ArticleDiabetology & metabolic syndrome2024
Construction and evaluation of sarcopenia risk prediction model for patients with diabetes: a study based on the China health and retirement longitudinal study (CHARLS).
Article in Diabetology & metabolic syndrome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction model performances for sarcopenia in middle-aged and elderly people: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- Sarcopenia in type 2 Diabetes mellitus among Asian populations: prevalence and risk factors based on AWGS- 2019: a systematic review and meta-analysis.BMC endocrine disorders · 2025Pooled it
- Development and validation of a nomogram for identifying prevalent sarcopenia in Chinese patients with Cardiovascular-Kidney-Metabolic Syndrome.Frontiers in public health · 2026Article
- Development and validation of a Four-Year predictive model for sarcopenia in older adults: insights from the CHARLS cohort.Aging clinical and experimental research · 2025Article
- Development and validation of nomogram and machine learning models to predict sarcopenia in patients with chronic kidney disease.Scientific reports · 2025Article
- Assessment of screening tools for diabetic sarcopenia in type 2 diabetes mellitus: evidence from a scoping review.Frontiers in endocrinology · 2025Article
- Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data.Frontiers in medicine · 2025Article
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2 authors.
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Abstract
purposeSarcopenia is a common complication of diabetes. Nevertheless, precise evaluation of sarcopenia risk among patients with diabetes is still a big challenge. The objective of this study was to develop a nomogram model which could serve as a practical tool to diagnose sarcopenia in patients with diabetes.
methodsA total of 783 participants with diabetes from China Health and Retirement Longitudinal Study (CHARLS) 2015 were included in this study. After oversampling process, 1,000 samples were randomly divided into the training set and internal validation set. To mitigate the overfitting effect caused by oversampling, data of CHARLS 2011 were utilized as the external validation set. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were employed to explore predictors. Subsequently, a nomogram was developed based on the 9 selected predictors. The model was assessed by area under receiver operating characteristic (ROC) curves (AUC) for discrimination, calibration curves for calibration, and decision curve analysis (DCA) for clinical efficacy. In addition, machine learning models were constructed to enhance the robustness of our findings and evaluate the importance of the predictors.
results9 factors were selected as predictors of sarcopenia for patients with diabetes. The nomogram model exhibited good discrimination in training, internal validation and external validation sets, with AUC of 0.808, 0.811 and 0.794. machine learning models revealed that age and hemoglobin were the most significant predictors. Calibration curves and DCA illustrated excellent calibration and clinical applicability of this model.
conclusionThis comprehensive nomogram presented high clinical predictability, which was a promising tool to evaluate the risk of sarcopenia in patients with diabetes.
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