Interpretable machine learning in type-2 diabetes prediction in patients with depressive symptoms: Insights from mendelian randomization and physical activity

= 0.027).ConclusionsOur MR results support a causal effect of depression on T2D risk, with the TyG index and physical activity (PA) serving as modulators. Our ML-driven prediction model provides an interpretable screening tool for T2D risk in patients with depressive symptoms.

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Publication Details

Journal
Science Progress
Published
2026-04-01
DOI
https://doi.org/10.1177/00368504261440964
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00
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article

Interpretable machine learning in type-2 diabetes prediction in patients with depressive symptoms: Insights from mendelian randomization and physical activity

Hyunsik KANG, Wenzhong Wu, Zhao Li, Xiaoming Qiu
Science Progress
Genetic Associations and Epidemiology
article

Interpretable machine learning in type-2 diabetes prediction in patients with depressive symptoms: Insights from mendelian randomization and physical activity

Hyunsik KANG, Wenzhong Wu, Zhao Li, Xiaoming Qiu
article en

Abstract

= 0.027).ConclusionsOur MR results support a causal effect of depression on T2D risk, with the TyG index and physical activity (PA) serving as modulators. Our ML-driven prediction model provides an interpretable screening tool for T2D risk in patients with depressive symptoms.

Science ProgressVol. 109(2)
Sungkyunkwan University (KR)
Zero hunger
Openalex Percentile: Top 34%
Genetic Associations and Epidemiology
DOIOpen Access PDFOpenAlex
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Interpretable machine learning in type-2 diabetes prediction in patients with depressive symptoms: Insights from mendelian randomization and physical activity — Hyunsik KANG, Wenzhong Wu, et al. · Science Progress (2026) | TGRS Research Map | TGRS