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作者

Rehanguli Maimaitituerxun Wenhang Chen Jingsha Xiang Yu Xie Atipatsa C Kaminga Xin Yin Wu Letao Chen Jianzhou Yang Aizhong Liu Wenjie Dai

作者单位

Affiliations ¹ Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China. ² Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China. ³ Department of Nephrology, Xiangya Hospital, Central South University, Changsha, China. ⁴ Human Resources Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, China. ⁵ Department of Mathematics and Statistics, Mzuzu University, Mzuzu, Malawi. ⁶ Infection Control Center, Xiangya Hospital, Central South University, Changsha, China. ⁷ Department of Preventive Medicine, Changzhi Medical College, Changzhi, China.

刊名

Journal of diabetes

年份

2023

页码

1

ISSN

1753-0407

关键词

diagnosis mild cognitive impairment model nomogram type 2 diabetes mellitus.

摘要

Background: Type 2 diabetes mellitus is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram-based model to detect mild cognitive impairment in T2DM patients. Methods: Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well-qualified investigators conducted face-to-face interviews with participants to retrospectively collect sociod...更多

Background: Type 2 diabetes mellitus is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram-based model to detect mild cognitive impairment in T2DM patients. Methods: Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well-qualified investigators conducted face-to-face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM-related information, and history of depression and anxiety. Cognitive function was assessed using the Mini-Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively. Results: A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% . Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 , and the threshold probability ranged from 35.0% to 85.0%. Conclusions: Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients.收起

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