Abstract: FR-OR028
Impact of Polygenic Scores on Kidney Failure Risk Prediction Models in IgAN
Session Information
- Glomerular Disease Outcomes: Measuring What Matters
November 07, 2025 | Location: Room 310A, Convention Center
Abstract Time: 04:30 PM - 04:40 PM
Category: Glomerular Diseases
- 1402 Glomerular Diseases: Clinical, Outcomes, and Therapeutics
Authors
- Ouyang, Yan, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China
- Zeng, Wenjie, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
- Liu, Lili, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
- Zanoni, Francesca, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
- Khan, Atlas, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
- Wang, Chen, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
- Xie, Jingyuan, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China
- Gharavi, Ali G., Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
- Kiryluk, Krzysztof, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
Group or Team Name
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University.
Background
To compare the performance of established kidney failure risk equations (KFREs) in predicting a 5-year risk of end-stage kidney disease (ESKD) in IgA nephropathy, and to evaluate the incremental predictive value of polygenic scores (PGS) when incorporated into the clinical risk models.
Methods
We first compared the performance of the three published KFRE models (Xie-IgAN-KFRE, Barbour-IgAN-KFRE, and Tangri-CKD-KFRE) in two independent cohorts, Ruijin (RJ, N=1045) and Columbia University (CU, N=483). Next, we assessed the added value of two published PGS (IgAN and CKD) in predicting ESKD. In total, four model configurations were tested for each KFRE: (1) clinical KFRE alone, (2) KFRE plus CKD-PGS, (3) KFRE plus IgAN-PGS, and (4) KFRE plus both PGSs combined. PGSs were calculated using published equations and standardized separately within each cohort. Model performance in predicting a 5-year ESKD risk was assessed using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Random-effects meta-analysis was conducted to derive pooled estimates of AUC and net benefit across cohorts.
Results
We systematically assessed the 5-year predictive performance of 12 KFRE models across both cohorts. The 5-year ESKD incidence rates were 17.7% for the RJ cohort and 23.0%, for the CU cohort. In the RJ discovery cohort, the Tangri-CKD-KFRE with both IgAN and CKD PGSs included demonstrated the highest AUC of 0.91 (95% CI: 0.87–0.94). Similarly, the same model had the best performance in the CU validation cohort, achieving AUC of 0.94 (95% CI: 0.90–0.99; net benefit = 0.021). Meta-analysis across both cohorts showed that including both PGSs improved the performance of the Tangri-based KFRE. The Tangri plus 2 PGS model demonstrated the highest overall performance in terms of AUC and net benefit, outperforming the Barbour- and Xie-based models with or without PGSs.
Conclusion
Among the 12 KFRE models evaluated across two independent cohorts, the Tangri-KFRE model with 2 PGS consistently demonstrated the best overall performance. These promising results motivate future work to improve integrated predictors of kidney disease progression in IgA nephropathy.