Construction of a logistic regression-based risk prediction model for male patients with type 2 diabetes mellitus complicated by sarcopenia and validation of its efficacy

This study aimed to construct a risk prediction model for male patients diagnosed with type 2 diabetes mellitus and sarcopenia, and subsequently assess its effectiveness in predicting efficacy. The study subjects consisted of male patients diagnosed with type 2 diabetes who were admitted to the hospital between August 2023 and February 2024. The participants were categorized into two groups: the sarcopenic group (n = 92) and the non-sarcopenic group (n = 196). Patients’ clinical data, lifestyle habits, comorbidities, medical history, and laboratory test markers were collected and subjected to statistical analysis. The findings from both univariate and multivariate logistic regression analyses indicate that age and uric acid (UA) are associated with an increased risk of developing sarcopenia in male patients with type 2 diabetes mellitus. Conversely, Body Mass Index (BMI) and vitamin D are associated with a decreased risk of sarcopenia in men with type 2 diabetes. The probability model for predicting the risk of Sarcopenia in male patients with type 2 diabetes: P = 1/[1 + exp(4.227 − 2.029X1 − 1.165X2 + 0.752X3+ 0.216X4)]. Hosmer and Lemeshow’s goodness-of-fit test showed that χ2 = 7.993, p = 0.434. Receiver Operator Characteristic Curve (ROC) curve analysis showed that Area Under the curve (AUC) was 0.911, 95% Confidence Interval (CI) was 0.879–0.944 respectively. The probability model value was 0.88, which is greater than 0.5, as indicated by the analysis of the overall model quality result. A high clinical predictive value was demonstrated by the risk prediction model of type 2 diabetes mellitus with sarcopenia, which can be used to facilitate early intervention and prevention of the disease.

Logistic regression analysis; Type 2 diabetes mellitus; Sarcopenia; Risk prediction; Model building

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