Abstract: Background　Sarcopenia is prone to increase the risk of hospitalization and death in elderly people，and its incidence is often higher in patients with multiple chronic diseases. Detecting comorbidities can detect and treat sarcopenia as early as possible，but previous studies have not fully considered the severity and combination of chronic diseases. Objective　To analyze the association between age-adjusted charlson comorbidity index（aCCI）and the risk of sarcopenia in elderly patients，and explore a new predictive models for sarcopenia in the elderly. Methods　Select 218 elderly patients who were treated at the 925th Hospital of Joint Logistics Support Force from December 2023 to May 2024. According to the diagnosis results，the patients were divided into two groups：sarcopenia group（n=69）and non-sarcopenia group（n=149）. Collect basic patient information，blood biochemical indicators，muscle related indicators，Mini Nutritional Assessment Short-Form（MNA-SF），etc.，and use aCCI to assess comorbidities. Using multiple logistic regression analysis to investigate the association between aCCI and other factors with sarcopenia in elderly patients，and drawing receiver operating characteristic（ROC）curves to evaluate the predictive value of aCCI and scoring models for sarcopenia in elderly patients. Results　The results of multiple logistic regression analysis showed that elevated aCCI（OR=1.661，95%CI=1.165-2.368，P=0.005）was a risk factor for sarcopenia in elderly patients，while elevated MNA-SF score（OR=0.682，95%CI=0.506-0.920，P=0.012）and calf circumference（OR=0.543，95%CI=0.413-0.714，P<0.001）were protective factors for sarcopenia in elderly patients. Based on the multiple logistic regression model equation Logit（P）=20.174 - MNA-SF score × 0.382 calf circumference × 0.611+aCCI score × 0.507，a nomogram prediction model for the risk of sarcopenia in elderly patients was constructed. The ROC curve analysis of MNA-SF score，calf circumference，aCCI，and nomogram prediction models for predicting sarcopenia in elderly patients showed that the area under the ROC curve（AUC）of MNA-SF score，calf circumference，and aCCI for predicting sarcopenia in elderly patients was 0.733（95%CI=0.654~0.813），0.853（95%CI=0.797~0.908），and 0.739（95%CI=0.662~0.815），respectively. The AUC of the nomogram prediction model for sarcopenia in elderly patients was 0.919（95%CI=0.878~0.959，P<0.001），with an optimal cutoff value of 0.37，sensitivity of 0.831，and specificity of 0.821. Conclusion　Our findings suggest that elevated aCCI is a risk factor for sarcopenia in elderly patients，while elevated MNA-SF score and calf circumference are protective factors. And the nomogram prediction model based on MNA-SF score，calf circumference，and aCCI has high predictive value for sarcopenia in elderly patients，which can provide a basis for early screening and prevention of sarcopenia.

Key words: Sarcopenia, Age-adjusted charlson comorbidity index, Mini Nutritional Assessment Short-Form, Calf circumference, Predictive, Logistic models

摘要： 背景　肌少症容易增加老年人住院和死亡的风险，在患有多种慢性病的患者中患病率更高，通过检测合并症能够尽早发现和治疗肌少症，但以往研究未能充分考虑到慢性病的严重程度和组合因素的影响。目的　分析年龄校正Charlson合并症指数（aCCI）与老年患者肌少症发生风险的关联，并探索新的老年肌少症预测模型。方法　选取2023年12月—2024年5月就诊于中国人民解放军联勤保障部队第925医院的老年患者218例为研究对象，根据诊断结果将患者分为两组：肌少症组（n=69）和非肌少症组（n=149）。收集患者基本信息、血生化指标、肌肉相关指标、微型营养评估（MNA-SF）等，采用aCCI评估合并症情况。利用多因素Logistic回归分析aCCI和其他因素与老年患者肌少症的关联，绘制受试者工作特征（ROC）曲线评估aCCI以及评分模型对老年患者肌少症的预测价值。结果　多因素Logistic回归分析结果显示，aCCI（OR=1.661，95%CI=1.165~2.368，P=0.005）升高是老年患者肌少症发生风险的危险因素，MNA-SF评分（OR=0.682，95%CI=0.506~0.920，P=0.012）和小腿围（OR=0.543，95%CI=0.413~0.714，P<0.001）升高是老年患者肌少症发生风险的保护因素。基于多因素Logistic回归模型方程Logit（P）=20.174-MNA-SF评分×0.382-小腿围×0.611+aCCI评分×0.507构建老年患者肌少症发生风险列线图预测模型，MNA-SF评分、小腿围、aCCI及列线图预测模型预测老年患者肌少症的ROC曲线分析结果显示，MNA-SF评分、小腿围和aCCI预测老年患者肌少症的ROC曲线下面积（AUC）分别为0.733（95%CI=0.654~0.813）、0.853（95%CI=0.797~0.908）和0.739（95%CI=0.662~0.815），而列线图预测模型预测老年患者肌少症的AUC为0.919（95%CI=0.878~0.959，P<0.001），最佳截断值为0.37，灵敏度为0.831，特异度为0.821。结论　aCCI升高是老年患者肌少症发生风险的危险因素，MNA-SF评分和小腿围升高是老年患者肌少症发生风险的保护因素，且基于MNA-SF评分、小腿围和aCCI构建的列线图预测模型对老年患者肌少症的预测价值较高，可为早期肌少症的筛查和防治提供依据。

关键词: 肌减少症, 年龄校正的 Charlson 合并症指数, 微型营养评估简化版量表, 小腿围, 预测, Logistic模型