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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.

Nettleton JA, McKeown NM, Kanoni S, Lemaitre RN, Hivert MF, Ngwa J, van Rooij FJ, Sonestedt E, Wojczynski MK, Ye Z, Tanaka T, Garcia M, Anderson JS, Follis JL, Djousse L, Mukamal K, Papoutsakis C, Mozaffarian D, Zillikens MC, Bandinelli S, Bennett AJ, Borecki IB, Feitosa MF, Ferrucci L, Forouhi NG, Groves CJ, Hallmans G, Harris T, Hofman A, Houston DK, Hu FB, Johansson I, Kritchevsky SB, Langenberg C, Launer L, Liu Y, Loos RJ, Nalls M, Orho-Melander M, Renstrom F, Rice K, Riserus U, Rolandsson O, Rotter JI, Saylor G, Sijbrands EJ, Sjogren P, Smith A, Steingrímsdóttir L, Uitterlinden AG, Wareham NJ, Prokopenko I, Pankow JS, van Duijn CM, Florez JC, Witteman JC, MAGIC InvestigatorsDupuis J, Dedoussis GV, Ordovas JM, Ingelsson E, Cupples L, Siscovick DS, Franks PW, Meigs JB - Diabetes Care (2010)

Bottom Line: For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant.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.

View Article: PubMed Central - PubMed

Affiliation: Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Sciences Center, Houston, Houston, Texas, USA. jennifer.a.nettleton@uth.tmc.edu

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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Related in: MedlinePlus

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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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).

Mentions: With adjustment for sex, age, and energy intake, greater whole-grain intake was associated with lower fasting glucose and insulin concentrations. For each one-daily-serving–greater intake of whole-grain foods, fasting glucose concentrations were 0.019 units lower (β [95% CI]: −0.019 mmol/l [−0.022 to −0.015], P < 0.0001) (Fig. 1A; Table 2) and fasting insulin concentrations were 0.021 units lower (β [95% CI]: −0.021 [ln] pmol/l [−0.025 to −0.017], P < 0.0001) (Fig. 1B; Table 2). Results from models 2–4 were similar (Table 2), showing only slight attenuation in the regression estimates (Table 2; see also supplemental Figs. S3 and S4 and supplemental Table S7).


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.

Nettleton JA, McKeown NM, Kanoni S, Lemaitre RN, Hivert MF, Ngwa J, van Rooij FJ, Sonestedt E, Wojczynski MK, Ye Z, Tanaka T, Garcia M, Anderson JS, Follis JL, Djousse L, Mukamal K, Papoutsakis C, Mozaffarian D, Zillikens MC, Bandinelli S, Bennett AJ, Borecki IB, Feitosa MF, Ferrucci L, Forouhi NG, Groves CJ, Hallmans G, Harris T, Hofman A, Houston DK, Hu FB, Johansson I, Kritchevsky SB, Langenberg C, Launer L, Liu Y, Loos RJ, Nalls M, Orho-Melander M, Renstrom F, Rice K, Riserus U, Rolandsson O, Rotter JI, Saylor G, Sijbrands EJ, Sjogren P, Smith A, Steingrímsdóttir L, Uitterlinden AG, Wareham NJ, Prokopenko I, Pankow JS, van Duijn CM, Florez JC, Witteman JC, MAGIC InvestigatorsDupuis J, Dedoussis GV, Ordovas JM, Ingelsson E, Cupples L, Siscovick DS, Franks PW, Meigs JB - Diabetes Care (2010)

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).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2992213&req=5

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).
Mentions: With adjustment for sex, age, and energy intake, greater whole-grain intake was associated with lower fasting glucose and insulin concentrations. For each one-daily-serving–greater intake of whole-grain foods, fasting glucose concentrations were 0.019 units lower (β [95% CI]: −0.019 mmol/l [−0.022 to −0.015], P < 0.0001) (Fig. 1A; Table 2) and fasting insulin concentrations were 0.021 units lower (β [95% CI]: −0.021 [ln] pmol/l [−0.025 to −0.017], P < 0.0001) (Fig. 1B; Table 2). Results from models 2–4 were similar (Table 2), showing only slight attenuation in the regression estimates (Table 2; see also supplemental Figs. S3 and S4 and supplemental Table S7).

Bottom Line: For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant.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.

View Article: PubMed Central - PubMed

Affiliation: Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Sciences Center, Houston, Houston, Texas, USA. jennifer.a.nettleton@uth.tmc.edu

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.

Show MeSH
Related in: MedlinePlus