Evaluate the Risk Factors of Mild Cognitive Impairment (MCI) in Patients with Type 2 Diabetes Mellitus (T2DM): A Cross-Sectional Study.

Abstract

Objective: To explore the potential incremental value of metabolic-inflammatory and insulin-resistance indices for diagnosing mild cognitive impairment (MCI) in type 2 diabetes (T2DM), and to furnish new candidate tools and insights for the risk stratification and early recognition of diabetes-related cognitive dysfunction.

Methods: This cross-sectional, observational study was jointly conducted by two tertiary endocrine centers in northwestern China. A uniform assessment of cognitive status was performed using the Chinese versions of the Montreal Cognitive Assessment (MoCA). MCI was defined as MoCA < 26. Baseline clinical characteristics, metabolic-inflammatory parameters and insulin-resistance indices were systematically compared between groups. Candidate predictors were first compressed via Lasso regression; key retained variables were then used to develop a binary logistic prediction model. The model's discrimination was assessed via ROC curves, and its calibration was evaluated by the Hosmer‑Lemeshow test combined with calibration plots. Decision-curve analysis (DCA) and clinical impact curves (CIC) were further employed to quantify net benefit and reclassification across a range of risk thresholds, providing a comprehensive appraisal of the model's clinical utility.

Results: This study included 293 patients with type 2 diabetes mellitus, of whom 187 (63.8%) had mild cognitive impairment (MoCA<26). Compared with the 106 cognitively normal controls, the T2DM-MCI group were older, had fewer years of education and a lower proportion of higher education (all p<0.05). The prevalence of diabetic nephropathy (32.6% vs 18.9%, p = 0.006) and previous cerebral infarction (29.9% vs 10.4%, p < 0.001) was significantly higher, while diabetic peripheral neuropathy and carotid atherosclerosis showed borderline differences (p ≈ 0.06). Concurrently, inflammatory-metabolic burden increased across the board-NMLR, PHR, BRI, ABSI, WWI and LAP were all elevated (p ≤ 0.048)-and adiposity index RFM together with insulin-resistance markers TyG-WtHR and HOMA-IR also rose (p ≤ 0.017). Notably, Lasso regression identified WWI, RFM, and TyG-WtHR as candidate markers. However, none of these variables achieved statistical significance in the subsequent multivariable Logistic regression (all p>0.05). This finding suggests that novel anthropometric and metabolic indices may be linked to cognitive decline primarily through indirect pathways associated with traditional risk factors, offering new mechanistic insights into the obesity-metabolism-cognition axis.

Conclusion: Univariate analysis revealed that WWI, RFM, and TyG-WtHR were significantly correlated with cognitive decline (r=0.232, 0.194, and 0.154, respectively; all p<0.01). Incorporation of these three readily obtainable anthropometric and laboratory parameters into the baseline clinical model increased the area under the curve (AUC) from 0.768 to 0.771, offering potential clues into the mechanistic link between obesity-metabolism and cognitive impairment.