS-CoV-2. Study Selection: Observational, descriptive studies and reports of mass screening for SARS-CoV-2 that were either cross-sectional or longitudinal in design; were published through 17 November 2020; and involved SARS-CoV-2 nucleic acid or antibody testing of a target population, regardless of current symptomatic status, over a defined period. Data Extraction: The authors collaboratively extracted data on the study design, type of testing performed, number of participants, criteria for determining symptomatic status, testing results, and setting. Data Synthesis: Sixty-one eligible studies and reports were identified, of which 43 used polymerase chain reaction (PCR) testing of nasopharyngeal swabs to detect current SARS-CoV-2 infection and 18 used antibody testing to detect current or prior infection. In the 14 studies with longitudinal data that reported information on the evolution of symptomatic status, nearly three quarters of persons who tested positive but had no symptoms at the time of testing remained asymptomatic. The highest-quality evidence comes from nationwide, representative serosurveys of England (n = 365 104) and Spain (n = 61 075), which suggest that at least one third of SARS-CoV-2 infections are asymptomatic. Limitation: For PCR-based studies, data are limited to distinguish presymptomatic from asymptomatic infection. Heterogeneity precluded formal quantitative syntheses. Conclusion: Available data suggest that at least one third of SARS-CoV-2 infections are asymptomatic. Longitudinal studies suggest that nearly three quarters of persons who receive a positive PCR test result but have no symptoms at the time of testing will remain asymptomatic. Control strategies for COVID-19 should be altered, taking into account the prevalence and transmission risk of asymptomatic SARS-CoV-2 infection. Primary Funding Source: National Institutes of Health.
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- 2021
20. Toward superhuman SARS-CoV-2 immunity?
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Dennis R, Burton and Eric J, Topol
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Immunity, Cellular ,Vaccines ,COVID-19 Vaccines ,SARS-CoV-2 ,Antibody Formation ,Animals ,Humans ,Infections - Published
- 2020
21. Digitising the prediction and management of sepsis
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Sachin, Kheterpal, Karandeep, Singh, and Eric J, Topol
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Sepsis ,Humans ,General Medicine - Published
- 2022
22. Has SARS-CoV-2 reached peak fitness?
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Eric J. Topol, Roberto Burioni, Burioni, R., and Topol, E. J.
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2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,SARS-CoV-2 ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,COVID-19 ,General Medicine ,Biological evolution ,Biology ,Biological Evolution ,Virology ,General Biochemistry, Genetics and Molecular Biology ,Mutation ,Host-Pathogen Interactions ,Mutation (genetic algorithm) ,Humans ,Immune Evasion - Published
- 2021
23. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
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Eric J. Topol, Milena A. Gianfrancesco, Ziad Obermeyer, Rima Arnaout, Bin Yu, Giorgio Quer, Ali Torkamani, Suchi Saria, Beau Norgeot, Raquel Dias, Brett K. Beaulieu-Jones, Atul J. Butte, and Isaac S. Kohane
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0301 basic medicine ,Diabetic Retinopathy ,Drug-Related Side Effects and Adverse Reactions ,Computer science ,business.industry ,Extramural ,MEDLINE ,General Medicine ,computer.software_genre ,General Biochemistry, Genetics and Molecular Biology ,Checklist ,Article ,Arthritis, Rheumatoid ,Machine Learning ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,Artificial Intelligence ,030220 oncology & carcinogenesis ,Humans ,Artificial intelligence ,business ,computer ,Natural language processing - Abstract
Here we present the MI-CLAIM checklist, a tool intended to improve transparent reporting of AI algorithms in medicine.
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- 2020
24. Welcoming new guidelines for AI clinical research
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Eric J, Topol
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Clinical Trials as Topic ,Artificial Intelligence ,Humans ,Guidelines as Topic - Published
- 2020
25. Reinventing the eye exam
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Pearse A. Keane and Eric J. Topol
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Ophthalmology ,business.industry ,Artificial Intelligence ,MEDLINE ,Optometry ,Medicine ,Humans ,Visual Field Tests ,General Medicine ,business ,Physical Examination - Published
- 2020
26. Genomic integrity of human induced pluripotent stem cells across nine studies in the NHLBI NextGen program
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Margaret A. Taub, Ivan Carcamo-Orive, Thomas Quertermous, Gustavo Mostoslavsky, Marlene Rabinovitch, Kelly A. Frazer, Kanika Kanchan, George J. Murphy, Ingo Ruczinski, Lewis C. Becker, Joshua W. Knowles, Wenli Yang, Daniel J. Rader, Lisa R. Yanek, Cashell E. Jaquish, Eric J. Topol, Claire Malley, Chad A. Cowan, Ulrich Broeckel, Rasika A. Mathias, Linzhao Cheng, Kruthika R. Iyer, Kristin K. Baldwin, Matteo D’Antonio, and Martin H. Steinberg
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0301 basic medicine ,Genome instability ,Medical and Health Sciences ,Transcriptome ,0302 clinical medicine ,and Blood Institute (U.S.) ,VanillaICE ,GWAS ,2.1 Biological and endogenous factors ,Copy-number variation ,Aetiology ,Induced pluripotent stem cell ,Lung ,lcsh:QH301-705.5 ,Cancer ,hiPSCs ,Stem Cell Research - Induced Pluripotent Stem Cell - Human ,Cell Differentiation ,General Medicine ,Genomics ,Biological Sciences ,DNA-Binding Proteins ,Biotechnology ,DNA Copy Number Variations ,Tumor suppressor genes ,Induced Pluripotent Stem Cells ,Computational biology ,Biology ,Article ,Genomic Instability ,03 medical and health sciences ,Genetic variation ,medicine ,Genetics ,Humans ,Gene ,Stem Cell Research - Induced Pluripotent Stem Cell ,Human Genome ,Cell Biology ,National Heart ,Oncogenes ,medicine.disease ,Stem Cell Research ,United States ,030104 developmental biology ,lcsh:Biology (General) ,Structural integrity ,GWAS, VanillaICE ,JNK cascade ,National Heart, Lung, and Blood Institute (U.S.) ,030217 neurology & neurosurgery ,Developmental Biology - Abstract
