Longitudinal serum uric acid transition patterns and their clinical, genetic, and dietary determinants: prospective prediction of incident hyperuricemia and gout in a Korean population-based cohort.

BACKGROUND: Longitudinal serum uric acid (SUA) transition patterns and their clinical, genetic, and dietary determinants remain poorly characterized. This prospective cohort study - a secondary analysis of the Korea Genome and Epidemiology Study Health Examinee (KoGES-HEXA) cohort - aimed to characterize SUA transition patterns, identify their determinants, and examine their associations with incident hyperuricemia and gout. METHODS: Genomic DNA was extracted from peripheral blood leukocytes and genotyped by the KoGES infrastructure; 58,701 participants with successfully genotyped data meeting KoGES quality control criteria were initially available. After applying study-specific exclusion criteria, 54,000 participants were included in the final analysis, yielding four SUA transition groups across two examination visits: Normal SUA (81.5%), Newly Developed Hyperuricemia (NDH; 12.4%), Recovered Hyperuricemia (RH; 5.6%), and Persistently Elevated Hyperuricemia (PH; 0.6%). Cox proportional hazards models evaluated incident hyperuricemia (n = 50,676) as the primary outcome and incident gout (n = 45,333) as the secondary outcome. Genetic risk scores (GRS) were constructed from genome-wide association analysis, and machine learning models identified key predictors of incident hyperuricemia. RESULTS: Lower eGFR and higher fatty liver index showed the strongest associations with adverse SUA transition groups (OR: 4.60-7.55). A 4-SNP GRS linked to urate transporter genes was strongly associated with hyperuricemia risk (OR: 3.64, 95% CI: 3.17-4.18), with nominally significant gene-lifestyle interactions for smoking, alcohol, and plant-based diet. Plant-based diet adherence was associated with reduced incident hyperuricemia risk (HR: 0.902) and attenuated genetic susceptibility across risk strata. Baseline hyperuricemia showed the strongest association with incident gout (HR: 9.34, 95% CI: 5.74-14.2). Machine learning models achieved good discrimination (AUROC: 0.862-0.885), with eGFR, GRS, sex, and BMI as top predictors. CONCLUSION: Longitudinal SUA transition patterns were strongly associated with incident hyperuricemia and gout risk, with reduced renal function, hepatic steatosis, and genetic susceptibility as the strongest determinants. Plant-based dietary patterns were associated with attenuated hyperuricemia risk across genetic risk strata, supporting integrated clinical, genetic, and lifestyle approaches to hyperuricemia prevention. Key Points • Longitudinal analysis identified four distinct serum uric acid trajectories, with 18.5% experiencing dynamic hyperuricemia patterns. • Impaired renal function and hepatic steatosis are the strongest predictors of adverse trajectories. • Plant-based dietary adherence significantly attenuates genetic susceptibility to hyperuricemia. • Machine learning integration of clinical, genetic, and lifestyle factors enables accurate hyperuricemia prediction.