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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 - 19 May 2026

Rahman Md Hafizur, Arian Ash, Konda Sreenivas, McGuinn Laura A, Islam Humayera, Chowdhury Muhammad Ashique Haider, Shahriar Mohammad Hasan, Rayna Shahrin Emdad, Farzana Shahla, Tasmin Saira, Manna Ridwana Maher, Haider Mohammad Rifat, Ahsan Habibul

Abstract excerpt

BACKGROUND: Evidence is limited on whether daily step counts are uniform across individuals or whether combining them with genetic risk improves prediction of type 2 diabetes (T2D), both essential for precision prevention. We aimed to assess whether objectively measured daily step count and Polygenic Risk Score (PRS) predict the incidence of T2D, and to evaluate the predictive performance of machine-learning...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 41396877
DOI: 10.1210/clinem/dgaf673
PMCID: PMC13185138

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

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