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Invited commentary: Gene x lifestyle interactions and complex disease traits--inferring cause and effect from observational data, sine qua non.

Author:
  • Paul Franks
  • J A Nettleton
Publishing year: 2010
Language: English
Pages: 992-997
Publication/Series: American Journal of Epidemiology
Volume: 172
Issue: 9
Document type: Journal article
Publisher: Oxford University Press

Abstract english

Observational epidemiology has made outstanding contributions to the discovery and elucidation of relations between lifestyle factors and common complex diseases such as type 2 diabetes. Recent major advances in the understanding of the human genetics of this disease have inspired studies that seek to determine whether the risk conveyed by bona fide risk loci might be modified by lifestyle factors such as diet composition and physical activity levels. A major challenge is to determine which of the reported findings are likely to represent causal interactions and which might be explained by other factors. The authors of this commentary use the Bradford-Hill criteria, a set of tried-and-tested guidelines for causal inference, to evaluate the findings of a recent study on interaction between variation at the cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) locus and total energy intake with respect to prevalent metabolic syndrome and hemoglobin A₁(c) levels in a cohort of 313 Japanese men. The current authors conclude that the study, while useful for hypothesis generation, does not provide overwhelming evidence of causal interactions. They overview ways in which future studies of gene × lifestyle interactions might overcome the limitations that motivated this conclusion.

Keywords

  • Public Health, Global Health, Social Medicine and Epidemiology
  • human
  • CDKAL1 protein
  • energy intake
  • hemoglobin A1c protein
  • Japan
  • metabolic syndrome X

Other

Published
  • Diabetes and Endocrinology
  • ISSN: 0002-9262
Paul Franks
E-mail: paul.franks [at] med.lu.se

Principal investigator

Genetic and Molecular Epidemiology

+46 40 39 11 49

60-12-021

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