Skip to main content
SpringerLink
Log in

NMR metabolomic profiles associated with long-term risk of prostate cancer

  • Original Article
  • Published:
Metabolomics Aims and scope Submit manuscript

Abstract

Introduction

Prostate cancer is a multifactorial disease whose aetiology is still not fully understood. Metabolomics, by measuring several hundred metabolites simultaneously, could enhance knowledge on the metabolic changes involved and the potential impact of external factors.

Objectives

The aim of the present study was to investigate whether pre-diagnostic plasma metabolomic profiles were associated with the risk of developing a prostate cancer within the following decade.

Methods

A prospective nested case-control study was set up among the 5141 men participant of the SU.VI.MAX cohort, including 171 prostate cancer cases, diagnosed between 1994 and 2007, and 171 matched controls. Nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples using NOESY1D and CPMG sequences. Multivariable conditional logistic regression models were computed for each individual NMR signal and for metabolomic patterns derived using principal component analysis.

Results

Men with higher fasting plasma levels of valine (odds ratio (OR) = 1.37 [1.07–1.76], p = .01), glutamine (OR = 1.30 [1.00–1.70], p = .047), creatine (OR = 1.37 [1.04–1.80], p = .02), albumin lysyl (OR = 1.48 [1.12–1.95], p = .006 and OR = 1.51 [1.13–2.02], p = .005), tyrosine (OR = 1.40 [1.06–1.85], p = .02), phenylalanine (OR = 1.39 [1.08–1.79], p = .01), histidine (OR = 1.46 [1.12–1.88], p = .004), 3-methylhistidine (OR = 1.37 [1.05–1.80], p = .02) and lower plasma level of urea (OR = .70 [.54–.92], p = .009) had a higher risk of developing a prostate cancer during the 13 years of follow-up.

Conclusions

This exploratory study highlighted associations between baseline plasma metabolomic profiles and long-term risk of developing prostate cancer. If replicated in independent cohort studies, such signatures may improve the identification of men at risk for prostate cancer well before diagnosis and the understanding of this disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

SU.VI.MAX:

Supplémentation en Vitamines et Minéraux Antioxydants

BMI:

Body mass index

NMR:

Nuclear magnetic resonance

OR:

Odds ratio

SD:

Standard deviation

CI:

Confidence interval

FDR:

False discovery rate

PSA:

Prostate-specific antigen

PCA:

Principal component analysis

HDL:

High density lipoproteins

LDL:

Low density lipoproteins

BCAA:

Branched chain amino acids

AAA:

Aromatic amino acids

References

  • Assi, N. (2015). A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study. Mutagenesis, 30(6), 743–753. https://doi.org/10.1093/mutage/gev045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Assi, N., Thomas, D. C., Leitzman, M., Stepien, M., Chajes, V., Philip, T., et al. (2018). Are metabolic signatures mediating the relationship between lifestyle factors and hepatocellular carcinoma risk? Results from a nested case-control study in EPIC. Cancer Epidemiology, Biomarkers & Prevention, 27(5), 531–540. https://doi.org/10.1158/1055-9965.EPI-17-0649.

  • Athersuch, T. J., & Keun, H. C. (2015). Metabolic profiling in human exposome studies. Mutagenesis, 30(6), 755–762. https://doi.org/10.1093/mutage/gev060.

    Article  CAS  PubMed  Google Scholar 

  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300.

    Google Scholar 

  • Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. https://doi.org/10.3322/caac.21492.

    Article  Google Scholar 

  • Bro, R., Kamstrup-Nielsen, M. H., Engelsen, S. B., Savorani, F., Rasmussen, M. A., Hansen, L., et al. (2015). Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics, 11(5), 1376–1380.

    Article  CAS  Google Scholar 

  • Brockmoller, S. F., Bucher, E., Muller, B. M., Budczies, J., Hilvo, M., Griffin, J. L., et al. (2012). Integration of metabolomics and expression of glycerol-3-phosphate acyltransferase (GPAM) in breast cancer-link to patient survival, hormone receptor status, and metabolic profiling. Journal of Proteome Research, 11, 850–860.

