Integrative Machine Learning Approaches for Predicting Metformin Responsein Type 2 Diabetes: A Multi Omics Perspective

Abstract

Background: Type 2 Diabetes Mellitus is a heterogeneous metabolic disorder characterized by variable therapeutic responses. Metformin remains the first line therapy; however, significant interindividual variability in glycemic r esponse limits its effectiveness. Objective: To review and synthesize current evidence on integrative machine learning approaches that combine multi omics and clinical data to predict metformin response in individuals with Type 2 Diabetes. Methods: A compr ehensive narrative review was conducted, focusing on studies integrating pharmacogenomic, metabolomic, and clinical datasets using machine learning techniques such as Random Forest, Support Vector Machines, and neural networks. Data integration strategies including early, intermediate, and late fusion were examined. Results: Individual predictors, including genetic variants (e.g., SLC22A1, ATM), clinical factors (age, BMI, baseline HbA1c), and metabolomic signatures (amino acids, lipids, gut derived metabol ites), explain only a fraction of variability in metformin response. Integrative machine learning models demonstrate improved predictive performance by capturi ng nonlinear interactions across biological layers. However, challenges such as data heterogeneit y, small sample sizes, lack of standardization, and limited external validation persist. Conclusion: Multi omics integration using machine learning offers a promising pathway toward precision medicine in Type 2 Diabetes. Nevertheless, methodological limita tions, interpretability issues, and translational barriers must be addressed before clinical implementation