Modified cardiometabolic index outperforms conventional indices in discriminating relative muscle loss: a cross-sectional analysis of NHANES 2011-2018

Traditional adiposity indices like the cardiometabolic index (CMI) assess central adiposity and lipid metabolism but do not directly reflect insulin resistance (IR). The modified cardiometabolic index (MCMI), incorporating fasting plasma glucose, may better reflect IR-related metabolic dysfunction relevant to skeletal muscle health. Muscle mass is a basic and objective component of sarcopenia, and relative muscle loss has been used as a proxy indicator for the low muscle mass dimension of sarcopenia-related phenotypes in some studies. This study evaluates the cross-sectional relationship between MCMI and relative muscle loss, comparing its discrimination ability with other indices (BMI, CMI, LAP, TyG, TyG-WC).

We conducted a cross-sectional analysis using data from 3559 U.S. participants aged 20-59 years, derived from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. Relative muscle loss was defined by the Foundation for the National Institutes of Health (FNIH) as characterized by appendicular lean mass (ALM) adjusted by BMI (ALM/BMI) < 0.512 for women and < 0.789 for men. Weighted analyses assessed the relationship between MCMI and the odds of relative muscle loss. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated through multivariable logistic regression analysis. Bonferroni-adjusted P values for MCMI quartiles and tests for linear trend were calculated to account for multiple comparisons. We applied restricted cubic spline (RCS) models and threshold effect analyses to assess non-linear trends and detect possible cutoff values. In addition, subgroup analyses were carried out to examine potential effect modification by age, sex, and other important covariates. The discriminatory ability of MCMI was compared with BMI, CMI, LAP, TyG, and TyG-WC using receiver operating characteristic (ROC) curve analysis. Key predictors of relative muscle loss were identified using LASSO regression with a 70/30 training-validation split and incorporated into an exploratory multivariable classification model for internal assessment. Model discrimination and calibration were examined using ROC curves, calibration plots, and decision curve analysis (DCA), and a nomogram was developed to visualize the odds of relative muscle loss.

In survey-weighted analyses, higher MCMI was strongly associated with greater odds of relative muscle loss (per 1-unit increase: OR = 2.68, 95% CI 2.21-3.25); participants in the highest MCMI quartile had markedly higher odds than those in the lowest quartile (OR = 21.31, 95% CI 10.16-44.70), and all quartile-based associations and the overall trend remained statistically significant after Bonferroni correction for multiple comparisons. Restricted cubic spline and threshold analyses suggested a non-linear association with an inflection point around MCMI 4.61: below this level, each 1-unit increase in MCMI was associated with substantially higher odds of relative muscle loss (OR = 3.52, 95% CI 2.78-4.45), whereas above the threshold the association appeared attenuated and statistically non‑significant (OR = 1.10, 95% CI 0.67-1.82). Associations were generally consistent across subgroups and appeared stronger in men (P for interaction = 0.002). In ROC analyses, MCMI showed the highest discrimination for prevalent relative muscle loss (AUC = 0.776) compared with BMI (0.727), CMI (0.690), LAP (0.708), TyG (0.661), and TyG-WC (0.718); a multivariable model that additionally included MCMI and selected sociodemographic and clinical covariates achieved an AUC of 0.828 in the training dataset, representing a modest improvement in statistical discrimination over MCMI alone, and the nomogram is provided as an exploratory communication tool for visualizing cross-sectional probability estimates derived from this model.

In this cross-sectional NHANES 2011-2018 analysis, higher MCMI was associated with greater odds of relative muscle loss and showed better cross-sectional discrimination than with widely used metabolic indices (BMI, CMI, LAP, TyG, TyG-WC). Prospective studies are needed to assess temporality and clinical utility.