    Article  Google Scholar 

  • Budczies, J., Brockmoller, S. F., Muller, B. M., Barupal, D. K., Richter-Ehrenstein, C., Kleine-Tebbe, A., et al. (2013). Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: Alterations in glutamine and beta-alanine metabolism. Journal of Proteomics, 94C, 279–288.

    Article  Google Scholar 

  • Carayol, M., Licaj, I., Achaintre, D., Sacerdote, C., Vineis, P., Key, T. J., et al. (2015). Reliability of serum metabolites over a two-year period: A targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS ONE, 10, e0135437.

  • Carr, H. Y., & Purcell, E. M. (1954). Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical Review, 94, 630–638.

    Article  CAS  Google Scholar 

  • Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245–276.

    Article  CAS  Google Scholar 

  • Claudino, W. M., Quattrone, A., Biganzoli, L., Pestrin, M., Bertini, I., & Di, L. A. (2007). Metabolomics: Available results, current research projects in breast cancer, and future applications. Journal of Clinical Oncology, 25, 2840–2846.

    Article  CAS  Google Scholar 

  • Costello, L. C., & Franklin, R. B. (2000). The intermediary metabolism of the prostate: A key to understanding the pathogenesis and progression of prostate malignancy. Oncology (Williston Park, N.Y.), 59(4), 269–282. https://doi.org/10.1159/000012183.

    Article  CAS  Google Scholar 

  • Defossez, G., Le Guyader–Peyrou, S., Uhry, Z., Grosclaude, P., Colonna, M., & Dantony, E. (2019). Estimations nationales de l’incidence et de la mortalité par cancer en France métropolitaine entre 1990 et 2018. In Synthèse (p. 20). Santé publique France.

  • Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., & Bax, A. (1995). NMRPipe: A multidimensional spectral processing system based on UNIX pipes. Journal of Biomolecular NMR, 6, 277–293.

    Article  CAS  Google Scholar 

  • Eidelman, E., Twum-Ampofo, J., Ansari, J., & Siddiqui, M. M. (2017). The metabolic phenotype of prostate cancer. Frontiers in Oncology, 7, 131. https://doi.org/10.3389/fonc.2017.00131.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fages, A., Duarte-Salles, T., Stepien, M., Ferrari, P., Fedirko, V., Pontoizeau, C., et al. (2015). Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort. BMC Medicine, 13, 242. https://doi.org/10.1186/s12916-015-0462-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Floegel, A., Drogan, D., Wang-Sattler, R., Prehn, C., Illig, T., Adamski, J., et al. (2011). Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS ONE, 6, e21103.

  • Giraudeau, P., Silvestre, V., & Akoka, S. (2015). Optimizing water suppression for quantitative NMR-based metabolomics: a tutorial review. Metabolomics, 11(5), 1041–1055.

  • Guenin, S., Schwartz, L., Morvan, D., Steyaert, J. M., Poignet, A., Madelmont, J. C., & Demidem, A. (2008). PP2A activity is controlled by methylation and regulates oncoprotein expression in melanoma cells: A mechanism which participates in growth inhibition induced by chloroethylnitrosourea treatment. International Journal of Oncology, 32, 49–57.

    CAS  PubMed  Google Scholar 

  • Hercberg, S., Galan, P., Preziosi, P., Bertrais, S., Mennen, L., Malvy, D., et al. (2004). The SU.VI.MAX study: A randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals. Archives of Internal Medicine, 164, 2335–2342.

    Article  CAS  Google Scholar 

  • Hercberg, S., Preziosi, P., Briancon, S., Galan, P., Triol, I., Malvy, D., et al. (1998). A primary prevention trial using nutritional doses of antioxidant vitamins and minerals in cardiovascular diseases and cancers in a general population: the SU.VI.MAX study–design, methods, and participant characteristics. SUpplementation en VItamines et Mineraux AntioXydants. Controlled Clinical Trials, 19, 336–351.

    Article  CAS  Google Scholar 

  • Howell, A. (2010). Can metabolomics in addition to genomics add to prognostic and predictive information in breast cancer? BMC Medicine, 8, 73.

