The blood metabolome of incident kidney cancer: A case–control study nested within the MetKid consortium

Author:

Guida FlorenceORCID,Tan Vanessa Y.ORCID,Corbin Laura J.ORCID,Smith-Byrne Karl,Alcala KarineORCID,Langenberg ClaudiaORCID,Stewart Isobel D.,Butterworth Adam S.,Surendran Praveen,Achaintre David,Adamski JerzyORCID,Amiano Pilar,Bergmann Manuela M.,Bull Caroline J.ORCID,Dahm Christina C.ORCID,Gicquiau Audrey,Giles Graham G.ORCID,Gunter Marc J.,Haller Toomas,Langhammer ArnulfORCID,Larose Tricia L.,Ljungberg BörjeORCID,Metspalu Andres,Milne Roger L.ORCID,Muller David C.,Nøst Therese H.ORCID,Pettersen Sørgjerd ElinORCID,Prehn CorneliaORCID,Riboli ElioORCID,Rinaldi Sabina,Rothwell Joseph A.ORCID,Scalbert AugustinORCID,Schmidt Julie A.ORCID,Severi GianlucaORCID,Sieri SabinaORCID,Vermeulen Roel,Vincent Emma E.ORCID,Waldenberger Melanie,Timpson Nicholas J.ORCID,Johansson MattiasORCID

Abstract

Background Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). Methods and findings We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case–control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10−8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10−5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some—but not all—metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., −0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10−5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10−3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. Conclusions This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI—the principal modifiable risk factor of kidney cancer.

Funder

World Cancer Research Fund

European Commission

Cancer Research UK Programme Grant

Wellcome Trust

Medical Research Council

Innovative Medicines Initiative

NIHR Imperial Biomedical Research Centre

Publisher

Public Library of Science (PLoS)

Subject

General Medicine

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