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

Florence Guida; Y Tan; Laura J Corbin; Caroline J Bull; Emma E Vincent; Nicholas John  Timpson; Mattias Johansson

doi:10.1371/journal.pmed.1003786

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

Florence Guida, Y Tan, Laura J Corbin, Caroline J Bull, Emma E Vincent, Nicholas John Timpson^*, Mattias Johansson^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

20 Citations (Scopus)

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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 five prospective cohort studies. Cases were diagnosed on average eight years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPL) were inversely associated with risk, including eight phosphatidylcholines (PC) and two plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds-ratio (OR) for one standard deviation (SD) increment of 0.75 (95% CI: 0.68 to 0.83, p=2.6x10-8). In contrast, four amino acids, including glutamate (OR for 1 SD=1.39, 95% CI: 1.20 - 1.60, p=1.6x10-5), 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 randomization 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 standard deviation change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p=3.4x10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p=1.5x10-3). Whilst 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 generalized 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.

Original language	English
Article number	e1003786
Number of pages	26
Journal	PLoS Medicine
Volume	18
Issue number	9
DOIs	https://doi.org/10.1371/journal.pmed.1003786
Publication status	Published - 20 Sept 2021

Bibliographical note

Funding Information:
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. The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC, MJ) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC, ER). The national cohorts are supported by: Danish Cancer Society (Denmark, CCD); Ligue Contre le Cancer (GS), Institut Gustave Roussy (GS), Mutuelle G?n?rale de l'Education Nationale (GS), Institut National de la Sant? et de la Recherche M?dicale (INSERM) (France, GS); German Cancer Aid, German Cancer Research Center (DKFZ, MMB), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE, MMB), Federal Ministry of Education and Research (BMBF) (Germany, MMB); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy, SC); Dutch Ministry of Public Health, Welfare and Sports (VWS, RV), Netherlands Cancer Registry (NKR, RV), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland, RV), World Cancer Research Fund (WCRF, RV), Statistics Netherlands (The Netherlands, RV); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andaluc?a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain, PAE); Swedish Cancer Society, Swedish Research Council and County Councils of Sk?ne and V?sterbotten (BJ). We thank the National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands, for their contribution and ongoing support to the EPIC Study (RV). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 MC-UU_12015/1 and MC_UU_00006/1, JAS) and Cancer Research UK (C864/A14136, JAS). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048, CL). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1, CL) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372 (CL). Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. Metabolon metabolomics assays as well as DNA extraction and genotyping were funded by the National Institute for Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014, ASB) [The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care]. The academic coordinating centre for INTERVAL was supported by core funding from the: NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024, ASB), UK Medical Research Council (MR/L003120/1, ASB), British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946, ASB) and NIHR Cambridge BRC (BRC-1215-20014, ASB). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. A complete list of the investigators and contributors to the INTERVAL trial is provided in reference 19 of Supplementary methods (Di Angelantonio, et al.). Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria (GG, RM). The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 (GG, RM) and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database (ACD). New South Wales (NSW) cancer registry data were obtained via the ACD with the assistance of the NSW Ministry of Health. NJT, VYT, FG and KSB are supported by the Cancer Research UK (CRUK) Integrative Cancer Epidemiology Programme (C18281/A29019). NJT, LJC and VYT work in the MRC IEU at the University of Bristol which is supported by the MRC (MC_UU_00011) and the University of Bristol. NJT is a Wellcome Trust Investigator (202802/Z/16/Z) and works within the University of Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). LJC is supported by NJT's Wellcome Trust Investigator grant (202802/Z/16/Z). PS was supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1. EEV is supported by Diabetes UK (17/0005587). EEV is supported by Diabetes UK (17/0005587) and the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme (IIG_2019_2009) and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A29019). CJB is supported by the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme (IIG_2019_2009) University of Tartu - Estonian Biobank was supported by NIH grant no 5R01 DK07 57 87 -13, under subward-agreement no GENFDOOO1B52751, the European Union through Horizon 2020 research and innovation programme under grant no 633589, the European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0125), the Estonian Research Council grant PUT (PRG687). The work of TLL was supported by Research Council of Norway Grant No. 267776/H10 within the framework of an agreement between the Research Council of Norway and the Norwegian University of Science and Technology. MMB was funded by the German Institute of Human Nutrition Potsdam-Rehbr?cke, a government-financed organization. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher 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.