    Article  Google Scholar 

  • Huang, J., Mondul, A. M., Weinstein, S. J., Koutros, S., Derkach, A., Karoly, E., et al. (2016). Serum metabolomic profiling of prostate cancer risk in the prostate, lung, colorectal, and ovarian cancer screening trial. British Journal of Cancer, 115, 1087–1095.

    Article  CAS  Google Scholar 

  • Jobard, E., Pontoizeau, C., Blaise, B. J., Bachelot, T., Elena-Herrmann, B., & Tredan, O. (2014). A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer. Cancer Letters, 343, 33–41.

    Article  CAS  Google Scholar 

  • Koutros, S., Meyer, T. E., Fox, S. D., Issaq, H. J., Veenstra, T. D., Huang, W.-Y., et al. (2013). Prospective evaluation of serum sarcosine and risk of prostate cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Carcinogenesis, 34(10), 2281–2285. https://doi.org/10.1093/carcin/bgt176.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kuhn, T., Floegel, A., Sookthai, D., Johnson, T., Rolle-Kampczyk, U., Otto, W., et al. (2016). Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Medicine, 14, 13.

    Article  Google Scholar 

  • Kühn, T., Sookthai, D., Graf, M. E., Schübel, R., Freisling, H., Johnson, T., et al. (2017). Albumin, bilirubin, uric acid and cancer risk: Results from a prospective population-based study. British Journal of Cancer, 117(10), 1572–1579. https://doi.org/10.1038/bjc.2017.313.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lécuyer, L., Dalle, C., Lyan, B., Demidem, A., Rossary, A., Vasson, M.-P., et al. (2019). Plasma metabolomic signatures associated with long-term breast cancer risk in the SU.VI.MAX prospective cohort. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology. https://doi.org/10.1158/1055-9965.EPI-19-0154.

  • Lécuyer, L., Bala, V., Deschasaux, A., Bouchemal, M., Triba, N. Nawfal, Vasson, M., M.-P., et al (2018). NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer. International Journal of Epidemiology, 47(2), 484–494. https://doi.org/10.1093/ije/dyx271.

    Article  PubMed  Google Scholar 

  • Leverve, X., Carpentier, F., Barnoud, D., Fontaine, E., & Guignier, M. (1992). Intérêt et limites de la 3-méthylhistidine pour la mesure du catabolisme musculaire. Nutrition Clinique et Métabolisme, 6(4), 219–225. https://doi.org/10.1016/S0985-0562(05)80371-1.

    Article  Google Scholar 

  • Liesenfeld, D. B., Habermann, N., Owen, R. W., Scalbert, A., & Ulrich, C. M. (2013). Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiology, Biomarkers & Prevention, 22, 2182–2201.

    Article  CAS  Google Scholar 

  • Lucarelli, G., Loizzo, D., Ferro, M., Rutigliano, M., Vartolomei, M. D., Cantiello, F., et al. (2019). Metabolomic profiling for the identification of novel diagnostic markers and therapeutic targets in prostate cancer: An update. Expert Review of Molecular Diagnostics, 19(5), 377–387. https://doi.org/10.1080/14737159.2019.1604223.

    Article  CAS  PubMed  Google Scholar 

  • Lucarelli, G., Rutigliano, M., Galleggiante, V., Giglio, A., Palazzo, S., Ferro, M., et al. (2015). Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer. Expert Review of Molecular Diagnostics, 15(9), 1211–1224. https://doi.org/10.1586/14737159.2015.1069711.

    Article  CAS  PubMed  Google Scholar 

  • Magnusson, M., Lewis, G. D., Ericson, U., Orho-Melander, M., Hedblad, B., Engstrom, G., et al. (2013). A diabetes-predictive amino acid score and future cardiovascular disease. European Heart Journal, 34, 1982–1989.

    Article  CAS  Google Scholar 

  • Maxeiner, A., Adkins, C. B., Zhang, Y., Taupitz, M., Halpern, E. F., McDougal, W. S., et al. (2010). Retrospective analysis of prostate cancer recurrence potential with tissue metabolomic profiles. Prostate, 70, 710–717.

    PubMed  PubMed Central  Google Scholar 

  • Merlot, A. M., Kalinowski, D. S., & Richardson, D. R. (2014). Unraveling the mysteries of serum albumin-more than just a serum protein. Frontiers in Physiology, 5, 299. https://doi.org/10.3389/fphys.2014.00299.

