Lucie Lécuyer1, Agnès Victor Bala2, Aicha Demidem3, Adrien Rossary3, Nadia Bouchemal2, Mohamed Nawfal Triba2, Pilar Galan1, Serge Hercberg1,4, Valentin Partula1, Bernard Srour1, Paule Latino-Martel1, Emmanuelle Kesse-Guyot1, Nathalie Druesne-Pecollo1, Marie-Paule Vasson3,5, Mélanie Deschasaux-Tanguy6, Philippe Savarin2, Mathilde Touvier1. 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. 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. 3. INRAE, UMR 1019, Human Nutrition Unit (UNH), Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Clermont Auvergne University, CRNH Auvergne, 63000, Clermont-Ferrand, France. 4. Public Health Department, Avicenne Hospital, 93000, Bobigny, France. 5. Anticancer Center Jean-Perrin, CHU Clermont-Ferrand, 63011, Clermont-Ferrand Cedex, France. 6. 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. m.deschasaux@eren.smbh.univ-paris13.fr.
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.
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 menparticipant 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.
Entities:
Keywords:
Metabolomics; Nuclear magnetic resonance; Prospective study; Prostate cancer risk
Authors: Giuseppe Lucarelli; Davide Loizzo; Matteo Ferro; Monica Rutigliano; Mihai Dorin Vartolomei; Francesco Cantiello; Carlo Buonerba; Giuseppe Di Lorenzo; Daniela Terracciano; Ottavio De Cobelli; Carlo Bettocchi; Pasquale Ditonno; Michele Battaglia Journal: Expert Rev Mol Diagn Date: 2019-04-24 Impact factor: 5.225
Authors: Samuel Guénin; Laurent Schwartz; Daniel Morvan; Jean Marc Steyaert; Amandine Poignet; Jean Claude Madelmont; Aicha Demidem Journal: Int J Oncol Date: 2008-01 Impact factor: 5.650
Authors: Stella Koutros; Tamra E Meyer; Stephen D Fox; Haleem J Issaq; Timothy D Veenstra; Wen-Yi Huang; Kai Yu; Demetrius Albanes; Lisa W Chu; Gerald Andriole; Robert N Hoover; Ann W Hsing; Sonja I Berndt Journal: Carcinogenesis Date: 2013-05-22 Impact factor: 4.944