Identification of plasma biomarkers for chronic kidney disease by integrating proteomics and machine learning

Background: Chronic kidney disease (CKD) is a major global health burden, and early identification of individuals at risk remains challenging. This study aimed to identify plasma proteomic biomarkers associated with incident CKD and develop a machine learning-based prediction model integrating proteomic and clinical data. Methods: We conducted a prospective cohort study of 27,429 UK Biobank participants without baseline CKD after excluding those with estimated glomerular filtration rate <60 mL/min/1.73 m2 or urinary albumin-to-creatinine ratio ≥3 mg/mmol. Among 2,920 plasma proteins, protein-wide Cox regression with Bonferroni correction was followed by least absolute shrinkage and selection operator (LASSO) regression and Light Gradient Boosting Machine (LightGBM)-based feature selection. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration metrics, reclassification indices, and landmark-based prediction analyses, with SHapley Additive exPlanations (SHAP), Kaplan-Meier, and sensitivity analyses. Results: A total of 205 proteins were significantly associated with incident CKD. LASSO identified 23 candidates, and LightGBM identified four key proteins (EDA2R, GDF15, HAVCR1, and SCARB2). The four-protein model showed comparable performance to the demographic model (AUC, 0.796 vs. 0.805; p = 0.14), while their combination improved discrimination (AUC, 0.830 [95% confidence interval, 0.806-0.855]; p < 0.001). The combined model also showed improved performance across 5-year, 10-year, and >10-year CKD prediction horizons. SHAP and Kaplan-Meier analyses consistently supported the predictive value of these proteins, and sensitivity analyses confirmed robustness. Conclusion: Integrating plasma proteomics with machine learning improves internal risk prediction for incident CKD. The parsimonious four-protein panel may provide complementary information for CKD risk stratification, although external validation is needed before clinical application.