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@article{767a308f8ffa44b0a9a0f29d34d2ec01,

title = "The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium",

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 five prospective cohort studies. Cases were diagnosed on average eight years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPL) were inversely associated with risk, including eight phosphatidylcholines (PC) and two plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds-ratio (OR) for one standard deviation (SD) increment of 0.75 (95% CI: 0.68 to 0.83, p=2.6x10-8). In contrast, four amino acids, including glutamate (OR for 1 SD=1.39, 95% CI: 1.20 - 1.60, p=1.6x10-5), 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 randomization 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 standard deviation change [{\ss}BMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p=3.4x10-5). BMI was also associated with increased levels of glutamate ({\ss}BMI: 0.12, p=1.5x10-3). Whilst 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 generalized 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.",

author = "Florence Guida and Y Tan and Corbin, {Laura J} and Bull, {Caroline J} and Vincent, {Emma E} and Timpson, {Nicholas John} and Mattias Johansson",

note = "Funding Information: 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. The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC, MJ) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC, ER). The national cohorts are supported by: Danish Cancer Society (Denmark, CCD); Ligue Contre le Cancer (GS), Institut Gustave Roussy (GS), Mutuelle G?n?rale de l'Education Nationale (GS), Institut National de la Sant? et de la Recherche M?dicale (INSERM) (France, GS); German Cancer Aid, German Cancer Research Center (DKFZ, MMB), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE, MMB), Federal Ministry of Education and Research (BMBF) (Germany, MMB); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy, SC); Dutch Ministry of Public Health, Welfare and Sports (VWS, RV), Netherlands Cancer Registry (NKR, RV), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland, RV), World Cancer Research Fund (WCRF, RV), Statistics Netherlands (The Netherlands, RV); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andaluc?a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain, PAE); Swedish Cancer Society, Swedish Research Council and County Councils of Sk?ne and V?sterbotten (BJ). We thank the National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands, for their contribution and ongoing support to the EPIC Study (RV). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 MC-UU_12015/1 and MC_UU_00006/1, JAS) and Cancer Research UK (C864/A14136, JAS). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048, CL). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1, CL) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372 (CL). Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. Metabolon metabolomics assays as well as DNA extraction and genotyping were funded by the National Institute for Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014, ASB) [The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care]. The academic coordinating centre for INTERVAL was supported by core funding from the: NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024, ASB), UK Medical Research Council (MR/L003120/1, ASB), British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946, ASB) and NIHR Cambridge BRC (BRC-1215-20014, ASB). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. A complete list of the investigators and contributors to the INTERVAL trial is provided in reference 19 of Supplementary methods (Di Angelantonio, et al.). Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria (GG, RM). The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 (GG, RM) and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database (ACD). New South Wales (NSW) cancer registry data were obtained via the ACD with the assistance of the NSW Ministry of Health. NJT, VYT, FG and KSB are supported by the Cancer Research UK (CRUK) Integrative Cancer Epidemiology Programme (C18281/A29019). NJT, LJC and VYT work in the MRC IEU at the University of Bristol which is supported by the MRC (MC_UU_00011) and the University of Bristol. NJT is a Wellcome Trust Investigator (202802/Z/16/Z) and works within the University of Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). LJC is supported by NJT's Wellcome Trust Investigator grant (202802/Z/16/Z). PS was supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1. EEV is supported by Diabetes UK (17/0005587). EEV is supported by Diabetes UK (17/0005587) and the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme (IIG_2019_2009) and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A29019). CJB is supported by the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme (IIG_2019_2009) University of Tartu - Estonian Biobank was supported by NIH grant no 5R01 DK07 57 87 -13, under subward-agreement no GENFDOOO1B52751, the European Union through Horizon 2020 research and innovation programme under grant no 633589, the European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0125), the Estonian Research Council grant PUT (PRG687). The work of TLL was supported by Research Council of Norway Grant No. 267776/H10 within the framework of an agreement between the Research Council of Norway and the Norwegian University of Science and Technology. MMB was funded by the German Institute of Human Nutrition Potsdam-Rehbr?cke, a government-financed organization. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 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.",

