Association of metabolic syndrome and its components with gout: an analysis based on a large-scale health survey database

ObjectiveTo investigate the association of metabolic syndrome (MetS) and its components with gout.MethodsThis cross-sectional study utilized data from 28 130 adult participants in the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018. Logistic regression was used to assess the associations of MetS and its components with gout. Restricted cubic spline (RCS) models were employed to examine non-linear dose-response relationships between metabolic indicators and gout. Subgroup analyses were performed by age, sex, and body mass index (BMI), with interaction effects evaluated using the Wald test by including multiplicative interaction terms in the logistic regression models.ResultsThe prevalence of MetS was substantially higher in individuals with gout than in those without (66.9% vs. 33.3%). After adjusting for age, sex, BMI, smoking, and alcohol consumption, MetS was independently associated with gout (OR=2.55, 95%CI: 1.55-4.20, P<0.01), with central obesity (OR=2.44, 95%CI: 1.55-3.85, P<0.01) and hypertension (OR=1.70, 95%CI: 1.04-2.78, P<0.05) showing the strongest associations. RCS analysis revealed J-shaped associations for BMI and fasting glucose, accelerating positive associations for waist circumference, mean systolic blood pressure, and triglyceride levels, and U-shaped associations for mean diastolic blood pressure and LDL-C levels (all P<0.05). HDL-C showed a significant non-linear association, with the odds of gout generally decreasing as HDL-C levels increased (P<0.05). Subgroup analyses indicated that the association of MetS with gout was significantly stronger in females than in males (OR: 5.07 vs. 2.34, interaction P=0.034), and the association of diabetes with gout was more pronounced in those aged ≥50 years (OR=4.58) and in non-obese individuals (OR=5.21) (both interaction P<0.01).ConclusionMetS is independently associated with gout, exhibiting significant non-linear dose-response characteristics and population heterogeneity.