TY - JOUR
T1 - Serum Metabolites and Kidney Outcomes
T2 - The Atherosclerosis Risk in Communities Study
AU - Bernard, Lauren
AU - Zhou, Linda
AU - Surapaneni, Aditya
AU - Chen, Jingsha
AU - Rebholz, Casey M.
AU - Coresh, Josef
AU - Yu, Bing
AU - Boerwinkle, Eric
AU - Schlosser, Pascal
AU - Grams, Morgan E.
N1 - Funding Information:
Dr Grams is supported by K24HL155861 and R01DK124399. Dr Rebholz is supported by K01DK107782, R03DK128386, and R56HL153178. Dr Schlosser is supported by DFG Project-ID 192904750 – CRC 992 Medical Epigenetics and Project-ID 431984000 – CRC 1453, by DFG grant SCHL 2292/1-1, and the EQUIP Program for Medical Scientists, Faculty of Medicine, University of Freiburg. The remaining authors declare that they have no relevant financial interests.
Funding Information:
Lauren Bernard, MHS, Linda Zhou, ScM, Aditya Surapaneni, PhD, Jingsha Chen, Casey M. Rebholz, PhD, Josef Coresh, MD, PhD, Bing Yu, PhD, Eric Boerwinkle, PhD, Pascal Schlosser, PhD, and Morgan E. Grams, MD, PhD, Research idea and study design: MEG; data acquisition: MEG, BY, JCo, EB; data analysis/interpretation: LB, MEG, CMR; statistical analysis: LB, LZ, AS, JCh, PS; supervision or mentorship: MEG. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). Metabolomics measurements were sponsored by the National Human Genome Research Institute (3U01HG004402-02S1). Dr Grams is supported by K24HL155861 and R01DK124399. Dr Rebholz is supported by K01DK107782, R03DK128386, and R56HL153178. Dr Schlosser is supported by DFG Project-ID 192904750 – CRC 992 Medical Epigenetics and Project-ID 431984000 – CRC 1453, by DFG grant SCHL 2292/1-1, and the EQUIP Program for Medical Scientists, Faculty of Medicine, University of Freiburg. The remaining authors declare that they have no relevant financial interests. The authors thank the staff and participants of the Atherosclerosis Risk in Communities (ARIC) study for their important contributions. Some of the data reported here were supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government. Received April 01, 2022 as a submission to the expedited consideration track with 2 external peer reviews. Direct editorial input from the Statistical Editor and the Editor-in-Chief. Accepted in revised form June 24, 2022.
Funding Information:
The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). Metabolomics measurements were sponsored by the National Human Genome Research Institute (3U01HG004402-02S1).
Publisher Copyright:
© 2022
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
KW - 1,5-anhydroglucitrol
KW - 1-Linoleoylglycerophosphocholine
KW - 5-oxoproline
KW - CKD progression
KW - end-stage kidney disease
KW - gamma-glutamylthreonine
KW - gamma-glutamyltyrosine
KW - glucose
KW - kidney failure
KW - mannose
KW - metabolic pathways
KW - metabolomics
UR - http://www.scopus.com/inward/record.url?scp=85136478843&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136478843&partnerID=8YFLogxK
U2 - 10.1016/j.xkme.2022.100522
DO - 10.1016/j.xkme.2022.100522
M3 - Article
C2 - 36046612
AN - SCOPUS:85136478843
SN - 2590-0595
VL - 4
JO - Kidney Medicine
JF - Kidney Medicine
IS - 9
M1 - 100522
ER -