Human induced pluripotent stem cell (hiPSC) lines have previously been generated through the NHLBI sponsored NextGen program at nine individual study sites. Here, we examined the structural integrity of 506 hiPSC lines as determined by copy number variations (CNVs). We observed that 149 hiPSC lines acquired 258 CNVs relative to donor DNA. We identified six recurrent regions of CNVs on chromosomes 1, 2, 3, 16 and 20 that overlapped with cancer associated genes. Furthermore, the genes mapping to regions of acquired CNVs show an enrichment in cancer related biological processes (IL6 production) and signaling cascades (JNK cascade & NFκB cascade). The genomic region of instability on chr20 (chr20q11.2) includes transcriptomic signatures for cancer associated genes such as ID1, BCL2L1, TPX2, PDRG1 and HCK. Of these HCK shows statistically significant differential expression between carrier and non-carrier hiPSC lines. Overall, while a low level of genomic instability was observed in the NextGen generated hiPSC lines, the observation of structural instability in regions with known cancer associated genes substantiates the importance of systematic evaluation of genetic variations in hiPSCs before using them as disease/research models.
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- 2020
27. Wearable sensor data and self-reported symptoms for COVID-19 detection
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Vik Kheterpal, Edward Ramos, Eric J. Topol, Lauren Ariniello, Matteo Gadaleta, Jennifer M. Radin, Giorgio Quer, Katie Baca-Motes, and Steven R. Steinhubl
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0301 basic medicine ,Adult ,Male ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Wearable computer ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Wearable Electronic Devices ,0302 clinical medicine ,Interquartile range ,Heart Rate ,Internal medicine ,Medicine ,Humans ,Mass Screening ,Sampling (medicine) ,Mass screening ,Aged ,Monitoring, Physiologic ,business.industry ,Activity tracker ,Area under the curve ,COVID-19 ,General Medicine ,Middle Aged ,United States ,030104 developmental biology ,030220 oncology & carcinogenesis ,Smartphone app ,Carrier State ,Female ,Self Report ,business ,Sleep - Abstract
Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.
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- 2020
28. Prevalence of Asymptomatic SARS-CoV-2 Infection : A Narrative Review
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Daniel P Oran and Eric J. Topol
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Pediatrics ,medicine.medical_specialty ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Pneumonia, Viral ,01 natural sciences ,Asymptomatic ,03 medical and health sciences ,Betacoronavirus ,0302 clinical medicine ,Public health surveillance ,Pandemic ,Internal Medicine ,medicine ,Prevalence ,Humans ,030212 general & internal medicine ,0101 mathematics ,Pandemics ,Subclinical infection ,biology ,Viral Epidemiology ,business.industry ,SARS-CoV-2 ,010102 general mathematics ,COVID-19 ,General Medicine ,medicine.disease ,biology.organism_classification ,Pneumonia ,Asymptomatic Diseases ,medicine.symptom ,business ,Coronavirus Infections - Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world since the first cases of coronavirus disease 2019 (COVID-19) were observed in December 2019 in Wuhan, China. It has been suspected that infected persons who remain asymptomatic play a significant role in the ongoing pandemic, but their relative number and effect have been uncertain. The authors sought to review and synthesize the available evidence on asymptomatic SARS-CoV-2 infection. Asymptomatic persons seem to account for approximately 40% to 45% of SARS-CoV-2 infections, and they can transmit the virus to others for an extended period, perhaps longer than 14 days. Asymptomatic infection may be associated with subclinical lung abnormalities, as detected by computed tomography. Because of the high risk for silent spread by asymptomatic persons, it is imperative that testing programs include those without symptoms. To supplement conventional diagnostic testing, which is constrained by capacity, cost, and its one-off nature, innovative tactics for public health surveillance, such as crowdsourcing digital wearable data and monitoring sewage sludge, might be helpful.
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- 2020
29. Telemedicine 2020 and the next decade
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Eric J. Topol and E. Ray Dorsey
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Adult ,Telemedicine ,Extramural ,Practice patterns ,business.industry ,MEDLINE ,Age Factors ,General Medicine ,Middle Aged ,medicine.disease ,Health Services Accessibility ,State Medicine ,United Kingdom ,United States ,Medicine ,Educational Status ,Humans ,Medical emergency ,Practice Patterns, Physicians' ,business ,Aged ,Forecasting - Published
- 2020
30. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
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Anthony C. Gordon, Mahiben Maruthappu, Eric J. Topol, Matthieu Komorowski, Christopher A. Lovejoy, Myura Nagendran, Gary S. Collins, Hugh Harvey, John P. A. Ioannidis, and Yang Chen
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Diagnostic Imaging ,medicine.medical_specialty ,Blinding ,MEDLINE ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,Bias ,Artificial Intelligence ,Physicians ,Image Processing, Computer-Assisted ,Medical imaging ,medicine ,Humans ,Medical physics ,Randomized Controlled Trials as Topic ,business.industry ,Deep learning ,Research ,Absolute risk reduction ,Consolidated Standards of Reporting Trials ,General Medicine ,Reference Standards ,Clinical trial ,Research Design ,030220 oncology & carcinogenesis ,Model risk ,Artificial intelligence ,business ,Algorithms - Abstract
Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design Systematic review. Data sources Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. Eligibility criteria for selecting studies Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. Review methods Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. Results Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal ( Conclusions Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. Study registration PROSPERO CRD42019123605.
