A Machine Learning Framework for Pathway-Driven Therapeutic Target Discovery in Metabolic Disorders

Metabolic disorders, such as type 2 diabetes mellitus (T2DM), pose substantial global health challenges, particularly affecting genetically predisposed populations like the Pima Indians. This study proposes a novel machine learning (ML) framework that integrates predictive modeling with gene-agnostic pathway mapping to identify high-risk individuals and discover therapeutic targets. Employing the PIMA Indian dataset, logistic regression and t-tests identified key predictors of T2DM, achieving a model accuracy of 78.43%. A pathway mapping strategy links these predictors to insulin signaling, AMPK, and PPAR pathways, facilitating mechanistic insights without molecular data. Therapeutic strategies encompass dual GLP-1/GIP receptor agonists, AMPK activators, SIRT1 modulators, and phytochemicals, validated through pathway enrichment. This framework advances precision medicine by providing interpretable, scalable solutions for early detection and targeted intervention in metabolic disorders and contributions:(1) An ML framework combining logistic regression and principal component analysis (PCA) for T2DM risk prediction; (2) A gene-agnostic pathway mapping method for mechanistic insights;(3) Novel therapeutic targets for high-risk populations.