Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

Qun Ding; Congcong Yu; Xiang Xu; Yinjiao Hou; Yingwen Miao; Sijue Yang; Shihua Chen; Xuelin Ma; Zhou Zhang; Yan Bi

doi:10.2147/DMSO.S448321

Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

Diabetes Metab Syndr Obes. 2024 Mar 7:17:1171-1182. doi: 10.2147/DMSO.S448321. eCollection 2024.

Authors

Qun Ding^#^{1

2

3

4}, Congcong Yu^#^{2

3}, Xiang Xu^{1

3}, Yinjiao Hou^{1

3}, Yingwen Miao^{2

3}, Sijue Yang^{2

3}, Shihua Chen^{2

3}, Xuelin Ma^{2

3}, Zhou Zhang^{2

3}, Yan Bi^{1

2

3}

Affiliations

¹ Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, People's Republic of China.
² Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China.
³ Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, People's Republic of China.
⁴ Department of Endocrinology, the Second People's Hospital of Lianyungang, Lianyungang, People's Republic of China.

^# Contributed equally.

Abstract

Aim: Numerous evidence suggests that diabetes increases the risk of cognitive impairment. This study aimed to develop and validate a multivariable risk score model to identify mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM).

Methods: This cross-sectional study included 1256 inpatients (age: 57.5 ± 11.2 years) with T2DM in a tertiary care hospital in China. MCI was diagnosed according to the criteria recommended by the National Institute on Aging-Alzheimer's Association Workgroup, and a MoCA score of 19-25 indicated MCI. Participants were randomly allocated into the derivation and validation sets at 7:3 ratio. Logistic regression models were used to identify predictors for MCI in the derivation set. A scoring system based on the predictors' beta coefficient was developed. Predictive ability of the risk score was tested by discrimination and calibration methods.

Results: Totally 880 (285 with MCI, 32.4%) and 376 (167 with MCI, 33.8%) patients were allocated in the derivation and validation set, respectively. Age, education, HbA_1c, self-reported history of severe hypoglycemia, and microvascular disease were identified as predictors for MCI and constituted the risk score. The AUCs (95% CI) of the risk score were 0.751 (0.717, 0.784) in derivation set and 0.776 (0.727, 0.824) in validation set. The risk score showed good apparent calibration of observed and predicted MCI probabilities and was capable of stratifying individuals into 3 risk categories by two cut-off points (low risk: ≤ 3, medium risk: 4-13, and high risk ≥ 14).

Conclusion: The risk score based on age, education, HbA_1c, self-reported history of severe hypoglycemia, and microvascular disease can effectively assess MCI risk in adults with T2DM at different age. It can serve as a practical prescreening tool for early detection of MCI in daily diabetes care.

Keywords: mild cognitive impairment; prescreening tool; risk score; type 2 diabetes mellitus.

Grants and funding

This work was supported by the National Natural Science Foundation of China Grant Awards (82030026, 81770819, 82000775, 82270883); the Key Research and Development Program of Jiangsu Province of China (BE2022666); the Natural Science Foundation of Jiangsu Province of China (BK20200114); and Fundings for Clinical Trails from the Affiliated Drum Tower Hospital, Medical School, Nanjing University (2022-LCYJ-ZD-03, and 2022-LCYJ-PY-02).