Skip to main content
Top
Published in: BMC Medicine 1/2017

Open Access 01-12-2017 | Research article

Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition

Authors: Julie A. Schmidt, Georgina K. Fensom, Sabina Rinaldi, Augustin Scalbert, Paul N. Appleby, David Achaintre, Audrey Gicquiau, Marc J. Gunter, Pietro Ferrari, Rudolf Kaaks, Tilman Kühn, Anna Floegel, Heiner Boeing, Antonia Trichopoulou, Pagona Lagiou, Eleutherios Anifantis, Claudia Agnoli, Domenico Palli, Morena Trevisan, Rosario Tumino, H. Bas Bueno-de-Mesquita, Antonio Agudo, Nerea Larrañaga, Daniel Redondo-Sánchez, Aurelio Barricarte, José Maria Huerta, J. Ramón Quirós, Nick Wareham, Kay-Tee Khaw, Aurora Perez-Cornago, Mattias Johansson, Amanda J. Cross, Konstantinos K. Tsilidis, Elio Riboli, Timothy J. Key, Ruth C. Travis

Published in: BMC Medicine | Issue 1/2017

Login to get access

Abstract

Background

Little is known about how pre-diagnostic metabolites in blood relate to risk of prostate cancer. We aimed to investigate the prospective association between plasma metabolite concentrations and risk of prostate cancer overall, and by time to diagnosis and tumour characteristics, and risk of death from prostate cancer.

Methods

In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition, pre-diagnostic plasma concentrations of 122 metabolites (including acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose and sphingolipids) were measured using targeted mass spectrometry (AbsoluteIDQ p180 Kit) and compared between 1077 prostate cancer cases and 1077 matched controls. Risk of prostate cancer associated with metabolite concentrations was estimated by multi-variable conditional logistic regression, and multiple testing was accounted for by using a false discovery rate controlling procedure.

Results

Seven metabolite concentrations, i.e. acylcarnitine C18:1, amino acids citrulline and trans-4-hydroxyproline, glycerophospholipids PC aa C28:1, PC ae C30:0 and PC ae C30:2, and sphingolipid SM (OH) C14:1, were associated with prostate cancer (p < 0.05), but none of the associations were statistically significant after controlling for multiple testing. Citrulline was associated with a decreased risk of prostate cancer (odds ratio (OR1SD) = 0.73; 95% confidence interval (CI) 0.62–0.86; p trend = 0.0002) in the first 5 years of follow-up after taking multiple testing into account, but not after longer follow-up; results for other metabolites did not vary by time to diagnosis. After controlling for multiple testing, 12 glycerophospholipids were inversely associated with advanced stage disease, with risk reduction up to 46% per standard deviation increase in concentration (OR1SD = 0.54; 95% CI 0.40–0.72; p trend = 0.00004 for PC aa C40:3). Death from prostate cancer was associated with higher concentrations of acylcarnitine C3, amino acids methionine and trans-4-hydroxyproline, biogenic amine ADMA, hexose and sphingolipid SM (OH) C14:1 and lower concentration of glycerophospholipid PC aa C42:4.

