Abstract: FR-PO1151
Urine Metabolite Risk Score Predicts CKD Progression in Black Patients with CKD
Session Information
- CKD: Screening, Diagnosis, Serum and Urine Biomarkers, and Scoring Indices
November 07, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: CKD (Non-Dialysis)
- 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention
Authors
- Zhang, Ruiyuan, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
- Liu, Yizhuo, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
- Sun, Yixi, University of Illinois Chicago, Chicago, Illinois, United States
- Huang, Zhijie, Tulane University, New Orleans, Louisiana, United States
- Kelly, Tanika, University of Illinois Chicago, Chicago, Illinois, United States
- Grams, Morgan, New York University Grossman School of Medicine, New York, New York, United States
- Chen, Jing, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
- He, Jiang, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
- Li, Changwei, The University of Texas Southwestern Medical Center, Dallas, Texas, United States
Background
End stage kidney disease (ESKD) imposes a substantial burden on the healthcare system. Early identification of individuals at risk for progression of chronic kidney disease (CKD) remains a priority, particularly among Black populations who are disproportionately influenced by CKD. Urinary metabolites may provide a non-invasive window into kidney health and offer predictive insights. We aimed to develop a urine metabolite risk score (MRS) for CKD progression among Black participants of the Chronic Renal Insufficiency Cohort (CRIC) study.
Methods
Untargeted metabolomics profiling was conducted on baseline 24-hour urine samples from 1,309 Black CKD patients. CKD progression, defined as the onset of ESKD or a 50% reduction in estimated glomerular filtration rate (eGFR), was tracked over a median follow-up of 5.13 years (up to 20 years). Metabolites associated with CKD progression were selected using elastic net regression model, and their coefficients were used to compute an MRS. The model was trained and tested using an 80:20 data split, with final risk scores calculated via leave-one-out cross-validation. Associations between the MRS, CKD progression, and traditional risk factors were evaluated. Model discrimination was assessed using the concordance index (C-index).
Results
The MRS included 89 urine metabolites and was significantly correlated with established CKD risk factors including age, diabetes, hypertension, baseline eGFR, urine protein-to-creatinine ratio, anemia, total cholesterol, and fibroblast growth factor 23 (all P<0.05). The MRS was strongly associated with CKD progression (hazard ratio [HR] per one standard deviation=3.02, 95% confidence interval [CI]: 2.79-3.26; P<0.0001), demonstrating good discrimination (C-index=0.812). Incorporating traditional risk factors modestly improved model performance (C-index=0.845), and the MRS remained independently predictive (HR=1.47, 95% CI: 1.26-1.71; P<0.0001).
Conclusion
This urine-based MRS captures a wide spectrum of biological pathways linked to CKD progression and independently predicts risks in Black CKD patients. Considering its performance and non-invasive nature, this MRS may offer a practical tool for early risk stratification and personalized CKD management.
Funding
- Other NIH Support