Please use this identifier to cite or link to this item:
Title: Identification Of Urinary Polyphenol Metabolite Patterns Associated With Polyphenol-rich Food Intake In Adults From Four European Countries
Author: Noh, Hwayoung
Freisling, Heinz
Assi, Nada
Zamora-Ros, Raul
Achaintre, David
Affret, Aurélie
Mancini, Francesca
Boutron-Ruault, Marie-Christine
Flögel, Anna
Boeing, Heiner
Kühn, Tilman
Schübel, Ruth
Trichopoulou, Antonia
Naska, Androniki
Kritikou, Maria
Palli, Domenico
Pala, Valeria
Tumino, Rosario
Ricceri, Fulvio
Santucci de Magistris, Maria
Cross, Amanda
Slimani, Nadia
Scalbert, Augustin
Ferrari, Pietro
Keywords: Polifenols
Marcadors bioquímics
Biochemical markers
Issue Date: 1-Aug-2017
Publisher: MDPI
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.
Note: Reproducció del document publicat a:
It is part of: Nutrients, 2017, vol. 9, num. 8
Related resource:
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

Files in This Item:
File Description SizeFormat 
NohH.pdf287.15 kBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons