Journal
NUTRIENTS
Volume 9, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/nu9080796
Keywords
dietary biomarker patterns; polyphenol metabolites; polyphenol-rich food; reduced rank regression (RRR); EPIC
Categories
Funding
- European Union [289511]
- Wereld Kanker Onderzoek Fonds [WCRF NL 2012/604]
- European Commission (DG-SANCO)
- International Agency for Research on Cancer
- Ligue Contre le Cancer (France)
- Institut Gustave Roussy (France)
- Mutuelle Generale de l'Education Nationale (France)
- Institut National de la Sante et de la Recherche Medicale (INSERM) (France)
- German Cancer Aid (Germany)
- German Cancer Research Center (DKFZ) (Germany)
- Federal Ministry of Education and Research (BMBF) (Germany)
- Deutsche Krebshilfe (Germany)
- Deutsches Krebsforschungszentrum (Germany)
- Federal Ministry of Education and Research (Germany)
- Hellenic Health Foundation (Greece)
- Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy (Italy)
- National Research Council (Italy)
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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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