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Association between eosinophil count and metabolically healthy obesity: A cross-sectional study based on NHANES 2005
1Department of Food Science and Biological Engineering, School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, China.
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Eosinophils, a pivotal component of the immune system, are known to contribute to both inflammatory responses and metabolic regulation. Nonetheless, the epidemiological link between eosinophil count and metabolically healthy obesity remains inconclusive. This study aims to examine the potential association between eosinophil levels and the odds of metabolically unhealthy obesity (MUO). The present analysis utilized data from the National Health and Nutrition Examination Survey 2005 to 2018 cycles, enrolling 4594 eligible obese adults. Participants were categorized as metabolically healthy obesity or MUO according to their metabolic status. Eosinophil counts, stratified by quartiles, served as the exposure variable. Weighted multivariate logistic regression and restricted cubic spline models were employed to evaluate their association with MUO odds. Additional subgroup and sensitivity analyses were performed to assess the robustness of the results. Among all obese participants, 84.33% were classified as MUO. Compared to the lowest quartile, individuals in the highest eosinophil count quartile had significantly elevated odds of MUO (adjusted odds ratio = 1.603, 95% confidence interval = 1.420-2.568, P < .001). When treated as a continuous variable, each 1 × 103 cells/μL increment in eosinophil count was associated with a 2.13-fold higher odds of MUO. The restricted cubic spline analysis revealed a nonlinear association, with an inflection point around 0.1 (103 cells/μL). The above associations were consistent across various subgroup and sensitivity analyses. Increased eosinophil levels were significantly associated with higher MUO odds in a nonlinear dose-response pattern. Eosinophil count may represent a promising epidemiological indicator for distinguishing metabolic obesity phenotypes.
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