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

Background and aims: Previous studies suggest 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 ≥18y. Dairy consumption was quantified by a food frequency questionnaire. Abdominal overweight was defined as waist circumference (WC) ≥88 (women) or ≥102 (men) cm (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 adjusted for relevant covariates. Total dairy was positively associated with abdominal overweight (OR Q1ref vs Q5: 1.09; 95% CI: 1.04, 1.14), and BMI-defined overweight (ORQ5 1.13; 95% CI: 1.08, 1.18) and obesity (ORQ5 1.09; 95% CI: 1.02, 1.16). Positive associations were also observed of skimmed, semi-skimmed, and non-fermented dairy with overweight categories. Full-fat dairy was inversely associated with overweight and obesity (ORQ5 for obesity: 0.78; 95% CI: 0.73, 0.83). Moreover, inverse associations were observed for yogurt and custard, and positive associations for milk, buttermilk, flavoured yogurt drinks, cheese, and cheese snacks. Fermented dairy, curd cheese and Dutch cheese were not consistently associated with overweight categories. Conclusions: Total, skimmed, semi-skimmed, and non-fermented dairy, milk, buttermilk, flavoured yogurt drinks, total cheese, and cheese snacks were positively associated with overweight categories, whereas full-fat dairy, custard, and yogurt were inversely associated with overweight categories

Dairy product consumption is associated with pre-diabetes and newly diagnosed type 2 diabetes in the Lifelines Cohort Study

Previous studies show associations between dairy product consumption and type 2 diabetes, but only a few studies conducted detailed analyses for a variety of dairy subgroups. Therefore, we examined cross-sectional associations of a broad variety of dairy subgroups with pre-diabetes and newly diagnosed type 2 diabetes (ND-T2DM) among Dutch adults. In total, 112 086 adults without diabetes completed a semi-quantitative FFQ and donated blood. Pre-diabetes was defined as fasting plasma glucose (FPG) between 5·6 and 6·9 mmol/l or HbA1c% of 5·7–6·4 %. ND-T2DM was defined as FPG ≥7·0 mmol/l or HbA1c ≥6·5 %. Logistic regression analyses were conducted by 100 g or serving increase and dairy tertiles (T1ref), while adjusting for demographic, lifestyle and dietary covariates. Median dairy product intake was 324 (interquartile range 227) g/d; 25 549 (23 %) participants had pre-diabetes; and 1305 (1 %) had ND-T2DM. After full adjustment, inverse associations were observed of skimmed dairy (OR100 g 0·98; 95 % CI 0·97, 1·00), fermented dairy (OR100 g 0·98; 95 % CI 0·97, 0·99) and buttermilk (OR150 g 0·97; 95 % CI 0·94, 1·00) with pre-diabetes. Positive associations were observed for full-fat dairy (OR100 g 1·003; 95 % CI 1·01, 1·06), non-fermented dairy products (OR100 g 1·01; 95 % CI 1·00, 1·02) and custard (ORserving/150 g 1·13; 95 % CI 1·03, 1·24) with pre-diabetes. Moreover, full-fat dairy products (ORT3 1·16; 95 % CI 0·99, 1·35), non-fermented dairy products (OR100 g 1·05; 95 % CI 1·01, 1·09) and milk (ORserving/150 g 1·08; 95 % CI 1·02, 1·15) were positively associated with ND-T2DM. In conclusion, our data showed inverse associations of skimmed and fermented dairy products with pre-diabetes. Positive associations were observed for full-fat and non-fermented dairy products with pre-diabetes and ND-T2DM

Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA

The intricate dependency structure of biological “omics” data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.</p