Serum and Urine Metabolites and Kidney Function

Author:

Yeo Wan-Jin1ORCID,Surapaneni Aditya L.1ORCID,Hasson Denise C.2ORCID,Schmidt Insa M.3ORCID,Sekula Peggy4ORCID,Köttgen Anna45ORCID,Eckardt Kai-Uwe67ORCID,Rebholz Casey M.58ORCID,Yu Bing9ORCID,Waikar Sushrut S.3ORCID,Rhee Eugene P.10,Schrauben Sarah J.1112ORCID,Feldman Harold I.1213ORCID,Vasan Ramachandran S.1415ORCID,Kimmel Paul L.16,Coresh Josef51718ORCID,Grams Morgan E.118ORCID,Schlosser Pascal4519ORCID

Affiliation:

1. Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York

2. Division of Pediatric Critical Care Medicine, Hassenfeld Children's Hospital, NYU Langone Health, New York, New York

3. Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts

4. Department of Data Driven Medicine, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany

5. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

6. Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen–Nürnberg, Erlangen, Germany

7. Department of Nephrology and Medical Intensive Care, Charité—Universitätsmedizin Berlin, Berlin, Germany

8. Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

9. Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas

10. Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts

11. Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

12. Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

13. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

14. School of Public Health, University of Texas Health San Antonio, San Antonio, Texas

15. Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts

16. Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland

17. Optimal Aging Institute, Departments of Population Health and Medicine, NYU Langone Health, New York, New York

18. Department of Population Health, NYU Langone Medical Center, New York, New York

19. Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany

Abstract

Key Points We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort.Metabolic profiling in serum and urine provides distinct and complementary insights into disease. Background Metabolites represent a read-out of cellular processes underlying states of health and disease. Methods We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses. Results At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m2 (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected P < 4.0E-5 for serum analyses and P < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], P = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, P = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, P = 2.9E-5). Conclusions Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Funder

National Institute of Diabetes and Digestive and Kidney Diseases

National Institutes of Health

Deutsche Forschungsgemeinschaft

National Heart, Lung, and Blood Institute

Publisher

Ovid Technologies (Wolters Kluwer Health)

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