Integrated cortical-cognitive signatures identified by machine learning enable early detection of MCI in type 2 diabetes

Abstract Type 2 Diabetes Mellitus (T2DM) confers a significant risk for Mild Cognitive Impairment (MCI), yet robust biomarkers for early detection remain limited. In this study, 150 age-matched participants (50 Healthy Controls, 50 T2DM, 50 T2DM with MCI) were assessed using high-resolution structural MRI, neuropsychological testing, and serological profiling to identify sensitive neuroanatomical and cognitive markers. Cortical thinning was observed, most prominently in the Left Pars Opercularis (LPO), which exhibited stepwise unidirectional atrophy across the diagnostic continuum, highlighting its potential as a structural marker for cognitive deterioration in T2DM. Significant deficits in episodic memory, processing speed, executive function, and verbal memory were also observed, reflecting disruptions in medial-temporal and frontoparietal networks. A Random Forest classifier integrating multimodal features achieved high discriminatory performance (AUC-ROC = 0.95) for distinguishing T2DM with MCI from T2DM patients. SHAP, an Explainable AI method, identified cortical thickness at LPO, and executive function assessed by TMTB as the most influential predictors. These findings establish the LPO as a key neuroanatomical substrate of T2DM-related cognitive impairment and demonstrate that combining targeted neuroimaging with domain-specific cognitive assessments provides a clinically viable framework for early identification of at-risk T2DM patients, offering critical opportunities for preventive intervention.