Because foods are consumed in combination, it is difficult in observational studies to separate the effects of single foods on the development of diseases. A possible way to examine the combined effect of food intakes is to derive dietary patterns by using appropriate statistical methods. The objective of this study was to apply a new statistical method, reduced rank regression (RRR), that is more flexible and powerful than the classic principal component analysis. RRR can be used efficiently in nutritional epidemiology by choosing disease-specific response variables and determining combinations of food intake that explain as much response variation as possible. The authors applied RRR to extract dietary patterns from 49 food groups, specifying four diabetes-related nutrients and nutrient ratios as responses. Data were derived from a nested German case-control study within the European Prospective Investigation into Cancer and Nutrition-Potsdam study consisting of 193 cases with incident type 2 diabetes identified until 2001 and 385 controls. The four factors extracted by RRR explained 93.1% of response variation, whereas the first four factors obtained by principal component analysis accounted for only 41.9%. In contrast to principal component analysis and other methods, the new RRR method extracted a significant risk factor for diabetes.

Human gut microbiota is an important determinant for health and disease, and recent studies emphasize the numerous factors shaping its diversity. Here we performed a genome-wide association study (GWAS) of the gut microbiota using two cohorts from northern Germany totaling 1,812 individuals. Comprehensively controlling for diet and non-genetic parameters, we identify genome-wide significant associations for overall microbial variation and individual taxa at multiple genetic loci, including the VDR gene (encoding vitamin D receptor). We observe significant shifts in the microbiota of Vdr−/− mice relative to control mice and correlations between the microbiota and serum measurements of selected bile and fatty acids in humans, including known ligands and downstream metabolites of VDR. Genome-wide significant (P < 5 × 10−8) associations at multiple additional loci identify other important points of host–microbe intersection, notably several disease susceptibility genes and sterol metabolism pathway components. Non-genetic and genetic factors each account for approximately 10% of the variation in gut microbiota, whereby individual effects are relatively small.

Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.

The purpose of the present literature review was to investigate and summarize the current evidence on associations between dietary patterns and biomarkers of inflammation, as derived from epidemiological studies. A systematic literature search was conducted using PubMed, Web of Science, and EMBASE, and a total of 46 studies were included in the review. These studies predominantly applied principal component analysis, factor analysis, reduced rank regression analysis, the Healthy Eating Index, or the Mediterranean Diet Score. No prospective observational study was found. Patterns identified by reduced rank regression as being statistically significantly associated with biomarkers of inflammation were almost all meat-based or "Western" patterns. Studies using principal component analysis or a priori-defined diet scores found that meat-based or "Western-like" patterns tended to be positively associated with biomarkers of inflammation, predominantly C-reactive protein, while vegetable- and fruit-based or "healthy" patterns tended to be inversely associated. While results of the studies were inconsistent, interventions with presumed healthy diets resulted in reductions of almost all investigated inflammatory biomarkers. In conclusion, prospective studies are warranted to confirm the reported findings and further analyze associations, particularly by investigating dietary patterns as risk factors for changes in inflammatory markers over time.

A very small inverse association between intake of total fruits and vegetables and cancer risk was observed in this study. Given the small magnitude of the observed associations, caution should be applied in their interpretation.

Estimating usual food intake distributions from short-term quantitative measurements is critical when occasionally or rarely eaten food groups are considered. To overcome this challenge by statistical modeling, the Multiple Source Method (MSM) was developed in 2006. The MSM provides usual food intake distributions from individual short-term estimates by combining the probability and the amount of consumption with incorporation of covariates into the modeling part. Habitual consumption frequency information may be used in 2 ways: first, to distinguish true nonconsumers from occasional nonconsumers in short-term measurements and second, as a covariate in the statistical model. The MSM is therefore able to calculate estimates for occasional nonconsumers. External information on the proportion of nonconsumers of a food can also be handled by the MSM. As a proof-of-concept, we applied the MSM to a data set from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Calibration Study (2004) comprising 393 participants who completed two 24-h dietary recalls and one FFQ. Usual intake distributions were estimated for 38 food groups with a proportion of nonconsumers > 70% in the 24-h dietary recalls. The intake estimates derived by the MSM corresponded with the observed values such as the group mean. This study shows that the MSM is a useful and applicable statistical technique to estimate usual food intake distributions, if at least 2 repeated measurements per participant are available, even for food groups with a sizeable percentage of nonconsumers.

Highlights d Obesity, but not type 2 diabetes, associated with gut microbiome variation d Medications and dietary supplements associated with gut microbiome variation d High iron intake affected microbiome composition in mice d Microbiome variation was also reflected in serum metabolite profiles

The prevalence of obesity, defined as a BMI of ‡ 30 . 0 kg/m 2 , has increased substantially over previous decades to about 20 % in industrialized countries, and a further increase is expected in the future. Epidemiological studies have shown that obesity is a risk factor for: postmenopausal breast cancer; cancers of the endometrium, colon and kidney; malignant adenomas of the oesophagus. Obese subjects have an approximately 1 . 5-3 . 5-fold increased risk of developing these cancers compared with normal-weight subjects, and it has been estimated that between 15 and 45 % of these cancers can be attributed to overweight (BMI 25 . 0-29 . 9 kg/m 2 ) and obesity in Europe. More recent studies suggest that obesity may also increase the risk of other types of cancer, including pancreatic, hepatic and gallbladder cancer. The underlying mechanisms for the increased cancer risk as a result of obesity are unclear and may vary by cancer site and also depend on the distribution of body fat. Thus, abdominal obesity as defined by waist circumference or waist :hip ratio has been shown to be more strongly related to certain cancer types than obesity as defined by BMI. Possible mechanisms that relate obesity to cancer risk include insulin resistance and resultant chronic hyperinsulinaemia, increased production of insulin-like growth factors or increased bioavailability of steroid hormones. Recent research also suggests that adipose tissue-derived hormones and cytokines (adipokines), such as leptin, adiponectin and inflammatory markers, may reflect mechanisms linked to tumourigenesis. Obesity: Cancer risk: Cancer site: Body fat distributionThe prevalence of obesity has increased substantially over previous decades in most industrialized countries, and a further increase is expected in the future