year = "2021",

month = sep,

day = "20",

doi = "10.1371/journal.pmed.1003786",

language = "English",

volume = "18",

journal = "PLoS Medicine",

issn = "1549-1277",

publisher = "Public Library of Science",

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}

TY - JOUR

T1 - The blood metabolome of incident kidney cancer

T2 - A case-control study nested within the MetKid consortium

AU - Guida, Florence

AU - Tan, Y

AU - Corbin, Laura J

AU - Bull, Caroline J

AU - Vincent, Emma E

AU - Timpson, Nicholas John

AU - Johansson, Mattias

N1 - Funding Information: 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. The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC, MJ) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC, ER). The national cohorts are supported by: Danish Cancer Society (Denmark, CCD); Ligue Contre le Cancer (GS), Institut Gustave Roussy (GS), Mutuelle G?n?rale de l'Education Nationale (GS), Institut National de la Sant? et de la Recherche M?dicale (INSERM) (France, GS); German Cancer Aid, German Cancer Research Center (DKFZ, MMB), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE, MMB), Federal Ministry of Education and Research (BMBF) (Germany, MMB); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy, SC); Dutch Ministry of Public Health, Welfare and Sports (VWS, RV), Netherlands Cancer Registry (NKR, RV), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland, RV), World Cancer Research Fund (WCRF, RV), Statistics Netherlands (The Netherlands, RV); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andaluc?a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain, PAE); Swedish Cancer Society, Swedish Research Council and County Councils of Sk?ne and V?sterbotten (BJ). We thank the National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands, for their contribution and ongoing support to the EPIC Study (RV). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 MC-UU_12015/1 and MC_UU_00006/1, JAS) and Cancer Research UK (C864/A14136, JAS). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048, CL). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1, CL) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372 (CL). Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. Metabolon metabolomics assays as well as DNA extraction and genotyping were funded by the National Institute for Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014, ASB) [The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care]. The academic coordinating centre for INTERVAL was supported by core funding from the: NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024, ASB), UK Medical Research Council (MR/L003120/1, ASB), British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946, ASB) and NIHR Cambridge BRC (BRC-1215-20014, ASB). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. A complete list of the investigators and contributors to the INTERVAL trial is provided in reference 19 of Supplementary methods (Di Angelantonio, et al.). Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria (GG, RM). The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 (GG, RM) and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database (ACD). New South Wales (NSW) cancer registry data were obtained via the ACD with the assistance of the NSW Ministry of Health. NJT, VYT, FG and KSB are supported by the Cancer Research UK (CRUK) Integrative Cancer Epidemiology Programme (C18281/A29019). NJT, LJC and VYT work in the MRC IEU at the University of Bristol which is supported by the MRC (MC_UU_00011) and the University of Bristol. NJT is a Wellcome Trust Investigator (202802/Z/16/Z) and works within the University of Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). LJC is supported by NJT's Wellcome Trust Investigator grant (202802/Z/16/Z). PS was supported by a Rutherford Fund Fellowship from the Medical Research Council grant MR/S003746/1. EEV is supported by Diabetes UK (17/0005587). EEV is supported by Diabetes UK (17/0005587) and the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme (IIG_2019_2009) and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A29019). CJB is supported by the World Cancer Research Fund (WCRF UK), as part of the World Cancer Research Fund International grant programme (IIG_2019_2009) University of Tartu - Estonian Biobank was supported by NIH grant no 5R01 DK07 57 87 -13, under subward-agreement no GENFDOOO1B52751, the European Union through Horizon 2020 research and innovation programme under grant no 633589, the European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0125), the Estonian Research Council grant PUT (PRG687). The work of TLL was supported by Research Council of Norway Grant No. 267776/H10 within the framework of an agreement between the Research Council of Norway and the Norwegian University of Science and Technology. MMB was funded by the German Institute of Human Nutrition Potsdam-Rehbr?cke, a government-financed organization. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher 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.

PY - 2021/9/20

Y1 - 2021/9/20

N2 - 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 five prospective cohort studies. Cases were diagnosed on average eight years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPL) were inversely associated with risk, including eight phosphatidylcholines (PC) and two plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds-ratio (OR) for one standard deviation (SD) increment of 0.75 (95% CI: 0.68 to 0.83, p=2.6x10-8). In contrast, four amino acids, including glutamate (OR for 1 SD=1.39, 95% CI: 1.20 - 1.60, p=1.6x10-5), 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 randomization 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 standard deviation change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p=3.4x10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p=1.5x10-3). Whilst 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 generalized 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.

AB - 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 five prospective cohort studies. Cases were diagnosed on average eight years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPL) were inversely associated with risk, including eight phosphatidylcholines (PC) and two plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds-ratio (OR) for one standard deviation (SD) increment of 0.75 (95% CI: 0.68 to 0.83, p=2.6x10-8). In contrast, four amino acids, including glutamate (OR for 1 SD=1.39, 95% CI: 1.20 - 1.60, p=1.6x10-5), 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 randomization 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 standard deviation change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p=3.4x10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p=1.5x10-3). Whilst 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 generalized 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.

UR - https://data.bris.ac.uk/data/dataset/33bq35s9lbos026r1xukxijoqu

U2 - 10.1371/journal.pmed.1003786

DO - 10.1371/journal.pmed.1003786

M3 - Article (Academic Journal)

C2 - 34543281

SN - 1549-1277

VL - 18

JO - PLoS Medicine

JF - PLoS Medicine

IS - 9

M1 - e1003786

ER -

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

Abstract

Bibliographical note

Research Groups and Themes

UN SDGs

Access to Document

Handle.net

Other files and links

Embargoed Document

Fingerprint

8074 (C18281/A29019) ICEP2 - Programme Award: Towards improved casual evidence and enhanced prediction of cancer risk and survival

Metabolite summary statistics for Mendelian randomization analyses

Cite this

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

Abstract

Bibliographical note

Research Groups and Themes

UN SDGs

Access to Document

Handle.net

Other files and links

Embargoed Document

Fingerprint

Projects

8074 (C18281/A29019) ICEP2 - Programme Award: Towards improved casual evidence and enhanced prediction of cancer risk and survival

Datasets

Metabolite summary statistics for Mendelian randomization analyses

Cite this