Risk prediction models for cognitive impairment in patients with type 2 diabetes mellitus: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

YIN Yunfang ¹ , LI Huijing ² , LIU Huan 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;

Corresponding?author：

LIU Ling, Email: 13808060753@139.com; JIN Rongjiang, Email: cdzyydxjrj@126.com

Keywords：

Type 2 diabetes mellitus; Cognitive impairment; Risk prediction model; Systematic review

DOI：

10.7507/1672-2531.202502135

Video：

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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 13^th, 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.

Citation： YIN Yunfang, LI Huijing, LIU Huan, WANG Qian, LIU Xiaobo, CHEN Jiayi, CHEN Xixi, GONG Yimeng, LUO Xiao, ZHONG Dongling, LI Juan, ZHANG Yue, ZHANG Furong, LIU Ling, JIN Rongjiang. Risk prediction models for cognitive impairment in patients with type 2 diabetes mellitus: a systematic review. Chinese Journal of Evidence-Based Medicine, 2025, 25(9): 1047-1054. doi: 10.7507/1672-2531.202502135 Copy

1.	楊曉妍, 吳先萍. 2型糖尿病危險因素研究進展. 預防醫學情報雜志, 2001, 17(1): 12-13.
2.	Sun H, Saeedi P, Karuranga S, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract, 2022, 183: 109119.
3.	Chen JF, Zhang YP, Han JX, et al. Systematic evaluation of the prevalence of cognitive impairment in elderly patients with diabetes in China. Clin Neurol Neurosurg, 2023, 225: 107557.
4.	Ding X, Yin L, Zhang L, et al. Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis. Alzheimers Dement, 2024, 20(7): 4583-4593.
5.	Pal K, Mukadam N, Petersen I, et al. Mild cognitive impairment and progression to dementia in people with diabetes, prediabetes and metabolic syndrome: a systematic review and meta-analysis. Soc Psychiatry Psychiatr Epidemiol, 2018, 53(11): 1149-1160.
6.	國家老年醫學中心, 中華醫學會老年醫學分會, 中國老年保健協會糖尿病專業委員會, 等. 中國老年糖尿病診療指南(2024版). 協和醫學雜志, 2024, 15(4): 771-800.
7.	中華醫學會糖尿病學分會. 中國2型糖尿病防治指南(2020年版)(上). 中國實用內科雜志, 2021, 41(8): 668-695.
8.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
9.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
10.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
11.	闞李梅, 孫莉, 張瑩, 等. 血小板活化、聚集功能及LDL-C對糖尿病認知功能障礙的預測價值及其列線圖模型的建立. 醫學研究雜志, 2023, 52(10): 54-84.
12.	張海鑫, 張一方, 謝芷蘭, 等. 基于機器學習構建老年糖尿病患者輕度認知障礙風險評估模型. 中華疾病控制雜志, 2024, 28(3): 1674-3679.
13.	劉敏麗, 邊紅艷. 2型糖尿病患者認知功能障礙風險預測模型的構建. 衛生職業教育, 2024, 42(3): 153-157.
14.	王志虎. 2型糖尿病患者輕度認知障礙風險預測模型構建與驗證. 天津: 天津中醫藥大學, 2023.
15.	張寧, 陳雁, 張紅梅, 等. 2型糖尿病患者輕度認知功能障礙發生風險的Logistic回歸和決策樹預測模型的構建. 中國實用護理雜志, 2023, 39(32): 2514-2520.
16.	張曉莉. 2型糖尿病患者輕度認知功能障礙風險預測模型的構建. 蘭州: 甘肅中醫藥大學, 2023.
17.	楊蓓, 葉紅芳, 相盧偉, 等. 中老年2型糖尿病患者輕度認知障礙風險預測模型的構建與驗證. 護理學報, 2024, 31(13): 18-23.
18.	梁運來, 尉文澤, 侯啟卓, 等. 2型糖尿病合并認知障礙臨床預測指標的篩選及評估. 中華預防醫學雜志, 2024, 58(8): 1184-1190.
19.	Teng Z, Feng J, Xie X, et al. A nomogram including total cerebral small vessel disease burden score for predicting mild vascular cognitive impairment in patients with type 2 diabetes mellitus. Diabetes Metab Syndr Obes, 2024, 17: 1553-1562.
20.	Maimaitituerxun R, Chen W, Xiang J, et al. Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: a CHAID decision tree analysis. Brain Behav, 2024, 14(3): e3456.
21.	Liu Y, Zhang S, He B, et al. Periphery biomarkers for objective diagnosis of cognitive decline in type 2 diabetes patients. Front Cell Dev Biol, 2021, 9: 752753.
