Serum Metabolites and Kidney Outcomes: The Atherosclerosis Risk in Communities StudyPlain Language Summary

• Published in: Kidney medicine Vol. 4; no. 9; p. 100522
• Main Authors: Lauren Bernard, Linda Zhou, Aditya Surapaneni, Jingsha Chen, Casey M. Rebholz, Josef Coresh, Bing Yu, Eric Boerwinkle, Pascal Schlosser, Morgan E. Grams
• Format: Journal Article
• Language: English
• Published: Elsevier 01.09.2022
• Subjects: 1,5-anhydroglucitrol; 1-Linoleoylglycerophosphocholine; 5-oxoproline; CKD progression; end-stage kidney disease; gamma-glutamylthreonine
• ISSN: 2590-0595; 2590-0595

Rationale & Objective: Novel metabolite biomarkers of kidney failure with replacement therapy (KFRT) may help identify people at high risk for adverse kidney outcomes and implicated pathways may aid in developing targeted therapeutics. Study Design: Prospective cohort. Setting & Participants: The cohort included 3,799 Atherosclerosis Risk in Communities study participants with serum samples available for measurement at visit 1 (1987-1989). Exposure: Baseline serum levels of 318 metabolites. Outcomes: Incident KFRT, kidney failure (KFRT, estimated glomerular filtration rate <15 mL/min/1.73 m2, or death from kidney disease). Analytical Approach: Because metabolites are often intercorrelated and represent shared pathways, we used a high dimension reduction technique called Netboost to cluster metabolites. Longitudinal associations between clusters of metabolites and KFRT and kidney failure were estimated using a Cox proportional hazards model. Results: Mean age of study participants was 53 years, 61% were African American, and 13% had diabetes. There were 160 KFRT cases and 357 kidney failure cases over a mean of 23 years. The 314 metabolites were grouped in 43 clusters. Four clusters were significantly associated with risk of KFRT and 6 were associated with kidney failure (including 3 shared clusters). The 3 shared clusters suggested potential pathways perturbed early in kidney disease: cluster 5 (15 metabolites involved in alanine, aspartate, and glutamate metabolism as well as 5-oxoproline and several gamma-glutamyl amino acids), cluster 26 (6 metabolites involved in sugar and inositol phosphate metabolism), and cluster 34 (21 metabolites involved in glycerophospholipid metabolism). Several individual metabolites were also significantly associated with both KFRT and kidney failure, including glucose and mannose, which were associated with higher risk of both outcomes, and 5-oxoproline, gamma-glutamyl amino acids, linoleoylglycerophosphocholine, 1,5-anhydroglucitol, which were associated with lower risk of both outcomes. Limitations: Inability to determine if the metabolites cause or are a consequence of changes in kidney function. Conclusions: We identified several clusters of metabolites reproducibly associated with development of KFRT. Future experimental studies are needed to validate our findings as well as continue unraveling metabolic pathways involved in kidney function decline.