使用列线图筛查2型糖尿病轻度认知障碍
Abstract Background Type 2 diabetes mellitus (T2DM) 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 (MCI) in T2DM patients. Methods Inpatients with T2DM in the en...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-05-01
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| Series: | Journal of Diabetes |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/1753-0407.13384 |
| _version_ | 1827950271919030272 |
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| author | Rehanguli Maimaitituerxun Wenhang Chen Jingsha Xiang Yu Xie Atipatsa C. Kaminga Xin Yin Wu Letao Chen Jianzhou Yang Aizhong Liu Wenjie Dai |
| author_facet | Rehanguli Maimaitituerxun Wenhang Chen Jingsha Xiang Yu Xie Atipatsa C. Kaminga Xin Yin Wu Letao Chen Jianzhou Yang Aizhong Liu Wenjie Dai |
| author_sort | Rehanguli Maimaitituerxun |
| collection | DOAJ |
| description | Abstract Background Type 2 diabetes mellitus (T2DM) 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 (MCI) 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% (95% confidence interval [CI]: 29.9%–38.3%). 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 (95% CI: 0.815–0.883), 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. |
| first_indexed | 2024-04-09T13:23:14Z |
| format | Article |
| id | doaj.art-71cca89e569948d9839c1992141ee36f |
| institution | Directory Open Access Journal |
| issn | 1753-0393 1753-0407 |
| language | English |
| last_indexed | 2024-04-09T13:23:14Z |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes |
| spelling | doaj.art-71cca89e569948d9839c1992141ee36f2023-05-11T00:50:48ZengWileyJournal of Diabetes1753-03931753-04072023-05-0115544845810.1111/1753-0407.13384使用列线图筛查2型糖尿病轻度认知障碍Rehanguli Maimaitituerxun0Wenhang Chen1Jingsha Xiang2Yu Xie3Atipatsa C. Kaminga4Xin Yin Wu5Letao Chen6Jianzhou Yang7Aizhong Liu8Wenjie Dai9Department of Epidemiology and Health Statistics, Xiangya School of Public Health Central South University Changsha ChinaDepartment of Nephrology, Xiangya Hospital Central South University Changsha ChinaHuman Resources Department Central Hospital Affiliated to Shandong First Medical University Jinan ChinaDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health Central South University Changsha ChinaDepartment of Mathematics and Statistics Mzuzu University Mzuzu MalawiDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health Central South University Changsha ChinaInfection Control Center, Xiangya Hospital Central South University Changsha ChinaDepartment of Preventive Medicine Changzhi Medical College Changzhi ChinaDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health Central South University Changsha ChinaDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health Central South University Changsha ChinaAbstract Background Type 2 diabetes mellitus (T2DM) 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 (MCI) 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% (95% confidence interval [CI]: 29.9%–38.3%). 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 (95% CI: 0.815–0.883), 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.https://doi.org/10.1111/1753-0407.13384诊断轻度认知障碍模型列线图2型糖尿病 |
| spellingShingle | Rehanguli Maimaitituerxun Wenhang Chen Jingsha Xiang Yu Xie Atipatsa C. Kaminga Xin Yin Wu Letao Chen Jianzhou Yang Aizhong Liu Wenjie Dai 使用列线图筛查2型糖尿病轻度认知障碍 Journal of Diabetes 诊断 轻度认知障碍 模型 列线图 2型糖尿病 |
| title | 使用列线图筛查2型糖尿病轻度认知障碍 |
| title_full | 使用列线图筛查2型糖尿病轻度认知障碍 |
| title_fullStr | 使用列线图筛查2型糖尿病轻度认知障碍 |
| title_full_unstemmed | 使用列线图筛查2型糖尿病轻度认知障碍 |
| title_short | 使用列线图筛查2型糖尿病轻度认知障碍 |
| title_sort | 使用列线图筛查2型糖尿病轻度认知障碍 |
| topic | 诊断 轻度认知障碍 模型 列线图 2型糖尿病 |
| url | https://doi.org/10.1111/1753-0407.13384 |
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