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

Metabolic disorders, particularly type 2 diabetes mellitus (T2DM), represent a significant global health burden, disproportionately impacting genetically predisposed populations such as the Pima Indians (a Native American tribe from south central Arizona). This study introduces a novel machine learning (ML) framework that integrates predictive modeling with gene-agnostic pathway mapping to identify high-risk individuals and uncover potential therapeutic targets. Using the Pima Indian dataset, logistic regression and t-tests were applied to identify key predictors of T2DM, yielding an overall model accuracy of 78.43%. To bridge predictive analytics with biological relevance, we developed a pathway mapping strategy that links identified predictors to critical signaling networks, including insulin signaling, AMPK, and PPAR pathways. This approach provides mechanistic insights without requiring direct molecular data. Building upon these connections, we propose therapeutic strategies such as dual GLP-1/GIP receptor agonists, AMPK activators, SIRT1 modulators, and phytochemical, further validated through pathway enrichment analyses. Overall, this framework advances precision medicine by offering interpretable and scalable solutions for early detection and targeted intervention in metabolic disorders. The key contributions of this work are: (1) development of an ML framework combining logistic regression and principal component analysis (PCA) for T2DM risk prediction; (2) introduction of a gene-agnostic pathway mapping approach to generate mechanistic insights; and (3) identification of novel therapeutic strategies tailored for high-risk populations.