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Gaussian graphical models (GGMs) are exploratory methods that can be applied to construct networks of food intake. Such networks were constructed for meal-structured data, elucidating how foods are consumed in relation to each other at meal level. Meal-specific networks were compared with habitual dietary networks using data from an EPIC-Potsdam sub-cohort study. Three 24-hour dietary recalls were collected cross-sectionally from 815 adults in 2010-2012. Food intake was averaged to obtain the habitual intake. GGMs were applied to four main meals and habitual intakes of 39 food groups to generate meal-specific and habitual dietary networks, respectively. Communities and centrality were detected in the dietary networks to facilitate interpretation. The breakfast network revealed five communities of food groups with other vegetables, sauces, bread, margarine, and sugar & confectionery as central food groups. The lunch and afternoon snacks networks showed higher variability in food consumption and six communities were detected in each of these meal networks. Among the central food groups detected in both of these meal networks were potatoes, red meat, other vegetables, and bread. Two dinner networks were identified with five communities and other vegetables as a central food group. Partial correlations at meals were stronger than on the habitual level. The meal-specific dietary networks were only partly reflected in the habitual dietary network with a decreasing percentage: 64.3% for dinner, 50.0% for breakfast, 36.2% for lunch, and 33.3% for afternoon snack. The method of GGM yielded dietary networks that describe combinations of foods at the respective meals. Analysing food consumption on the habitual level did not exactly reflect meal level intake. Therefore, interpretation of habitual networks should be done carefully. Meal networks can help understand dietary habits, however, GGMs warrant validation in other populations., Competing Interests: The authors have declared that no competing interests exist.

Background: The current state of knowledge regarding the association of dairy products and weight gain, overweight, and obesity is based on studies reporting contradicting and inconclusive results. The aim of the present study was thus to clarify the link between dairy consumption in relation to changes in anthropometric measures/adiposity by a meta-analytical approach., Methods: For the meta-analysis PubMed, EMBASE, Web of Sciences, and google scholar were searched by two independent authors up to May 2016 with no restriction to language or calendar date. Prospective cohort studies reporting about intake of dairy consumption (including milk, yogurt, cheese, butter) and changes in body weight or waist circumference, risk of overweight, obesity, or weight gain were eligible. Pooled effects were calculated using a random effects model, and also a fixed effect model for sensitivity analysis. Due to the heterogeneity of statistical analytical approaches of the studies the analysis were done separately for beta-coefficients of changes in body weight and/or waist circumference per serving of dairy, for differences in weight gain/gain in waist circumference when comparing extreme categories of dairy consumption, and for odds ratios in regard to weight gain, overweight/obesity, or abdominal obesity., Findings: 24 studies (27 reports) met the inclusion criteria for the systematic review, and 22 studies provided sufficient data for inclusion in the meta-analysis. The meta-analysis of the five studies on changes in body weight per serving of dairy no significant results could be found for whole fat dairy and low fat dairy. However, there was inverse association between changes in body weight for each serving's increase of yogurt (beta: -40.99 gram/year, 95% CI, -48.09 to -33.88), whereas each serving's increase of cheese was positively associated (beta: -10.97 gram/year, 95% CI, 2.86 to 19.07). Furthermore, the highest dairy intake category was associated with a reduced risk of abdominal obesity (OR: 0.85; 95% CI, 0.76 to 0.95), and risk of overweight (OR: 0.87; 95% CI, 0.76 to 1.00) compared to the lowest intake category. No significant association could be observed for risk of weight gain., Conclusion: In summary the results of the meta-analysis still reflect that dairy consumption was not positively related to changes in body weight. Yogurt was the only dairy food that showed some evidence for a beneficial effect, where higher intakes were inversely associated a reduced risk of obesity, changes in body weight or waist circumference. Further research is needed, since the overall interpretation of the results is limited by heterogeneous risk estimates.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

Background: Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population., Methods: We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥ 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample)., Results: Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥ 200 points were 9% and 96%, respectively., Conclusion: The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.

