Correlation between Different Obesity Metabolic Phenotypes and Atherosclerosis in a Young Population: Uric Acid as Its Potential Factor

Background Obesity and metabolic abnormalities are associated with atherosclerosis, and different metabolic phenotypes of obesity have different risks of AS. The relationship between serum uric acid (SUA) levels and AS is unclear. Objective Using brachial-ankle pulse wave velocity (baPWV) as an early predictor of AS, we investigated the correlation between different metabolic phenotypes of obesity and the risk of AS, with a focus on metabolically healthy obese populations, and further explored whether uric acid serves as a potential influencing factor. Methods A total of 622 young medical check-ups at a street community health center from December 2021 to January 2022 were selected for questionnaire survey, physical examination, and laboratory tests. The study subjects were categorized into four groups based on BMI and metabolic status, metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO). Pearson's correlation was used to analyse the correlation between SUA and baPWV and various clinical indicators. Multiple linear regression models were used to analyze the correlation between SUA and baPWV and various clinical indicators, and scatter plots were drawn. Multifactorial Logistic regression models were used to analyze the correlation between different obesity metabolic phenotypes and high baPWV and hyperuricemia. Results After grouping according to different obesity metabolic phenotypes, the prevalence of high baPWV levels in the MHNO, MHO, MUNO and MUO groups gradually increased to 3.9%, 9.8%, 14.5% and 29.4%, respectively, with a statistically significant difference (χ2 trend=60.722, P<0.001); and the prevalence of hyperuricemia in the MHNO, MHO, MUNO and MUO groups. The prevalence rates were 3.6%, 8.9%, 10.1% and 26.9%, respectively, and the difference was statistically significant (χ2 trend=56.444, P<0.001). The results of multivariate linear regression analysis correcting for confounders showed SUA was an independent risk factor for atherosclerosis. The results of multifactorial Logistic regression modeling showed that after correcting for gender, age, current smoking, current alcohol consumption, and literacy, the risk of high baPWV was significantly higher in the four groups of MHNO, MHO, MUNO, and MUO in that order, and the risk of hyperuricemia was also significantly higher in that order. Conclusion In the young population, there is a significant correlation between different obesity metabolic phenotypes and atherosclerosis, and uric acid may be a potential influencing factor. In addition, we should pay attention to the young MHO population for early lifestyle intervention and health management.