Deep Neural Network-Based Nutrition-Gene Interaction Framework for Predicting Type 2 Diabetes Risk
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Abstract
Type 2 diabetes mellitus (T2DM) arises through complex mechanisms, and diet-gene interactions are not yet fully defined. We built a deep neural network (DNN) framework that combines dietary and genetic data to predict T2DM risk. Food-frequency questionnaire data from 300,000 partici-pants were merged with genome-wide association study (GWAS) data covering about nine mil-lion single-nucleotide polymorphisms. A multitask design yields two outputs: T2DM risk and individual dietary response. Dietary variables were reduced with sparse coding, and a Trans-former-based embedding layer was used to represent genetic features. In an independent mul-ticenter validation cohort (n = 15,000), the model achieved an area under the receiv-er-operating-characteristic curve of 0.92 for T2DM prediction and 78 % accuracy for die-tary-response prediction. SHAP analysis showed a significant interaction between omega-3 fat-ty-acid intake and the TCF7L2 locus. This study is the first to model nutrition-gene interactions on a large scale. It overcomes the limits of single-modality risk scores and provides a solid basis for precision-nutrition interventions aimed at preventing T2DM.
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