ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2024
Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Prevalence, risk factors, and early prediction of cognitive impairment in patients with diabetes mellitus: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- A multi-stage validation of the C-reactive protein-triglyceride-glucose index for predicting mild cognitive impairment: evidence from clinical and nationwide prospective cohorts.Frontiers in endocrinology · 2026Article
- Automatic diagnosis of type 2 diabetes mellitus with mild cognitive impairment using artificial intelligence based on routine T1-weighted MRI.Frontiers in neurology · 2025Article
- Predicting Mild Cognitive Impairment in Type 2 Diabetes: A Machine Learning Approach.Journal of diabetes research · 2025Article
- Risk Prediction Models for Mild Cognitive Impairment in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Review
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 Conclusion: The risk score based on age, education, HbA
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