Sugar-sweetened beverage intake associations with fasting glucose and insulin concentrations are not modified by selected genetic variants in a ChREBP-FGF21 pathway : a meta-analysis

Aims/hypothesis Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormone fibroblast growth factor 21 (FGF21) may influence sugar metabolism and, thereby, contribute to fructose-induced metabolic disease. We hypothesise that common variants in 11 genes involved in fructose metabolism and the ChREBP-FGF21 pathway may interact with SSB intake to exacerbate positive associations between higher SSB intake and glycaemic traits. Methods Data from 11 cohorts (six discovery and five replication) in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided association and interaction results from 34,748 adults of European descent. SSB intake (soft drinks, fruit punches, lemonades or other fruit drinks) was derived from food-frequency questionnaires and food diaries. In fixed-effects meta-analyses, we quantified: (1) the associations between SSBs and glycaemic traits (fasting glucose and fasting insulin); and (2) the interactions between SSBs and 18 independent SNPs related to the ChREBP-FGF21 pathway. Results In our combined meta-analyses of discovery and replication cohorts, after adjustment for age, sex, energy intake, BMI and other dietary covariates, each additional serving of SSB intake was associated with higher fasting glucose (beta +/- SE 0.014 +/- 0.004 [mmol/l], p = 1.5 x 10(-3)) and higher fasting insulin (0.030 +/- 0.005 [log(e) pmol/l], p = 2.0 x 10(-10)). No significant interactions on glycaemic traits were observed between SSB intake and selected SNPs. While a suggestive interaction was observed in the discovery cohorts with a SNP (rs1542423) in the beta-Klotho (KLB) locus on fasting insulin (0.030 +/- 0.011 log(e) pmol/l, uncorrected p = 0.006), results in the replication cohorts and combined meta-analyses were non-significant. Conclusions/interpretation In this large meta-analysis, we observed that SSB intake was associated with higher fasting glucose and insulin. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 pathway was observed in the discovery cohorts, this observation was not confirmed in the replication analysis.Peer reviewe

A Common Genetic Variant at 15q25 Modifies the Associations of Maternal Smoking during Pregnancy with Fetal Growth: The Generation R Study

Objective: Maternal smoking during pregnancy is associated with fetal growth retardation. We examined whether a common genetic variant at chromosome 15q25 (rs1051730), which is known to be involved in nicotine metabolism, modifies the associations of maternal smoking with fetal growth characteristics. Methods: This study was performed in 3,563 European mothers participating in a population-based prospective cohort study from early pregnancy onwards. Smoking was assessed by postal questionnaires and fetal growth characteristics were measured by ultrasound examinations in each trimester of pregnancy. Results: Among mothers who did not smoke during pregnancy (82.9%), maternal rs1051730 was not consistently associated with any fetal growth characteristic. Among mothers who continued smoking during pregnancy (17.1%), maternal rs1051730 was not associated with head circumference. The T-allele of maternal rs1051730 was associated with a smaller second and third trimester fetal femur length [differences 20.23 mm (95%CI 20.45 to 20.00) and 20.41 mm (95%CI 20.69 to 20.13), respectively] and a smaller birth length [difference 22.61 mm (95%CI 25.32 to 0.11)]. The maternal T-allele of rs1051730 was associated with a lower third trimester estimated fetal weight [difference 233 grams (95%CI 255 to 210)], and tended to be associated with birth weight [difference 238 grams (95%CI 289 to 13)]. This association persisted after adjustment for smoking quantity

Lutein intake at the age of 1 year and cardiometabolic health at the age of 6 years: the Generation R Study

Prevention, Population and Disease management (PrePoD)Public Health and primary car

Subject characteristics of the mothers per smoking status (n = 3,563)¹.

1<p>Values are means (SD) or percentages.</p>2<p>Median (95% range).</p><p>Differences in distributions between groups were evaluated using a Student T-test for continuous variables and Chi-square tests for categorical variables.</p>*<p><i>P</i>-value<0.05;</p>**<p><i>P</i>-value<0.01.</p

Cross-sectional associations of maternal rs1051730 genotype with fetal growth characteristics in different trimesters¹ (n = 3,563).

1<p>Effect estimates (with 95% confidence interval) reflect the differences in each growth characteristic for each additional copy of the T-allele of rs1051730 (assuming an additive model).</p>2<p>Interaction term = maternal genotype×smoking status.</p>*<p><i>P</i>-value<0.05;</p>**<p><i>P</i>-value<0.01.</p><p>All analyses were adjusted for gestational age at visit and sex. Analyses in the total group were additionally adjusted for smoking status (yes, no). Birth length and head circumference at birth were additionally adjusted for source of the birth measurements.</p

Effect of maternal genotype and smoking status on fetal growth characteristics (SD scores)¹.

<p>¹Values are based on repeated linear regression models and reflect the differences in growth in gender and gestational age specific standard deviation scores (SDS) between the number of risk alleles and smoking status compared to the reference group (genotype G/G, non-smokers). Estimates are given in the Supplementary <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034584#pone.0034584.s002" target="_blank">Table S2</a>. *<i>P</i>-value<0.05; **<i>P</i>-value<0.01. (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034584#pone-0034584-g001" target="_blank">Figure 1a</a> = head circumference growth, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034584#pone-0034584-g001" target="_blank">Figure 1b</a> = length growth, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034584#pone-0034584-g001" target="_blank">Figure 1c</a> = weight growth).</p

Flow chart of participants included for analysis.

<p>Flow chart of participants included for analysis.</p

A Priori and a Posteriori Dietary Patterns during Pregnancy and Gestational Weight Gain: The Generation R Study

Abnormal gestational weight gain (GWG) is associated with adverse pregnancy outcomes. We examined whether dietary patterns are associated with GWG. Participants included 3374 pregnant women from a population-based cohort in the Netherlands. Dietary intake during pregnancy was assessed with food-frequency questionnaires. Three a posteriori-derived dietary patterns were identified using principal component analysis: a “Vegetable, oil and fish”, a “Nuts, high-fiber cereals and soy”, and a “Margarine, sugar and snacks” pattern. The a priori-defined dietary pattern was based on national dietary recommendations. Weight was repeatedly measured around 13, 20 and 30 weeks of pregnancy; pre-pregnancy and maximum weight were self-reported. Normal weight women with high adherence to the “Vegetable, oil and fish” pattern had higher early-pregnancy GWG than those with low adherence (43 g/week (95% CI 16; 69) for highest vs. lowest quartile (Q)). Adherence to the “Margarine, sugar and snacks” pattern was associated with a higher prevalence of excessive GWG (OR 1.45 (95% CI 1.06; 1.99) Q4 vs. Q1). Normal weight women with higher scores on the “Nuts, high-fiber cereals and soy” pattern had more moderate GWG than women with lower scores (−0.01 (95% CI −0.02; −0.00) per SD). The a priori-defined pattern was not associated with GWG. To conclude, specific dietary patterns may play a role in early pregnancy but are not consistently associated with GWG