Conclusions

Several metabolites, i.e. C18:1, citrulline, trans-4-hydroxyproline, three glycerophospholipids and SM (OH) C14:1, might be related to prostate cancer. Analyses by time to diagnosis indicated that citrulline may be a marker of subclinical prostate cancer, while other metabolites might be related to aetiology. Several glycerophospholipids were inversely related to advanced stage disease. More prospective data are needed to confirm these associations.
Appendix
Available only for authorised users
Literature
1.
go back to reference Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, et al. GLOBOCAN 2012 v1.0, Cancer incidence and mortality worldwide: IARC CancerBase No. 11. International Agency for Research on Cancer, Lyon, France. 2013. http://globocan.iarc.fr. Accessed 18 Nov 2016. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, et al. GLOBOCAN 2012 v1.0, Cancer incidence and mortality worldwide: IARC CancerBase No. 11. International Agency for Research on Cancer, Lyon, France. 2013. http://​globocan.​iarc.​fr. Accessed 18 Nov 2016.
2.
go back to reference Travis RC, Appleby PN, Martin RM, Holly JM, Albanes D, Black A, et al. A meta-analysis of individual participant data reveals an association between circulating levels of IGF-I and prostate cancer risk. Cancer Res. 2016;76:2288–300.CrossRefPubMedPubMedCentral Travis RC, Appleby PN, Martin RM, Holly JM, Albanes D, Black A, et al. A meta-analysis of individual participant data reveals an association between circulating levels of IGF-I and prostate cancer risk. Cancer Res. 2016;76:2288–300.CrossRefPubMedPubMedCentral
3.
go back to reference Scalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J, et al. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014;99:1286–308.CrossRefPubMed Scalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J, et al. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014;99:1286–308.CrossRefPubMed
4.
go back to reference Kelly RS, Vander Heiden MG, Giovannucci E, Mucci LA. Metabolomic biomarkers of prostate cancer: prediction, diagnosis, progression, prognosis, and recurrence. Cancer Epidemiol Biomarkers Prev. 2016;25:887–906.CrossRefPubMedPubMedCentral Kelly RS, Vander Heiden MG, Giovannucci E, Mucci LA. Metabolomic biomarkers of prostate cancer: prediction, diagnosis, progression, prognosis, and recurrence. Cancer Epidemiol Biomarkers Prev. 2016;25:887–906.CrossRefPubMedPubMedCentral
5.
go back to reference Dunn WB, Goodacre R, Broadhurst DI, Atherton HJ, Griffin JL. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev. 2011;40:387–426.CrossRefPubMed Dunn WB, Goodacre R, Broadhurst DI, Atherton HJ, Griffin JL. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev. 2011;40:387–426.CrossRefPubMed
6.
go back to reference Fiehn O. Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol. 2002;48:155–71.CrossRefPubMed Fiehn O. Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol. 2002;48:155–71.CrossRefPubMed
7.
go back to reference Scalbert A, Brennan L, Fiehn O, Hankemeier T, Kristal BS, van Ommen B, et al. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics. 2009;5:435–58.CrossRefPubMedPubMedCentral Scalbert A, Brennan L, Fiehn O, Hankemeier T, Kristal BS, van Ommen B, et al. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics. 2009;5:435–58.CrossRefPubMedPubMedCentral
8.
go back to reference Mondul AM, Moore SC, Weinstein SJ, Mannisto S, Sampson JN, Albanes D. 1-stearoylglycerol is associated with risk of prostate cancer: results from serum metabolomic profiling. Metabolomics. 2014;10:1036–41.CrossRefPubMedPubMedCentral Mondul AM, Moore SC, Weinstein SJ, Mannisto S, Sampson JN, Albanes D. 1-stearoylglycerol is associated with risk of prostate cancer: results from serum metabolomic profiling. Metabolomics. 2014;10:1036–41.CrossRefPubMedPubMedCentral
9.