- Published
- 2020
31. A brighter future for kidney disease?
- Author
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Eric J. Topol and Evan D. Muse
- Subjects
medicine.medical_specialty ,business.industry ,MEDLINE ,Renal function ,General Medicine ,medicine.disease ,Kidney Function Tests ,Mobile Applications ,Machine Learning ,Internal medicine ,medicine ,Humans ,Kidney Diseases ,business ,Biomarkers ,Kidney disease - Published
- 2020
32. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study
- Author
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Steven R. Steinhubl, Nathan E. Wineinger, Eric J. Topol, and Jennifer M. Radin
- Subjects
Outbreak response ,Adult ,Male ,Activities of daily living ,Population ,Medicine (miscellaneous) ,Wearable computer ,Health Informatics ,lcsh:Computer applications to medicine. Medical informatics ,Article ,Wearable Electronic Devices ,Individual health ,Health Information Management ,Influenza, Human ,Medicine ,Humans ,Decision Sciences (miscellaneous) ,education ,education.field_of_study ,Influenza-like illness ,business.industry ,Disease control ,United States ,Population based study ,Population Surveillance ,lcsh:R858-859.7 ,Female ,business ,Demography - Abstract
Summary Background Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data. Methods We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates. Findings We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3–32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. Interpretation Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks. Funding Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.
- Published
- 2020
33. Home Monitoring of Blood Pressure: Short–Term Changes During Serial Measurements for 56398 Subjects
- Author
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Eric J. Topol, Angela Chieh, Giorgio Quer, Alexis Normand, Nima Nikzad, Steven R. Steinhubl, and Matthieu Vegreville
- Subjects
Adult ,Male ,medicine.medical_specialty ,Databases, Factual ,Diastole ,Blood Pressure ,030204 cardiovascular system & hematology ,Article ,03 medical and health sciences ,0302 clinical medicine ,Health Information Management ,Internal medicine ,medicine ,Humans ,Clinical significance ,030212 general & internal medicine ,Electrical and Electronic Engineering ,Stroke ,Aged ,Aged, 80 and over ,Models, Statistical ,Home environment ,business.industry ,Blood Pressure Determination ,Middle Aged ,medicine.disease ,Home Care Services ,Home setting ,Computer Science Applications ,Term (time) ,Surgery ,Blood pressure ,Cardiology ,Female ,Ischemic heart ,business ,Biotechnology - Abstract
Hypertension is one of the greatest contributors to premature morbidity and mortality worldwide. It has been demonstrated that lowering blood pressure (BP) by just a few mmHg can bring substantial clinical benefits, reducing the risk of stroke and ischemic heart disease. Properly managing high BP is one of the most pressing global health issues, but accurate methods to continuously monitoring BP at home are still under discussion. Indeed, the BP for any given individual can fluctuate significantly during intervals as short as a few minutes. In clinical settings, the guidelines suggest to wait for 5 or 10 minutes in seated rest before taking the measure, in order to alleviate the effect of the stress induced by the clinical environment. Alternatively, BP measured in the home environment is thought to provide a more accurate measure free of the stress of a clinical environment, but there is currently a lack of extensive studies on the trajectory of serial BP measurements over minutes in the home setting. In this paper, we aim at filling this gap by analyzing a large dataset of more than 16 million BP measurements taken at home with commercial BP monitoring devices. In particular, we propose new techniques to analyze this dataset, taking into account the limitations due to the uncontrolled data collection, and we study the characteristics of the BP trajectory for consecutive measures over several minutes. We show that the BP values significantly decrease after 10 minutes minutes from the initial measurement (4.1 and 6.6 mmHg for the diastolic and systolic BP, respectively), and continue to decrease for about 25 minutes. We also describe statistically the clinical relevance of this change, observing more than 50% misclassifications for measurements in the hypertension region. We then propose a model to study the inter-subject variability, showing significant variations in the expected decrease in systolic BP. These results may provide the initial evidence for future large clinical studies using participant-monitored BP.
