Research on the development and validation of a nomogram model for sarcopenia risk in patients with type 2 diabetes mellitus
10.3969/j.issn.1006-6187.2025.11.007
- VernacularTitle:2型糖尿病患者肌少症风险列线图模型开发与验证的研究
- Author:
Hui XU
1
;
Yong FAN
Author Information
1. 830000 乌鲁木齐,新疆医科大学第一附属医院内分泌科;新疆昌吉市人民医院内分泌科
- Publication Type:Journal Article
- Keywords:
Diabetes mellitus,type 2;
Sarcopenia;
Nomograms;
Risk prediction
- From:
Chinese Journal of Diabetes
2025;33(11):833-838
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop and internally validate a nomogram prediction model for evaluating the risk of sarcopenia in patients with type 2 diabetes mellitus(T2DM).Methods A total of 766 patients with T2DM hospitalized in the Department of Endocrinology of our hospital from January 2023 to July 2024 were selected and divided into a simple T2DM group(n=627)and a combined sarcopenia(Sar)group(n=139)according to the presence or absence of sarcopenia.The general data and biochemical indexes of the two groups were compared.LASSO regression and Logistic regression were used to screen predictors and construct a nomogram model.Bootstrap resampling was used for internal validation.Receiver operating characteristic(ROC)curve analysis,Hosmer-Lemeshow test calibration curve analysis,and decision curve analysis(DCA)were used to evaluate the performance of the nomogram prediction model.Results The proportion of females,age,DM duration,proportion of diabetic peripheral neuropathy,proportion of diabetic retinopathy(DR),hemoglobin A1c(HbA1c)and high-density lipoprotein cholesterol in the Sar group were higher than those in the T2DM group(P<0.05),while body mass index(BMI),serum creatinine/cystatin C ratio(CCR),triglyceride,albumin(Alb)and alanine aminotransferase were lower than those in the T2DM group(P<0.05).The predictive variables screened by LASSO regression analysis were age,gender,ethnicity,DM duration,hypertension,DR,BMI,HbA1c,CCR,25(OH)D,HDL-C,and Alb.Logistic regression analysis showed that age,HbA1c,CCR,BMI and ethnicity were the influencing factors of sarcopenia in patients with T2DM.Based on this,a nomogram was constructed.ROC curve analysis showed that the AUC of the nomogram prediction model was 0.91,and internal validation indicated that the corrected AUC remained 0.91.The Hosmer-Lemeshow test results showed that the predicted values were highly consistent with the actual observed values(P>0.05).The DCA results show that when the threshold probability is between 0.1 and 1.0,the net benefit brought by applying this model for prediction is superior to the strategy assuming that all patients receive or none receive treatment.Conclusions The nomogram model effectively predicts the risk of sarcopenia in patients with T2DM,providing a convenient and practical screening tool for clinical use.
