Dairy shows different associations with abdominal and BMI-defined overweight: Cross-sectional analyses exploring a variety of dairy products.

BACKGROUND AND AIMS: Previous studies have suggested weight-regulatory properties for several dairy nutrients, but population-based studies on dairy and body weight are inconclusive. We explored cross-sectional associations between dairy consumption and indicators of overweight. METHODS AND RESULTS: We included 114,682 Dutch adults, aged ≥18 years. Dairy consumption was quantified by a food frequency questionnaire. Abdominal overweight was defined as waist circumference (WC) ≥88 cm (women) or ≥102 cm (men) (n = 37,391), overweight as BMI ≥25-30 kg/m2 (n = 44,772) and obesity as BMI ≥30 kg/m2 (n = 15,339). Associations were quantified by logistic (abdominal overweight, no/yes), multinomial logistic (BMI-defined overweight and obesity) and linear regression analyses (continuous measures of WC and BMI), and they were adjusted for relevant covariates. Total dairy showed a positive association with abdominal overweight (OR Q1 ref vs. Q5: 1.09; 95% CI: 1.04-1.14) and with BMI-defined overweight (OR Q5 1.13; 95% CI: 1.08-1.18) and obesity (OR Q5 1.09; 95% CI: 1.02-1.16). Skimmed, semi-skimmed and non-fermented dairy also showed positive associations with overweight categories. Full-fat dairy showed an inverse association with overweight and obesity (OR Q5 for obesity: 0.78; 95% CI: 0.73-0.83). Moreover, inverse associations were observed for yoghurt and custard and positive associations for milk, buttermilk, flavoured yoghurt drinks, cheese and cheese snacks. Fermented dairy, curd cheese and Dutch cheese did not show a consistent association with overweight categories. CONCLUSIONS: Total, skimmed, semi-skimmed and non-fermented dairy; milk; buttermilk; flavoured yoghurt drinks; total cheese and cheese snacks showed a positive association with overweight categories, whereas full-fat dairy, custard and yoghurt showed an inverse association with overweight categories.

Intake of different types of dairy and its prospective association with risk of type 2 diabetes: The Rotterdam Study.

BACKGROUND AND AIMS: The prevalence of type 2 diabetes (T2DM) is increasing. Several studies have suggested a beneficial effect of several major dairy nutrients on insulin production and sensitivity. Conversely, harmful effects have been suggested as well. This study aimed to investigate the impact of the full-range of dairy products and its association with incidence T2DM in Dutch adults aged ≥55 years participating in the Rotterdam Study. METHODS AND RESULTS: Dairy intake was assessed with a validated FFQ, including total, skimmed, semi-skimmed, full-fat, fermented, and non-fermented dairy, and subclasses of these product groups. Verified prevalent and incident diabetes were documented. Cox proportional hazards regression and spline regression were used to analyse data, adjusting for age, sex, alcohol, smoking, education, physical activity, body mass index, intake of total energy, energy-adjusted meat, and energy-adjusted fish intake. Median total dairy intake was 398 g/day (IQR 259-559 g/day). Through 9.5 ± 4.1 years of follow-up, 393 cases of incident T2DM were reported. Cox and spline regression did not point towards associations of total dairy consumption, dairy consumption based on fat content, non-fermented or fermented dairy consumption, or individual dairy product consumption with incident T2DM. The HR for total dairy intake and T2DM was 0.93 (95% CI: 0.70-1.23) in the upper quartile (P-for trend 0.76). CONCLUSIONS: This prospective cohort study did not point towards an association between dairy consumption and T2DM.

Shared genetic variance between the features of the metabolic syndrome: heritability studies.