go back to reference Mondul AM, Moore SC, Weinstein SJ, Karoly ED, Sampson JN, Albanes D. Metabolomic analysis of prostate cancer risk in a prospective cohort: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Int J Cancer. 2015;137:2124–32.CrossRefPubMedPubMedCentral Mondul AM, Moore SC, Weinstein SJ, Karoly ED, Sampson JN, Albanes D. Metabolomic analysis of prostate cancer risk in a prospective cohort: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Int J Cancer. 2015;137:2124–32.CrossRefPubMedPubMedCentral
10.
go back to reference Kuhn T, Floegel A, Sookthai D, Johnson T, Rolle-Kampczyk U, Otto W, et al. Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Med. 2016;14:13.CrossRefPubMedPubMedCentral Kuhn T, Floegel A, Sookthai D, Johnson T, Rolle-Kampczyk U, Otto W, et al. Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Med. 2016;14:13.CrossRefPubMedPubMedCentral
11.
go back to reference Huang J, Mondul AM, Weinstein SJ, Koutros S, Derkach A, Karoly E, et al. Serum metabolomic profiling of prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Br J Cancer. 2016;115:1087–95.CrossRefPubMedPubMedCentral Huang J, Mondul AM, Weinstein SJ, Koutros S, Derkach A, Karoly E, et al. Serum metabolomic profiling of prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Br J Cancer. 2016;115:1087–95.CrossRefPubMedPubMedCentral
12.
go back to reference Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 2002;5:1113–24.CrossRefPubMed Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 2002;5:1113–24.CrossRefPubMed
13.
go back to reference Price AJ, Allen NE, Appleby PN, Crowe FL, Travis RC, Tipper SJ, et al. Insulin-like growth factor-I concentration and risk of prostate cancer: results from the European Prospective Investigation into Cancer and Nutrition. Cancer Epidemiol Biomarkers Prev. 2012;21:1531–41.CrossRefPubMed Price AJ, Allen NE, Appleby PN, Crowe FL, Travis RC, Tipper SJ, et al. Insulin-like growth factor-I concentration and risk of prostate cancer: results from the European Prospective Investigation into Cancer and Nutrition. Cancer Epidemiol Biomarkers Prev. 2012;21:1531–41.CrossRefPubMed
14.
go back to reference Byrne KS, Castaño JM, Chirlaque MD, Lilja H, Agudo A, Ardanaz E, et al. Vasectomy and prostate cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). J Clin Oncol. 2017;35:1297–303.CrossRefPubMed Byrne KS, Castaño JM, Chirlaque MD, Lilja H, Agudo A, Ardanaz E, et al. Vasectomy and prostate cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). J Clin Oncol. 2017;35:1297–303.CrossRefPubMed
15.
go back to reference Cole LK, Vance JE, Vance DE. Phosphatidylcholine biosynthesis and lipoprotein metabolism. Biochim Biophys Acta. 1821;2012:754–61. Cole LK, Vance JE, Vance DE. Phosphatidylcholine biosynthesis and lipoprotein metabolism. Biochim Biophys Acta. 1821;2012:754–61.
16.
go back to reference Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol. 1995;57:289–300. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol. 1995;57:289–300.
17.
go back to reference Yu Z, Kastenmuller G, He Y, Belcredi P, Moller G, Prehn C, et al. Differences between human plasma and serum metabolite profiles. PLoS One. 2011;6:e21230.CrossRefPubMedPubMedCentral Yu Z, Kastenmuller G, He Y, Belcredi P, Moller G, Prehn C, et al. Differences between human plasma and serum metabolite profiles. PLoS One. 2011;6:e21230.CrossRefPubMedPubMedCentral
18.
go back to reference Carayol M, Licaj I, Achaintre D, Sacerdote C, Vineis P, Key TJ, et al. Reliability of serum metabolites over a two-year period: a targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS One. 2015;10:e0135437.CrossRefPubMedPubMedCentral Carayol M, Licaj I, Achaintre D, Sacerdote C, Vineis P, Key TJ, et al. Reliability of serum metabolites over a two-year period: a targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS One. 2015;10:e0135437.CrossRefPubMedPubMedCentral
19.
go back to reference Breuillard C, Cynober L, Moinard C. Citrulline and nitrogen homeostasis: an overview. Amino Acids. 2015;47:685–91.CrossRefPubMed Breuillard C, Cynober L, Moinard C. Citrulline and nitrogen homeostasis: an overview. Amino Acids. 2015;47:685–91.CrossRefPubMed
20.
