A deep learning-derived digital biomarker of dysglycemia and its association with genetic risk of type 2 diabetes.

Shao J, Pan Y, Xue J, Pan H, Wang J, Li S, Nie Z, Li Y, Tian Z, Zhao Y, Feng H, Zhou K

NPJ Metab Health Dis 3 (1) 46 [2025-12-02; online 2025-12-02]

Type 2 diabetes is a global health burden driven by genetic and environmental factors. Continuous glucose monitoring (CGM) can effectively guide lifestyle interventions in non-diabetic. However, predefined CGM metrics fail to fully capture the dysglycemic information contained in the high-dimensional time-series CGM data. This study employed deep learning to learn dysglycemia features from CGM data associated with diabetes and derived a digital biomarker of dysglycemia, validated against traditional dysglycemic biomarkers and diabetes polygenic risk score (PRS). Output of the deep learning model, called the deep learning-score, was significantly associated with multiple existing dysglycemic biomarkers and PRS of diabetes (P = 0.007). Moreover, existing CGM metrics were not associated with prevalent diabetes after adjusting for the deep learning-score, while the deep learning-score remained significantly associated with prevalent diabetes (P < 0.001) in a regression analysis. This digital biomarker demonstrated potential for providing dynamic feedback on dysglycemia and improving long-term intervention adherence.

PubMed 41331126

DOI 10.1038/s44324-025-00089-8

Crossref 10.1038/s44324-025-00089-8

pmc: PMC12672723
pii: 10.1038/s44324-025-00089-8