- Published
- 2018
34. Incidental Detection of Maternal Neoplasia in Noninvasive Prenatal Testing
- Author
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Ron McCullough, Dirk van den Boom, Juan-Sebastian Saldivar, Daniel H. Farkas, Nilesh Dharajiya, Eric J. Topol, Eyad Almasri, Daniel S. Grosu, Youting Sun, Taylor J. Jensen, Sung K. Kim, and Mathias Ehrich
- Subjects
Adult ,0301 basic medicine ,medicine.medical_specialty ,Uterine fibroids ,Clinical Biochemistry ,Population ,Laboratory testing ,Article ,Circulating Tumor DNA ,Cohort Studies ,03 medical and health sciences ,Pregnancy ,Prenatal Diagnosis ,medicine ,Humans ,Neoplasm ,education ,Pathological ,Gynecology ,Incidental Findings ,education.field_of_study ,Fetus ,business.industry ,Obstetrics ,Biochemistry (medical) ,High-Throughput Nucleotide Sequencing ,Cancer ,medicine.disease ,030104 developmental biology ,Biomarker (medicine) ,Female ,business ,Cell-Free Nucleic Acids ,Pregnancy Complications, Neoplastic - Abstract
BACKGROUND Noninvasive prenatal testing (NIPT) uses cell-free DNA (cfDNA) as an analyte to detect copy-number alterations in the fetal genome. Because maternal and fetal cfDNA contributions are comingled, changes in the maternal genome can manifest as abnormal NIPT results. Circulating tumor DNA (ctDNA) present in cases of maternal neoplasia has the potential to distort the NIPT readout to a degree that prevents interpretation, resulting in a nonreportable test result for fetal aneuploidy. METHODS NIPT cases that showed a distortion from normal euploid genomic representation were communicated to the caregiving physician as nonreportable for fetal aneuploidy. Follow-up information was subsequently collected for these cases. More than 450000 pregnant patients who submitted samples for clinical laboratory testing >3 years are summarized. Additionally, in-depth analysis was performed for >79000 research-consented samples. RESULTS In total, 55 nonreportable NIPT cases with altered genomic profiles were cataloged. Of these, 43 had additional information available to enable follow-up. A maternal neoplasm was confirmed in 40 of these cases: 18 malignant, 20 benign uterine fibroids, and 2 with radiological confirmation but without pathological classification. CONCLUSIONS In a population of pregnant women who submitted a blood sample for cfDNA testing, an abnormal genomic profile not consistent with fetal abnormalities was detected in about 10 out of 100000 cases. A subset of these observations (18 of 43; 41.9%) was attributed to maternal malignant neoplasms. These observational results suggest the need for a controlled trial to evaluate the potential of using cfDNA as an early biomarker of cancer.
- Published
- 2018
35. Direct to Consumer Fitness DNA Testing
- Author
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Eric J. Topol and Emily G. Spencer
- Subjects
Adult ,0301 basic medicine ,media_common.quotation_subject ,Clinical Biochemistry ,030204 cardiovascular system & hematology ,03 medical and health sciences ,0302 clinical medicine ,Resource (project management) ,Order (exchange) ,Direct-To-Consumer Screening and Testing ,medicine ,Humans ,Genetic Testing ,Marketing ,Child ,media_common ,Genetic testing ,medicine.diagnostic_test ,Ownership ,Biochemistry (medical) ,DNA ,Purchasing ,Test (assessment) ,030104 developmental biology ,Harm ,Aptitude ,Consumer confidence index ,Business - Abstract
The direct to consumer (DTC)2 genetic testing market was initiated in 2007 with the introduction of 23andMe. Since then, there has been marked expansion and, at the same time, considerable efforts to scrutinize the potential benefits and harms of such testing (1). The various tests are heterogeneous, with output ranging from medical data to ancestry to lifestyle. Along the way, fitness-centered DTC genetic tests have cropped up, encouraging consumers to pay, as we will briefly describe here, high costs for what we feel to be low value information. These tests promise insights about nutrition, healthy aging, weight loss, and athletic aptitude (even for specific sports). They market faux scientific authority, engendering confusion and the potential for harm, no less diminishing consumer confidence in validated, actionable, genetic tests. The genetic testing market is currently divided into 2 main categories with a couple of exceptions that operate in both arenas. Clinical tests require that a physician order each test, are subject to regulatory oversight, and are aimed at diagnosing or screening for mutations related to specific medical conditions. In contrast, DTC tests are purchased without any need for physician involvement. Current surveys identify approximately 40 companies offering DTC fitness genetic tests (2) and several companies that serve as a marketplace for purchasing a variety of such tests interpreted by third parties. Although registries exist to provide some assistance for medical genetics professionals to choose between the approximately 75000 clinical genetic tests available (3), there is no such resource for consumers. DTC genetic test prices range from under $30 to well over $1000. The less expensive tests are generally for individual single nucleotide variants, and marketing by some DTC companies drives sales of multiple tests per customer. The DTC genetic testing market is estimated to total about $100 million in 2017, with …
- Published
- 2019
36. Prevalence of Asymptomatic SARS-CoV-2 Infection
- Author
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Eric J. Topol and Daniel P Oran
- Subjects
2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,viruses ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Reviews ,01 natural sciences ,Asymptomatic ,03 medical and health sciences ,0302 clinical medicine ,Pandemic ,Prevalence ,Internal Medicine ,Humans ,Medicine ,030212 general & internal medicine ,0101 mathematics ,Asymptomatic Diseases ,SARS-CoV-2 ,business.industry ,010102 general mathematics ,COVID-19 ,virus diseases ,General Medicine ,Virology ,Severe acute respiratory syndrome coronavirus ,medicine.symptom ,Coronavirus Infections ,business - Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world since the first cases of coronavirus disease 2019 (COVID-19) were observed in December 2019 in Wuhan, China. It has been suspected that infected persons who remain asymptomatic play a significant role in the ongoing pandemic, but their relative number and effect have been uncertain. The authors sought to review and synthesize the available evidence on asymptomatic SARS-CoV-2 infection. Asymptomatic persons seem to account for approximately 40% to 45% of SARS-CoV-2 infections, and they can transmit the virus to others for an extended period, perhaps longer than 14 days. Asymptomatic infection may be associated with subclinical lung abnormalities, as detected by computed tomography. Because of the high risk for silent spread by asymptomatic persons, it is imperative that testing programs include those without symptoms. To supplement conventional diagnostic testing, which is constrained by capacity, cost, and its one-off nature, innovative tactics for public health surveillance, such as crowdsourcing digital wearable data and monitoring sewage sludge, might be helpful., Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world. Infected persons who remain asymptomatic may play a role in the ongoing pandemic, but their relative number and effect have been uncertain. This article reviews the available evidence on asymptomatic SARS-CoV-2 infection.
