Franziska Jannasch1,2,3, Janine Kröger1,2, Claudia Agnoli4, Aurelio Barricarte5,6,7, Heiner Boeing8, Valerie Cayssials9, Sandra Colorado-Yohar5,10,11, Christina C Dahm12, Courtney Dow13, Guy Fagherazzi13, Paul W Franks14,15, Heinz Freisling16, Marc J Gunter16, Nicola D Kerrison17, Timothy J Key18, Kay-Tee Khaw19, Tilman Kühn20, Cecilie Kyro21, Francesca Romana Mancini13, Olatz Mokoroa5,22, Peter Nilsson14, Kim Overvad12,23, Domenico Palli24, Salvatore Panico25, Jose Ramón Quirós García26, Olov Rolandsson15, Carlotta Sacerdote27,28, Mariá-José Sánchez5,29, Mohammad Sediq Sahrai16, Ruth Schübel20, Ivonne Sluijs30, Annemieke M W Spijkerman31, Anne Tjonneland21, Tammy Y N Tong18, Rosario Tumino32,33, Elio Riboli34, Claudia Langenberg17, Stephen J Sharp17, Nita G Forouhi17, Matthias B Schulze1,2,35, Nicholas J Wareham17. 1. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Germany. 2. German Center for Diabetes Research (DZD), München-Neuherberg, Germany. 3. NutriAct-Competence Cluster Nutrition Research Berlin-Potsdam, Nuthetal, Germany. 4. Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 5. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. 6. Navarra Public Health Institute, Pamplona, Spain. 7. IdiSNA, Navarra Institute for Health Research, Pamplona, Spain. 8. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. 9. Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain. 10. Department of Epidemiology, Murcia Health Council, IMIB-Arrixaca, Spain. 11. Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia. 12. Section for Epidemiology, Department of Public Health, Aarhus University, Denmark. 13. Inserm, Centre for Research in Epidemiology and Population Health [CESP], Villejuif, France; Université Paris-Sud, Villejuif, France. 14. Department of Clinical Sciences, Lund University, Malmö, Sweden. 15. Department of Public Health and Clinical Medicine, Umeå University, Sweden. 16. Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France. 17. MRC Epidemiology Unit, University of Cambridge, United Kingdom. 18. Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom. 19. Department of Public Health and Primary Care, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom. 20. German Cancer Research Center [DKFZ], Heidelberg, Germany. 21. Danish Cancer Society Research Center, Copenhagen, Denmark. 22. Public Health Division of Gipuzkoa, Biodonostia Research Institute, San Sebastian, Spain. 23. Department of Cardiology, Center for Cardiovascular Research, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark. 24. Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network-ISPRO, Florence, Italy. 25. Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy. 26. Public Health Directorate, Asturias, Spain. 27. Unit of Cancer Epidemiology, Citta'della Salute e della Scienza Hospital-University of Turin and Center for Cancer Prevention (CPO), Torino, Italy. 28. Human Genetics Foundation (HuGeF), Torino, Italy. 29. Andalusian School of Public Health, Granada, Spain. 30. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands. 31. National Institute for Public Health and the Environment [RIVM], Bilthoven, Netherlands. 32. Cancer Registry and Histopathology Unit, 'Civile - M.P. Arezzo' Hospital, Ragusa, Italy. 33. Associazone Iblea per la Ricerca Epidemiologica-Onlus, Ragusa, Italy. 34. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom. 35. Institute of Nutritional Sciences, University of Potsdam, Nuthetal, Germany.
