ArticleDiabetologia2009
IRS1 G972R polymorphism and type 2 diabetes: a paradigm for the difficult ascertainment of the contribution to disease susceptibility of 'low-frequency-low-risk' variants.
Article in Diabetologia, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Relationship between melatonin receptor 1B and insulin receptor substrate 1 polymorphisms with gestational diabetes mellitus: a systematic review and meta-analysis.Scientific reports · 2014Pooled it
- Pooled it
- Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine.Diabetes/metabolism research and reviews · 2019Review
- Pharmacogenetics of oral antidiabetes drugs: evidence for diverse signals at the IRS1 locus.The pharmacogenomics journal · 2018Article
- Lack of association between the insulin receptor substrates-1 Gly972Arg polymorphism and type-2 diabetes mellitus among Saudis from Eastern Saudi Arabia.Saudi medical journal · 2015Article
- Association of the genetic variants of insulin receptor substrate 1 (IRS-1) with type 2 diabetes mellitus in a Saudi population.Endocrine · 2014Article
- Current Insights into the Joint Genetic Basis of Type 2 Diabetes and Coronary Heart Disease.Current cardiovascular risk reports · 2014Article
- Insulin receptor substrate-1 (IRS-1) Gly927Arg: correlation with gestational diabetes mellitus in Saudi women.BioMed research international · 2014Article
- Association study of a common variant near IRS1 with type 2 diabetes mellitus in Chinese Han population.Endocrine · 2013Article
- Insulin receptor substrate 1 (IRS1) variants confer risk of diabetes in the Boston Puerto Rican Health Study.Asia Pacific journal of clinical nutrition · 2013Article
- Variants of insulin-signaling inhibitor genes in type 2 diabetes and related metabolic abnormalities.International journal of genomics · 2013Article
- IRS1 gene variants, dysglycaemic metabolic changes and type-2 diabetes risk.Nutrition, metabolism, and cardiovascular diseases : NMCD · 2012Article
- Genetic prediction of common diseases. Still no help for the clinical diabetologist!Nutrition, metabolism, and cardiovascular diseases : NMCD · 2012Review
- Individualized therapy for type 2 diabetes: clinical implications of pharmacogenetic data.Molecular diagnosis & therapy · 2012Review
- Variants in GLIS3 and CRY2 are associated with type 2 diabetes and impaired fasting glucose in Chinese Hans.PloS one · 2011Article
- Insulin signaling regulating genes: effect on T2DM and cardiovascular risk.Nature reviews. Endocrinology · 2009Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
aims/hypothesisThe aim of the study was to determine the association between IRS1 G972R polymorphism and type 2 diabetes; published data concerning this association have been conflicting. To obtain further insight into this topic, we performed a meta-analysis of all available case-control studies.
methodsWe performed a meta-analysis of 32 studies (12,076 cases and 11,285 controls).
resultsThe relatively infrequent R972 variant was not significantly associated with type 2 diabetes (OR 1.09, 95% CI 0.96-1.23, p = 0.184 under a dominant model). Some evidence of heterogeneity was observed across studies (p = 0.1). In the 14 studies (9,713 individuals) in which the mean age at type 2 diabetes diagnosis was available, this variable explained 52% of the heterogeneity (p = 0.03). When these studies were subdivided into tertiles of mean age at diagnosis, the OR for diabetes was 1.48 (95% CI 1.17-1.87), 1.22 (95% CI 0.97-1.53) and 0.88 (95% CI 0.68-1.13) in the youngest, intermediate and oldest tertile, respectively (p = 0.0022 for trend of ORs). CONCLUSIONS/
interpretationOur findings illustrate the difficulties of ascertaining the contribution of 'low-frequency-low-risk' variants to type 2 diabetes susceptibility. In the specific context of the R972 variant, approximately 200,000 study individuals would be needed to have 80% power to identify a 9% increase in diabetes risk at a genome-wide significance level. Under these circumstances, a strategy aimed at improving outcome definition and decreasing its heterogeneity may critically enhance our ability to detect genetic effects, thereby decreasing the required sample size. Our data suggest that focusing on early-onset diabetes, which is characterised by a stronger genetic background, may be part of such a strategy.
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