Construction and validation of a predictive model for the risk of sarcopenia in middle-aged and elderly patients with type 2 diabetes mellitus
Objective To analyze the inflnencing factors affecting the occurrence of sarcopenia in middle-aged and elderly patients with type 2 diabetes mellitus(T2DM),and to construct and validate an individualized clinical risk prediction model.Methods From January 2020 to January 2023,a total of 329 middle-aged and elderly patients with type 2 diabetes mellitus in the Department of Endocrinology of the Second People's Hospital of Shaanxi Province were selected as study subjects,and they were randomly divided into the modeling group and the validation group according to 7∶3.Lasso regression optimization was used to screen independent risk factors,and multivariate Logistic regression analysis was used to determine predictor variables.Receiver operating characteristic(ROC)curves,Hosmer-Lemeshow goodness-of-fit,and decision curve analysis(DCA)tests were used to validate and evaluate the model's discrimination,calibration and clinical validity.Results The detection rate of sarcopenia in the overall subjects was 12.16％(40/329).Age,smoking behavior and upper arm circumference were influencing factors for the development of sarcopenia in middle-aged and elderly T2DM patients.The areas under the ROC curves for the modeling and the validation groups were 0.861(95％confidence interval CI:0.801-0.921)and 0.814(95％CI:0.680-0.947),respectively.The Hosmer-Lemeshow goodness-of-fit test indicated that the model had good calibration(P=0.818,P=0.914,respectively),and DCA showed that the model had good clinical validity.Conclusions The constructed risk prediction model for sarcopenia in middle-aged and elderly people with T2DM has a good predictive effect,which facilitates healthcare professionals to recognize the occurrence of sarcopenia in T2DM patients and take intervention measures timely.
type 2 diabetes mellitussarcopeniaprediction modelnomogram
NETL
NSTL
万方数据