- Published
- 2021
37. Digitising the vision test
- Author
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Ali Ahmad Malik, Eric J. Topol, and Chris Piech
- Subjects
Digital Technology ,Computer science ,Vision Tests ,MEDLINE ,Humans ,Optometry ,General Medicine ,Vision test - Published
- 2021
38. Assessment of Prolonged Physiological and Behavioral Changes Associated With COVID-19 Infection
- Author
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Jennifer M. Radin, Eric J. Topol, Katie Baca-Motes, Giorgio Quer, Matteo Gadaleta, Edward Ramos, and Steven R. Steinhubl
- Subjects
Adult ,Male ,2019-20 coronavirus outbreak ,Adolescent ,Coronavirus disease 2019 (COVID-19) ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Wearable Electronic Devices ,Young Adult ,Post-Acute COVID-19 Syndrome ,Heart Rate ,mental disorders ,Research Letter ,Humans ,Medicine ,Aged ,SARS-CoV-2 ,business.industry ,Research ,COVID-19 ,General Medicine ,Middle Aged ,Virology ,Online Only ,Infectious Diseases ,Female ,Wearable Electronic Device ,business - Abstract
This cohort study examines the duration and variation of recovery among COVID-19–positive verses COVID-19–negative individuals.
- Published
- 2021
39. Cardiac rehabilitation in the digital era
- Author
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Eric J. Topol, Amitabh C. Pandey, and Jessica R. Golbus
- Subjects
Cardiac Rehabilitation ,Rehabilitation ,business.industry ,Digital era ,medicine.medical_treatment ,MEDLINE ,Fitness Trackers ,General Medicine ,medicine.disease ,Mobile Applications ,Exercise Therapy ,Artificial Intelligence ,Humans ,Medicine ,Medical emergency ,business - Published
- 2021
40. Artificial intelligence, bias, and patients' perspectives
- Author
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Ziad Obermeyer and Eric J. Topol
- Subjects
Patients ,business.industry ,MEDLINE ,General Medicine ,computer.software_genre ,Racism ,Text mining ,Artificial Intelligence ,Diagnosis ,Humans ,Artificial intelligence ,business ,Psychology ,computer ,Algorithms ,Prejudice ,Natural language processing - Published
- 2021
41. Genome-Wide Linkage Analysis of Large Multiple Multigenerational Families Identifies Novel Genetic Loci for Coronary Artery Disease
- Author
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Stephen G. Ellis, Stephen Arckacki, Qing Kenneth Wang, Eric J. Topol, Carlos A. Hubbard, Fan Wang, Yang Guo, Lin Li, Hanxiang Gao, Qiuyun Chen, John Barnard, and Isabel Z. Wang
- Subjects
0301 basic medicine ,Male ,Genetic Linkage ,Science ,Genome-wide association study ,Coronary Artery Disease ,Biology ,Article ,Coronary artery disease ,03 medical and health sciences ,Genetic linkage ,medicine ,SNP ,Chromosomes, Human ,Humans ,Family ,Genetic Predisposition to Disease ,Gene ,Nuclear family ,Genetics ,Multidisciplinary ,Chromosome ,Middle Aged ,medicine.disease ,Human genetics ,Pedigree ,030104 developmental biology ,Genetic Loci ,Medicine ,Female ,Genome-Wide Association Study - Abstract
Coronary artery disease (CAD) is the leading cause of death, and genetic factors contribute significantly to risk of CAD. This study aims to identify new CAD genetic loci through a large-scale linkage analysis of 24 large and multigenerational families with 433 family members (GeneQuest II). All family members were genotyped with markers spaced by every 10 cM and a model-free nonparametric linkage (NPL-all) analysis was carried out. Two highly significant CAD loci were identified on chromosome 17q21.2 (NPL score of 6.20) and 7p22.2 (NPL score of 5.19). We also identified four loci with significant NPL scores between 4.09 and 4.99 on 2q33.3, 3q29, 5q13.2 and 9q22.33. Similar analyses in individual families confirmed the six significant CAD loci and identified seven new highly significant linkages on 9p24.2, 9q34.2, 12q13.13, 15q26.1, 17q22, 20p12.3, and 22q12.1, and two significant loci on 2q11.2 and 11q14.1. Two loci on 3q29 and 9q22.33 were also successfully replicated in our previous linkage analysis of 428 nuclear families. Moreover, two published risk variants, SNP rs46522 in UBE2Z and SNP rs6725887 in WDR12 by GWAS, were found within the 17q21.2 and 2q33.3 loci. These studies lay a foundation for future identification of causative variants and genes for CAD.
