Integrated Risk Prediction of Kidney Failure
Alana C. Jones, Atlas Khan, Amit Patki, Vinodh Srinivasasainagendra, Nicole D. Armstrong, Hemant K. Tiwari, Bertha A. Hidalgo, Nita A. Limdi, Donna K. Arnett, Devin M. Absher, Krzysztof Kiryluk, Brittney Davis, Stephen S. Rich, Jerome I. Rotter, Josyf Mychaleckyj, Holly J. Kramer, Yongmei Liu, Marguerite R. IrvinBackground:
Kidney failure affects approximately 800,000 adults in the United States, and African Americans carry a disproportionate burden of disease. Robust algorithms for clinical risk prediction, such as the Kidney Failure Risk Equation (KFRE), have previously been validated, whereas the clinical utility of polygenic (PRS) and methylation risk scores (MRS), have not been as well characterized. 1
Methods:
In this study, we evaluated the capacity of PRS and MRS for CKD to predict multiple clinical endpoints, including incident kidney failure and decline in kidney function. Leveraging genomic, epigenomic, and clinical data from African American participants of the Hypertension Genetic Epidemiology Network (HyperGEN), Multi-Ethnic Study of Atherosclerosis (MESA), and Electronic Medical Records and Genomics (eMERGE) Network, we evaluated the performance of these risk scores in comparison to and in conjunction with the widely validated KFRE.
Results:
In HyperGEN, a higher PRS was associated with a higher risk of kidney failure. Similarly, the MRS was associated with incident kidney failure in both HyperGEN and MESA. Neither PRS nor MRS were associated with the rate of decline in kidney function in eMERGE or MESA. The KFRE outperformed both PRS and MRS as a single predictor. However, multi-score models suggested a synergistic effect of PRS and MRS to the predictive accuracy of the KFRE, with the greatest risk of kidney failure among individuals in high-risk strata of KFRE, PRS, and MRS.
Conclusions:
Results from this exploratory analysis suggest that, while PRS and MRS may not be superior to clinical algorithms, inclusion of ‘-omics’ information may improve disease risk prediction. Future studies should seek to increase sample size to better evaluate these risk models and improve their capacity.