A new food-composition database for 437 polyphenols in 19, 899 raw and prepared foods used to estimate polyphenol intakes in adults from 10 European countries. Issue 3 (21st June 2018)

• Record Type: Journal Article
• Authors: Knaze, Viktoria; Rothwell, Joseph A; Zamora-Ros, Raul; Moskal, Aurelie; Kyrø, Cecilie; Jakszyn, Paula; Skeie, Guri; Weiderpass, Elisabete; Santucci de Magistris, Maria; Agnoli, Claudia; Westenbrink, Susanne; Sonestedt, Emily; Trichopoulou, Antonia; Vasilopoulou, Effie; Peppa, Eleni; Ardanaz, Eva; Huerta, José María; Boeing, Heiner; Mancini, Francesca Romana; Scalbert, Augustin; Slimani, Nadia
• Is Part Of: American journal of clinical nutrition. Volume 108:Issue 3(2018)
• Page Start: 517
• Page End: 524
• Publication Date: 2018-06-21
• Subjects: EPIC -- Phenol-Explorer database -- food composition -- polyphenol intake -- retention factors; Diet therapy -- Periodicals; Nutrition -- Periodicals; Dietetics -- Periodicals; 613.205
• DOI: 10.1093/ajcn/nqy098
• Languages: English
• ISSNs: 0002-9165

Abstract

ABSTRACT: BACKGROUND: Accurate assessment of polyphenol intakes is needed in epidemiologic research in order to study their health effects, and this can be particularly challenging in international study settings. OBJECTIVE: The purpose of this work is to describe the procedures to prepare a comprehensive polyphenol food-composition database that was used to calculate standardized polyphenol intakes from 24-h diet recalls (24HDRs) and dietary questionnaires (DQs) in the European Prospective Investigation into Cancer and Nutrition (EPIC). Design: With the use of the comparable food classification and facet-descriptor system of the computerized 24HDR program EPIC-Soft (renamed GloboDiet), foods reported in the 24HDR ( n = 74, 626) were first aggregated following a stepwise process. Multi-ingredient and generic foods were broken down into ingredients or more-specific foods with consideration of regional consumption habits before matching to foods in the Phenol-Explorer database. Food-composition data were adjusted by using selected retention factors curated in Phenol-Explorer. DQ foods ( n = 13, 946) were matched to a generated EPIC 24HDR polyphenol-composition database before calculation of daily intakes from the 24HDR and DQ. RESULTS: Food matching yielded 2.0% and 2.7% of foods with missing polyphenol content in the 24HDR and DQ food data sets, respectively. Process-specific retention factors for 42 different polyphenol compounds were applied to adjust the polyphenol content in 35 prioritized Phenol-Explorer foods, thereby adjusting the polyphenol content in 70% of all of the prepared 24 food occurrences. A detailed food-composition database was finally generated for 437 polyphenols in 19, 899 aggregated raw and prepared foods reported by 10 EPIC countries in the 24HDR. Conclusions: An efficient procedure was developed to build the most-comprehensive food-composition database for polyphenols, thereby standardizing the calculations of dietary polyphenol intakes obtained from different dietary assessment methods and European populations. The whole database is accessible online. This procedure could equally be used for other food constituents and in other cohorts.