    Article  PubMed  PubMed Central  Google Scholar 

  • Mondul, A. M., Moore, S. C., Weinstein, S. J., Karoly, E. D., Sampson, J. N., & Albanes, D. (2015). Metabolomic analysis of prostate cancer risk in a prospective cohort: The alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study. International Journal of Cancer, 137, 2124–2132.

    Article  CAS  Google Scholar 

  • Mondul, A. M., Moore, S. C., Weinstein, S. J., Mannisto, S., Sampson, J. N., & Albanes, D. (2014). 1-stearoylglycerol is associated with risk of prostate cancer: Results from serum metabolomic profiling. Metabolomics, 10, 1036–1041.

    Article  CAS  Google Scholar 

  • Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., et al. (2009). A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metabolism, 9, 311–326.

    Article  CAS  Google Scholar 

  • O’Connell, T. M. (2013). The complex role of branched chain amino acids in diabetes and cancer. Metabolites, 3, 931–945.

    Article  Google Scholar 

  • Ren, J.-G., Seth, P., Ye, H., Guo, K., Hanai, J.-I., Husain, Z., & Sukhatme, V. P. (2017). Citrate Suppresses tumor growth in multiple models through inhibition of glycolysis, the tricarboxylic acid cycle and the IGF-1R pathway. Scientific Reports, 7(1), 4537. https://doi.org/10.1038/s41598-017-04626-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Scalbert, A., Brennan, L., Manach, C., Andres-Lacueva, C., Dragsted, L. O., Draper, J., et al. (2014). The food metabolome: A window over dietary exposure. The American Journal of Clinical Nutrition, 99(6), 1286–1308.

    Article  CAS  Google Scholar 

  • Schmidt, J. A., Fensom, G. K., Rinaldi, S., Scalbert, A., Appleby, P. N., Achaintre, D., et al. (2017). Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition. BMC Medicine, 15(1), 122. https://doi.org/10.1186/s12916-017-0885-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sousa, S. A., Magalhaes, A., & Ferreira, M. M. (2013). Optimized bucketing for NMR spectra: Three case studies. Chemometrics and Intelligent Laboratory Systems, 122, 93–102.

    Article  CAS  Google Scholar 

  • Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., et al. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457, 910–914.

    Article  CAS  Google Scholar 

  • Stepien, M., Duarte-Salles, T., Fedirko, V., Floegel, A., Barupal, D. K., Rinaldi, S., et al. (2016). Alteration of amino acid and biogenic amine metabolism in hepatobiliary cancers: Findings from a prospective cohort study. International Journal of Cancer, 138, 348–360.

    Article  CAS  Google Scholar 

  • Vandenbroucke, J. P., & Pearce, N. (2012). Case-control studies: Basic concepts. International Journal of Epidemiology, 41(5), 1480–1489. https://doi.org/10.1093/ije/dys147.

    Article  PubMed  Google Scholar 

  • WHO. (1993). ICD-10, International Classification of Diseases and related health problems, 10th revision. World Health Organization.

    Google Scholar 

  • Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., et al. (2018). HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Research, 46(D1), D608–D617. https://doi.org/10.1093/nar/gkx1089.

    Article  CAS  PubMed  Google Scholar 

  • Wood, S. L., Westbrook, J. A., & Brown, J. E. (2014). Omic-profiling in breast cancer metastasis to bone: Implications for mechanisms, biomarkers and treatment. Cancer Treatment Reviews, 40, 139–152.

    Article  CAS  Google Scholar 

  • World Cancer Research Fund/American Institute for Cancer Research. (2014). Continuous Update Project Expert Report 2018. Diet, nutrition, physical activity and prostate cancer.  World Cancer Research Fund/American Institute for Cancer Research.

  • World Cancer Research Fund/American Institute for Cancer Research. (2018). Continuous Update Project Expert Report 2018. The cancer process. http://www.dietandcancerreport.org.

