Karina Meidtner1,2, Clara Podmore3, Janine Kröger1, Yvonne T van der Schouw4, Benedetta Bendinelli5, Claudia Agnoli6, Larraitz Arriola7,8,9, Aurelio Barricarte9,10,11, Heiner Boeing12, Amanda J Cross13, Courtney Dow14,15,16, Kim Ekblom17, Guy Fagherazzi14,15,16, Paul W Franks18,19, Marc J Gunter20, José María Huerta8,21, Paula Jakszyn22,23, Mazda Jenab20, Verena A Katzke24, Timothy J Key25, Kay Tee Khaw26, Tilman Kühn24, Cecilie Kyrø27, Francesca Romana Mancini14,15,16, Olle Melander18, Peter M Nilsson18, Kim Overvad28,29, Domenico Palli5, Salvatore Panico30, J Ramón Quirós31, Miguel Rodríguez-Barranco9,32, Carlotta Sacerdote33,34, Ivonne Sluijs4, Magdalena Stepien20, Anne Tjonneland27, Rosario Tumino35,36, Nita G Forouhi3, Stephen J Sharp3, Claudia Langenberg3, Matthias B Schulze37,2, Elio Riboli13, Nicholas J Wareham3. 1. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. 2. German Center for Diabetes Research (DZD), München-Neuherberg, Germany. 3. Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, U.K. 4. University Medical Center Utrecht, Utrecht, the Netherlands. 5. Cancer Research and Prevention Institute (ISPO), Florence, Italy. 6. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 7. Public Health Division of Gipuzkoa, San Sebastian, Spain. 8. Instituto BIO-Donostia, Basque Government, San Sebastian, Spain. 9. Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. 10. Navarre Public Health Institute (ISPN), Pamplona, Spain. 11. Navarra Institute for Health Research (IdiSNA) Pamplona, Spain. 12. German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. 13. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K. 14. INSERM U1018, Centre de recherche en Épidémiologie et Santé des Populations, Villejuif, France. 15. University Paris-Saclay, University Paris-Sud, Villejuif, France. 16. Gustave Roussy Institute, Villejuif, France. 17. Clinical Chemistry, Department of Medical Biosciences, Umeå University, Umeå, Sweden. 18. Department of Clinical Sciences, Lund University, Malmö, Sweden. 19. Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden. 20. International Agency for Research on Cancer, Lyon, France. 21. Department of Epidemiology, Murcia Regional Health Council, Instituto Murciano de Investigación Biosanitaria-Arrixaca, Murcia, Spain. 22. Nutrition and Cancer Unit, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Barcelona, Spain. 23. Blanquerna Health Sciences Faculty, Universitat Ramon Llull, Barcelona, Spain. 24. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 25. University of Oxford, Oxford, U.K. 26. University of Cambridge, Cambridge, U.K. 27. Danish Cancer Society Research Center, Copenhagen, Denmark. 28. Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark. 29. Aalborg University Hospital, Aalborg, Denmark. 30. Dipartimento di Medicina Clinica e Chirurgia, Federico II University of Naples, Naples, Italy. 31. Public Health Directorate, Asturias, Spain. 32. Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.GRANADA, Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain. 33. Unit of Cancer Epidemiology, Città della Salute e della Scienza Hospital-University of Turin and Center for Cancer Prevention (CPO), Torino, Italy. 34. Human Genetics Foundation (HuGeF), Torino, Italy. 35. Azienda Sanitaria Provinciale di Ragusa, Ragusa, Italy. 36. L'Associazione Iblea per la Ricerca Epidemiologica-Organizazione Non Lucrativa di Utilità Sociale, Ragusa, Italy. 37. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany mschulze@dife.de.
Abstract
OBJECTIVE: Meat intake has been consistently shown to be positively associated with incident type 2 diabetes. Part of that association may be mediated by body iron status, which is influenced by genetic factors. We aimed to test for interactions of genetic and dietary factors influencing body iron status in relation to the risk of incident type 2 diabetes. RESEARCH DESIGN AND METHODS: The case-cohort comprised 9,347 case subjects and 12,301 subcohort participants from eight European countries. Single nucleotide polymorphisms (SNPs) were selected from genome-wide association studies on iron status biomarkers and candidate gene studies. A ferritin-related gene score was constructed. Multiplicative and additive interactions of heme iron and SNPs as well as the gene score were evaluated using Cox proportional hazards regression. RESULTS: Higher heme iron intake (per 1 SD) was associated with higher ferritin levels (β = 0.113 [95% CI 0.082; 0.144]), but not with transferrin (-0.019 [-0.043; 0.006]) or transferrin saturation (0.016 [-0.006; 0.037]). Five SNPs located in four genes (rs1799945 [HFE H63D], rs1800562 [HFE C282Y], rs236918 [PCK7], rs744653 [SLC40A1], and rs855791 [TMPRSS6 V736A]) were associated with ferritin. We did not detect an interaction of heme iron and the gene score on the risk of diabetes in the overall study population (Padd = 0.16, Pmult = 0.21) but did detect a trend toward a negative interaction in men (Padd = 0.04, Pmult = 0.03). CONCLUSIONS: We found no convincing evidence that the interplay of dietary and genetic factors related to body iron status associates with type 2 diabetes risk above the level expected from the sum or product of the two individual exposures.
OBJECTIVE: Meat intake has been consistently shown to be positively associated with incident type 2 diabetes. Part of that association may be mediated by body iron status, which is influenced by genetic factors. We aimed to test for interactions of genetic and dietary factors influencing body iron status in relation to the risk of incident type 2 diabetes. RESEARCH DESIGN AND METHODS: The case-cohort comprised 9,347 case subjects and 12,301 subcohort participants from eight European countries. Single nucleotide polymorphisms (SNPs) were selected from genome-wide association studies on iron status biomarkers and candidate gene studies. A ferritin-related gene score was constructed. Multiplicative and additive interactions of heme iron and SNPs as well as the gene score were evaluated using Cox proportional hazards regression. RESULTS: Higher heme iron intake (per 1 SD) was associated with higher ferritin levels (β = 0.113 [95% CI 0.082; 0.144]), but not with transferrin (-0.019 [-0.043; 0.006]) or transferrin saturation (0.016 [-0.006; 0.037]). Five SNPs located in four genes (rs1799945 [HFE H63D], rs1800562 [HFE C282Y], rs236918 [PCK7], rs744653 [SLC40A1], and rs855791 [TMPRSS6 V736A]) were associated with ferritin. We did not detect an interaction of heme iron and the gene score on the risk of diabetes in the overall study population (Padd = 0.16, Pmult = 0.21) but did detect a trend toward a negative interaction in men (Padd = 0.04, Pmult = 0.03). CONCLUSIONS: We found no convincing evidence that the interplay of dietary and genetic factors related to body iron status associates with type 2 diabetes risk above the level expected from the sum or product of the two individual exposures.
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