Abstract: PO0435
Discovery of Obesity and Adiposity-Related CKD Subgroups and Preliminary Metabolomics Findings: The CRIC Study
Session Information
- CKD Epidemiology, Biomarkers, Predictors
October 22, 2020 | Location: On-Demand
Abstract Time: 10:00 AM - 12:00 PM
Category: CKD (Non-Dialysis)
- 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention
Authors
- Zheng, Zihe, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Li, Hongzhe, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Rhee, Eugene P., Massachusetts General Hospital, Boston, Massachusetts, United States
- Yang, Wei, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Waikar, Sushrut S., Boston Medical Center, Boston, Massachusetts, United States
- Urbanowicz, Ryan, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Anderson, Amanda Hyre, Tulane University, New Orleans, Louisiana, United States
- Xie, Dawei, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Feldman, Harold I., University of Pennsylvania, Philadelphia, Pennsylvania, United States
Group or Team Name
- Chronic Renal Insufficiency Cohort Study
Background
Obesity/adiposity perturbs the plasma metabolome, as does chronic kidney disease (CKD). Understanding the complex relationships across CKD patient subphenotypes, obesity, and the metabolome may shed light on finding novel risk factors and the mechanisms for CKD progression.
Methods
Among 1,529 participants in the Chronic Renal Insufficiency Cohort (CRIC) Study for whom metabolomics data (Broad Institute) were generated, we first applied consensus clustering with K-means on 20 baseline clinical adiposity-obesity-related attributes to identify patient subgroups. We individually examined the association of 634 known metabolites with the identified subgroups using separate multivariable linear models. Finally, Cox model was used to examine the prospective association of the adiposity-obesity subgroups (the biggest subgroup as reference) with CKD progression, ESRD, a composite cardiovascular disease outcome, and death.
Results
We identified four distinct adiposity-obesity-related CKD subgroups: Subgroup 1 (N=429) - favorable obesity/diabetes profiles and elevated lipid levels; Subgroup 2 (N=349) - favorable diabetes profiles, but slightly obese; Subgroup 3 (N=357) - less favorable diabetes profile, lower lipid levels and severe obesity; and Subgroup 4 (N=394) - less favorable diabetes profiles, but less obese. Among the 634 known metabolites, after adjusting for demographics, health history, eGFR and UACR, 260 were significantly associated with CKD subgroups at Bonferroni-adjusted p<7.9×10-5. Survival analyses showed that compared to Subgroup 1 (ref), Subgroup 4 had the highest risk for CKD progression (HR 1.78, 95% CI 1.40, 2.26) and ESRD (HR 1.92, 95% CI 1.45, 2.52), and Subgroup 3 had the highest risk for the composite CVD outcome (HR 1.87, 95% CI 1.40, 2.50) and death (HR 1.51, 95% CI 1.09, 2.10).
Conclusion
With consensus clustering and metabolomics analysis, we discovered four distinct adiposity-obesity-related subgroups of CKD patients that were associated with numerous metabolites and different risks of clinical endpoints. Novel biomarkers that co-segregate with patient subgroups of high risk could reveal new insights into the obesity related biology of CKD progression and subsequent CVD events, and potentially suggest tailored therapeutic targets among CKD patients.
Funding
- NIDDK Support