go back to reference Paschos A, Pandya R, Duivenvoorden WC, Pinthus JH. Oxidative stress in prostate cancer: changing research concepts towards a novel paradigm for prevention and therapeutics. Prostate Cancer Prostatic Dis. 2013;16:217–25.CrossRefPubMed Paschos A, Pandya R, Duivenvoorden WC, Pinthus JH. Oxidative stress in prostate cancer: changing research concepts towards a novel paradigm for prevention and therapeutics. Prostate Cancer Prostatic Dis. 2013;16:217–25.CrossRefPubMed
21.
go back to reference Fernández-Peralbo M, Gómez-Gómez E, Calderón-Santiago M, Carrasco-Valiente J, Ruiz-García J, Requena-Tapia M, et al. Prostate cancer patients—negative biopsy controls discrimination by untargeted metabolomics analysis of urine by LC-QTOF: upstream information on other omics. Sci Rep. 2016;6:38243.CrossRefPubMedPubMedCentral Fernández-Peralbo M, Gómez-Gómez E, Calderón-Santiago M, Carrasco-Valiente J, Ruiz-García J, Requena-Tapia M, et al. Prostate cancer patients—negative biopsy controls discrimination by untargeted metabolomics analysis of urine by LC-QTOF: upstream information on other omics. Sci Rep. 2016;6:38243.CrossRefPubMedPubMedCentral
22.
go back to reference Heller W, Harzmann R, Bichler KH, Schmidt K. Urinary hydroxyproline in healthy patients and in prostate patients with and without bone metastases. Curr Probl Clin Biochem. 1979;9:249–56. Heller W, Harzmann R, Bichler KH, Schmidt K. Urinary hydroxyproline in healthy patients and in prostate patients with and without bone metastases. Curr Probl Clin Biochem. 1979;9:249–56.
23.
go back to reference Mooppan MM, Wax SH, Kim H, Wang JC, Tobin MS. Urinary hydroxyproline excretion as a marker of osseous metastasis in carcinoma of the prostate. J Urol. 1980;123:694–6.PubMed Mooppan MM, Wax SH, Kim H, Wang JC, Tobin MS. Urinary hydroxyproline excretion as a marker of osseous metastasis in carcinoma of the prostate. J Urol. 1980;123:694–6.PubMed
24.
go back to reference Phang JM, Donald SP, Pandhare J, Liu Y. The metabolism of proline, a stress substrate, modulates carcinogenic pathways. Amino Acids. 2008;35:681–90.CrossRefPubMed Phang JM, Donald SP, Pandhare J, Liu Y. The metabolism of proline, a stress substrate, modulates carcinogenic pathways. Amino Acids. 2008;35:681–90.CrossRefPubMed
25.
go back to reference Pallister T, Jennings A, Mohney RP, Yarand D, Mangino M, Cassidy A, et al. Characterizing blood metabolomics profiles associated with self-reported food intakes in female twins. PLoS One. 2016;11:e0158568.CrossRefPubMedPubMedCentral Pallister T, Jennings A, Mohney RP, Yarand D, Mangino M, Cassidy A, et al. Characterizing blood metabolomics profiles associated with self-reported food intakes in female twins. PLoS One. 2016;11:e0158568.CrossRefPubMedPubMedCentral
26.
go back to reference Ross AB, Svelander C, Undeland I, Pinto R, Sandberg AS. Herring and beef meals lead to differences in plasma 2-aminoadipic acid, beta-alanine, 4-hydroxyproline, cetoleic acid, and docosahexaenoic acid concentrations in overweight men. J Nutr. 2015;145:2456–63.CrossRefPubMed Ross AB, Svelander C, Undeland I, Pinto R, Sandberg AS. Herring and beef meals lead to differences in plasma 2-aminoadipic acid, beta-alanine, 4-hydroxyproline, cetoleic acid, and docosahexaenoic acid concentrations in overweight men. J Nutr. 2015;145:2456–63.CrossRefPubMed
27.
go back to reference Wu K, Spiegelman D, Hou T, Albanes D, Allen NE, Berndt SI, et al. Associations between unprocessed red and processed meat, poultry, seafood and egg intake and the risk of prostate cancer: a pooled analysis of 15 prospective cohort studies. Int J Cancer. 2016;138:2368–82.CrossRefPubMedPubMedCentral Wu K, Spiegelman D, Hou T, Albanes D, Allen NE, Berndt SI, et al. Associations between unprocessed red and processed meat, poultry, seafood and egg intake and the risk of prostate cancer: a pooled analysis of 15 prospective cohort studies. Int J Cancer. 2016;138:2368–82.CrossRefPubMedPubMedCentral
28.
go back to reference Liesenfeld DB, Habermann N, Owen RW, Scalbert A, Ulrich CM. Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiol Biomarkers Prev. 2013;22:2182–201.CrossRefPubMedPubMedCentral Liesenfeld DB, Habermann N, Owen RW, Scalbert A, Ulrich CM. Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiol Biomarkers Prev. 2013;22:2182–201.CrossRefPubMedPubMedCentral
