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Correlation of neutrophil- and high-density lipoprotein cholesterol-related inflammatory markers with sarcopenia: Insights from a cross-sectional study

J Int Med Res. 2026 Jan;54(1):3000605261416714. doi: 10.1177/03000605261416714. Epub 2026 Jan 31.

ABSTRACT

ObjectivesThis study aimed to comprehensively investigate the possible associations between neutrophil- and high-density lipoprotein cholesterol (HDL-C)-related inflammatory markers and the incidence of sarcopenia among adults in the United States.MethodsThis cross-sectional study analyzed data from 8878 participants in the National Health and Nutrition Examination Survey. The potential associations between neutrophil- and HDL-C-related inflammatory markers and sarcopenia were evaluated using multiple statistical methods, including weighted logistic regression analysis, sensitivity analyses, restricted cubic spline models, and subgroup analyses. A receiver operating characteristic curve was employed to thoroughly analyze the predictive accuracy of various inflammatory markers for sarcopenia risk.ResultsAll examined inflammatory markers were significantly associated with sarcopenia. The association between the neutrophil-to-HDL-C ratio and sarcopenia was nonlinear, with risk increasing notably beyond a certain threshold (breakpoint = 2.791), whereas the neutrophil percentage-to-albumin ratio demonstrated a linear association. Further threshold effect analyses identified specific breakpoints for neutrophil-to-HDL-C ratio, platelet-to-HDL-C ratio, lymphocyte-to-HDL-C ratio, and monocyte-to-HDL-C ratio. Notably, the area under the curve value for neutrophil-to-HDL-C ratio exceeded those for other inflammatory markers in receiver operating characteristic analysis (area under the curve = 0.632, 95% confidence interval: 0.612-0.652), suggesting its potential as a promising predictor of sarcopenia prevalence.ConclusionsThe findings suggest positive associations between inflammatory biomarkers and sarcopenia, with the neutrophil-to-HDL-C ratio demonstrating superior predictive performance.

PMID:41618762 | DOI:10.1177/03000605261416714

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