Correlation of lung function and sarcopenia in elderly patients with chronic obstructive pulmonary disease and analysis of factors influencing sarcopenia.

Objective: This study aims to investigate the correlation between lung function and sarcopenia in elderly patients with chronic obstructive pulmonary disease (COPD) and to analyze the factors influencing sarcopenia. The goal is to provide evidence for comprehensive management of COPD patients.

Methods: A total of 294 elderly COPD patients admitted to Lanzhou City No. 1 People's Hospital from March 2022 to March 2024 were selected as study subjects. The patients were divided into a training group (n=205) and a validation group (n=89) in a 7:3 ratio. Lung function was assessed through pulmonary function tests, and sarcopenia was defined by evaluating muscle mass and muscle quality using bioelectrical impedance analysis. Based on the diagnostic criteria for sarcopenia, patients were categorized into a sarcopenia group (n=113) and a non-sarcopenia group (n=181). Basic information, lifestyle habits, and medical history were collected to analyze the correlation between lung function and sarcopenia, as well as influencing factors. Additionally, logistic regression analysis was conducted to identify independent risk factors, and a nomogram model was developed for risk prediction.

Results: Multivariate logistic regression analysis revealed that age (P<0.001, OR=0.053), weight (P=0.032, OR=3.321), Cys-C (P=0.018, OR=0.283), Hb (P=0.001, OR=7.014), FVC (P=0.04, OR=3.605), FEV1 (P=0.001, OR=9.674), and CAT score (P<0.001, OR=0.085) were independent risk factors for sarcopenia in COPD patients. The nomogram model based on these independent risk factors demonstrated good predictive performance for sarcopenia in elderly COPD patients. ROC curve analysis showed that the area under the curve (AUC) of the nomogram model was 0.886 (95% CI: 0.819-0.932) in the training group and 0.809 (95% CI: 0.726-0.883) in the validation group, indicating a high predictive accuracy. Additionally, ROC curve analysis showed that the AUCs for age, BMI, and FEV1/FVC in diagnosing sarcopenia in elderly COPD patients were 0.710 (95% CI: 0.747-0.863), 0.647 (95% CI: 0.766-0.878), and 0.682 (95% CI: 0.701-0.833), respectively. Gene Set Enrichment Analysis (GSEA) revealed that pathways significantly enriched in the high Cys-C expression group included oxidative phosphorylation, fatty acid biosynthesis, the AMPK signaling pathway, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis pathways, which may play important roles in energy metabolism and muscle function regulation in sarcopenic patients.

Conclusion: Lung function decline in elderly COPD patients is closely associated with the occurrence of sarcopenia. Increasing age is an independent risk factor for sarcopenia in COPD patients, while higher BMI and FEV1/FVC are protective factors. The nomogram model based on these independent risk factors can effectively predict sarcopenia in elderly COPD patients.