A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus

Abstract

BackgroundA high risk of developing mild cognitive impairment (MCI) is faced by elderly patients with type 2 diabetes mellitus (T2DM). In this study, independent risk factors for MCI in elderly patients with T2DM were investigated, and an individualized nomogram model was developed.MethodsIn this study, clinical data of elderly patients with T2DM admitted to the endocrine ward of the hospital from November 2021 to March 2023 were collected to evaluate cognitive function using the Montreal Cognitive Assessment scale. To screen the independent risk factors for MCI in elderly patients with T2DM, a logistic multifactorial regression model was employed. In addition, a nomogram to detect MCI was developed based on the findings of logistic multifactorial regression analysis. Furthermore, the accuracy of the prediction model was evaluated using calibration and receiver operating characteristic curves. Finally, decision curve analysis was used to evaluate the clinical utility of the nomogram.ResultsIn this study, 306 patients were included. Among them, 186 patients were identified as having MCI. The results of multivariate logistic regression analysis demonstrated that educational level, duration of diabetes, depression, glycated hemoglobin, walking speed, and sedentary duration were independently correlated with MCI, and correlation analyses showed which influencing factors were significantly correlated with cognitive function (p <0.05). The nomogram based on these factors had an area under the curve of 0.893 (95%CI:0.856-0.930)(p <0.05), and the sensitivity and specificity were 0.785 and 0.850, respectively. An adequate fit of the nomogram in the predictive value was demonstrated by the calibration plot.ConclusionsThe nomogram developed in this study exhibits high accuracy in predicting the occurrence of cognitive dysfunction in elderly patients with T2DM, thereby offering a clinical basis for detecting MCI in patients with T2DM.