Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes.

Emerging evidence implicates cerebellar degeneration-related protein 1 antisense transcript (CDR1as) and alpha-synuclein (α-Syn) in diabetes and cognitive decline, suggesting their potential to serve as biomarkers. This study aimed to evaluate their diagnostic efficacy for type 2 diabetes mellitus-associated mild cognitive impairment (T2DM-MCI) and establish a corresponding diagnostic model using machine learning. T2DM-MCI-associated differentially expressed genes were identified from the hippocampus using the Gene Expression Omnibus and predictive databases. The study involved 529 T2DM patients in two phases (training set = 408, test set = 121). Levels of plasma CDR1as, α-Syn, Aβ1-40/1-42, and ApoE genotype were detected. Three diagnostic models were identified using least absolute shrinkage and selection operator (LASSO) and logistic regression. Their performance was assessed using the area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). The best-performing model was selected from nine machine learning algorithms. Personalized risk assessment was achieved with the SHapley Additive exPlanations (SHAP). Bio-informatics analysis identified CDR1as and α-Syn as potential biomarkers. Their inclusion in multi-modal panels markedly improved diagnostic accuracy. The Random Forest (RF) model outperformed eight other machine learning classifiers (AUC = 0.975, 95% CI: 0.946-0.999) with the lowest Brier score (0.054). When evaluated on a test set, the RF model maintained robust performance (AUC = 0.901, accuracy = 0.850). An online diagnostic tool was established ( https://www.xsmartanalysis.com/model/list/predict/model/html?mid=6734&symbol=816GyBh9ag1Wr9367341 ). The study preliminarily identified CDR1as and α-Syn as promising biomarkers for T2DM-MCI. Integrating these markers with conventional blood tests showed potential clinical utility for improving diagnostic precision.