22.	Kang H, Kim J, Kim M, et al. Prediction model for mild cognitive impairment in patients with type 2 diabetes using the autonomic function test. Neurol Sci, 2024, 45(8): 3757-3766.
23.	Jiang Y, Liu X, Gao H, et al. A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne), 2024, 15: 1307837.
24.	Exalto LG, Biessels GJ, Karter AJ, et al. Risk score for prediction of 10 year dementia risk in individuals with type 2 diabetes: a cohort study. Lancet Diabetes Endocrinol, 2013, 1(3): 183-190.
25.	Ding Q, Yu C, Xu X, et al. 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, 17: 1171-1182.
26.	Yu Q, Jiang X, Yan J, et al. Development and validation of a risk prediction model for mild cognitive impairment in elderly patients with type 2 diabetes mellitus. Geriatr Nurs, 2024, 58: 119-126.
27.	Li CI, Li TC, Liu CS, et al. Risk score prediction model for dementia in patients with type 2 diabetes. Eur J Neurol, 2018, 25(7): 976-983.
28.	王為, 楊小華, 王姝琴, 等. 2型糖尿病并發認知功能障礙的危險因素分析及Nomogram模型構建. 皖南醫學院學報, 2024, 43(3): 254-257.
29.	Zhou X, Dai N, Yu D, et al. Development and validation of Galectin-3 and CVAI-based model for predicting cognitive impairment in type 2 diabetes. J Endocrinol Invest, 2025, 48(4): 1017-1031.
30.	Zhang J, Tam WWS, Lu J, et al. Cognitive risk stratification score in middle-aged and older adults with type 2 diabetes: a cross-sectional study. J Clin Endocrinol Metab, 2025.
31.	Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
32.	莫航灃, 陳亞萍, 韓慧, 等. 臨床預測模型研究方法與步驟. 中國循證醫學雜志, 2024, 24(2): 228-236.
33.	Steyerberg EW, Harrell FE, Borsboom GJ, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol, 2001, 54(8): 774-781.
34.	Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ, 2006, 332(7549): 1080.
35.	Zhang Q, Wang L. Abstract: evaluating methods for moderation analysis with missing data in the predictors. Multivariate Behav Res, 2015, 50(1): 144-145.
36.	Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
37.	許汝福. Logistic回歸變量篩選及回歸方法選擇實例分析. 中國循證醫學雜志, 2016, 16(11): 1360-1364.
38.	Whalley LJ, Deary IJ, Appleton CL, et al. Cognitive reserve and the neurobiology of cognitive aging. Ageing Res Rev, 2004, 3(4): 369-382.
39.	Logroscino G, Kang JH, Grodstein F. Prospective study of type 2 diabetes and cognitive decline in women aged 70-81 years. BMJ, 2004, 328(7439): 548.
40.	Katon W, Lyles CR, Parker MM, et al. Association of depression with increased risk of dementia in patients with type 2 diabetes: the diabetes and aging study. Arch Gen Psychiatry, 2012, 69(4): 410-417.
41.	李代欽, 黃永清. 抑郁癥對于認知影響因素. 臨床醫學進展, 2022, 12(5): 4105-4109.
42.	Xue M, Xu W, Ou YN, et al. Diabetes mellitus and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 144 prospective studies. Ageing Res Rev, 2019, 55: 100944.
43.	Lin CH, Sheu WH. Hypoglycaemic episodes and risk of dementia in diabetes mellitus: 7-year follow-up study. J Intern Med, 2013, 273(1): 102-110.
44.	Fanelli CG, Porcellati F, Pampanelli S, et al. Insulin therapy and hypoglycaemia: the size of the problem. Diabetes Metab Res Rev, 2004, 20(Suppl 2): 32-42.
45.	Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA, 2017, 318(14): 1377-1384.
46.	Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol, 2010, 5(9): 1315-1316.

