Metabolic and renal implications of hyperuricemia: a cross-sectional study with cluster and principal component analysis

Hyperuricemia is a well-established metabolic abnormality associated with gout, chronic kidney disease (CKD), cardiovascular disorders, and components of metabolic syndrome. However, the interplay between serum uric acid (SUA), renal function parameters, and lipid profile remains incompletely characterized in clinical datasets. This study aimed to examine associations between hyperuricemia and selected renal and metabolic markers using multivariate statistical approaches. In this cross-sectional study, 224 adults aged 18–65 years attending a tertiary care outpatient department were screened between November 2023 and June 2024. Among them, 108 individuals had SUA > 8.0 mg/dL. After applying strict eligibility criteria, 30 participants were included for detailed analysis. Biochemical parameters assessed included serum creatinine, blood urea, lipid profile (total cholesterol, triglycerides, HDL, LDL), and body mass index (BMI). Statistical analyses included independent t-tests, correlation analysis, linear regression, K-means clustering, and principal component analysis (PCA). Given the small sample size, findings may be interpreted as exploratory. The mean SUA among analyzed participants was 9.28 ± 1.00 mg/dL. A modest positive correlation was observed between SUA and serum creatinine (r = 0.33), whereas correlations with lipid parameters were weak. Linear regression demonstrated that SUA accounted for 10.8% of the variance in serum creatinine (R² = 0.108, p = 0.169), indicating a non-significant association in this dataset. Clustering identified three distinct metabolic phenotypes, while PCA demonstrated differential contributions of renal and lipid parameters to overall variance. Clustering analysis identified three biochemical phenotypes with varying profiles of uric acid, creatinine, and lipid parameters. PCA revealed that renal function markers and lipid variables contributed differently to overall variance within the dataset. In this exploratory cross-sectional analysis, serum uric acid demonstrated modest associations with renal function parameters and minimal associations with lipid markers. These findings should be interpreted cautiously due to the limited sample size. Larger, longitudinal studies incorporating comprehensive renal function markers are warranted to better elucidate the metabolic and renal correlates of hyperuricemia. These findings provide preliminary, hypothesis-generating insights into the metabolic and renal interplay in hyperuricemia, which may assist in identifying early biochemical patterns in clinical settings.