Serum and Urine Metabolites and Kidney Function

Yeo WJ, Surapaneni AL, Hasson DC, Schmidt IM, Sekula P, Köttgen A, Eckardt KU, Rebholz CM, Yu B, Waikar SS, Rhee EP, Schrauben SJ, Feldman HI, Vasan RS, Kimmel PL, Coresh J, Grams ME, Schlosser P (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Journal of the American Society of Nephrology American Society of Nephrology

DOI: 10.1681/ASN.0000000000000403

Abstract

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 5 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P 5 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P 5 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 model50.79, clinical1metabolites model50.87, P 5 8.1E-6; for UACR doubling: clinical model50.66, clinical1metabolites model50.73, P 5 2.9E-5). Conclusions Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Authors with CRIS profile

Kai-Uwe Eckardt Department of Medicine 4 – Nephrology and Hypertension

Involved external institutions

Perelman School of Medicine University of Pennsylvania US United States (USA) (US) Johns Hopkins University (JHU) US United States (USA) (US) University of Texas Health Science Center at Houston (UTHealth) US United States (USA) (US) Boston Medical Center (BMC) US United States (USA) (US) Massachusetts General Hospital US United States (USA) (US) University of Texas Health Science Center at San Antonio (UTHSCSA) US United States (USA) (US) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK / NIDDKD) US United States (USA) (US) New York University (NYU) US United States (USA) (US) Universitätsklinikum Freiburg DE Germany (DE) NYU Langone Medical Center US United States (USA) (US)

How to cite

APA:

Yeo, W.J., Surapaneni, A.L., Hasson, D.C., Schmidt, I.M., Sekula, P., Köttgen, A.,... Schlosser, P. (2024). Serum and Urine Metabolites and Kidney Function. Journal of the American Society of Nephrology. https://doi.org/10.1681/ASN.0000000000000403

MLA:

Yeo, Wan Jin, et al. "Serum and Urine Metabolites and Kidney Function." Journal of the American Society of Nephrology (2024).

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