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.
Original language | Undefined/Unknown |
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Article number | 796 |
Number of pages | 14 |
Journal | Nutrients |
Volume | 9 |
Issue number | 8 |
DOIs | |
Publication status | Published - 25 Jul 2017 |