Supplemental Material for: Serum Uric Acid and Blood Urea Nitrogen as Primary Determinants of eGFR in 23,960 Chinese Adults: A Cross-Sectional Study with Machine Learning and Longitudinal Validation

Methods

We conducted a population-based cross-sectional study of 23,960 unique adult participants (first visit per patient) recruited across five annual waves (2011–2015) in Eastern China, with a longitudinal validation subset of 14,309 patients with two visits. eGFR was calculated using the CKD-EPI 2021 race-free equation. LightGBM and traditional regression were applied to 35 clinical and laboratory predictors, explicitly excluding serum creatinine and cystatin C to prevent tautological prediction.

Patient-level data splitting (70/15/15) ensured no individual appeared in both training and test sets.</p><p dir="ltr">Results: CKD (eGFR <60 mL/min/1.73m²) was present in 2.3% (n=554) of participants; mean eGFR was 91.9±15.3 mL/min/1.73m². LightGBM achieved R²=0.461 (RMSE=11.63 mL/min/1.73m²) compared to linear regression R²=0.450. Feature importance analysis identified serum uric acid (SUA, 10.1%), blood urea nitrogen (BUN, 9.6%), and age (7.2%) as primary eGFR determinants.

Multivariable regression confirmed age (β=−0.809 per year, 95% CI: −0.843 to −0.774) and SUA (β=−0.064 per μmol/L, 95% CI: −0.069 to −0.060) as independent predictors (both p<0.001). In the longitudinal subset, mean eGFR declined by 4.44±12.50 mL/min/1.73m² between visits. CKD prevalence increased from 2.5% (2011) to 3.6% (2015; p<0.001).</p><p dir="ltr">Conclusions: Using a methodologically rigorous approach that excludes GFR-derived metabolites from predictor sets, SUA and BUN are identified as primary eGFR determinants in a large Chinese population.

The modest ML advantage over traditional regression (ΔR²≈0.011) reflects capture of non-linear interactions. These findings support targeted screening of individuals with elevated uric acid and BUN for CKD prevention.</p>