ArticleScientific reports2024
Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Development and validation of a trans-ancestry polygenic risk score for type 1 diabetes.Diabetologia · 2026Article
- Predicting incident type 2 diabetes using wearable activity and polygenic risk: A survival-modeling study in All of Us.The Journal of clinical endocrinology and metabolism · 2026Article
- Evaluation of the genome-informed risk assessment (GIRA) approach from eMERGE in an independent health system.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
- Statistical uncertainty explains the poor agreement in polygenic scoring for type 2 diabetes.medRxiv : the preprint server for health sciences · 2026Article
- Metabolic gene polymorphisms and type 2 diabetes mellitus risk: A systematic review and meta-analyses.Experimental and therapeutic medicine · 2026Article
- Type 5 diabetes mellitus: nutritional-imprinted β-cell insufficiency, diagnostic gaps, and emerging therapeutic strategies.Frontiers in endocrinology · 2026Review
- Article
- Improving type 2 diabetes polygenic risk scores by incorporating rare, low-frequency, and population-specific variants.medRxiv : the preprint server for health sciences · 2025Article
- Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.Scientific reports · 2025Article
- Calibrated prediction intervals for polygenic scores across diverse contexts.Nature genetics · 2024Article
- Multi-ancestry polygenic mechanisms of type 2 diabetes.Nature medicine · 2024Article
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Abstract
The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10
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