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Meta-Analysis
. 2010 Dec;33(12):2684-91.
doi: 10.2337/dc10-1150. Epub 2010 Aug 6.

Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies

Jennifer A Nettleton  1 , Nicola M McKeown, Stavroula Kanoni, Rozenn N Lemaitre, Marie-France Hivert, Julius Ngwa, Frank J A van Rooij, Emily Sonestedt, Mary K Wojczynski, Zheng Ye, Tosh Tanaka, Melissa Garcia, Jennifer S Anderson, Jack L Follis, Luc Djousse, Kenneth Mukamal, Constantina Papoutsakis, Dariush Mozaffarian, M Carola Zillikens, Stefania Bandinelli, Amanda J Bennett, Ingrid B Borecki, Mary F Feitosa, Luigi Ferrucci, Nita G Forouhi, Christopher J Groves, Goran Hallmans, Tamara Harris, Albert Hofman, Denise K Houston, Frank B Hu, Ingegerd Johansson, Stephen B Kritchevsky, Claudia Langenberg, Lenore Launer, Yongmei Liu, Ruth J Loos, Michael Nalls, Marju Orho-Melander, Frida Renstrom, Kenneth Rice, Ulf Riserus, Olov Rolandsson, Jerome I Rotter, Georgia Saylor, Eric J G Sijbrands, Per Sjogren, Albert Smith, Laufey Steingrímsdóttir, André G Uitterlinden, Nicholas J Wareham, Inga Prokopenko, James S Pankow, Cornelia M van Duijn, Jose C Florez, Jacqueline C M Witteman; MAGIC Investigators; Josée Dupuis, George V Dedoussis, Jose M Ordovas, Erik Ingelsson, L Adrienne Cupples, David S Siscovick, Paul W Franks, James B Meigs
Collaborators, Affiliations
Meta-Analysis

Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies

Jennifer A Nettleton et al. Diabetes Care. 2010 Dec.

Erratum in

  • Diabetes Care. 2011 Mar;34(3):785-6. multiple author names added; multiple investigator names added

Abstract

Objective: Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin.

Research design and methods: Via meta-analysis of data from 14 cohorts comprising ∼ 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant.

Results: Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele.

Conclusions: Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.

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Figures

Figure 1
Figure 1
Associations between daily whole-grain intake (A) and fasting glucose (B) and fasting insulin in 14 cohorts. A: Regression coefficient (β [95% CI]) representing expected change in fasting glucose (mmol/l) per one-daily-serving–greater whole-grain intake. B: Regression coefficient (β [95% CI]) representing expected change in fasting insulin [(ln)pmol/l] per one-daily-serving–greater whole-grain intake. Data are adjusted for model one covariates: age, sex, energy intake, field center, or population structure (Note: energy intake was not estimated in the AGES cohort; field center was included as a covariate in Health ABC, CHS, ARIC, FamHS, and InCHIANTI; population structure by principal components in FHS and FamHS).
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