29.
go back to reference Taniguchi M, Okazaki T. The role of sphingomyelin and sphingomyelin synthases in cell death, proliferation and migration—from cell and animal models to human disorders. Biochim Biophys Acta. 1841;2014:692–703. Taniguchi M, Okazaki T. The role of sphingomyelin and sphingomyelin synthases in cell death, proliferation and migration—from cell and animal models to human disorders. Biochim Biophys Acta. 1841;2014:692–703.
30.
go back to reference Narayan P, Dahiya R. Alterations in sphingomyelin and fatty acids in human benign prostatic hyperplasia and prostatic cancer. Biomed Biochim Acta. 1991;50:1099–108.PubMed Narayan P, Dahiya R. Alterations in sphingomyelin and fatty acids in human benign prostatic hyperplasia and prostatic cancer. Biomed Biochim Acta. 1991;50:1099–108.PubMed
31.
go back to reference Zhou X, Mao J, Ai J, Deng Y, Roth MR, Pound C, et al. Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics. PLoS One. 2012;7:e48889.CrossRefPubMedPubMedCentral Zhou X, Mao J, Ai J, Deng Y, Roth MR, Pound C, et al. Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics. PLoS One. 2012;7:e48889.CrossRefPubMedPubMedCentral
33.
go back to reference Ridgway ND. The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Crit Rev Biochem Mol Biol. 2013;48:20–38.CrossRefPubMed Ridgway ND. The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Crit Rev Biochem Mol Biol. 2013;48:20–38.CrossRefPubMed
34.
go back to reference Floegel A, Stefan N, Yu Z, Muhlenbruch K, Drogan D, Joost HG, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62:639–48.CrossRefPubMedPubMedCentral Floegel A, Stefan N, Yu Z, Muhlenbruch K, Drogan D, Joost HG, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62:639–48.CrossRefPubMedPubMedCentral
35.
go back to reference Jian Gang P, Mo L, Lu Y, Runqi L, Xing Z. Diabetes mellitus and the risk of prostate cancer: an update and cumulative meta-analysis. Endocr Res. 2015;40:54–61.CrossRefPubMed Jian Gang P, Mo L, Lu Y, Runqi L, Xing Z. Diabetes mellitus and the risk of prostate cancer: an update and cumulative meta-analysis. Endocr Res. 2015;40:54–61.CrossRefPubMed
36.
go back to reference Tsilidis KK, Allen NE, Appleby PN, Rohrmann S, Nothlings U, Arriola L, et al. Diabetes mellitus and risk of prostate cancer in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer. 2015;136:372–81.CrossRefPubMed Tsilidis KK, Allen NE, Appleby PN, Rohrmann S, Nothlings U, Arriola L, et al. Diabetes mellitus and risk of prostate cancer in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer. 2015;136:372–81.CrossRefPubMed
38.
go back to reference Baenke F, Peck B, Miess H, Schulze A. Hooked on fat: the role of lipid synthesis in cancer metabolism and tumour development. Dis Model Mech. 2013;6:1353–63.CrossRefPubMedPubMedCentral Baenke F, Peck B, Miess H, Schulze A. Hooked on fat: the role of lipid synthesis in cancer metabolism and tumour development. Dis Model Mech. 2013;6:1353–63.CrossRefPubMedPubMedCentral
39.
go back to reference Floegel A, Drogan D, Wang-Sattler R, Prehn C, Illig T, Adamski J, et al. Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS One. 2011;6:e21103.CrossRefPubMedPubMedCentral Floegel A, Drogan D, Wang-Sattler R, Prehn C, Illig T, Adamski J, et al. Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS One. 2011;6:e21103.CrossRefPubMedPubMedCentral
40.
go back to reference Townsend MK, Clish CB, Kraft P, Wu C, Souza AL, Deik AA, et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem. 2013;59:1657–67.CrossRefPubMed Townsend MK, Clish CB, Kraft P, Wu C, Souza AL, Deik AA, et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem. 2013;59:1657–67.CrossRefPubMed
41.
go back to reference Sampson JN, Boca SM, Shu XO, Stolzenberg-Solomon RZ, Matthews CE, Hsing AW, et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev. 2013;22:631–40.CrossRefPubMedPubMedCentral Sampson JN, Boca SM, Shu XO, Stolzenberg-Solomon RZ, Matthews CE, Hsing AW, et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev. 2013;22:631–40.CrossRefPubMedPubMedCentral
