4 resultados para 098051


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BACKGROUND Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.

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This study was supported by the UK Natural Environment Research Council (NE/H019456/1) to CJvdG, by the Wellcome Trust (WT 098051) to AWW and JP for sequencing costs, and by The Anna Trust (KB2008) to KDB. AWW and The Rowett Institute of Nutrition and Health, University of Aberdeen, receive core funding support from the Scottish Government Rural and Environmental Science and Analysis Service (RESAS). We thank Paul Scott, Richard Rance and the Wellcome Trust Sanger Institute’s sequencing team for generating 16S rRNA gene sequence data.

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IA, JNP, and MP were partly supported by the NIH, grants R01-AI-100947 to MP, and R21-GM-107683 to Matthias Chung, subcontract to MP. JNP was partly supported by an NSF graduate fellowship number DGE750616. IA, JNP, BRL, OCS and MP were supported in part by the Bill and Melinda Gates Foundation, award number 42917 to OCS. JP and AWW received core funding support from The Wellcome Trust (grant number 098051). AWW, and the Rowett Institute of Nutrition and Health, University of Aberdeen, receive core funding support from the Scottish Government Rural and Environmental Science and Analysis Service (RESAS).

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This article is protected by copyright. All rights reserved. Acknowledgements: The authors acknowledge support from the Scottish Government Food Land and People programme (RESAS). We would like to thank Lorraine Scobbie and Gary Duncan for technical support. Funding for JP, AWW and 454 pyrosequencing was provided by the Wellcome Trust (grant number 098051).