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Abstract: SA-OR20

Integrated Proteomic and Metabolomic Modules Associated with Risk of Kidney Function Decline

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Schlosser, Pascal, Johns Hopkins University Department of Epidemiology, Baltimore, Maryland, United States
  • Surapaneni, Aditya L., Division of Precision Medicine, New York University School of Medicine, New York, New York, United States
  • Rhee, Eugene P., Massachusetts General Hospital, Boston, Massachusetts, United States
  • Coresh, Josef, Johns Hopkins University Department of Epidemiology, Baltimore, Maryland, United States
  • Grams, Morgan, Division of Precision Medicine, New York University School of Medicine, New York, New York, United States

Group or Team Name

  • On Behalf of the CRIC and CKD BioCOn Writing Group.
Background

Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes. Many molecules have been linked to kidney disease, and several of them are potentially representing pathways or endophenotypes.

Methods

We present an integrated analysis of proteomics (4,091 proteins) and metabolomics (634 metabolites) via a dimensionality reduction clustering method in the Chronic Renal Insufficiency Cohort (CRIC) Study. We split the 1,708 participants (mean age 59; mean eGFR=42.8 mL/min/1.73m2) with a random split in discovery (2/3) and replication (1/3). Linear regressions (eGFR decline) and Cox proportional hazards models (CKD progression, ESKD) were comprehensively adjusted for demographics and risk factors including eGFR and PCR. Multiple testing in discovery and replication was accounted for by Bonferroni adjustment. Identified modules were characterized through pathway enrichment analyses.

Results

We identified 139 modules of correlated proteins and metabolites in the discovery data. The mean module size was 34 proteins / metabolites. There were 286 principal components (PCs) used to represent the 139 modules. Module membership and PC directions were projected onto the replication dataset.
The average follow-up period was 9.5 and 7.4 years for ESKD and CKD progression respectively (537 and 685 events). Eight module PC to endpoint associations originating from four different modules were identified and replicated (1 eGFR decline; 3 CKD progression; 4 ESKD). One module showed associations with all three traits and ESKD concordance in the replication dataset was improved (87% to 88%, p=0.03) by the addition of this module to the full model of ten covariables. All five protective protein components of this module (ATF6, CLSTN1, EGFR, GHR, C1GALT1C1) were transmembrane proteins and several transmembrane related terms were significantly enriched among the 298 module components. Transmembrane-ephrin receptor activity displayed the largest odds ratio among these (OR = 13.2, P-value = 5.5e-5).

Conclusion

In summary, this study demonstrates that the integration of the proteome and metabolome can identify functions of (patho-)physiologic importance in human health and disease. Specifically, the ephrin receptor activity pathway might play a role on a systemic level.

Funding

  • NIDDK Support