A multidimensional muscle-fat-metabolism model for severity stratification in COPD: a multi-center cross-sectional study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a systemic disorder characterized by significant body composition alterations, including muscle loss (sarcopenia) and abnormal fat distribution. These changes contribute to a high metabolic burden, with increased resting energy expenditure and systemic inflammation accelerating disease progression. Traditional assessments like body mass index (BMI) fail to capture these complex interactions. This study aimed to evaluate a multidimensional "muscle-fat-metabolism" framework for stratifying COPD risk and severity. METHODS: In this multi-center, cross-sectional study, 377 participants (213 stable COPD patients, 164 healthy controls) underwent bioelectrical impedance analysis (BIA) to assess body composition parameters, including fat-free mass index (FFMI), appendicular skeletal muscle mass index (ASMI), visceral fat area (VFA), and basal metabolic rate (BMR) and other indicators. Spirometry was used to confirm COPD diagnosis and grade severity according to GOLD criteria. Statistical analyses included between-group comparisons, correlation analyses, and multivariate logistic regression to develop predictive models. RESULTS: COPD patients exhibited significantly lower skeletal muscle mass (FFMI, ASMI) and higher systemic fat levels (FM, BFP) compared with controls. Logistic regression analysis suggested that FFMI and ASMI may be protective against COPD risk and severity, whereas FM and BFP may be risk factors for it. Appendicular and truncal fat decreased progressively with advancing GOLD stage. The integrated muscle-fat-metabolism model achieved excellent predictive performance for severe COPD. Body composition and metabolic markers substantially improved risk stratification in patients with COPD. CONCLUSION: An integrative assessment of muscle, fat, metabolic, inflammatory, and functional indicators provided a more effective tool for stratifying COPD risk and severity than conventional metrics. This framework facilitates early identification of high-risk phenotypes like sarcopenic obesity and supports the development of personalized management strategies for COPD patients.