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

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 C...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579590/
https://www.ncbi.nlm.nih.gov/pubmed/28757581
http://dx.doi.org/10.3390/nu9080796
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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
author_facet 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
author_sort Noh, Hwayoung
collection PubMed
description 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.
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spelling pubmed-55795902017-09-06 Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries 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 Nutrients Article 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. MDPI 2017-07-25 /pmc/articles/PMC5579590/ /pubmed/28757581 http://dx.doi.org/10.3390/nu9080796 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
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
Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries
title Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries
title_full Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries
title_fullStr Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries
title_full_unstemmed Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries
title_short Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries
title_sort identification of urinary polyphenol metabolite patterns associated with polyphenol-rich food intake in adults from four european countries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579590/
https://www.ncbi.nlm.nih.gov/pubmed/28757581
http://dx.doi.org/10.3390/nu9080796
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