Association between the novel inflammatory marker white blood cell count-to-mean platelet volume ratio and metabolic syndrome: A cross-sectional study based on NHANES 2011–2020

Background Metabolic syndrome (MetS) is a chronic non-infective syndrome characterized by a set of vascular risk factors, including insulin resistance, hypertension, abdominal obesity, impaired glucose metabolism, and dyslipidaemia. This study aims to investigate the potential association between a novel inflammatory marker, the white blood cell count-to-mean platelet volume ratio (WMR), and MetS. By examining this association, we seek to provide data supporting the effective prevention of MetS through the improvement of inflammatory responses. Methods We conducted a cross-sectional study using data from adult participants in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2020. Comprehensive data on complete blood count parameters and MetS were collected. MetS was defined according to the Adult Treatment Program III of the National Cholesterol Education Program. The formula for calculating WMR is: WMR = white blood cell count (1000 cells/μl)/ mean platelet volume (fL). Participants were stratified into four quartile groups (Q1 to Q4) based on their WMR levels, and chi-square tests along with rank-sum tests were utilized to assess differences in variables. Spearman correlation analysis was employed to evaluate the association between WMR and risk factors linked to MetS and other clinical indicators. Logistic regression analysis and subgroup analysis were conducted to investigate the independent interaction between WMR and MetS, and to further explore the association between WMR levels and the five specific components of MetS. Finally, receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of WMR for MetS. Results A total of 4917 participants were included in this study, comprising 2460 males and 2457 females. Among them, 1717 individuals (34.92%) were diagnosed with MetS. As quartile groups of WMR increased, the rates of MetS occurrence and its components, including Elevated FPG, Elevated TG, Elevated WC, and Low HDL-C, also increased. Spearman correlation analysis demonstrated a positive correlation between WMR and the insulin resistance index HOMA-IR. Logistic regression analysis, after adjusting for multiple confounders, revealed that each standard deviation increase in WMR was associated with a significant 3.185-fold increase in the odds of MetS prevalence (95% CI, 2.399–4.229; P < 0.001). In logistic regression analysis based on WMR quartiles (Q1 to Q4), the risks of MetS were 1.285 (95% CI, 1.045–1.582; P < 0.001), 1.586 (95% CI, 1.288–1.953; P < 0.001), and 2.548 (95% CI, 2.067–3.140; P < 0.001), respectively. After adjustment for multiple confounders, WMR levels were positively associated with Elevated FPG (OR = 2.126; 95% CI, 1.599–2.826; P < 0.001), Elevated TG (OR = 2.893; 95% CI, 2.095–3.995; P < 0.001), Elevated WC (OR = 2.678; 95% CI, 1.969–3.643; P < 0.001), and Low HDL-C (OR = 2.770; 95% CI, 2.049–3.744; P < 0.001). Subgroup analysis and interaction tests demonstrated that gender, age, race, education, smoking status, and physical activity modified the positive association between WMR and MetS (p for interaction < 0.05). Additionally, ROC curve analysis showed that the optimal cutoff value for WMR predicting MetS was 0.7974 (sensitivity: 58.4%; specificity: 59.9%; AUC: 0.621). Conclusions Increasing WMR levels are significantly associated with the risk of MetS and its components: Elevated FPG, Elevated TG, Low HDL-C, and Elevated WC. This suggests that WMR could potentially serve as a valuable and reliable biomarker for MetS, highlighting the importance of closely monitoring patients with elevated WMR to improve prevention and mitigate the development of MetS. However, prospective cohort studies are warranted to confirm these associations and to further explore the causal relationships between WMR and the development of MetS.