Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study

Langenberg, Claudia; Sharp, Stephen J.; Forouhi, Nita G.; Franks, Paul W.; Schulze, Matthias B.; Kerrison, Nicola D.; Ekelund, Ulf; Barroso, Inês; Panico, Salvatore; Tormo, María-José; Spranger, Joachim; Griffin, Simon J.; Schouw, Yvonne T. van der; Amiano, Pilar; Ardanaz, Eva; Arriola, Larraitz; Balkau, Beverley; Barricarte, Aurelio; Beulens, Joline W.J.; Boeing, Heiner; Bueno-de-Mesquita, H. Bas; Buijsse, Brian; López, María Dolores Chirlaque; Clavel‐Chapelon, Françoise; Crowe, Francesca L.; Lauzon-Guillan, Blandine de; Deloukas, Panos; Dorronsoro, Miren; Drogan, Dagmar; Froguel, Philippe; González, Carlos; Grioni, Sara; Groop, Leif; Groves, Chris; Hainaut, Pierre; Halkjær, Jytte; Hallmans, Göran; Hansen, Torben; Huerta, José María; Kaaks, Rudolf; Key, Timothy J.; Khaw, Kay‐Tee; Koulman, Albert; Mattiello, Amalia; Navarro, Carmen; Nilsson, Peter M.; Norat, Teresa; Overvad, Kim; Palla, Luigi; Palli, Domenico; Pedersen, Oluf; Peeters, Petra H.M.; Quiŕos, J. Ramón; Ramachandran, A.; Rodriguez-Suarez, L.; Rolandsson, Olov; Romaguera, Dora; Romieu, Isabelle; Sacerdote, Carlotta; Sánchez, María José; Sandbæk, Annelli; Slimani, Nadia; Sluijs, Ivonne; Spijkerman, Annemieke M.W.; Teucher, Birgit; Tjønneland, Anne; Tumino, Rosario; Verschuren, W M Monique; Tuomilehto, Jaakko; Feskens, Edith J. M.; McCarthy, Mark I.; Riboli, Elio; Wareham, Nicholas J.

doi:10.1007/s00125-011-2182-9

Cited by 162 publications

(231 citation statements)

References 32 publications

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“…This allows a pooling of detailed data on dietary self-reports, developed and validated within each of the eight participating countries and including an assessment of nutritional biomarkers [9]. Standardising and validating the procedure to assess exposure to these factors provides a more accurate estimate of the true underlying relationship between an exposure and an outcome.…”

Section: Strengths and Weaknessesmentioning

confidence: 99%

“…As an example, given the high prevalence of undiagnosed type 2 diabetes [11], it is unclear how many of the 1,058 individuals with incident diabetes in the first and second year of follow-up had prevalent diabetes and should therefore have been excluded from the analyses [9]. There is also a potential for underestimating the incidence of type 2 diabetes due to the large number of individuals whose diabetes can only be detected by fasting blood glucose or oral glucose tolerance tests.…”

Section: Strengths and Weaknessesmentioning

confidence: 99%

“…Generally, InterAct comprises heterogeneous populations with different methods of ascertaining their diabetes (see above), with different age ranges and sex distributions [9]. Furthermore, the recruitment strategies of the EPIC cohorts varied widely [9].…”

Section: Strengths and Weaknessesmentioning

confidence: 99%

“…Furthermore, the recruitment strategies of the EPIC cohorts varied widely [9]. Thus, it will be important to evaluate whether the results are consistent across studies.…”

Section: Strengths and Weaknessesmentioning

confidence: 99%

“…The goal of the InterAct consortium is to carry out a meta-analysis of gene-lifestyle interactions in terms of the risk of type 2 diabetes based on EPIC cohorts from eight European countries [9].…”

mentioning

confidence: 99%

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Type 2 diabetes: unravelling the interaction between genetic predisposition and lifestyle

2011

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Section: Strengths and Weaknessesmentioning

confidence: 99%

Section: Strengths and Weaknessesmentioning

confidence: 99%

Section: Strengths and Weaknessesmentioning

confidence: 99%

“…Furthermore, the recruitment strategies of the EPIC cohorts varied widely [9]. Thus, it will be important to evaluate whether the results are consistent across studies.…”

Section: Strengths and Weaknessesmentioning

confidence: 99%

mentioning

confidence: 99%

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Type 2 diabetes: unravelling the interaction between genetic predisposition and lifestyle

2011

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Association Between Low-Density Lipoprotein Cholesterol–Lowering Genetic Variants and Risk of Type 2 Diabetes