42.
go back to reference Barri T, Dragsted LO. UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: effect of experimental artefacts and anticoagulant. Anal Chim Acta. 2013;768:118–28.CrossRefPubMed Barri T, Dragsted LO. UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: effect of experimental artefacts and anticoagulant. Anal Chim Acta. 2013;768:118–28.CrossRefPubMed
43.
go back to reference Gonzalez-Covarrubias V, Dane A, Hankemeier T, Vreeken RJ. The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling. Metabolomics. 2013;9:337–48.CrossRef Gonzalez-Covarrubias V, Dane A, Hankemeier T, Vreeken RJ. The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling. Metabolomics. 2013;9:337–48.CrossRef
44.
go back to reference Fages A, Ferrari P, Monni S, Dossus L, Floegel A, Mode N, et al. Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method. Metabolomics. 2014;10:1074–83.CrossRef Fages A, Ferrari P, Monni S, Dossus L, Floegel A, Mode N, et al. Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method. Metabolomics. 2014;10:1074–83.CrossRef
45.
go back to reference Schmidt JA, Rinaldi S, Ferrari P, Carayol M, Achaintre D, Scalbert A, et al. Metabolic profiles of male meat eaters, fish eaters, vegetarians, and vegans from the EPIC-Oxford cohort. Am J Clin Nutr. 2015;102:1518–26.CrossRefPubMedPubMedCentral Schmidt JA, Rinaldi S, Ferrari P, Carayol M, Achaintre D, Scalbert A, et al. Metabolic profiles of male meat eaters, fish eaters, vegetarians, and vegans from the EPIC-Oxford cohort. Am J Clin Nutr. 2015;102:1518–26.CrossRefPubMedPubMedCentral
46.
go back to reference Townsend MK, Bao Y, Poole EM, Bertrand KA, Kraft P, Wolpin BM, et al. Impact of pre-analytic blood sample collection factors on metabolomics. Cancer Epidemiol Biomarkers Prev. 2016;25:823–9.CrossRefPubMedPubMedCentral Townsend MK, Bao Y, Poole EM, Bertrand KA, Kraft P, Wolpin BM, et al. Impact of pre-analytic blood sample collection factors on metabolomics. Cancer Epidemiol Biomarkers Prev. 2016;25:823–9.CrossRefPubMedPubMedCentral
47.
go back to reference Allsbrook Jr WC, Mangold KA, Johnson MH, Lane RB, Lane CG, Epstein JI. Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. Hum Pathol. 2001;32:81–8.CrossRefPubMed Allsbrook Jr WC, Mangold KA, Johnson MH, Lane RB, Lane CG, Epstein JI. Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. Hum Pathol. 2001;32:81–8.CrossRefPubMed
50.
go back to reference Persson J, Wilderang U, Jiborn T, Wiklund PN, Damber JE, Hugosson J, et al. Interobserver variability in the pathological assessment of radical prostatectomy specimens: findings of the Laparoscopic Prostatectomy Robot Open (LAPPRO) study. Scand J Urol. 2014;48:160–7.CrossRefPubMed Persson J, Wilderang U, Jiborn T, Wiklund PN, Damber JE, Hugosson J, et al. Interobserver variability in the pathological assessment of radical prostatectomy specimens: findings of the Laparoscopic Prostatectomy Robot Open (LAPPRO) study. Scand J Urol. 2014;48:160–7.CrossRefPubMed
Metadata
Title
Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition
Authors
Julie A. Schmidt
Georgina K. Fensom
Sabina Rinaldi
Augustin Scalbert
Paul N. Appleby
David Achaintre
Audrey Gicquiau
Marc J. Gunter
Pietro Ferrari
Rudolf Kaaks
Tilman Kühn
Anna Floegel
Heiner Boeing
Antonia Trichopoulou
Pagona Lagiou
Eleutherios Anifantis
Claudia Agnoli
Domenico Palli
Morena Trevisan
Rosario Tumino
H. Bas Bueno-de-Mesquita
Antonio Agudo
Nerea Larrañaga
Daniel Redondo-Sánchez
Aurelio Barricarte
José Maria Huerta
J. Ramón Quirós
Nick Wareham
Kay-Tee Khaw
Aurora Perez-Cornago
Mattias Johansson
Amanda J. Cross
Konstantinos K. Tsilidis
Elio Riboli
Timothy J. Key
Ruth C. Travis
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medicine / Issue 1/2017
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/s12916-017-0885-6

Other articles of this Issue 1/2017

BMC Medicine 1/2017 Go to the issue