- Published
- 2017
42. COVID-19 can affect the heart
- Author
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Eric J. Topol
- Subjects
Heart Failure ,2019-20 coronavirus outbreak ,Multidisciplinary ,Heart Diseases ,Coronavirus disease 2019 (COVID-19) ,SARS-CoV-2 ,business.industry ,Extramural ,Myocardium ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Pneumonia, Viral ,MEDLINE ,COVID-19 ,Arrhythmias, Cardiac ,Heart ,Bioinformatics ,Affect (psychology) ,Betacoronavirus ,Myocarditis ,Viral Tropism ,Humans ,Medicine ,Coronavirus Infections ,business ,Pandemics - Abstract
COVID-19 has a spectrum of potential heart manifestations with diverse mechanisms
- Published
- 2020
43. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
- Author
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Lucas M. Bachmann, Eric J. Topol, Joseph R. Ledsam, Mohith Shamdas, Livia Faes, Christoph Kern, Gabriella Moraes, Pearse A. Keane, Siegfried K Wagner, Thushika Mahendiran, Konstantinos Balaskas, Dun Jack Fu, Aditya Kale, Xiaoxuan Liu, Alice Bruynseels, Alastair K Denniston, and Martin Schmid
- Subjects
Diagnostic Imaging ,Contingency table ,medicine.medical_specialty ,business.industry ,Health Personnel ,Deep learning ,Citation index ,education ,Science Citation Index ,Medicine (miscellaneous) ,Health Informatics ,Sample (statistics) ,lcsh:Computer applications to medicine. Medical informatics ,Deep Learning ,Health Information Management ,Meta-analysis ,Health care ,Medical imaging ,Humans ,Medicine ,lcsh:R858-859.7 ,Decision Sciences (miscellaneous) ,Medical physics ,Artificial intelligence ,business - Abstract
Summary Background Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging. Methods In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. We excluded studies that used medical waveform data graphics material or investigated the accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176. Findings Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging from 9·7% to 100·0% (mean 79·1%, SD 0·2) and specificity ranging from 38·9% to 100·0% (mean 88·3%, SD 0·1). An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning models and health-care professionals in the same sample. Comparison of the performance between health-care professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the highest accuracy, found a pooled sensitivity of 87·0% (95% CI 83·0–90·2) for deep learning models and 86·4% (79·9–91·0) for health-care professionals, and a pooled specificity of 92·5% (95% CI 85·1–96·4) for deep learning models and 90·5% (80·6–95·7) for health-care professionals. Interpretation Our review found the diagnostic performance of deep learning models to be equivalent to that of health-care professionals. However, a major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology. Funding None.
- Published
- 2019
44. Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults
- Author
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Eric J. Topol, Pishoy Gouda, Steven R. Steinhubl, Giorgio Quer, and Michael Galarnyk
- Subjects
Male ,Pulmonology ,Physiology ,030204 cardiovascular system & hematology ,Body Mass Index ,Cohort Studies ,Electrocardiography ,0302 clinical medicine ,Endocrinology ,Heart Rate ,Reproductive Physiology ,Medicine and Health Sciences ,Medicine ,030212 general & internal medicine ,Longitudinal Studies ,Young adult ,media_common ,2. Zero hunger ,Aged, 80 and over ,education.field_of_study ,Multidisciplinary ,medicine.diagnostic_test ,Age Factors ,Middle Aged ,Bioassays and Physiological Analysis ,Research Design ,Engineering and Technology ,Female ,Seasons ,Cohort study ,Research Article ,Adult ,Adolescent ,Science ,media_common.quotation_subject ,Rest ,Population ,Cardiology ,Equipment ,Research and Analysis Methods ,03 medical and health sciences ,Young Adult ,Sex Factors ,Heart rate ,Humans ,Adults ,education ,Menstrual cycle ,Measurement Equipment ,Menstrual Cycle ,Aged ,Retrospective Studies ,Endocrine Physiology ,business.industry ,Electrophysiological Techniques ,Biology and Life Sciences ,Retrospective cohort study ,Asthma ,Age Groups ,People and Places ,Population Groupings ,Cardiac Electrophysiology ,business ,Sleep ,Physiological Processes ,Body mass index ,Demography - Abstract
BackgroundHeart rate is routinely measured as part of the clinical examination but is rarely acted upon unless it is well outside a population-based normal range. With wearable sensor technologies, heart rate can now be continuously measured, making it possible to accurately identify an individual's "normal" heart rate and potentially important variations in it over time. Our objective is to describe inter- and intra-individual variability in resting heart rate (RHR) collected over the course of two years using a wearable device, studying the variations of resting heart rate as a function of time of year, as well as individuals characteristics like age, sex, average sleep duration, and body mass index (BMI).Methods and findingsOur retrospective, longitudinal cohort study includes 92,457 de-identified individuals from the United States (all 50 states), who consistently-over at least 35 weeks in the period from March 2016 to February 2018, for at least 2 days per week, and at least 20 hours per day-wore a heart rate wrist-worn tracker. In this study, we report daily RHR and its association with age, BMI, sex, and sleep duration, and its variation over time. Individual daily RHR was available for a median of 320 days, providing nearly 33 million daily RHR values. We also explored the range in daily RHR variability between individuals, and the long- and short-term changes in the trajectory of an individual's daily RHR. Mean daily RHR was 65 beats per minute (bpm), with a range of 40 to 109 bpm among all individuals. The mean RHR differed significantly by age, sex, BMI, and average sleep duration. Time of year variations were also noted, with a minimum in July and maximum in January. For most subjects, RHR remained relatively stable over the short term, but 20% experienced at least 1 week in which their RHR fluctuated by 10 bpm or more.ConclusionsIndividuals have a daily RHR that is normal for them but can differ from another individual's normal by as much as 70 bpm. Within individuals, RHR was much more consistent over time, with a small but significant seasonal trend, and detectable discrete and infrequent episodes outside their norms.
