Based on Bayesian multivariate skewed regression analysis: the interaction between skeletal muscle mass and left ventricular mass.

OBJECTIVE

This study aims to investigate the association between skeletal muscle mass (SMM) and left ventricular mass (LVM), providing a basis for health management and cardiac health interventions in sarcopenic populations.

METHODS

We conducted a retrospective analysis of participants who underwent SMM assessment at Linyi People's Hospital from January 2017 to December 2023, including a total of 278 individuals. The study employed Bayesian multivariate skewed regression analysis, incorporating ridge regression as a prior distribution to address the skewness and heavy-tailed characteristics of the LVM data. Data collection included clinical information, SMM, and cardiac function metrics. Posterior inference was conducted using Markov Chain Monte Carlo (MCMC) methods, and model convergence was assessed through Gelman-Rubin diagnostics.

RESULTS

The results of ridge regression indicate that age ( = 4.54, = 1.23-7.85) and appendicular lean mass (ALM) ( = 16.82, = 2.87-30.77) are significantly positively correlated with LVM. In contrast, Bayesian multivariate skewed regression analysis demonstrates that the skeletal muscle index (SMI) ( = 22.22, = 2.41-39.07) exerts a significant positive effect on LVM. Additionally, locally weighted scatterplot smoothing (LOWESS) analysis reveals that LVM tends to increase with higher levels of both ALM and SMI.

CONCLUSION

This study found that skeletal muscle mass (such as ALM and SMI) is significantly associated with LVM, suggesting that there is an association between improvements in skeletal muscle and a potential positive impact on cardiac health, highlighting the importance of regional muscle mass. These findings provide new insights for cardiac health management in sarcopenic populations, indicating that there is a relationship where interventions could potentially involve enhancing ALM.