The “All of Us” Research Program

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Abstract

Knowledge gained from observational cohort studies has dramatically advanced the prevention and treatment of diseases. Many of these cohorts, however, are small, lack diversity, or do not provide comprehensive phenotype data. The All of Us Research Program plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health. The program aims to make the research results accessible to participants, and it is developing new approaches to generate, access, and make data broadly available to approved researchers. All of Us opened for enrollment in May 2018 and currently enrolls participants 18 years of age or older from a network of more than 340 recruitment sites. Elements of the program protocol include health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens. As of July 2019, more than 175,000 participants had contributed biospecimens. More than 80% of these participants are from groups that have been historically underrepresented in biomedical research. EHR data on more than 112,000 participants from 34 sites have been collected. The All of Us data repository should permit researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.

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Most cited references 18

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Mendelian Randomization.

Amit Khera, Sekar Kathiresan, Connor Emdin (2017)

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Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Riccardo Miotto, Li Li, Brian A Kidd … (2016)

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

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Association of coronary heart disease incidence with carotid arterial wall thickness and major risk factors: the Atherosclerosis Risk in Communities (ARIC) Study, 1987-1993.

L E Chambless, G Heiss, A Folsom … (1997)

Few studies have determined whether greater carotid artery intima-media thickness (IMT) in asymptomatic individuals is associated prospectively with increased risk of coronary heart disease (CHD). In the Atherosclerosis Risk in Communities Study, carotid IMT, an index of generalized atherosclerosis, was defined as the mean of IMT measurements at six sites of the carotid arteries using B-mode ultrasound. The authors assessed its relation to CHD incidence over 4-7 years of follow-up (1987-1993) in four US communities (Forsyth County, North Carolina; Jackson, Mississippi; Minneapolis, Minnesota; and Washington County, Maryland) from samples of 7,289 women and 5,552 men aged 45-64 years who were free of clinical CHD at baseline. There were 96 incident events for women and 194 for men. In sex-specific Cox proportional hazards models adjusted only for age, race, and center, the hazard rate ratio comparing extreme mean IMT (> or = 1 mm) to not extreme (< 1 mm) was 5.07 for women (95% confidence interval 3.08-8.36) and 1.85 for men (95% confidence interval 1.28-2.69). The relation was graded (monotonic), and models with cubic splines indicated significant nonlinearity. The strength of the association was reduced by including major CHD risk factors, but remained elevated at higher IMT. Up to 1 mm mean IMT, women had lower adjusted annual event rates than did men, but above 1 mm their event rate was closer to that of men. Thus, mean carotid IMT is a noninvasive predictor of future CHD incidence.