- Published
- 2019
45. Re-analysis of whole-exome sequencing data uncovers novel diagnostic variants and improves molecular diagnostic yields for sudden death and idiopathic diseases
- Author
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Emily G. Spencer, Glenn N. Wagner, Ali Torkamani, Jonathan R. Lucas, Eric J. Topol, Manuel Rueda, Steven Campman, Evan D. Muse, Elias L. Salfati, and Sarah E. Topol
- Subjects
0301 basic medicine ,Male ,Myosin Light Chains ,lcsh:QH426-470 ,Adenosine Deaminase ,Ubiquitin-Protein Ligases ,lcsh:Medicine ,030105 genetics & heredity ,Bioinformatics ,Sudden death ,03 medical and health sciences ,Death, Sudden ,Young Adult ,Rare Diseases ,Molecular autopsy ,Nucleotidases ,Databases, Genetic ,Exome Sequencing ,Genetics ,Medicine ,Humans ,Idiopathic disease ,Exome ,Automated periodic re-analysis ,Child ,Molecular Biology ,Gene ,Rare and undiagnosed diseases ,Genetics (clinical) ,Exome sequencing ,Likely pathogenic ,business.industry ,Research ,lcsh:R ,Medical genetics ,Genetic Variation ,Sudden unexplained death ,Phenotype ,3. Good health ,lcsh:Genetics ,030104 developmental biology ,Child, Preschool ,Whole-exome sequencing ,Molecular Medicine ,Female ,business ,Rare disease - Abstract
Background Whole-exome sequencing (WES) has become an efficient diagnostic test for patients with likely monogenic conditions such as rare idiopathic diseases or sudden unexplained death. Yet, many cases remain undiagnosed. Here, we report the added diagnostic yield achieved for 101 WES cases re-analyzed 1 to 7 years after initial analysis. Methods Of the 101 WES cases, 51 were rare idiopathic disease cases and 50 were postmortem “molecular autopsy” cases of early sudden unexplained death. Variants considered for reporting were prioritized and classified into three groups: (1) diagnostic variants, pathogenic and likely pathogenic variants in genes known to cause the phenotype of interest; (2) possibly diagnostic variants, possibly pathogenic variants in genes known to cause the phenotype of interest or pathogenic variants in genes possibly causing the phenotype of interest; and (3) variants of uncertain diagnostic significance, potentially deleterious variants in genes possibly causing the phenotype of interest. Results Initial analysis revealed diagnostic variants in 13 rare disease cases (25.4%) and 5 sudden death cases (10%). Re-analysis resulted in the identification of additional diagnostic variants in 3 rare disease cases (5.9%) and 1 sudden unexplained death case (2%), which increased our molecular diagnostic yield to 31.4% and 12%, respectively. Conclusions The basis of new findings ranged from improvement in variant classification tools, updated genetic databases, and updated clinical phenotypes. Our findings highlight the potential for re-analysis to reveal diagnostic variants in cases that remain undiagnosed after initial WES.
- Published
- 2019
46. A decade of digital medicine innovation
- Author
-
Eric J. Topol
- Subjects
Engineering ,business.industry ,Genomics ,General Medicine ,030204 cardiovascular system & hematology ,Digital medicine ,Data science ,Field (computer science) ,Clinical trial ,Wearable Electronic Devices ,03 medical and health sciences ,0302 clinical medicine ,Inventions ,Humans ,Medicine ,Smartphone ,030212 general & internal medicine ,business ,Randomized Controlled Trials as Topic - Abstract
The field of digital medicine has matured over the past decade, but validation will require careful randomized, controlled clinical trials.
