A prediction nomogram for mild cognitive impairment in type 2 diabetes mellitus based on the Chinese visceral adiposity index

Visceral adiposity has been proposed to be closely linked to cognitive impairment. This cross-sectional study aimed to evaluate the predictive value of Chinese Visceral Adiposity Index (CVAI) for mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) and to develop a quantitative risk assessment model. A total of 337 hospitalized patients with T2DM were included and randomly assigned to a training cohort (70%, n = 236) and a validation cohort (30%, n = 101). Demographic, clinical, and neuropsychological data were collected. CVAI levels were compared between patients with MCI and those with normal cognition. Associations between CVAI and cognitive performance were assessed using Spearman correlation and multivariable linear regression. Predictors of MCI were identified through Lasso regression followed by univariate and multivariate logistic regression analyses. A nomogram incorporating age, gender, education level, and CVAI was constructed and validated using calibration plots, ROC curve analysis, and decision curve analysis (DCA). Patients with MCI exhibited significantly higher CVAI values and lower MoCA and MMSE scores compared to those with normal cognition (all P < 0.001). CVAI was independently and negatively associated with MoCA and MMSE scores (β = -0.22, P < 0.001 for both) after adjustment. Multivariate logistic regression confirmed CVAI as an independent risk factor for MCI (P = 0.002). The nomogram demonstrated good discrimination, with an AUC of 0.765 in the training cohort and 0.690 in the validation cohort, and exhibited favorable clinical utility based on DCA. These findings suggest that CVAI is a valuable biomarker for the early identification and risk stratification of MCI in T2DM, and that the CVAI-based nomogram provides a practical tool for individualized clinical decision-making.