  • Wu, S., Zhu, W., Thompson, P., & Hannun, Y. A. (2018). Evaluating intrinsic and non-intrinsic cancer risk factors. Nature Communications, 9(1), 3490. https://doi.org/10.1038/s41467-018-05467-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wurtz, P., Soininen, P., Kangas, A. J., Ronnemaa, T., Lehtimaki, T., Kahonen, M., et al. (2013). Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care, 36(3), 648–655. https://doi.org/10.2337/dc12-0895.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank all participants of the SU.VI.MAX study as well as Younes Esseddik, Thi Hong Van Duong, Régis Gatibelza, and Jagatjit Mohinder (computer scientists), Cédric Agaesse (dietitian), Fabien Szabo de Edelenyi, PhD, Julien Allègre, Nathalie Arnault, Laurent Bourhis (data-managers/biostatisticians), and Fatoumata Diallo, MD, Roland Andrianasolo, MD and Sandrine Kamdem (physicians), for their technical contribution. This work was conducted in the framework of the French network for Nutrition And Cancer Research (NACRe network), and received the NACRe Partnership Label.

Funding

This work was supported by the Fondation de France [Grant Number 2015 00060743 for the project], by the French National Cancer Institute [PhD Grant Number INCa_11323 for L. Lecuyer]; and the Federative Institute for Biomedical Research IFRB Paris 13. The funders had no role in the design, analysis, or writing of this article.

Author information

Authors and Affiliations

  1. Inserm U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center – University of Paris (CRESS), Sorbonne Paris Nord University, SMBH Paris 13, 74 rue Marcel Cachin, 93017, Bobigny Cedex, France

    Lucie Lécuyer, Pilar Galan, Serge Hercberg, Valentin Partula, Bernard Srour, Paule Latino-Martel, Emmanuelle Kesse-Guyot, Nathalie Druesne-Pecollo, Mélanie Deschasaux-Tanguy & Mathilde Touvier

  2. Chemistry Structures Properties of Biomaterials and Therapeutic Agents (CSPBAT), Nanomédecine Biomarqueurs Détection (NBD), The National Center for Scientific Research (CNRS) 7244, Sorbonne Paris Nord University, 93017, Bobigny Cedex, France

    Agnès Victor Bala, Nadia Bouchemal, Mohamed Nawfal Triba & Philippe Savarin

  3. INRAE, UMR 1019, Human Nutrition Unit (UNH), Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Clermont Auvergne University, CRNH Auvergne, 63000, Clermont-Ferrand, France

    Aicha Demidem, Adrien Rossary & Marie-Paule Vasson

  4. Public Health Department, Avicenne Hospital, 93000, Bobigny, France

    Serge Hercberg

  5. Anticancer Center Jean-Perrin, CHU Clermont-Ferrand, 63011, Clermont-Ferrand Cedex, France

    Marie-Paule Vasson

Authors
  1. Lucie Lécuyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Agnès Victor Bala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aicha Demidem
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adrien Rossary
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nadia Bouchemal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamed Nawfal Triba
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pilar Galan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Serge Hercberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Valentin Partula
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bernard Srour
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Paule Latino-Martel
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Emmanuelle Kesse-Guyot
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Nathalie Druesne-Pecollo
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Marie-Paule Vasson
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mélanie Deschasaux-Tanguy
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Philippe Savarin
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Mathilde Touvier
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The author’s responsibilities were as follow—LL and MT: designed the research; SH, PG, MT, EKG: conducted the research; NB, AVB: acquired NMR spectra; LL: performed statistical analysis; MT, MNT: supervised statistical analysis; LL and MT: wrote the paper; LL, AVB, AD, AR, NB, MNT, PG, SH, VP, LLM, BS, PLM, EKG, NDP, MD, PS, MT: contributed to the data interpretation and revised each draft for important intellectual content; and MT had primary responsibility for final content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mélanie Deschasaux-Tanguy.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to disclose.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent for publication

All authors have approved the manuscript before submission. Participants signed informed consent regarding publishing their data.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lécuyer, L., Victor Bala, A., Demidem, A. et al. NMR metabolomic profiles associated with long-term risk of prostate cancer. Metabolomics 17, 32 (2021). https://doi.org/10.1007/s11306-021-01780-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11306-021-01780-9

Keywords

Search

Navigation