- Published
- 2019
47. Long data from the electrocardiogram
- Author
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Evan D. Muse, Eric J. Topol, Steven R. Steinhubl, and Giorgio Quer
- Subjects
medicine.medical_specialty ,Electrocardiography ,Physical medicine and rehabilitation ,Artificial neural network ,business.industry ,MEDLINE ,Medicine ,Humans ,General Medicine ,Neural Networks, Computer ,business ,Mobile Applications - Published
- 2019
48. Characteristics of the modern-day physician house call
- Author
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Shannon Fortin Ensign, Steven R. Steinhubl, Katie Baca-Motes, and Eric J. Topol
- Subjects
Adult ,Male ,Opportunity cost ,healthcare delivery models ,Primary health care ,Observational Study ,Primary care ,Health Services Accessibility ,03 medical and health sciences ,0302 clinical medicine ,Help-Seeking Behavior ,Healthcare delivery ,House call ,medicine ,Humans ,030212 general & internal medicine ,Practice Patterns, Physicians' ,Aged ,Potential impact ,Primary Health Care ,business.industry ,Age Factors ,primary care accessibility ,Infant ,General Medicine ,medicine.disease ,Mobile Applications ,Quality Improvement ,United States ,3. Good health ,House Calls ,Patient Satisfaction ,030220 oncology & carcinogenesis ,Health Care Surveys ,Female ,Medical emergency ,Smartphone ,business ,on-demand physician house calls ,Research Article - Abstract
Many barriers to primary healthcare accessibility in the United States exist including an increased opportunity cost associated with seeking primary care. New models of healthcare delivery aimed at addressing these problems are emerging. The potential impact that on-demand primary care physician house calls services can have on healthcare accessibility, patient care, and satisfaction by both patients and physicians is poorly characterized. We performed a retrospective observational analysis on data from 13,849 patients who utilized Heal, Inc, an application (app)-based, on-demand house calls platform between August 2016 and July 2017. We assessed house call wait time and visit duration, diagnoses by International Classification of Diseases, tenth revision, Inc (ICD10) codes, and house call outcomes by post-visit prescription and lab requests, and patient satisfaction survey. Patients who utilized this physician house call service had a bimodal age distribution peaking at age 1 year and 36 years. Same day acute sick exams (93.9% of pediatric (Ped) and 66.9% of adult requests) for fever and/or acute upper respiratory infection represented the most common use. The mean wait time for as soon as possible house calls were 96.1 minutes, with an overall mean house call duration of 27.1 minutes. A house call was primarily chosen over an Urgent Care Clinic or Doctor's office (46.2% and 41.6% of respondents, respectively), due to convenience or fastest appointment available (69.6% and 33.8% of respondents, respectively). Most survey respondents (94.2%) would schedule house calls again. On-demand physician house calls programs can expand access options to primary healthcare, primarily used by younger individuals with acute illness and preference for a smartphone app-based home visit.
- Published
- 2019
49. Unveiling the Role of the Most Impactful Cardiovascular Risk Locus through Haplotype Editing
- Author
-
Evan L. Teng, Aditya Kumar, Kristin K. Baldwin, Pavel Chubukov, Lei Zhang, Valentina Lo Sardo, Adam J. Engler, Michael A. Duran, Fyodor D. Urnov, Ali Torkamani, Gregory J. Cost, William S. Ferguson, and Eric J. Topol
- Subjects
0301 basic medicine ,Male ,Transcription, Genetic ,coronary artery ,Coronary Artery Disease ,arterial wall ,Cardiovascular ,Medical and Health Sciences ,Muscle, Smooth, Vascular ,lncRNA ,Genome editing ,Risk Factors ,Smooth Muscle ,cardiovascular disease ,disease modeling ,Leukocytes ,80 and over ,vascular smooth muscle cells ,2.1 Biological and endogenous factors ,Aetiology ,Induced pluripotent stem cell ,Aged, 80 and over ,Genetics ,Gene Editing ,Single Nucleotide ,Middle Aged ,Biological Sciences ,Phenotype ,Heart Disease ,Cardiovascular Diseases ,Muscle ,RNA, Long Noncoding ,Female ,Long Noncoding ,Smooth ,Stem cell ,Chromosomes, Human, Pair 9 ,Transcription ,Human ,Pair 9 ,Genotype ,Myocytes, Smooth Muscle ,Mononuclear ,Induced Pluripotent Stem Cells ,iPSCs ,Locus (genetics) ,and over ,Biology ,Polymorphism, Single Nucleotide ,Article ,General Biochemistry, Genetics and Molecular Biology ,Chromosomes ,03 medical and health sciences ,Genetic ,stem cells ,Clinical Research ,Vascular ,Humans ,genome editing ,Genetic Predisposition to Disease ,Polymorphism ,Gene ,Heart Disease - Coronary Heart Disease ,Aged ,Myocytes ,Stem Cell Research - Induced Pluripotent Stem Cell ,Haplotype ,Human Genome ,Stem Cell Research ,Atherosclerosis ,030104 developmental biology ,HEK293 Cells ,Haplotypes ,Leukocytes, Mononuclear ,RNA ,Human genome ,Genome-Wide Association Study ,Developmental Biology - Abstract
The 9p21.3 cardiovascular disease locus is the most influential common genetic risk factor for coronary artery disease (CAD), accounting for ~10%−15% of disease in non-African populations. The ~60 kb risk haplotype is human-specific and lacks coding genes, hindering efforts to decipher its function. Here, we produce induced pluripotent stem cells (iPSCs) from risk and non-risk individuals, delete each haplo-type using genome editing, and generate vascular smooth muscle cells (VSMCs). Risk VSMCs exhibit globally altered transcriptional networks that intersect with previously identified CAD risk genes and pathways, concomitant with aberrant adhesion, contraction, and proliferation. Unexpectedly, deleting the risk haplotype rescues VSMC stability, while expressing the 9p21.3-associated long non-coding RNA ANRIL induces risk phenotypes in non-risk VSMCs. This study shows that the risk haplotype selectively predisposes VSMCs to adopt a cell state associated with CAD phenotypes, defines new VSMC-based networks of CAD risk genes, and es-tablishes haplotype-edited iPSCs as powerful tools for functionally annotating the human genome.
- Published
- 2018
50. Monitoring Jet Engines and the Health of People
- Author
-
Eric J. Topol and Lionel Tarassenko
- Subjects
0301 basic medicine ,Signal processing ,Information retrieval ,Aircraft ,business.industry ,MEDLINE ,Signal Processing, Computer-Assisted ,General Medicine ,Models, Biological ,Jet engine ,law.invention ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,law ,Medicine ,Humans ,Computer Simulation ,business ,030217 neurology & neurosurgery ,Algorithms ,Monitoring, Physiologic - Published
- 2018
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