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Guida, F.* ; Tan, V.Y.* ; Corbin, L.J.* ; Smith-Byrne, K.* ; Alcala, K.* ; Langenberg, C.* ; Stewart, I.D.* ; Butterworth, A.S.* ; Surendran, P.* ; Achaintre, D.* ; Adamski, J. ; Amiano Exezarreta, P.* ; Bergmann, M.M.* ; Bull, C.J.* ; Dahm, C.C.* ; Gicquiau, A.* ; Giles, G.G.* ; Gunter, M.J.* ; Haller, T.* ; Langhammer, A.* ; Larose, T.L.* ; Ljungberg, B.* ; Metspalu, A.* ; Milne, R.L.* ; Müller, D.C.* ; Nøst, T.H.* ; Pettersen Sørgjerd, E.* ; Prehn, C. ; Riboli, E.* ; Rinaldi, S.* ; Rothwell, J.A.* ; Scalbert, A.* ; Schmidt, J.A.* ; Severi, G.* ; Sieri, S.* ; Vermeulen, R.* ; Vincent, E.E.* ; Waldenberger, M. ; Timpson, N.J.* ; Johansson, M.*

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

PLoS Med. 18:e1003786 (2021)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Renal-cell Carcinoma; Mendelian Randomization; Serum; Risk; Bias; Quantification; Spectroscopy; Plasma
Language english
Publication Year 2021
HGF-reported in Year 2021
ISSN (print) / ISBN 1549-1277
e-ISSN 1549-1676
Journal PLoS Medicine
Quellenangaben Volume: 18, Issue: 9, Pages: , Article Number: e1003786 Supplement: ,
Publisher Public Library of Science (PLoS)
Publishing Place 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Reviewing status Peer reviewed
Institute(s) Institute of Experimental Genetics (IEG)
CF Metabolomics & Proteomics (CF-MPC)
Institute of Epidemiology (EPI)
POF-Topic(s) 30201 - Metabolic Health
30203 - Molecular Targets and Therapies
30505 - New Technologies for Biomedical Discoveries
30202 - Environmental Health
Research field(s) Genetics and Epidemiology
Enabling and Novel Technologies
PSP Element(s) G-500600-001
G-505700-001
A-630710-001
G-504091-001
Grants German Research Center for Environmental Health (GmbH), Neuherberg, Germany
Institut Gustave Roussy
Ligue contre le Cancer
Danish Cancer Society (Denmark)
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London
International Agency for Research on Cancer (IARC)
Wellcome Trust
Cancer Research UK Programme Grant [The Integrative Cancer Epidemiology Programme, ICEP]
European Commission
Mutuelle Generale de l'Education Nationale
Institut National de la Sante et de la Recherche Medicale (INSERM) (France)
Helmholtz Zentrum Munchen
National Research Council (Italy)
Compagnia di SanPaolo
Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy
Federal Ministry of Education and Research (BMBF) (Germany)
German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE)
German Cancer Research Center (DKFZ)
German Cancer Aid
World Cancer Research Fund
DOI 10.1371/journal.pmed.1003786
WOS ID WOS:000724245200002
Scopus ID 85116286972
PubMed ID 34543281
Erfassungsdatum 2021-10-15
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