Diabetes Genetic Clusters and Clinical Outcomes in the Chronic Renal Insufficiency Cohort

Kaylia M. Reynolds, Department of Epidemiology, University of North Carolina, Chapel Hill, NC.
Daohang Sha, Departments of Medicine and Epidemiology and Biostatistics, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
Quan Sun, Department of Biostatistics, University of North Carolina, Chapel Hill, NC.
Afshin Parsa, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD.
Jing Chen, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA.
Hernan Rincon Choles, Department of Kidney Medicine, Medical Specialty Institute, Cleveland Clinic Foundation, Cleveland, OH.
Ruth Dubin, Division of Nephrology, University of Texas Southwestern Medical Center, Dallas, TX.
Jiang He, Department of Kidney Medicine, Medical Specialty Institute, Cleveland Clinic Foundation, Cleveland, OH.
Chi-Yuan Hsu, Division of Nephrology, University of California, San Francisco, San Francisco, CA.
Kristine Yaffe, Departments of Psychiatry, Neurology and Epidemiology and Biostatistics, University of California, San Francisco, CA.
Vallabh Shah, Division of Nephrology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM.
Dominic Raj, Division of Renal Diseases and Hypertension, George Washington University School of Medicine and Health Sciences, Washington, DC.
Sylvia E. Rosas, Joslin Diabetes Center, Harvard University, Boston, MA.
James P. Lash, Division of Nephrology, University of Illinois Chicago, Chicago, IL.
Andrew P. Morris, Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.
Nora Franceschini, Department of Epidemiology, University of North Carolina, Chapel Hill, NC.

Document Type

Journal Article

Publication Date

11-25-2025

Journal

Clinical journal of the American Society of Nephrology : CJASN

DOI

10.2215/CJN.0000000882

Abstract

BACKGROUND: Type 2 diabetes (T2D) exhibits biological and pathophysiological heterogeneity, which may contribute to variations in diabetes-related complications, particularly in high-risk populations such as individuals with chronic kidney disease (CKD). Prior research has explored T2D mechanisms using partitioned polygenic scores (PGS), derived from distinct clusters of variants according to their associations with cardiometabolic traits. In this study, we investigated whether cluster-specific partitioned PGS are associated with cardiovascular outcomes and CKD progression in individuals with CKD. METHODS: We used genome-wide genotype data from the Chronic Renal Insufficiency Cohort (CRIC) to calculate both a total PGS for T2D and partitioned PGS for T2D clusters. We examined their associations with cardiometabolic traits and conducted time-to-event analysis to assess their association with mortality (overall and cardiovascular), incident cardiovascular outcomes (myocardial infarction, stroke, heart failure, atrial fibrillation, peripheral artery disease, and a composite of major cardiovascular events) and CKD progression. RESULTS: Among 3,577 CRIC participants (mean age 58 years, 45% were women, 49% had T2D), the total PGS was significantly associated with higher hemoglobin A1C (p=4.8 x 10-21) among nondiabetic participants. Cluster-specific partitioned PGS in CRIC captured distinct cardiometabolic profiles corresponding to T2D mechanistic subtypes, particularly the obesity cluster, the β-cell dysfunction with positive proinsulin cluster, and the lipodystrophy cluster. The obesity cluster partitioned PGS was significantly associated with incident atrial fibrillation (p=2.89 x 10-4) and overall mortality (p= 4.29 x 10-4), but the association with atrial fibrillation was attenuated when accounting for competing risk of death (p=0.002). The β-cell dysfunction with negative proinsulin cluster was inversely associated with CKD progression (p=2.48 x 10-5). CONCLUSIONS: Our findings suggest that T2D driven by insulin resistance and obesity contributes to a higher risk of overall death in individuals with CKD and explain in part the higher risk of atrial fibrillation. These results highlight the importance of considering T2D heterogeneity when assessing cardiovascular risk in this population.

Department

Medicine

Link to Full Text

Share

COinS