ArticleGenome medicine2024
Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study.
Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries.The lancet. Diabetes & endocrinology · 2026Pooled it
- Impact of Parental or First-Degree Family History of Diabetes on Diabetes Incidence and Progression During Long-term Follow-up in the Diabetes Prevention Program Outcomes Study.Diabetes care · 2025 · on this mapTrial
- Refining the Genetic Contribution to Type 2 Diabetes Subtypes.Diabetes, obesity & metabolism · 2026Article
- Multi-ancestry polygenic risk scores: toward equitable prediction of type 2 diabetes and its complications.Acta diabetologica · 2026Article
- Evaluation of Sex-Stratified Polygenic Risk Scores for Type 2 Diabetes Mellitus and Glycemic Traits in the Framingham Heart Study.Diabetes & metabolism journal · 2026Article
- Quantifying the utility of type 2 diabetes polygenic risk score for predicting incident diabetes: an analysis of large US-based cohort studies.BMC medical genomics · 2026Article
- Evaluating Individual Level Performance of Polygenic Risk Scores Using Early Onset High Genetic Risk Coronary Artery Disease as a Benchmark.medRxiv : the preprint server for health sciences · 2026Article
- Statistical uncertainty explains the poor agreement in polygenic scoring for type 2 diabetes.medRxiv : the preprint server for health sciences · 2026Article
- Improving type 2 diabetes polygenic risk scores by incorporating rare, low-frequency, and population-specific variants.medRxiv : the preprint server for health sciences · 2025Article
- Genetic risk and polygenic risk score assessment of prediabetes and progression to type 2 diabetes.Diabetes, obesity & metabolism · 2025Article
- Validation of GenProb-T1D and its clinical utility for differentiating types of diabetes in a biobank from a US healthcare system.Journal of diabetes investigation · 2025Article
- The PRIMED Consortium: Reducing disparities in polygenic risk assessment.American journal of human genetics · 2024Article
- Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.PLoS medicine · 2024Article
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7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D.
methodsWe tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS.
resultsPGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)).
conclusionsGenetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
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