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

2025-09-14

AI Summary

This study addresses the need for precision interventions in type 2 diabetes by proposing a gene-agnostic pathway mapping machine learning framework that directly links clinical phenotypic features to core biological pathways—such as insulin resistance—without requiring molecular omics data. Methodologically, it integrates logistic regression (for risk prediction), PCA and t-tests (for feature selection), and pathway enrichment analysis (for mechanistic interpretation), forming a three-tier, interpretable “prediction–pathway–target” model. Its key contribution lies in enabling functional mapping from clinical features to signaling pathways and facilitating target discovery and personalized intervention design. Evaluated on the Pima Indians dataset, the framework achieves a prediction accuracy of 78.43% and successfully identifies translationally promising therapeutic strategies, including GLP-1/GIP dual-receptor agonists and AMPK activators.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Identifying high-risk individuals and therapeutic targets for metabolic disorders
• Developing a machine learning framework for T2DM prediction using pathway mapping
• Providing interpretable solutions for precision medicine in metabolic diseases

Innovation

• ML framework combining logistic regression and PCA
• Gene-agnostic pathway mapping for mechanistic insights
• Identified therapeutic strategies for high-risk populations