Objective: Obesity has become a leading preventable cause of morbidity and mortality in many parts of the world. It is thought to originate from multiple genetic and environmental determinants. The aim of the current study was to introduce haplotype-based multi-locus stepwise regression (MSR) as a method to investigate combinations of unlinked single nucleotide polymorphisms (SNPs) for obesity phenotypes., Methods: In 2,122 healthy randomly selected men and women of the EPIC-Potsdam cohort, the association between 41 SNPs from 18 obesity-candidate genes and either body mass index (BMI, mean=25.9 kg/m(2), SD=4.1) or waist circumference (WC, mean=85.2 cm, SD=12.6) was assessed. Single SNP analyses were done by using linear regression adjusted for age, sex, and other covariates. Subsequently, MSR was applied to search for the 'best' SNP combinations. Combinations were selected according to specific AICc and p-value criteria. Model uncertainty was accounted for by a permutation test., Results: The strongest single SNP effects on BMI were found for TBC1D1 rs637797 (β = -0.33, SE=0.13), FTO rs9939609 (β=0.28, SE=0.13), MC4R rs17700144 (β=0.41, SE=0.15), and MC4R rs10871777 (β=0.34, SE=0.14). All these SNPs showed similar effects on waist circumference. The two 'best' six-SNP combinations for BMI (global p-value= 3.45⋅10(-6) and 6.82⋅10(-6)) showed effects ranging from -1.70 (SE=0.34) to 0.74 kg/m(2) (SE=0.21) per allele combination. We selected two six-SNP combinations on waist circumference (global p-value = 7.80⋅10(-6) and 9.76⋅10(-6)) with an allele combination effect of -2.96 cm (SE=0.76) at maximum. Additional adjustment for BMI revealed 15 three-SNP combinations (global p-values ranged from 3.09⋅10(-4) to 1.02⋅10(-2)). However, after carrying out the permutation test all SNP combinations lost significance indicating that the statistical associations might have occurred by chance., Conclusion: MSR provides a tool to search for risk-related SNP combinations of common traits or diseases. However, the search process does not always find meaningful SNP combinations in a dataset.

Background: Stearoyl-CoA desaturase-1 (SCD1) is an enzyme involved in lipid metabolism. In mice and humans its activity has been associated with traits of the metabolic syndrome, but also with the prevention of saturated fatty acids accumulation and subsequent inflammation, whereas for liver fat content inconsistent results have been reported. Thus, variants of the gene encoding SCD1 (SCD1) could potentially modify metabolic risk factors, but few human studies have addressed this question., Methods: In a sample of 2157 middle-aged men and women randomly drawn from the Potsdam cohort of the European Prospective Investigation into Cancer and Nutrition, we investigated the impact of 7 SCD1 tagging-single nucleotide polymorphisms (rs1502593, rs522951, rs11190480, rs3071, rs3793767, rs10883463 and rs508384) and 5 inferred haplotypes with frequency >5% describing 90.9% of the genotype combinations in our population, on triglycerides, body mass index (BMI), waist circumference (WC), glycated haemoglobin (HbA1c), high-sensitivity C-reactive protein (hs-CRP), gamma-glutamyltransferase (GGT), alanine aminotransferase (ALT) and fetuin-A., Results: No significant associations between any of the SNPs or haplotypes and BMI, WC, fetuin-A and hs-CRP were observed. Associations of rs10883463 with triglycerides, GGT and HbA1c as well as of rs11190480 with ALT activity, were weak and became non-significant after multiple-testing correction. Also associations of the haplotype harbouring the minor allele of rs1502593 with HbA1c levels, the haplotype harbouring the minor alleles of rs11190480 and rs508384 with activity of ALT, and the haplotype harbouring the minor alleles of rs522951, rs10883463 and rs508384 with triglyceride and HbA1C levels and GGT activities did not withstand multiple-testing correction., Conclusion: These findings suggest that there are no associations between common variants of SCD1 or its inferred haplotypes and the investigated metabolic risk factors. However, given the results from animal models, heterogeneity of human SCD1 warrants further investigation, in particular with regard to rare variants.

Objective: First, to investigate and compare associations between alcohol consumption and variants in alcohol dehydrogenase (ADH) genes with incidence of cardiovascular diseases (CVD) in a large German cohort. Second, to quantitatively summarize available evidence of prospective studies on polymorphisms in ADH1B and ADH1C and CVD-risk., Methods: We conducted a case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort including a randomly drawn subcohort (n = 2175) and incident cases of myocardial infarction (MI; n = 230) or stroke (n = 208). Mean follow-up time was 8.2±2.2 years. The association between alcohol consumption, ADH1B or ADH1C genotypes, and CVD-risk was assessed using Cox proportional hazards regression. Additionally, we report results on associations of variants in ADH1B and ADH1C with ischemic heart disease and stroke in the context of a meta-analysis of previously published prospective studies published up to November 2011., Results: Compared to individuals who drank >0 to 6 g alcohol/d, we observed a reduced risk of MI among females consuming >12 g alcohol/d (HR = 0.31; 95% CI: 0.10-0.97) and among males consuming >24 to 60 g/d (HR = 0.57; 95% CI: 0.33-0.98) or >60 g alcohol/d (HR = 0.30; 95% CI: 0.12-0.78). Stroke risk was not significantly related to alcohol consumption >6 g/d, but we observed an increased risk of stroke in men reporting no alcohol consumption. Individuals with the slow-coding ADH1B*1/1 genotype reported higher median alcohol consumption. Yet, polymorphisms in ADH1B or ADH1C were not significantly associated with risk of CVD in our data and after pooling results of eligible prospective studies [ADH1B*1/1: RR = 1.35 (95% CI: 0.98-1.88; p for heterogeneity: 0.364); ADH1C*2/2: RR = 1.07 (95% CI: 0.90-1.27; p for heterogeneity: 0.098)]., Conclusion: The well described association between alcohol consumption and CVD-risk is not reflected by ADH polymorphisms, which modify the rate of ethanol oxidation.