Florence Guida, Vanessa Y. Tan , Laura J. Corbin, Karl Smith-Byrne, Karine Alcala, Claudia Langenberg, Isobel D. Stewart, Adam S. Butterworth, PraveenSurendran, David Achaintre, Jerzy Adamski, Pilar Amiano, Manuela M. Bergmann, Caroline J. Bull, Christina C. Dahm, AudreyGicquiau, 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.
Abstract
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).
Introduction
Kidney cancer is the 14th most common cancer worldwide, with renal cell carcinoma (RCC) making up the majority of cases [1]. There are important geographical variations in kidney cancer incidence that are only partly understood [2]. Excess bodyweight and related conditions, such as hypertension, diabetes, and related metabolic perturbations, are among the most robustly implicated risk factors for kidney cancer, with support from both traditional observational studies and genetic studies [2–7]. For instance, in the United Kingdom, an estimated 24% of kidney cancer cases are attributable to overweight and obesity, making this the leading modifiable risk factor for the disease [8]. Germline mutations responsible for an inherited predisposition to kidney cancer (a small proportion of kidney cancer cases) have a key role in regulating cellular metabolism [9], and this, together with evidence of extensive metabolic reprogramming within tumours themselves [10], have led to the characterisation of kidney cancer as a metabolic disease.
Method
The primary analysis was predefined and involved investigating the association between circulating levels of metabolites and kidney cancer risk using pre-diagnostic metabolomics measurements in a case–control study nested within multiple large-scale prospective cohorts (the MetKid consortium). Adjustment for known risk factors for kidney cancer (BMI, hypertension, alcohol consumption, and smoking) [2] was then carried out to evaluate the extent to which these could explain the associations between blood metabolites and kidney cancer risk.
Discussion
This study describes the relationship between the pre-diagnostic blood-metabolome and risk of developing kidney cancer based on data from 5 longitudinal population cohorts. This is the first comprehensive metabolomics analysis of incident kidney cancer to be conducted using a prospective design, and as such, complements existing work characterising the metabolic profile (in tissue and biofluids) of the disease itself [16–26]. We investigated 1,416 metabolites in relation to the occurrence of kidney cancer using 2 complementary analytical methods and observed 25 metabolites to be robustly associated with risk. These metabolites included 14 GPLs inversely associated with risk, 5 amino acids positively associated, and 1 inversely associated with risk, as well as risk associations for a carotenoid, 2 peptides, a nucleotide, and an unidentified feature. Results of an MR analysis designed to evaluate the extent to which BMI influences the key risk-associated metabolites suggest that differences in BMI may be responsible for part of the metabolite profile associated with the development of kidney cancer.
Conclusion
This study points to a particularly important role of the blood metabolome in kidney cancer aetiology, specifically by identifying positive risk associations for several amino acids, as well as negative risk associations with multiple lipids, including PCs, LPCs, and plasmalogens. Downstream analyses indicated that some—but not all—risk metabolites are influenced by BMI, which partly explains their associations with kidney cancer risk, whereas the risk associations for other metabolites could not be explained by known risk factors. These results provide important insight into the metabolic pathways underpinning the central role of obesity in kidney cancer aetiology and clues to novel pathways involved in kidney cancer aetiology.
Citation: Guida F, Tan VY, Corbin LJ, Smith-Byrne K, Alcala K, Langenberg C, et al. (2021) The blood metabolome of incident kidney cancer: A case–control study nested within the MetKid consortium. PLoS Med 18(9): e1003786. https://doi.org/10.1371/journal.pmed.1003786
Academic Editor: Maarten W. Taal, Royal Derby Hospital, UNITED KINGDOM
Received: January 12, 2021; Accepted: August 27, 2021; Published: September 20, 2021.
Copyright:© 2021 Guida et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The MetKid metabolomics data were generated on samples that were transferred from the originating institutes under agreements that prevent us from making the individual level data freely available online. However, individual level data can be made available for investigators from bona fide research organizations upon request and assuming that the necessary data transfer agreements have been established with the originating institutes.
Funding: The metabolomics analysis of this study was supported by World Cancer Research Fund (reference: 2014/1193, MJ) and the European Commission (FP7: BBMRI-LPC; reference: 313010, MJ). The work was supported by a Cancer Research UK Programme Grant [The Integrative Cancer Epidemiology Programme, ICEP] (C18281/A19169, NJT). This research was funded in whole, or in part, by the Wellcome Trust (202802/Z/16/Z, NJT). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CL is an Academic Editor on PLOS Medicine’s editorial board; ASB reports institutional grants outside of this work from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Novartis, Regeneron and Sanofi; during the course of this project, PS became a full-time employee of GSK. No other conflicts of interest have been declared by the authors.
Abbreviations: BMI, body mass index; CI, confidence interval; CV, coefficients of variation; ENT, effective number of independent tests; EPIC, The European Prospective Investigation into Cancer and Nutrition; Estonian BB, University of Tartu—Estonian Biobank; GIANT, Genetic Investigation of Anthropometric Traits; GPC, Glycerophosphocholine; GPL, glycerophospholipid; GWAS, genome-wide association study; HUNT, The Trøndelag Health Study; IARC, International Agency for Research on Cancer; ICD-O-3, International Classification of Diseases for Oncology3rd Edition; IVW, inverse-variance weighted; LLOQ, lower limit of quantification; LOD, limit of detection; LPC, lysophosphatidylcholine; MCCS, The Melbourne Collaborative Cohort Study; MR, mendelian randomisation; MS, mass spectrometry; NMR, nuclear magnetic resonance; NSHDS, Northern Sweden Health and Disease study; OR, odds ratio; PC, phosphatidylcholine; QC, quality control; RCC, renal cell carcinoma; SD, standard deviation; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology; TCA, tricarboxylic acid; ULOQ, upper limit of quantification; UPLC–MS/MS, ultra-high performance liquid chromatography—tandem mass spectrometry.