Association of the neutrophil percentage-to-albumin ratio with sarcopenia in U.S. adults: Evidence from NHANES 2011–2018 and machine learning–based analyses

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.