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Abstract: FR-PO918

Serum Metabolomic Markers of Protein Intake and Incident CKD Risk

Session Information

Category: CKD (Non-Dialysis)

  • 2201 CKD (Non-Dialysis): Epidemiology‚ Risk Factors‚ and Prevention

Authors

  • Bernard, Lauren, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
  • Chen, Jingsha, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
  • Kim, Hyunju, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
  • Wong, Kari E., Metabolon Inc, Research Triangle Park, North Carolina, United States
  • Steffen, Lyn M., University of Minnesota School of Public Health, Minneapolis, Minnesota, United States
  • Yu, Bing, The University of Texas Health Science Center at Houston, Houston, Texas, United States
  • Boerwinkle, Eric, The University of Texas Health Science Center at Houston, Houston, Texas, United States
  • Rhee, Eugene P., Massachusetts General Hospital, Boston, Massachusetts, United States
  • Levey, Andrew S., Tufts University School of Medicine, Boston, Massachusetts, United States
  • Grams, Morgan, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • Rebholz, Casey, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States
Background

Untargeted metabolomics is a promising approach for capturing dietary intake with less bias than traditional methods of dietary assessment. An important application of this technique is the identification of biomarkers of specific dietary protein sources, which may explain the diet-chronic kidney disease (CKD) relationship.

Methods

We analyzed dietary data collected from an interviewer-administered questionnaire and 359 serum metabolites at visit 1 (1987-1989) in the Atherosclerosis Risk in Communities (ARIC) study (n=3,724). Multivariable linear regression models were used to estimate cross-sectional associations between specific sources of protein (red and processed meat, nuts, legumes) previously associated with CKD risk in the ARIC study and serum metabolites. For metabolites that were significantly associated with these dietary protein sources, we assessed their prospective associations with incident CKD using Cox regression models. Bonferroni correction was used to account for multiple comparisons in the cross-sectional analysis. A priori, we expected metabolites positively associated with red and processed meat to be associated with higher risk of CKD and metabolites positively associated with nuts or legumes to be inversely associated with CKD.

Results

There were 31 significant protein-metabolite associations. Six metabolites were representative of red and processed meat consumption. Five metabolites were associated with nut intake, including 4-vinylphenol sulfate and tryptophan betaine, and no metabolites were associated with legume intake. Higher levels of two metabolites (glucose, 10-nonadecenoate) were associated with higher intake of red and processed meat and higher risk of incident CKD (Table). Higher levels of catechol sulfate were positively associated with nut intake and higher risk of incident CKD.

Conclusion

We identified candidate biomarkers of specific dietary protein sources. Glucose and 10-nonadecenoate may serve as metabolomic markers of the red and processed meat-CKD association.

Funding

  • NIDDK Support