YIN Yunfang 1 , LI Huijing 2 , LIU Huang 3 , WANG Qian 4 , LIU Xiaobo 1 , CHEN Jiayi 1 , CHEN Xixi 1 , GONG Yimeng 5 , LUO Xiao 5 , ZHONG Dongling 1 , LI Juan 1 , ZHANG Yue 1 , ZHANG Furong 1 , LIU Ling 6 , JIN Rongjiang 1
  • 1. School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, P. R. China;
  • 2. School of Clinical medical, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, P. R. China;
  • 3. People’s Hospital of Nanbu County, Nanchong 637000, P. R. China;
  • 4. Chengdu Tianfu New Area Care Alliance Hetai Rehabilitation Hospital, Chengdu 610075, P. R. China;
  • 5. School of Acupuncture-Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, P. R. China;
  • 6. Chengdu Bayi Orthopaedic Hospital, China RongTong Medical Healthcare Group Co.Ltd, Chendu 610012, P. R. China;
LIU Ling, Email: 13808060753@139.com; JIN Rongjiang, Email: cdzyydxjrj@126.com
Type 2 diabetes mellitus; Cognitive impairment; Risk prediction model; Systematic review
10.7507/1672-2531.202502135
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Objective To systematically review the research status of risk prediction models for cognitive impairment in patients with T2DM. Methods The CNKI, WanFang Data, VIP, CBM, PubMed, Embase, Web of Science, Cochrane Library databases and Clinical trial registration platform were electronically searched to collect relevant literature on risk prediction models for cognitive impairment in patients with T2DM from inception to February 13th 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies, and then qualitative description and meta-analysis was performed. Results A total of 20 studies were included, involving 25 risk prediction models. In terms of the risk of bias, 20 studies were considered as high risk. With regards to applicability, 20 studies were high applicability. The pooled area under the curve (AUC) for modeling set was 0.83 (95%CI 0.79 to 0.88) and for the validation set was 0.83 (95% CI 0.79 to 0.87). It suggested that the model had good discrimination ability. The most common predictors included age, education level, duration of diabetes and depression. Conclusion The overall performance of the risk prediction model for cognitive impairment in patients with T2DM is good, but the quality of the model needs to be improved.

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