ArticleBMC geriatrics2023
Development and validation of a risk prediction model for frailty in patients with diabetes.
Article in BMC geriatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 4 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 4 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Risk Prediction Model for Frailty in Older Chinese Patients With Type 2 Diabetes Mellitus: A Systematic Review.Geriatrics & gerontology international · 2026Pooled it
- Characterization of Models for Identifying Physical and Cognitive Frailty in Older Adults With Diabetes: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Evaluation of the Risk Prediction Model for Frailty in Diabetic Patients: A Systematic Review and Meta-Analysis.Journal of diabetes research · 2026Pooled it
- Interplay between frailty and cardiometabolic disorders: from pathophysiology to clinical implications.Cardiovascular diabetology · 2025Pooled it
- Analysis of the role of BAP31-related exosomal miRNAs as potential prognostic biomarkers for gastric cancer.Functional & integrative genomics · 2026Article
- Dual trajectories of intrinsic capacity and frailty: a joint predictor framework for dementia risk in middle-aged and older adults.BMC public health · 2026Article
- Pain and depression mediate the relationship between multimorbidity and frailty in older adults with diabetes.Scientific reports · 2026Article
- Multidimensional associations of physical performance, balance, wellness and daily activities with frailty in older adults with coexisting frailty and diabetes.Journal of clinical nursing · 2026Article
- The association between sarcopenia and frailty in older adults: a moderated mediation model.BMC geriatrics · 2026Article
- Development and internal validation of a frailty prediction model in adults aged ≥ 45 years with obesity: evidence from the China Health and Retirement Longitudinal Study (CHARLS).BMC public health · 2026Article
- Frailty in older adults with type 2 diabetes: the importance of metabolic control and treatment adherence.BMC geriatrics · 2026Article
- Explainable Machine Learning Approaches Predict Frailty and Adverse Outcomes in Older Adults: Development and Validation with Two Longitudinal Cohorts.Journal of clinical medicine · 2026Article
- Development and validation of a risk prediction model for mild cognitive impairment in older Chinese adults with chronic diseases.BMC geriatrics · 2026Observational
- Vision-Focused Nomogram for Assessing the Risk of Depression in Diabetic Retinopathy: Integrating Visual Function, Metabolic Factors and Psychosocial Status.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Frailty prediction in heart failure patients with acute infections: the potential role of thiazide diuretics?PloS one · 2026Article
- Prediction Model for Frailty in Middle-Aged and Older Adults with Cardiovascular Disease.Vascular health and risk management · 2026Article
- Bayesian Analysis of Frailty Risk Factors in Chronic Kidney Disease: A Nationwide Cross-Sectional Survey.Health science reports · 2026Article
- Associations of chronic lung disease, insufficient sleep, and pain with incident frailty in Chinese middle-aged and older adults.Epidemiology and health · 2026Article
- Development and validation of a risk prediction model for postoperative frailty in breast cancer patients.PeerJ · 2026Article
- Frailty progression before and after diabetes onset among older adults in china: a nationwide cohort study.BMC geriatrics · 2025Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFrailty is the third most common complication of diabetes after macrovascular and microvascular complications. The aim of this study was to develop a validated risk prediction model for frailty in patients with diabetes.
methodsThe research used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Twenty-five indicators, including socio-demographic variables, behavioral factors, health status, and mental health parameters, were analyzed in this study. The study cohort was randomly divided into a training set and a validation set at a ratio of 70 to 30%. LASSO regression analysis was used to screen the variables for the best predictors of the model based on a 10-fold cross-validation. The logistic regression model was applied to explore the associated factors of frailty in patients with diabetes. A nomogram was constructed to develop the prediction model. Calibration curves were applied to evaluate the accuracy of the nomogram model. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance.
resultsOne thousand four hundred thirty-six patients with diabetes from the CHARLS database collected in 2013 (n = 793) and 2015 (n = 643) were included in the final analysis. A total of 145 (10.9%) had frailty symptoms. Multivariate logistic regression analysis showed that marital status, activities of daily living, waist circumference, cognitive function, grip strength, social activity, and depression as predictors of frailty in people with diabetes. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.912 (95%CI 0.887-0.937) and 0.881 (95% CI 0.829-0.934). Hosmer-Lemeshow test values were P = 0.824 and P = 0.608 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance.
conclusionsComprehensive nomogram constructed in this study was a promising and convenient tool to evaluate the risk of frailty in patients with diabetes, and contributed clinicians to screening the high-risk population.
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