The predicted skeletal muscle mass index (pSMI), derived from the serum creatinine-to-cystatin C ratio (CCR), has emerged as a novel biomarker for predicting the onset of type 2 diabetes mellitus. However, its application remains primarily limited to East Asian populations, and the relationship between pSMI and mortality in general populations remains unclear. Therefore, this study aimed to investigate the association between pSMI and all-cause mortality in a nationally representative US adult population. We analyzed data from three cycles (1999–2004) of the National Health and Nutrition Examination Survey (NHANES). pSMI levels were analyzed both as a continuous variable and categorized into tertiles. To assess the association between pSMI and all-cause mortality, we performed multivariable Cox regression, restricted cubic spline (RCS) analysis, and Kaplan-Meier survival analysis. During a median follow-up of 193.2 months (2217 deaths), multivariable-adjusted analyses revealed that higher pSMI levels were significantly associated with reduced all-cause mortality (HR 0.76, 95% CI 0.72–0.80; p < 0.001). Compared to the lowest tertile (T1:4.98–7.83), T2 (7.84–9.18) and T3 (9.19–19.24) showed progressively lower mortality risks (T2: HR 0.79, 95% CI 0.67–0.94, p = 0.009; T3: HR 0.66, 95% CI 0.50–0.88, p = 0.004). Restricted cubic spline analysis demonstrated an L-shaped association (p for non-linear = 0.003) with an inflection point at 10.0 (HR 0.632, 95% CI 0.543–0.721; p < 0.001). Sex-stratified analyses revealed inflection points at 10.5 (males) and 7.6 (females). Kaplan-Meier analysis confirmed significantly improved survival with higher pSMI levels (all p < 0.001 for total population, males and females). This study identifies pSMI as an independent predictor of lower all-cause mortality, revealing a nonlinear L-shaped association with a distinct threshold effect. The protective relationship remains consistent across both sexes, though with differing inflection points. These findings highlight the clinical importance of assessing skeletal muscle mass for mortality risk stratification.
Chen et al. (Sat,) studied this question.