Optimal skeletal muscle mass index and incident cardiovascular events: insights from a longitudinal analysis
Year: 2025
Type: article
Study design: Observational Study
Abstract: AIMS: Although several body sizes have been used in the adjustment of skeletal muscle mass (SMM), their predictive ability for cardiovascular events remained unclear. We aimed to assess and compare the predictive performance of SMM indices for cardiovascular events in the middle-aged and older population and evaluate their association according to different body shape subgroups. METHODS AND RESULTS: This study included 1374 individuals (43.5% men) aged 50-80 years from four communities. The follow-up was conducted in 2021-22 with a mean follow-up of 7.5 years. Cardiovascular events were recorded by phone calls and further validated using electronic medical records. Baseline SMM was estimated using a bioelectrical impedance analyzer and adjusted for weight (SMM/Wt), body mass index (SMM/BMI), and visceral fat area (SMM/VFA), respectively. The visceral fat area was measured using magnetic resonance imaging. During a median follow-up of 7.5 years, adjusted hazard ratios for the lowest tertile of SMM/Wt, SMM/BMI, and SMM/VFA were 1.80 (95% CI 1.17-2.77), 1.83 (95% CI 1.15-2.91), and 1.56 (95% CI 0.99-2.46) compared with the highest tertile, respectively. Regarding C-statistics, net reclassification improvement, and integrated discrimination improvement, the greatest improvement was observed when adding SMM/BMI to the model compared with SMM/Wt or SMM/VFA. Moreover, the association between SMM/BMI and cardiovascular events persisted regardless of age, sex, and different body shape subgroups (all P < 0.05). CONCLUSION: SMM indices were significantly associated with the incidence of cardiovascular events in the middle-aged and older population. SMM/BMI appeared to be a better muscle-relevant risk factor for cardiovascular events.
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,792
Awards: G178959341, G3985899199, G7618385859
OpenAlex ID: W4411224551
