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Nutrients 2017, 9(8), 796; doi:10.3390/nu9080796

Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries

1
Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC), 69372 Lyon CEDEX 08, France
2
Unit of Nutrition and Cancer, Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
3
Université Paris-Saclay, Université Paris-Sud, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Le Centre de recherche en Epidémiologie et Santé des Population (CESP), Institut National de la Santé et de la Recherche Médicale (INSERM), 94800 Villejuif, France
4
Gustave Roussy, 94800 Villejuif, France
5
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, 14558 Nuthetal, Germany
6
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
7
Hellenic Health Foundation, 115 27 Athens, Greece
8
WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, 157 72 Athens, Greece
9
Cancer Risk Factors and Life-Style Epidemiology Unit, Cancer Research and Prevention Institute (ISPO), 50139 Florence, Italy
10
Epidemiology and Prevention Unit, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milano, Italy
11
Cancer Registry and Histopathology Unit, “Civic-M.P.Arezzo” Hospital, ASP Ragusa, 97100 Ragusa, Italy
12
Unit of Epidemiology, Regional Health Service ASL TO3, 10095 Grugliasco (TO), Italy
13
Unit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, 10124 Turin, Italy
14
Azienda Ospedaliera Universitaria (AOU) Federico II, 80131 Naples, Italy
15
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, UK
*
Author to whom correspondence should be addressed.
Received: 30 June 2017 / Revised: 18 July 2017 / Accepted: 18 July 2017 / Published: 25 July 2017
(This article belongs to the Special Issue Effects of Polyphenol-Rich Foods on Human Health)
View Full-Text   |   Download PDF [287 KB, uploaded 25 July 2017]

Abstract

We identified urinary polyphenol metabolite patterns by a novel algorithm that combines dimension reduction and variable selection methods to explain polyphenol-rich food intake, and compared their respective performance with that of single biomarkers in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. The study included 475 adults from four European countries (Germany, France, Italy, and Greece). Dietary intakes were assessed with 24-h dietary recalls (24-HDR) and dietary questionnaires (DQ). Thirty-four polyphenols were measured by ultra-performance liquid chromatography–electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS-MS) in 24-h urine. Reduced rank regression-based variable importance in projection (RRR-VIP) and least absolute shrinkage and selection operator (LASSO) methods were used to select polyphenol metabolites. Reduced rank regression (RRR) was then used to identify patterns in these metabolites, maximizing the explained variability in intake of pre-selected polyphenol-rich foods. The performance of RRR models was evaluated using internal cross-validation to control for over-optimistic findings from over-fitting. High performance was observed for explaining recent intake (24-HDR) of red wine (r = 0.65; AUC = 89.1%), coffee (r = 0.51; AUC = 89.1%), and olives (r = 0.35; AUC = 82.2%). These metabolite patterns performed better or equally well compared to single polyphenol biomarkers. Neither metabolite patterns nor single biomarkers performed well in explaining habitual intake (as reported in the DQ) of polyphenol-rich foods. This proposed strategy of biomarker pattern identification has the potential of expanding the currently still limited list of available dietary intake biomarkers. View Full-Text
Keywords: dietary biomarker patterns; polyphenol metabolites; polyphenol-rich food; reduced rank regression (RRR); EPIC dietary biomarker patterns; polyphenol metabolites; polyphenol-rich food; reduced rank regression (RRR); EPIC
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Noh, H.; Freisling, H.; Assi, N.; Zamora-Ros, R.; Achaintre, D.; Affret, A.; Mancini, F.; Boutron-Ruault, M.-C.; Flögel, A.; Boeing, H.; Kühn, T.; Schübel, R.; Trichopoulou, A.; Naska, A.; Kritikou, M.; Palli, D.; Pala, V.; Tumino, R.; Ricceri, F.; Santucci de Magistris, M.; Cross, A.; Slimani, N.; Scalbert, A.; Ferrari, P. Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries. Nutrients 2017, 9, 796.

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