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.