The association between eight complete blood count-derived inflammatory markers and muscle health

Abstract

Background: Most studies have evaluated sarcopenia and muscle health solely based on muscle mass. This study comprehensively examined the associations between eight inflammatory indicators and muscle mass and strength, with the aim of identifying an indicator capable of evaluating muscle health across multiple dimensions.

Methods: This study included 10,440 participants from the National Health and Nutrition Examination Survey (NHANES, 2011-2018) and 5,384 participants from NHANES (2011-2014). Multivariate logistic regression, smooth curve fitting, restricted cubic spline (RCS) analysis, subgroup analysis, and Spearman's correlation were used to comprehensively assess the associations between the eight inflammatory indicators and muscle mass and strength. Receiver operating characteristic (ROC) curves were used to compare the predictive abilities of the different indices for low muscle mass and muscle strength. Additionally, NHANES data were cross-validated with data from 554 patients at our hospital to evaluate the ability of the systemic immune inflammatory index (SII) to distinguish between low muscle mass and strength.

Results: After controlling for all potential confounding factors, multiple logistic regression analysis revealed that apart from the platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and derived NLR (dNLR), the neutrophil-to-monocyte-plus-lymphocyte ratio (NMLR), neutrophil-to-lymphocyte ratio (NLR), SII, systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV) were significantly negatively correlated with muscle mass and strength. However, NMLR and NLR were significantly associated with changes in muscle mass only in Q4 (P < 0.05). In the stratified analysis by body mass index (BMI), only the SII, NLR, and NMLR were unaffected by BMI. In the cross-validation, the predictive performance of the SII for low muscle mass [area under the curve (AUC) = 0.699, 0.677, and 0.685] and low muscle strength (AUC = 0.857, 0.849, and 0.840) demonstrated a good reference value. RCS and smooth curve fitting analyses indicated that most inflammatory markers were linearly correlated with muscle health (P < 0.05).

Conclusion: Compared with other inflammatory markers (e.g., PIV and dNLR), the SII demonstrated a more robust predictive ability, was less influence by covariates, and exhibited high generalization performance in internal and external validation. SII may be crucial in identifying "hidden sarcopenia" and the early stages of muscle functional decline.

Keywords: NHANES; indicators; inflammation; muscle mass; sarcopenia.

Figure 1

Inclusion and exclusion criteria of the study population. (A) Muscle mass; (B) muscle strength.

Figure 2

Correlations between inflammatory markers and low muscle mass (sarcopenia) and low muscle strength (LMS) were analyzed using RCS curves. (A–H) Associations between MLR, NMLR, NLR, dNLR, SII, SIRI, PLR, and PIV and the risk of low muscle mass. (I–P) Associations between the markers and risk of LMS. The red dots in the figures indicate key turning points, while the red shaded areas represent the 95% confidence intervals.

Figure 3

Smoothed curve fitting was conducted stratified by obesity or overweight status. The relationships between various inflammatory markers and muscle mass (A–D, I–L) and muscle strength (E–H, M–P) were analyzed. In the figures, the red curve represents non-obese/non-overweight individuals, while the blue curve represents obese/overweight individuals.

Figure 4

ROC analysis, used to evaluate the predictive accuracy of SII and other inflammatory markers for low muscle mass and strength. (A) Low muscle mass; (B) low muscle strength.