PLoS Medicine (Sep 2021)

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

  • Florence Guida,
  • Vanessa Y Tan,
  • Laura J Corbin,
  • Karl Smith-Byrne,
  • Karine Alcala,
  • Claudia Langenberg,
  • Isobel D Stewart,
  • Adam S Butterworth,
  • Praveen Surendran,
  • David Achaintre,
  • Jerzy Adamski,
  • Pilar Amiano,
  • Manuela M Bergmann,
  • Caroline J Bull,
  • Christina C Dahm,
  • Audrey Gicquiau,
  • Graham G Giles,
  • Marc J Gunter,
  • Toomas Haller,
  • Arnulf Langhammer,
  • Tricia L Larose,
  • Börje Ljungberg,
  • Andres Metspalu,
  • Roger L Milne,
  • David C Muller,
  • Therese H Nøst,
  • Elin Pettersen Sørgjerd,
  • Cornelia Prehn,
  • Elio Riboli,
  • Sabina Rinaldi,
  • Joseph A Rothwell,
  • Augustin Scalbert,
  • Julie A Schmidt,
  • Gianluca Severi,
  • Sabina Sieri,
  • Roel Vermeulen,
  • Emma E Vincent,
  • Melanie Waldenberger,
  • Nicholas J Timpson,
  • Mattias Johansson

DOI
https://doi.org/10.1371/journal.pmed.1003786
Journal volume & issue
Vol. 18, no. 9
p. e1003786

Abstract

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BackgroundExcess 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 findingsWe 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.ConclusionsThis 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.