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.

Identification of urinary polyphenol metabolite patterns associated with polyphenol-rich food intake in adults from four European Countries

Ricceri F.;
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.
9
8,796
1
14
Dietary biomarker patterns; EPIC; Polyphenol metabolites; Polyphenol-rich food; Reduced rank regression (RRR); Adult; Aged; Biomarkers; Body Mass Index; Coffee; Europe; European Continental Ancestry Group; Exercise; Female; Humans; Male; Mental Recall; Middle Aged; Nutrition Assessment; Olea; Polyphenols; Prospective Studies; Surveys and Questionnaires; Wine; Diet
Noh H.; Freisling H.; Assi N.; Zamora-Ros R.; Achaintre D.; Affret A.; Mancini F.; Boutron-Ruault M.-C.; Flogel A.; Boeing H.; Kuhn T.; Schubel R.; Trichopoulou A.; Naska A.; Kritikou M.; Palli D.; Pala V.; Tumino R.; Ricceri F.; De Magistris M.S.; Cross A.; Slimani N.; Scalbert A.; Ferrari P.
File in questo prodotto:
File Dimensione Formato  
Nohetal2017_Nutrients.pdf

Accesso aperto

Tipo di file: PDF EDITORIALE
Dimensione 278.76 kB
Formato Adobe PDF
278.76 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1766512
Citazioni
  • ???jsp.display-item.citation.pmc??? 7
  • Scopus 17
  • ???jsp.display-item.citation.isi??? 14
social impact