1. 楊曉妍, 吳先萍. 2型糖尿病危險因素研究進展. 預防醫學情報雜志, 2001, 17(1): 12-13.
2. Sun H, Saeedi P, Karuranga S, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract, 2022, 183: 109119.
3. Chen JF, Zhang YP, Han JX, et al. Systematic evaluation of the prevalence of cognitive impairment in elderly patients with diabetes in China. Clin Neurol Neurosurg, 2023, 225: 107557.
4. Ding X, Yin L, Zhang L, et al. Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis. Alzheimers Dement, 2024, 20(7): 4583-4593.
5. Pal K, Mukadam N, Petersen I, et al. Mild cognitive impairment and progression to dementia in people with diabetes, prediabetes and metabolic syndrome: a systematic review and meta-analysis. Soc Psychiatry Psychiatr Epidemiol, 2018, 53(11): 1149-1160.
6. 國家老年醫學中心, 中華醫學會老年醫學分會, 中國老年保健協會糖尿病專業委員會, 等. 中國老年糖尿病診療指南(2024版). 協和醫學雜志, 2024, 15(4): 771-800.
7. 中華醫學會糖尿病學分會. 中國2型糖尿病防治指南(2020年版)(上). 中國實用內科雜志, 2021, 41(8): 668-695.
8. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
9. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
10. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
11. 闞李梅, 孫莉, 張瑩, 等. 血小板活化、聚集功能及LDL-C對糖尿病認知功能障礙的預測價值及其列線圖模型的建立. 醫學研究雜志, 2023, 52(10): 54-84.
12. 張海鑫, 張一方, 謝芷蘭, 等. 基于機器學習構建老年糖尿病患者輕度認知障礙風險評估模型. 中華疾病控制雜志, 2024, 28(3): 1674-3679.
13. 劉敏麗, 邊紅艷. 2型糖尿病患者認知功能障礙風險預測模型的構建. 衛生職業教育, 2024, 42(3): 153-157.
14. 王志虎. 2型糖尿病患者輕度認知障礙風險預測模型構建與驗證. 天津: 天津中醫藥大學, 2023.
15. 張寧, 陳雁, 張紅梅, 等. 2型糖尿病患者輕度認知功能障礙發生風險的Logistic回歸和決策樹預測模型的構建. 中國實用護理雜志, 2023, 39(32): 2514-2520.
16. 張曉莉. 2型糖尿病患者輕度認知功能障礙風險預測模型的構建. 蘭州: 甘肅中醫藥大學, 2023.
17. 楊蓓, 葉紅芳, 相盧偉, 等. 中老年2型糖尿病患者輕度認知障礙風險預測模型的構建與驗證. 護理學報, 2024, 31(13): 18-23.
18. 梁運來, 尉文澤, 侯啟卓, 等. 2型糖尿病合并認知障礙臨床預測指標的篩選及評估. 中華預防醫學雜志, 2024, 58(8): 1184-1190.
19. Teng Z, Feng J, Xie X, et al. A nomogram including total cerebral small vessel disease burden score for predicting mild vascular cognitive impairment in patients with type 2 diabetes mellitus. Diabetes Metab Syndr Obes, 2024, 17: 1553-1562.
20. Maimaitituerxun R, Chen W, Xiang J, et al. Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: a CHAID decision tree analysis. Brain Behav, 2024, 14(3): e3456.
21. Liu Y, Zhang S, He B, et al. Periphery biomarkers for objective diagnosis of cognitive decline in type 2 diabetes patients. Front Cell Dev Biol, 2021, 9: 752753.
22. Kang H, Kim J, Kim M, et al. Prediction model for mild cognitive impairment in patients with type 2 diabetes using the autonomic function test. Neurol Sci, 2024, 45(8): 3757-3766.
23. Jiang Y, Liu X, Gao H, et al. A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne), 2024, 15: 1307837.
24. Exalto LG, Biessels GJ, Karter AJ, et al. Risk score for prediction of 10 year dementia risk in individuals with type 2 diabetes: a cohort study. Lancet Diabetes Endocrinol, 2013, 1(3): 183-190.
25. Ding Q, Yu C, Xu X, et al. 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, 17: 1171-1182.
