成年人不同肥胖代谢表型与高尿酸血症之间的关联:基于NHANES的横断面研究OA

Objective The aim of this study was to explore the correlation between different obesity metabolic phenotypes and the risk of hyperuricemia(HUA)in adults.Methods A cross-sectional study was conducted using data from the 2011-2018 National Health and Nutrition Examination Survey(NHANES)database,extracting adult samples(n=7394)aged≥20 years with complete measurements of body mass index,waist circumference,serum uric acid,fasting blood glucose,high-density lipoprotein cholesterol,triglycerides,systolic blood pressure,and diastolic blood pressure.The sample was categorised into four groups based on obesity and metabolic status:metabolically healthy non-obese(MHNO),metabolically unhealthy non-obese(MUNO),metabolically healthy obe-sity(MHO),and metabolically unhealthy obesity(MUO).We employed weighted multivariate logistic regression models to analyse associations between obesity-metabolic phenotypes and HUA,utilising restricted cubic splines to assess dose-response relationships between obesity and metabolic indicators and HUA risk.Robustness of findings was evaluated through subgroup analyses and interac-tion tests.Results The prevalence of HUA in the study cohort was 22.7%.Using the MHNO group as reference,after multivariate adjustment,the risk of HUA progressively increased in the MUNO,MHO,and MUO groups,with odds ratios(OR)and 95%confi-dence intervals(95%CI)being 2.12(1.50～3.00),2.16(1.69～2.76),and 4.53(3.41～6.02),respectively,all with P values<0.001.Restricted cubic spline analysis revealed a non-linear dose-response relationship between HUA risk and each obesity/metabolic indicator.Subgroup analyses revealed significant interactions between obesity-metabolic phenotypes and HUA risk across gender,eth-nicity,educational attainment,and alcohol consumption status(P for interaction<0.05).This association was particularly pronounced among males and younger individuals(aged 20～39 years).Conclusion Individuals with the MUO phenotype carry the highest risk of HUA,which suggests that clinical assessment of HUA risk should extend beyond traditional BMI categories to include a comprehensive integration of metabolic indicators such as blood pressure,blood glucose,and lipids.This approach enables precision identification,stratified management,and early intervention for high-risk populations.