ArticlePloS one2020
Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study.Cardiovascular diabetology · 2022Pooled it
- A Clinical Comprehensive Evaluation of Long-Acting GLP-1 Receptor Agonists in Type 2 Diabetes Management.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Performance of diabetes risk prediction models: a systematic review and meta-analysis.Endocrine connections · 2025Article
- Type 2 Diabetes Prediction Model in China: A Five-Year Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Development and validation of a nomogram of all-cause mortality in adult Americans with diabetes.Scientific reports · 2024Article
- New risk score model for identifying individuals at risk for diabetes in southwest China.Preventive medicine reports · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
purposeTo derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China.
methodsA total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort.
resultsIn the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity.
conclusionModels A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.
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