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Diet, Lifestyle Behaviour and Other Risk Factors Associated With Type 2 Diabetes Beyond Body Mass Index: A Mendelian Randomization Study

  • Yiming Jia
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Daoxia Guo
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China

    School of Nursing, Medical College of Soochow University, Suzhou, China
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  • Lulu Sun
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Mengyao Shi
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Kaixin Zhang
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Pinni Yang
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Yuhan Zang
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Yu Wang
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Fanghua Liu
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Guo-Chong Chen
    Affiliations
    Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States
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  • Yonghong Zhang
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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  • Zhengbao Zhu
    Correspondence
    Address correspondence to: Zhengbao Zhu MD, PhD, Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province 215123, China.
    Affiliations
    Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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      Abstract

      Objectives

      Our aim in this study was to identify promising targets for the prevention of type 2 diabetes in addition to weight loss. We conducted a Mendelian randomization (MR) study to investigate the body mass index (BMI)-independent associations of 16 risk factors, including diet, lifestyle behaviour and others with type 2 diabetes.

      Methods

      We selected genetic variants as instrumental variables for diet, sleep traits, smoking, physical activity, education and blood pressure (BP) from European-descent genome-wide association studies (GWASs). Summary statistics for type 2 diabetes were derived from a recent GWAS with 74,124 European cases and 824,006 European controls. The inverse-variance weighted MR method was used to assess the associations of the risk factors with type 2 diabetes, followed by validation of robustness using different MR methods in sensitivity analyses.

      Results

      Genetically predicted insomnia (odds ratio [OR], 1.10; 95% confidence interval [CI], 1.06 to 1.15), smoking initiation (OR, 1.14; 95% CI, 1.06 to 1.21), educational level (OR, 0.69; 95% CI, 0.65 to 0.74), hypertension (OR, 6.50; 95% CI, 3.13 to 13.50), systolic BP (OR, 1.02; 95% CI, 1.02 to 1.03) and diastolic BP (OR, 1.03; 95% CI, 1.02 to 1.03) had BMI-independent effects on type 2 diabetes risk. In addition, alcohol dependence (OR, 1.10 95% CI, 1.05 to 1.16; BMI-adjusted OR, 1.04; 95% CI, 0.98 to 1.09) and vegetarian diet (OR, 0.50; 95% CI, 0.33 to 0.74; BMI-adjusted OR, 0.78; 95% CI, 0.57 to 1.06) appeared to be correlated with type 2 diabetes via a BMI-mediated pathway. Sensitivity analyses further confirmed the relationship between these factors and type 2 diabetes.

      Conclusions

      In this systematic MR study, insomnia, smoking, education and BP had BMI-independent causal effects on the risk of type 2 diabetes, whereas alcohol dependence and vegetarian diet were associated with type 2 diabetes through BMI.

      Résumé

      Objectifs

      L’objectif de notre étude était de déterminer les cibles prometteuses dans la prévention du diabète de type 2, outre la perte de poids. Nous avons mené une étude de randomisation mendélienne (RM) pour examiner les associations indépendantes de l’indice de masse corporelle (IMC) de 16 facteurs de risque, à savoir le régime alimentaire, le comportement lié au mode de vie, etc., au diabète de type 2.

      Méthodes

      Nous avons sélectionné les variants génétiques tels que les variables instrumentales du régime alimentaire, des caractéristiques du sommeil, du tabagisme, de l’activité physique, de la scolarité et de la pression artérielle (PA) issues d’études d’association pangénomiques (GWAS, de l’anglais genome-wide association studies) qui portaient sur des participants d’ascendance européenne. Les statistiques sommaires du diabète de type 2 provenaient d’une GWAS récente auprès de 74 124 cas européens et de 824 006 témoins européens. Nous avons eu recours à la méthode RM pondérée par l’inverse de la variance pour évaluer les associations des facteurs de risque avec le diabète de type 2, puis nous avons effectué la validation de la robustesse au moyen de différentes méthodes RM dans les analyses de sensibilité.

      Résultats

      L’insomnie génétiquement prévisible (rapport de cotes [RC], 1,10; intervalle de confiance [CI] à 95 %, de 1,06 à 1,15), le début du tabagisme (RC, 1,14; IC à 95 %, de 1,06 à 1,21), le niveau de scolarité (RC, 0,69; IC à 95 %, de 0,65 à 0,74), l’hypertension (RC, 6,50; IC à 95 %, de 3,13 à 13,50), la PA systolique (RC, 1,02; IC à 95 %, de 1,02 à 1,03) et la PA diastolique (RC, 1,03; IC à 95 %, de 1,02 à 1,03) ont montré des effets indépendants de l’IMC sur le risque de diabète de type 2. De plus, la dépendance à l’alcool (RC, 1,10; IC à 95 %, de 1,05 à 1,16; RC ajusté à l’IMC, 1,04; IC à 95 %, de 0,98 à 1,09) et le régime végétarien (RC, 0,50; IC à 95 %, de 0,33 à 0,74; RC ajusté à l’IMC, 0,78; IC à 95 %, de 0,57 à 1,06) semblaient être en corrélation avec le diabète de type 2 par l’intermédiaire des effets médiateurs de l’IMC. Les analyses de sensibilité ont par ailleurs confirmé la relation entre ces facteurs et le diabète de type 2.

      Conclusions

      Dans cette étude systématique RM, l’insomnie, le tabagisme, la scolarité et la PA ont montré des effets causaux indépendants de l’IMC sur le risque de diabète de type 2, alors que la dépendance à l’alcool et le régime végétarien ont été associés au diabète de type 2 par l’intermédiaire de l’IMC.

      Keywords

      Mots clés

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