Sarcopenia is an age-related muscle disorder driven by complex interactions between chronic inflammation and nutritional imbalance. The neutrophil percentage-to-albumin ratio (NPAR), an emerging biomarker integrating systemic inflammatory burden and nutritional status, has been associated with adverse outcomes in several chronic diseases. However, population-based evidence regarding the relationship between NPAR and sarcopenia remains limited. Using data from the U.S. National Health and Nutrition Examination Survey (NHANES) 2011–2018, we conducted a cross-sectional analysis of 10,287 adults aged ≥20 years. Sarcopenia was defined according to the Foundation for the National Institutes of Health (FNIH) criteria using the sarcopenia index derived from dual-energy X-ray absorptiometry. NPAR was calculated as neutrophil percentage divided by serum albumin concentration. Survey-weighted logistic regression models were applied to examine the association between NPAR and sarcopenia. Restricted cubic spline and threshold effect analyses were used to assess nonlinear dose–response relationships. The incremental predictive value of NPAR was evaluated using receiver operating characteristic curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Machine learning–based models were further constructed to evaluate the predictive contribution of NPAR, with SHapley Additive exPlanations (SHAP) applied to enhance model interpretability. Among the study population, 900 participants (8.7%) had sarcopenia. Higher NPAR levels were independently associated with increased odds of sarcopenia after full adjustment for sociodemographic factors, lifestyle behaviors, and comorbidities (odds ratio per unit increase = 1.11, 95% CI: 1.06–1.15). When analyzed by quartiles, participants in the highest NPAR quartile had a 69% higher risk of sarcopenia compared with those in the lowest quartile. A significant nonlinear association was observed, with an inflection point at NPAR = 13.042; above this threshold, sarcopenia risk increased markedly. Adding NPAR to conventional risk models significantly improved discrimination and reclassification (NRI = 0.170, IDI = 0.004; all P < 0.001). Machine learning analyses consistently identified NPAR as an important predictor of sarcopenia, with SHAP analyses demonstrating a positive and monotonic association between NPAR and sarcopenia risk. Elevated NPAR is independently and nonlinearly associated with a higher risk of sarcopenia in U.S. adults and provides incremental predictive value beyond traditional risk factors. As a simple, inexpensive, and routinely available biomarker, NPAR may serve as a promising tool for early identification and risk stratification of sarcopenia in both clinical and public health settings. • Higher NPAR was independently associated with higher odds of sarcopenia. • A significant nonlinear association was observed between NPAR and sarcopenia. • NPAR improved risk discrimination and reclassification beyond traditional factors. • Machine learning analyses supported the predictive relevance of NPAR. • NPAR may serve as a simple biomarker for sarcopenia risk stratification.
Xie et al. (Thu,) studied this question.