26. Yu Q, Jiang X, Yan J, et al. Development and validation of a risk prediction model for mild cognitive impairment in elderly patients with type 2 diabetes mellitus. Geriatr Nurs, 2024, 58: 119-126.
27. Li CI, Li TC, Liu CS, et al. Risk score prediction model for dementia in patients with type 2 diabetes. Eur J Neurol, 2018, 25(7): 976-983.
28. 王為, 楊小華, 王姝琴, 等. 2型糖尿病并發認知功能障礙的危險因素分析及Nomogram模型構建. 皖南醫學院學報, 2024, 43(3): 254-257.
29. Zhou X, Dai N, Yu D, et al. Development and validation of Galectin-3 and CVAI-based model for predicting cognitive impairment in type 2 diabetes. J Endocrinol Invest, 2025, 48(4): 1017-1031.
30. Zhang J, Tam WWS, Lu J, et al. Cognitive risk stratification score in middle-aged and older adults with type 2 diabetes: a cross-sectional study. J Clin Endocrinol Metab, 2025.
31. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
32. 莫航灃, 陳亞萍, 韓慧, 等. 臨床預測模型研究方法與步驟. 中國循證醫學雜志, 2024, 24(2): 228-236.
33. Steyerberg EW, Harrell FE, Borsboom GJ, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol, 2001, 54(8): 774-781.
34. Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ, 2006, 332(7549): 1080.
35. Zhang Q, Wang L. Abstract: evaluating methods for moderation analysis with missing data in the predictors. Multivariate Behav Res, 2015, 50(1): 144-145.
36. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
37. 許汝福. Logistic回歸變量篩選及回歸方法選擇實例分析. 中國循證醫學雜志, 2016, 16(11): 1360-1364.
38. Whalley LJ, Deary IJ, Appleton CL, et al. Cognitive reserve and the neurobiology of cognitive aging. Ageing Res Rev, 2004, 3(4): 369-382.
39. Logroscino G, Kang JH, Grodstein F. Prospective study of type 2 diabetes and cognitive decline in women aged 70-81 years. BMJ, 2004, 328(7439): 548.
40. Katon W, Lyles CR, Parker MM, et al. Association of depression with increased risk of dementia in patients with type 2 diabetes: the diabetes and aging study. Arch Gen Psychiatry, 2012, 69(4): 410-417.
41. 李代欽, 黃永清. 抑郁癥對于認知影響因素. 臨床醫學進展, 2022, 12(5): 4105-4109.
42. Xue M, Xu W, Ou YN, et al. Diabetes mellitus and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 144 prospective studies. Ageing Res Rev, 2019, 55: 100944.
43. Lin CH, Sheu WH. Hypoglycaemic episodes and risk of dementia in diabetes mellitus: 7-year follow-up study. J Intern Med, 2013, 273(1): 102-110.
44. Fanelli CG, Porcellati F, Pampanelli S, et al. Insulin therapy and hypoglycaemia: the size of the problem. Diabetes Metab Res Rev, 2004, 20(Suppl 2): 32-42.
45. Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA, 2017, 318(14): 1377-1384.
46. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol, 2010, 5(9): 1315-1316.

Chinese Journal of Evidence-Based Medicine

Risk prediction models for cognitive impairment in patients with type 2 diabetes mellitus: a systematic review

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