Shengyuan Luo1,2, Aditya Surapaneni1,2, Zihe Zheng3, Eugene P Rhee4, Josef Coresh1,2, Adriana M Hung5,6, Girish N Nadkarni7,8,9, Bing Yu10, Eric Boerwinkle10,11, Adrienne Tin1,2, Dan E Arking12, Inga Steinbrenner13, Pascal Schlosser13, Anna Köttgen1,13, Morgan E Grams14,2,15. 1. Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland. 2. Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland. 3. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 4. Division of Nephrology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. 5. Geriatric Research Education Clinical Center, Veteran Administration Tennessee Valley Health Care System, Nashville, Tennessee. 6. Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. 7. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. 8. BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York. 9. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. 10. Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, Texas. 11. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas. 12. McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland. 13. Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany. 14. Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland mgrams2@jhmi.edu. 15. Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland.
Abstract
BACKGROUND AND OBJECTIVES: Genetic variants in NAT8, a liver- and kidney-specific acetyltransferase encoding gene, have been associated with eGFR and CKD in European populations. Higher circulating levels of two NAT8-associated metabolites, N-δ-acetylornithine and N-acetyl-1-methylhistidine, have been linked to lower eGFR and higher risk of incident CKD in the Black population. We aimed to expand upon prior studies to investigate associations between rs13538, a missense variant in NAT8, N-acetylated amino acids, and kidney failure in multiple, well-characterized cohorts. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted analyses among participants with genetic and/or serum metabolomic data in the African American Study of Kidney Disease and Hypertension (AASK; n=962), the Atherosclerosis Risk in Communities (ARIC) study (n=1050), and BioMe, an electronic health record-linked biorepository (n=680). Separately, we evaluated associations between rs13538, urinary N-acetylated amino acids, and kidney failure in participants in the German CKD (GCKD) study (n=1624). RESULTS: Of 31 N-acetylated amino acids evaluated, the circulating and urinary levels of 14 were associated with rs13538 (P<0.05/31). Higher circulating levels of five of these N-acetylated amino acids, namely, N-δ-acetylornithine, N-acetyl-1-methylhistidine, N-acetyl-3-methylhistidine, N-acetylhistidine, and N2,N5-diacetylornithine, were associated with kidney failure, after adjustment for confounders and combining results in meta-analysis (combined hazard ratios per two-fold higher amino acid levels: 1.48, 1.44, 1.21, 1.65, and 1.41, respectively; 95% confidence intervals: 1.21 to 1.81, 1.22 to 1.70, 1.08 to 1.37, 1.29 to 2.10, and 1.17 to 1.71, respectively; all P values <0.05/14). None of the urinary levels of these N-acetylated amino acids were associated with kidney failure in the GCKD study. CONCLUSIONS: We demonstrate significant associations between an NAT8 gene variant and 14 N-acetylated amino acids, five of which had circulation levels that were associated with kidney failure.
BACKGROUND AND OBJECTIVES: Genetic variants in NAT8, a liver- and kidney-specific acetyltransferase encoding gene, have been associated with eGFR and CKD in European populations. Higher circulating levels of two NAT8-associated metabolites, N-δ-acetylornithine and N-acetyl-1-methylhistidine, have been linked to lower eGFR and higher risk of incident CKD in the Black population. We aimed to expand upon prior studies to investigate associations between rs13538, a missense variant in NAT8, N-acetylated amino acids, and kidney failure in multiple, well-characterized cohorts. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted analyses among participants with genetic and/or serum metabolomic data in the African American Study of Kidney Disease and Hypertension (AASK; n=962), the Atherosclerosis Risk in Communities (ARIC) study (n=1050), and BioMe, an electronic health record-linked biorepository (n=680). Separately, we evaluated associations between rs13538, urinary N-acetylated amino acids, and kidney failure in participants in the German CKD (GCKD) study (n=1624). RESULTS: Of 31 N-acetylated amino acids evaluated, the circulating and urinary levels of 14 were associated with rs13538 (P<0.05/31). Higher circulating levels of five of these N-acetylated amino acids, namely, N-δ-acetylornithine, N-acetyl-1-methylhistidine, N-acetyl-3-methylhistidine, N-acetylhistidine, and N2,N5-diacetylornithine, were associated with kidney failure, after adjustment for confounders and combining results in meta-analysis (combined hazard ratios per two-fold higher amino acid levels: 1.48, 1.44, 1.21, 1.65, and 1.41, respectively; 95% confidence intervals: 1.21 to 1.81, 1.22 to 1.70, 1.08 to 1.37, 1.29 to 2.10, and 1.17 to 1.71, respectively; all P values <0.05/14). None of the urinary levels of these N-acetylated amino acids were associated with kidney failure in the GCKD study. CONCLUSIONS: We demonstrate significant associations between an NAT8 gene variant and 14 N-acetylated amino acids, five of which had circulation levels that were associated with kidney failure.
Keywords:
AASK (African American Study of Kidney Disease and Hypertension); acetylation; amino acids; chronic kidney disease; end-stage renal disease; human genetics; metabolism
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