Abstract
BACKGROUND: Population-specificity of exploratory dietary patterns limits their generalizability in investigations with type 2 diabetes incidence. OBJECTIVE: The aim of this study was to derive country-specific exploratory dietary patterns, investigate their association with type 2 diabetes incidence, and replicate diabetes-associated dietary patterns in other countries. METHODS: Dietary intake data were used, assessed by country-specific questionnaires at baseline of 11,183 incident diabetes cases and 14,694 subcohort members (mean age 52.9 y) from 8 countries, nested within the European Prospective Investigation into Cancer and Nutrition study (mean follow-up time 6.9 y). Exploratory dietary patterns were derived by principal component analysis. HRs for incident type 2 diabetes were calculated by Prentice-weighted Cox proportional hazard regression models. Diabetes-associated dietary patterns were simplified or replicated to be applicable in other countries. A meta-analysis across all countries evaluated the generalizability of the diabetes-association. RESULTS: Two dietary patterns per country/UK-center, of which overall 3 dietary patterns were diabetes-associated, were identified. A risk-lowering French dietary pattern was not confirmed across other countries: pooled HRFrance per 1 SD: 1.00; 95% CI: 0.90, 1.10. Risk-increasing dietary patterns, derived in Spain and UK-Norfolk, were confirmed, but only the latter statistically significantly: HRSpain: 1.09; 95% CI: 0.97, 1.22 and HRUK-Norfolk: 1.12; 95% CI: 1.04, 1.20. Respectively, this dietary pattern was characterized by relatively high intakes of potatoes, processed meat, vegetable oils, sugar, cake and cookies, and tea. CONCLUSIONS: Only few country/center-specific dietary patterns (3 of 18) were statistically significantly associated with diabetes incidence in this multicountry European study population. One pattern, whose association with diabetes was confirmed across other countries, showed overlaps in the food groups potatoes and processed meat with identified diabetes-associated dietary patterns from other studies. The study demonstrates that replication of associations of exploratory patterns with health outcomes is feasible and a necessary step to overcome population-specificity in associations from such analyses.
BACKGROUND: Population-specificity of exploratory dietary patterns limits their generalizability in investigations with type 2 diabetes incidence. OBJECTIVE: The aim of this study was to derive country-specific exploratory dietary patterns, investigate their association with type 2 diabetes incidence, and replicate diabetes-associated dietary patterns in other countries. METHODS: Dietary intake data were used, assessed by country-specific questionnaires at baseline of 11,183 incident diabetes cases and 14,694 subcohort members (mean age 52.9 y) from 8 countries, nested within the European Prospective Investigation into Cancer and Nutrition study (mean follow-up time 6.9 y). Exploratory dietary patterns were derived by principal component analysis. HRs for incident type 2 diabetes were calculated by Prentice-weighted Cox proportional hazard regression models. Diabetes-associated dietary patterns were simplified or replicated to be applicable in other countries. A meta-analysis across all countries evaluated the generalizability of the diabetes-association. RESULTS: Two dietary patterns per country/UK-center, of which overall 3 dietary patterns were diabetes-associated, were identified. A risk-lowering French dietary pattern was not confirmed across other countries: pooled HRFrance per 1 SD: 1.00; 95% CI: 0.90, 1.10. Risk-increasing dietary patterns, derived in Spain and UK-Norfolk, were confirmed, but only the latter statistically significantly: HRSpain: 1.09; 95% CI: 0.97, 1.22 and HRUK-Norfolk: 1.12; 95% CI: 1.04, 1.20. Respectively, this dietary pattern was characterized by relatively high intakes of potatoes, processed meat, vegetable oils, sugar, cake and cookies, and tea. CONCLUSIONS: Only few country/center-specific dietary patterns (3 of 18) were statistically significantly associated with diabetes incidence in this multicountry European study population. One pattern, whose association with diabetes was confirmed across other countries, showed overlaps in the food groups potatoes and processed meat with identified diabetes-associated dietary patterns from other studies. The study demonstrates that replication of associations of exploratory patterns with health outcomes is feasible and a necessary step to overcome population-specificity in associations from such analyses.
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Authors: Franziska Jannasch; Stefan Dietrich; Tom R P Bishop; Matthew Pearce; Anouar Fanidi; Gráinne O'Donoghue; Donal O'Gorman; Pedro Marques-Vidal; Peter Vollenweider; Maira Bes-Rastrollo; Liisa Byberg; Alicja Wolk; Maryam Hashemian; Reza Malekzadeh; Hossein Poustchi; Vivian C Luft; Sheila M Alvim de Matos; Jihye Kim; Mi Kyung Kim; Yeonjung Kim; Dalia Stern; Martin Lajous; Dianna J Magliano; Jonathan E Shaw; Tasnime Akbaraly; Mika Kivimaki; Gertraud Maskarinec; Loïc Le Marchand; Miguel Ángel Martínez-González; Sabita S Soedamah-Muthu; Nicholas J Wareham; Nita G Forouhi; Matthias B Schulze Journal: Eur J Nutr Date: 2022-06-01 Impact factor: 4.865