Metabolic Fingerprints of Diabetes: Machine Learning Reveals Distinct Biomarkers in Type 2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) and obesity have reached epidemic levels globally. Although T2DM strain a heavy burden on population health, limited information is known about its metabolic signature, particularly in Emirati population.We explored Emiratis with controlled and uncontrolled T2DM metabolic profiles employing advanced machine-learning methods e.g. predictor randomization and bootstrap aggregation.Alpha-ketoisovaleric acid, 2-pyrrolidinone, and uridine were identified as significant metabolic markers related to inflammation and metabolic stress between the two groups. Notable interactions between metabolites in the tryptophan metabolic pathway, including L-tryptophan and indole compounds, were observed, highlighting their potential role in T2DM progression. Alterations in Tricarboxylic Acid cycle intermediates and increased activation of Pentose Phosphate Pathway suggested adaptive responses to oxidative stress. Furthermore, metabolic changes were observed across the prediabetic, controlled, and uncontrolled diabetes stages, with metabolites such as 5-Dodecenoic acid and phosphatidylcholine identified as potential markers for distinguishing between these stages.Our novel findings reveal the complex metabolic alterations associated with T2DM, providing an in-depth insight into the molecular level. These insights suggest that specific metabolites could serve as reliable biomarkers for early diagnosis and personalized treatment strategies. This approach could revolutionize the chronic conditions management, with effective and tailored interventions.