Heritability estimates of MetS range from approximately 10%-30%. The genetic variation that is shared among MetS features can be calculated by genetic correlation coefficients. The objective of this paper is to identify MetS feature as well as MetS related features which have much genetic variation in common, by reviewing the literature regarding genetic correlation coefficients. Identification of features, that have much genetic variation in common, may eventually facilitate the search for pleitropic genetic variants that may explain the clustering of MetS features. A PubMed search with the search terms "(metabolic syndrome OR insulin resistance syndrome) and (heritability OR genetic correlation OR pleiotropy)" was performed. Studies published before 7th July 2011, which presented genetic correlation coefficients between the different MetS features and genetic correlation coefficients of MetS and its features with adipose tissue-, pro-inflammatory and pro-thrombotic biomarkers were included. Nine twin and 19 family studies were included in the review. Genetic correlations varied, but were strongest between waist circumference and HOMA-IR (r(2): 0.36 to 0.79, median: 0.50), HDL cholesterol and triglycerides (r(2): -0.05 to -0.59, median -0.45), adiponectin and MetS (r(2): -0.32 to -0.43; median -0.38), adiponectin and insulin (r(2): -0.10 to -0.60; median -0.30) and between adiponectin and HDL-cholesterol (r(2): -0.22 to -0.51, median -0.29). In conclusion, heritability studies suggest that genetic pleiotropy exist especially between certain MetS features, as well as between MetS and adiponectin. Further research on actual genetic variants responsible for the genetic pleiotropy of these combinations will provide more insight into the etiology of MetS.

Genetic variants and the metabolic syndrome: a systematic review.

Several candidate gene studies on the metabolic syndrome (MetS) have been conducted. However, for most single nucleotide polymorphisms (SNPs) no systematic review on their association with MetS exists. A systematic electronic literature search was conducted until the 2nd of June 2010, using HuGE Navigator. English language articles were selected. Only genes of which at least one SNP-MetS association was studied in an accumulative total population ≥ 4000 subjects were included. Meta-analyses were conducted on SNPs with three or more studies available in a generally healthy population. In total 88 studies on 25 genes were reviewed. Additionally, for nine SNPs in seven genes (GNB3, PPARG, TCF7L2, APOA5, APOC3, APOE, CETP) a meta-analysis was conducted. The minor allele of rs9939609 (FTO), rs7903146 (TCF7L2), C56G (APOA5), T1131C (APOA5), C482T (APOC3), C455T (APOC3) and 174G>C (IL6) were more prevalent in subjects with MetS, whereas the minor allele of Taq-1B (CETP) was less prevalent in subjects with the MetS. After having systematically reviewed the most studied SNP-MetS associations, we found evidence for an association with the MetS for eight SNPs, mostly located in genes involved in lipid metabolism.

Glucose levels and genetic variants across transcriptional pathways: interaction effects with BMI.

OBJECTIVE: Much of the genetic variation in glucose levels remains to be discovered. Especially, research on gene-environment interactions is scarce. Overweight is one of the main risk factors for hyperglycemia. As transcriptional regulation is important for both weight maintenance and glucose control, we analyzed 353 single nucleotide polymorphisms (SNPs), occurring in transcriptional pathways of glucose and lipid metabolism in interaction with body mass index (BMI) on glucose levels. RESEARCH DESIGN AND METHODS: SNPs were measured in 3244 participants of the Doetichem cohort. Non-fasting glucose levels and BMI were measured twice in 6 years. SNP x BMI interactions were analyzed by mixed models and adjusted for age, sex, time since last meal, and follow-up time. False discovery rate (FDR) <0.2 was used to adjust for multiple testing. RESULTS: Two SNPs in the PPARGC1A gene (rs8192678, FDR=0.07; rs3755863, FDR=0.17) showed a significant interaction with BMI. The rare allele of both SNPs was associated with significantly lower glucose levels in subjects with a BMI28 kg m(-2). A small intervention study (n=120) showed similar, though non-significant, results. CONCLUSIONS: Using a pathway-based approach, we found that BMI significantly modified the association between two SNPs in the PPARGC1A gene and glucose levels. The association between glucose and PPARGC1A was only present in lean subjects. This suggests that the effect of the PPARGC1A gene, which is involved both in fatty acid oxidation and glucose metabolism, is modified by BMI.