Kidney Week

Abstract: TH-PO994

Metabolites Associated with Inflammatory Proteins in the AASK Study

Session Information

• CKD: Epidemiology, Risk Factors, and Prevention - 1
  October 24, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Chen, Teresa K., University of California San Francisco, San Francisco, California, United States

Teresa K. Chen, MD

• Surapaneni, Aditya L., New York University, New York, New York, United States

Aditya L. Surapaneni, PhD

• Estrella, Michelle M., University of California San Francisco, San Francisco, California, United States

Michelle M. Estrella, MD

• Appel, Lawrence J., Johns Hopkins University, Baltimore, Maryland, United States

Lawrence J. Appel, MD, MPH

• Obeid, Wassim, Johns Hopkins University, Baltimore, Maryland, United States

Wassim Obeid, PhD

• Parikh, Chirag R., Johns Hopkins University, Baltimore, Maryland, United States

Chirag R. Parikh, MD, PhD, FASN

• Grams, Morgan, New York University, New York, New York, United States

Morgan Grams, MD, PhD

Background

Inflammation is associated with greater risk of adverse outcomes, including CKD progression, CVD, and mortality, and may also be impacted by the metabolic milieu. Using an untargeted metabolomics approach, we identified metabolites associated with 9 inflammatory proteins (TNFR1, TNFR2, TNF-α, IFN-γ, IL-6, IL-8, IL-10, UMOD, EGF) in the African American Study of Kidney Disease and Hypertension (AASK).

Methods

Among 491 AASK participants (37% female, mean age 54 y and GFR 45 mL/min/1.73m², median UPCR 91 mg/g), we measured 812 serum metabolites by untargeted mass spectrometry (Metabolon) at study baseline and serum proteins by targeted immunoassays (MesoScale Discovery) at baseline, 12- and 24-months. Using multivariable linear regression, linear mixed-effects, and Cox PH models, we evaluated associations of metabolites with proteins at baseline, over time, and with ESKD risk, respectively. We used Metaboanalyst to identify enriched metabolomic pathways for each protein.

Results

At baseline, 367 associations between metabolites and inflammatory proteins were significant after Bonferroni correction: with more positive associations for TNFR1 (97%), TNFR2 (97%), IL-8 (77%), and IL-10 (100%); more negative associations for UMOD (80%) and EGF (97%); and variable for TNF-α, IFN-γ, and IL-6. Enriched pathways differed across inflammatory proteins (Table). For TNFR1 and TNFR2, these included inositol phosphate metabolism, ascorbate and aldarate metabolism, and tryptophan metabolism. Several metabolites were associated with changes in TNFR1, TNFR2, TNF-α, IL-8, UMOD, or EGF. Higher levels of tigylcarnitine and N2,N5 diacetylornithine were associated with 2-year increases in TNFR1 and/or TNFR2, and notably of ESKD (Figure).

Conclusion

Using an untargeted approach, multiple metabolites were cross-sectionally and longitudinally associated with inflammatory proteins.

Funding

• NIDDK Support