Viktoria Knaze1, Joseph A Rothwell1, Raul Zamora-Ros2, Aurelie Moskal1, Cecilie Kyrø3, Paula Jakszyn2, Guri Skeie4, Elisabete Weiderpass4,5,6,7, Maria Santucci de Magistris8, Claudia Agnoli9, Susanne Westenbrink10, Emily Sonestedt11, Antonia Trichopoulou12,13, Effie Vasilopoulou12,13, Eleni Peppa12, Eva Ardanaz14,15,16, José María Huerta16,17, Heiner Boeing18, Francesca Romana Mancini19,20, Augustin Scalbert1, Nadia Slimani1. 1. Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France. 2. Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain. 3. Danish Cancer Society Research Center, Copenhagen, Denmark. 4. Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway. 5. Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway. 6. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 7. Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland. 8. AOU Federico II, Naples, Italy. 9. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 10. Rijksinstituut voor Volksgezondheid en Milieu (RIVM)/National Institute for Public Health and the Environment, Center for Nutrition, Prevention, and Health Services, Bilthoven, Netherlands. 11. Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden. 12. Hellenic Health Foundation, Athens, Greece. 13. WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece. 14. Navarra Public Health Institute, Pamplona, Spain. 15. IdiSNA, Navarra Institute for Health Research, Pamplona, Spain. 16. CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain. 17. Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain. 18. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany. 19. Center for Research in Epidemiology and Population Health (CESP), Faculté de Médecine-Université Paris-Sud, Faculté de Médecine-UVSQ, French National Institute of Health and Medical Research (INSERM), Université Paris-Saclay, Villejuif, France. 20. Institut Gustave Roussy, Villejuif, France.
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.
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.
Authors: Daniel O Clark; Huiping Xu; Lyndsi Moser; Philip Adeoye; Annie W Lin; Christy C Tangney; Shannon L Risacher; Andrew J Saykin; Robert V Considine; Frederick W Unverzagt Journal: Contemp Clin Trials Date: 2019-07-18 Impact factor: 2.226
Authors: Olatz Mompeo; Tim D Spector; Marisa Matey Hernandez; Caroline Le Roy; Geoffrey Istas; Melanie Le Sayec; Massimo Mangino; Amy Jennings; Ana Rodriguez-Mateos; Ana M Valdes; Cristina Menni Journal: Nutrients Date: 2020-06-23 Impact factor: 6.706
Authors: Nicola P Bondonno; Frederik Dalgaard; Kevin Murray; Raymond J Davey; Catherine P Bondonno; Aedin Cassidy; Joshua R Lewis; Cecilie Kyrø; Gunnar Gislason; Augustin Scalbert; Anne Tjønneland; Jonathan M Hodgson Journal: J Nutr Date: 2021-11-02 Impact factor: 4.798