Interplay between genetic predisposition, macronutrient intake and type 2 diabetes incidence: analysis within EPIC-InterAct across eight European countries View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-03-17

AUTHORS

Sherly X. Li, Fumiaki Imamura, Matthias B. Schulze, Jusheng Zheng, Zheng Ye, Antonio Agudo, Eva Ardanaz, Dagfinn Aune, Heiner Boeing, Miren Dorronsoro, Courtney Dow, Guy Fagherazzi, Sara Grioni, Marc J. Gunter, José María Huerta, Daniel B. Ibsen, Marianne Uhre Jakobsen, Rudolf Kaaks, Timothy J. Key, Kay-Tee Khaw, Cecilie Kyrø, Francesca Romana Mancini, Elena Molina-Portillo, Neil Murphy, Peter M. Nilsson, N. Charlotte Onland-Moret, Domenico Palli, Salvatore Panico, Alaitz Poveda, J. Ramón Quirós, Fulvio Ricceri, Ivonne Sluijs, Annemieke M. W. Spijkerman, Anne Tjonneland, Rosario Tumino, Anna Winkvist, Claudia Langenberg, Stephen J. Sharp, Elio Riboli, Robert A. Scott, Nita G. Forouhi, Nicholas J. Wareham

ABSTRACT

Aims/hypothesisGene–macronutrient interactions may contribute to the development of type 2 diabetes but research evidence to date is inconclusive. We aimed to increase our understanding of the aetiology of type 2 diabetes by investigating potential interactions between genes and macronutrient intake and their association with the incidence of type 2 diabetes.MethodsWe investigated the influence of interactions between genetic risk scores (GRSs) for type 2 diabetes, insulin resistance and BMI and macronutrient intake on the development of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a prospective case-cohort study across eight European countries (N = 21,900 with 9742 incident type 2 diabetes cases). Macronutrient intake was estimated from diets reported in questionnaires, including proportion of energy derived from total carbohydrate, protein, fat, plant and animal protein, saturated, monounsaturated and polyunsaturated fat and dietary fibre. Using multivariable-adjusted Cox regression, we estimated country-specific interaction results on the multiplicative scale, using random-effects meta-analysis. Secondary analysis used isocaloric macronutrient substitution.ResultsNo interactions were identified between any of the three GRSs and any macronutrient intake, with low-to-moderate heterogeneity between countries (I2 range 0–51.6%). Results were similar using isocaloric macronutrient substitution analyses and when weighted and unweighted GRSs and individual SNPs were examined.Conclusions/interpretationGenetic susceptibility to type 2 diabetes, insulin resistance and BMI did not modify the association between macronutrient intake and incident type 2 diabetes. This suggests that macronutrient intake recommendations to prevent type 2 diabetes do not need to account for differences in genetic predisposition to these three metabolic conditions. More... »

PAGES

1325-1332

Journal

TITLE

Diabetologia

ISSUE

6

VOLUME

61

Author Affiliations

  • MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, CB2 0QQ, Cambridge, UK
  • German Center for Diabetes Research (DZD), München-Neuherberg, Germany
  • Department of Medical Genetics, Cambridge Institute for Medical Research (CIMR), University of Cambridge, Cambridge, UK
  • Catalan Institute of Oncology (ICO), Barcelona, Spain
  • CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
  • Bjørknes University College, Oslo, Norway
  • Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
  • Instituto BIO-Donostia, Basque Government, San Sebastian, Spain
  • CESP, Faculty of Medicine, University Paris-South, Faculty of Medicine, University Versailles-St Quentin, Inserm U1018, University Paris-Saclay, Villejuif, France
  • Epidemiology and Prevention Unit, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
  • International Agency for Research on Cancer, Lyon, France
  • Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
  • Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark
  • National Food Institute, Division for Diet, Disease Prevention and Toxicology, Technical University of Denmark, Kongens Lyngby, Denmark
  • Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
  • Cancer Epidemiology Unit, University of Oxford, Oxford, UK
  • Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
  • Danish Cancer Society, Copenhagen, Denmark
  • Andalusian School of Public Health, Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain
  • Department of History of Medicine, Lund University, Malmö, Sweden
  • Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
  • Cancer Risk Factors and Life-Style Epidemiology Unit, Cancer Research and Prevention Institute – ISPO, Florence, Italy
  • Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy
  • Department of Nutritional Research, Umeå University, Umeå, Sweden
  • Public Health Directorate, Asturias, Spain
  • Unit of Epidemiology, Regional Health Service ASL TO3, Grugliasco, (TO), Italy
  • National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
  • Danish Cancer Society Research Center, Copenhagen, Denmark
  • Associazione Iblea per la Ricerca Epidemiologica (AIRE-ONLUS), Ragusa, Italy
  • The Sahlgrenska Academy, Department of Internal Medicine and Clinical Nutrition, University of Gothenburg, Göteborg, Sweden
  • School of Public Health, Imperial College London, London, UK
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00125-018-4586-2

    DOI

    http://dx.doi.org/10.1007/s00125-018-4586-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1101555417

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29549418


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    35 schema:description Aims/hypothesisGene–macronutrient interactions may contribute to the development of type 2 diabetes but research evidence to date is inconclusive. We aimed to increase our understanding of the aetiology of type 2 diabetes by investigating potential interactions between genes and macronutrient intake and their association with the incidence of type 2 diabetes.MethodsWe investigated the influence of interactions between genetic risk scores (GRSs) for type 2 diabetes, insulin resistance and BMI and macronutrient intake on the development of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a prospective case-cohort study across eight European countries (N = 21,900 with 9742 incident type 2 diabetes cases). Macronutrient intake was estimated from diets reported in questionnaires, including proportion of energy derived from total carbohydrate, protein, fat, plant and animal protein, saturated, monounsaturated and polyunsaturated fat and dietary fibre. Using multivariable-adjusted Cox regression, we estimated country-specific interaction results on the multiplicative scale, using random-effects meta-analysis. Secondary analysis used isocaloric macronutrient substitution.ResultsNo interactions were identified between any of the three GRSs and any macronutrient intake, with low-to-moderate heterogeneity between countries (I2 range 0–51.6%). Results were similar using isocaloric macronutrient substitution analyses and when weighted and unweighted GRSs and individual SNPs were examined.Conclusions/interpretationGenetic susceptibility to type 2 diabetes, insulin resistance and BMI did not modify the association between macronutrient intake and incident type 2 diabetes. This suggests that macronutrient intake recommendations to prevent type 2 diabetes do not need to account for differences in genetic predisposition to these three metabolic conditions.
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    42 schema:keywords Aims/hypothesisGene–macronutrient interactions
    43 BMI
    44 Conclusions/interpretationGenetic susceptibility
    45 Cox regression
    46 EPIC-InterAct
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    49 MethodsWe
    50 Prospective Investigation
    51 ResultsNo interaction
    52 SNPs
    53 analysis
    54 animal protein
    55 association
    56 cancer
    57 carbohydrates
    58 case-cohort study
    59 conditions
    60 countries
    61 country-specific interaction results
    62 date
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    95 macronutrient intake
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    97 macronutrient substitution analyses
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