估计的葡萄糖处理率与患有肌少症的老年人代谢综合征之间的关联

摘要(中文译文)

肌少症是一种与年龄相关的综合征，以肌肉质量和功能下降为特征，与多种不良临床结局密切相关。胰岛素抵抗（IR）已成为连接肌少症与代谢综合征（MetS）的关键环节，显著影响二者的病理生理相互作用。本研究旨在探讨肌少症患者中估计葡萄糖处置率（eGDR）与MetS之间的潜在相关性，为分层防治策略提供新思路。共纳入2023年1月至2024年12月在中国人民解放军联勤保障部队第九二一医院就诊的970名老年人。参与者被分为三组，包括非肌少症组（=613）、肌少症组（=307）和肌少症合并MetS组（=50）。采用单因素筛选、支持向量机递归特征消除（SVM-RFE）算法和Boruta算法筛选关键变量。构建了三个逻辑回归模型以评估eGDR与MetS之间的关联。老年肌少症患者中MetS的患病率为14.01%（50/357），显著高于非肌少症者（3.10%，19/613），比值比（OR）为5.09（：2.95-8.79）。在357例肌少症患者中，肌少症合并MetS组的平均eGDR水平为9.60（9.21，10.03）mg/kg/min，低于肌少症组的10.82（10.55，11.09）mg/kg/min（=2.64，<0.01）。Boruta算法确定了构建逻辑回归模型的关键变量，显示eGDR的OR为0.54（95%CI：0.30-0.84）。衰弱和高胆固醇水平的OR分别为1.47（95%CI：1.01-2.94）和3.94（95%CI：2.47-6.69）。模型性能比较显示，基于Boruta和SVM-RFE的模型ROC曲线下面积值（分别为0.78和0.75）高于单因素筛选模型（<0.05）。此外，亚组分析证实了eGDR与老年肌少症患者MetS之间关联的稳健性。在老年人群中，肌少症患者较非肌少症者发生代谢综合征（MetS）的可能性更高。除eGDR水平较低外，衰弱、尿酸升高、BMI和MDA水平与肌少症个体中MetS的较高患病率相关。基于机器学习的回归模型在预测MetS风险方面优于单因素筛选模型。

Abstract

Sarcopenia, an age-related syndrome marked by muscle mass and function decline, is closely linked to various adverse clinical outcomes. Insulin resistance (IR) has emerged as a pivotal link between sarcopenia and metabolic syndrome (MetS), significantly influencing their pathophysiological interactions. This study aimed to investigate the potential correlation between estimated glucose disposal rate (eGDR) and MetS among patients with sarcopenia, offering new insights into stratified prevention and treatment strategies. A total of 970 elderly individuals who visited the 921 Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army from January 2023 to December 2024 were enrolled. Participants were categorized into three groups, including the non-sarcopenia group ( = 613), the sarcopenia group ( = 307) and the sarcopenia with MetS group ( = 50). Key variables were selected using univariate screening, the support vector machine recursive feature elimination (SVM-RFE) algorithm, and the Boruta algorithm. Three logistic regression models were constructed to evaluate the association between eGDR and MetS. The prevalence of MetS in elderly individuals with sarcopenia was 14.01% (50/357), significantly higher than that of those without sarcopenia (3.10%, 19/613), with an odds ratio (OR) of 5.09 (: 2.95-8.79). Among the 357 sarcopenia patients, the average eGDR level in the sarcopenia with MetS group was 9.60 (9.21, 10.03) mg/kg/min, which was lower than that in the sarcopenia group, 10.82 (10.55, 11.09) mg/kg/min ( = 2.64, < 0.01). The Boruta algorithm identified crucial variables for developing a logistic regression model, indicating an OR for eGDR of 0.54 (95% CI: 0.30-0.84). The ORs for frailty and elevated cholesterol levels were 1.47 (95% CI: 1.01-2.94) and 3.94 (95% CI: 2.47-6.69), respectively. Model performance comparison showed that the Boruta and SVM-RFE-based models had higher area under the ROC curve values (0.78 and 0.75, respectively) than the univariate screening model ( < 0.05). Furthermore, subgroup analysis confirmed the robustness of the association between eGDR and MetS in elderly patients with sarcopenia. In the older adult group, patients with sarcopenia have a higher possibility of developing metabolic syndrome (MetS) compared with those without sarcopenia. In addition to lower eGDR levels, frailty, elevated uric acid, BMI, and MDA levels are associate with a higher prevalence of MetS in individuals with sarcopenia. Machine learning-based regression models outperform univariate screening models in predicting MetS risk.

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