Residual cholesterol correlates with mild cognitive impairment, particularly for executive dysfunction among male individuals with type 2 diabetes mellitus
Background Residual cholesterol (RC) is derived from lipids, especially for cholesterol metabolism indexes, correlated with excessive nutrient intake, has recently been suggested to influence cognitive function. It not only reflects the nutrition metabolism status, but also is closely associated with diabetic complications. Despite this, how this nutrition-related metabolic marker relates to cognitive dysfunction in type 2 diabetes mellitus (T2DM) patients is still poorly understood. This study sought to assess the link between RC levels and mild cognitive impairment (MCI) among those living with T2DM. Methods A total of 195 individuals with T2DM participated in the study and were divided by cognitive status into a normal cognition control group and an MCI group. Between these two cohorts, baseline features were compared. To determine the association between RC and cognitive impairment, both correlation analysis and logistic regression were applied. Sex-based subgroup analyses were conducted, and ROC curve analysis was used to evaluate the diagnostic performance of RC in detecting MCI. Results RC levels were notably higher in individuals with MCI than in their cognitively normal counterparts. Even after accounting for age, sex, and how long diabetes had lasted, elevated RC stood out as an independent risk factor for MCI. Among males, RC showed a negative link with MoCA scores—a marker of overall cognitive performance—as well as with verbal fluency test results, pointing to executive deficits. No such relationship emerged in females. According to ROC curve analysis, an RC cutoff of 0.675 mmol/L yielded 80.0% sensitivity and 68.3% specificity (AUC = 0.784) for detecting MCI in the male subgroup. Conclusions In male patients with T2DM, elevated RC is one of the risk factors for early cognitive dysfunction, especially affecting the executive function, and a promising biomarker for MCI detection.