Lotta

Sharp

Burgess

et al. 2016

JAMA

324

234

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Importance Low-density lipoprotein (LDL) cholesterol-lowering alleles in or near NPC1L1 or HMGCR, encoding the respective molecular targets of ezetimibe and statins, have previously been used as proxies to study the efficacy of these lipid-lowering drugs. Alleles near HMGCR are associated with a higher risk of type 2 diabetes, mimicking the increased incidence of new-onset diabetes associated with statin treatment in randomized clinical trials. It is unknown whether alleles near NPC1L1 are also associated with the risk of type 2 diabetes. Objective To investigate whether LDL-lowering alleles in or near NPC1L1 and other genes encoding current or prospective molecular targets of lipid-lowering therapy (i.e. HMGCR, PCSK9, ABCG5/G8, LDLR) are associated with the risk of type 2 diabetes. Design, Setting and Participants The associations with type 2 diabetes and coronary artery disease of LDL-lowering genetic variants were investigated in meta-analyses of genetic association studies. Meta-analyses included 50,775 individuals with type 2 diabetes and 270,269 controls including three studies and 60,801 individuals with coronary artery disease and 123,504 controls from a published meta-analysis. Data collection took place in Europe and the United States between 1991 and 2016. Exposure LDL-lowering alleles in or near NPC1L1, HMGCR, PCSK9, ABCG5/G8, LDLR. Main Outcomes and Measures Odds ratio of type 2 diabetes and coronary artery disease. Results LDL-lowering genetic variants at NPC1L1 were inversely associated with coronary artery disease (odds ratio for a genetically-predicted reduction of 1 mmol/L in LDL cholesterol, 0.61; 95% confidence interval, 0.42-0.88; p=0.008) and directly associated with type 2 diabetes (2.42, 1.70-3.43; p<0.001). The odds ratio of type 2 diabetes for PCSK9 genetic variants was 1.19 (95% confidence interval, 1.02-1.38, p=0.03). For a given reduction in LDL cholesterol, genetic variants were associated with a similar reduction in coronary artery disease risk (I-squared for heterogeneity in genetic associations=0.0%; p=0.93). However, associations with type 2 diabetes were heterogeneous (I-squared=77.2%; p=0.002), indicating gene-specific associations with metabolic risk for LDL-lowering alleles. Conclusions and Relevance In this meta-analysis, exposure to LDL-cholesterol lowering genetic variants in or near NPC1L1 and other genes was associated with a higher risk of type 2 diabetes. These data provide insights into potential adverse effects of LDL cholesterol-lowering therapy.

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Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors

Lotta

Wittemans

Zuber

et al. 2018

JAMA

165

140

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IMPORTANCE Body fat distribution, usually measured using waist-to-hip ratio (WHR), is an important contributor to cardiometabolic disease independent of body mass index (BMI). Whether mechanisms that increase WHR via lower gluteofemoral (hip) or via higher abdominal (waist) fat distribution affect cardiometabolic risk is unknown. OBJECTIVE To identify genetic variants associated with higher WHR specifically via lower gluteofemoral or higher abdominal fat distribution and estimate their association with cardiometabolic risk. DESIGN, SETTING, AND PARTICIPANTS Genome-wide association studies (GWAS) for WHR combined data from the UK Biobank cohort and summary statistics from previous GWAS (data collection: 2006-2018). Specific polygenic scores for higher WHR via lower gluteofemoral or via higher abdominal fat distribution were derived using WHR-associated genetic variants showing specific association with hip or waist circumference. Associations of polygenic scores with outcomes were estimated in 3 population-based cohorts, a case-cohort study, and summary statistics from 6 GWAS (data collection: 1991-2018). EXPOSURES More than 2.4 million common genetic variants (GWAS); polygenic scores for higher WHR (follow-up analyses). MAIN OUTCOMES AND MEASURES BMI-adjusted WHR and unadjusted WHR (GWAS); compartmental fat mass measured by dual-energy x-ray absorptiometry, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, triglycerides, fasting glucose, fasting insulin, type 2 diabetes, and coronary disease risk (follow-up analyses). RESULTS Among 452 302 UK Biobank participants of European ancestry, the mean (SD) age was 57 (8) years and the mean (SD) WHR was 0.87 (0.09). In genome-wide analyses, 202 independent genetic variants were associated with higher BMI-adjusted WHR (n = 660 648) and unadjusted WHR (n = 663 598). In dual-energy x-ray absorptiometry analyses (n = 18 330), the hip-and waist-specific polygenic scores for higher WHR were specifically associated with lower gluteofemoral and higher abdominal fat, respectively. In follow-up analyses (n = 636 607), both polygenic scores were associated with higher blood pressure and triglyceride levels and higher risk of diabetes (waist-specific score: odds ratio [OR], 1.57 [95% CI, 1.34-1.83], absolute risk increase per 1000 participant-years [ARI], 4.4 [95% CI,

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Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study

Cited by 162 publications

References 32 publications

Type 2 diabetes: unravelling the interaction between genetic predisposition and lifestyle

Type 2 diabetes: unravelling the interaction between genetic predisposition and lifestyle

Association Between Low-Density Lipoprotein Cholesterol–Lowering Genetic Variants and Risk of Type 2 Diabetes

Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors

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