ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Neuroendocrine Science
Development and Validation of a Prediction Model for Cognitive Impairment in Elderly Patients with Type 2 Diabetes
Sijie Li 1
Yan Jiang 2
Shanshan Liu 2
ling Ke 1
Libingxue Huang 1
Xiangpeng Zhu 1
QI Zhou 1
1. Tianyou Hospital Affiliated to Wuhan University of Science & Technology, Wuhan, China
2. Qingdao Mental Health Center, Qingdao, China
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Abstract
Background: China is experiencing rapid population aging, accompanied by a rising prevalence of T2DM and its complex complications. Cognitive impairment is one of the major complications of T2DM and currently lacks effective treatment. These two conditions can interact and aggravate each other. Objective: This study aimed to identify reliable early predictors of cognitive impairment among elderly individuals with T2DM, in order to facilitate early intervention and delay disease progression. Methods: A total of 202 elderly patients with T2DM hospitalized at Tianyou Hospital, affiliated with Wuhan University of Science and Technology, between May and September 2025 were enrolled. Cognitive function was assessed using MoCA with a cutoff score of 26. Seventy-two participants scoring ≥26 were assigned to the normal cognition group, and 130 participants scoring ≤25 were assigned to the cognitive impairment group. Demographic information, hematological and imaging parameters, and scale scores related to sleep quality, anxiety–depression status, and activities of daily living were collected. Statistical analyses were conducted using R version 4.5. Results: LASSO regression selected 14 predictors. After analyzing the data, four factors remained independently associated with T2DM related cognitive impairment: age (OR = 1.96, 95% CI: 1.31–2.95, P = 0.001), HADS-D score (OR = 1.87, 95% CI: 1.25–2.80, P = 0.002), WMD (OR = 2.44, 95% CI: 1.14–5.25, P = 0.022), and HbA1c (OR = 1.53, 95% CI: 1.01–2.30, P = 0.043). The model demonstrated an AUC of 0.812 (95% CI: 0.778–0.891) and was well-calibrated (Hosmer-Lemeshow P = 0.661). After bootstrap validation, the optimism-corrected AUC was 0.751, indicating minimal overfitting. At the optimal cut-off of 0.685, the model achieved a sensitivity of 69.2% and a specificity of 81.9%, with a positive predictive value of 87.4% and a negative predictive value of 59.6%. DCA demonstrated a positive net benefit across threshold probabilities from 0.02 to 0.86, supporting the model's clinical value. Conclusion:This study developed a prediction model for T2DM related cognitive impairment in elderly Chinese patients. The model showed good discrimination, calibration, and clinical value, supporting its potential role for identifying high-risk populations. However,before using this model,more research is needed to confirm it's performance in different people.
Summary
Keywords
cognitive impairment, Elderly, Risk prediction model, The old, type 2 diabetes
Received
12 November 2025
Accepted
20 February 2026
Copyright
© 2026 Li, Jiang, Liu, Ke, Huang, Zhu and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: QI Zhou
Disclaimer
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