Genome-wide association studies have so far identified 56 loci associated with risk of coronary artery disease (CAD). Many CAD loci show pleiotropy; that is, they are also associated with other diseases or traits. This study sought to systematically test if genetic variants identified for non-CAD diseases/traits also associate with CAD and to undertake a comprehensive analysis of the extent of pleiotropy of all CAD loci. In discovery analyses involving 42,335 CAD cases and 78,240 control subjects we tested the association of 29,383 common (minor allele frequency >5%) single nucleotide polymorphisms available on the exome array, which included a substantial proportion of known or suspected single nucleotide polymorphisms associated with common diseases or traits as of 2011. Suggestive association signals were replicated in an additional 30,533 cases and 42,530 control subjects. To evaluate pleiotropy, we tested CAD loci for association with cardiovascular risk factors (lipid traits, blood pressure phenotypes, body mass index, diabetes, and smoking behavior), as well as with other diseases/traits through interrogation of currently available genome-wide association study catalogs. We identified 6 new loci associated with CAD at genome-wide significance: on 2q37 (KCNJ13-GIGYF2), 6p21 (C2), 11p15 (MRVI1-CTR9), 12q13 (LRP1), 12q24 (SCARB1), and 16q13 (CETP). Risk allele frequencies ranged from 0.15 to 0.86, and odds ratio per copy of the risk allele ranged from 1.04 to 1.09. Of 62 new and known CAD loci, 24 (38.7%) showed statistical association with a traditional cardiovascular risk factor, with some showing multiple associations, and 29 (47%) showed associations at p < 1 × 10-4 with a range of other diseases/traits. We identified 6 loci associated with CAD at genome-wide significance. Several CAD loci show substantial pleiotropy, which may help us understand the mechanisms by which these loci affect CAD risk.

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized.A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state‐of‐the‐art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative “A Healthy Diet for a Healthy Life” (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful “tips and tricks” along the analytical workflow.

Whereas most concepts of personalized nutrition (PN) in the past, included genotyping, recent years have brought new approaches that include microbiome analysis to optimize recommendations for diet and lifestyle changes. The new approach, offered by companies, that microbiome analysis provides a real benefit to either more concise recommendations or for increased compliance to PN, is largely lacking scientific validation. Although the microbiome field shows enormous proliferation, it has some major flaws that make its use in the public health domain currently critical. Starting with the quality and representative character of the stool samples, its processing and analysis as well as assembly of metagenome data and the interpretation. Moreover, there is still no consensus of what constitutes a “normal/healthy” microbiome, nor what features characterize a dysbiotic microbiome. And, based on hundreds of individual parameters and environmental factors, the intestinal microbiome shows a huge variability and consequently changing one factor—such as food intake—is likely to have a limited impact in achieving optimized health. The present review intends to summarize the state of consolidated knowledge on human gut microbiome in the context of diet and disease, its key features, and its influencing factors as well as its “add‐on” quality for PN offers.

Introduction: Exercise metabolomics research has revealed significant exercise-induced metabolic changes and identified several exerkines as mediators of physiological adaptations to exercise. However, the effect of exercise intensity on metabolic changes and circulating exerkine levels remains to be examined. Objectives: This study compared the metabolic responses to moderate-intensity and vigorous-intensity aerobic exercise. Methods: A two-period crossover trial was conducted under controlled conditions at the Max Rubner-Institute in Karlsruhe, Germany. Seventeen young, healthy, and physically active men performed 30 min moderate-intensity (50% VO_2peak) and vigorous-intensity (75% VO_2peak) aerobic exercise using two bicycle ergometer protocols in a randomized sequence. Blood samples obtained immediately before exercise and at four time points after exercise were analyzed in an untargeted metabolomics approach, and separate linear mixed models were applied to over 1000 metabolites. Results: Vigorous-intensity exercise induced a greater metabolic response than moderate-intensity exercise. Several intensity-dependent metabolites were identified, primarily involved in amino acid metabolism and energy conversion pathways, including N-lactoyl-amino acids, TCA cycle intermediates, N-acetylated amino acids, and acylcholines. The exerkines N-lactoyl-phenylalanine, lactate, and succinate were among the most intensity-dependent metabolites. N-acetylated amino acids and acylcholines were systematically altered by exercise intensity, indicating potential physiological functions. Conclusion: Exercise intensity significantly affects exercise-induced metabolic alterations and changes in exerkine levels. Our results expand the knowledge about exerkine dynamics and emphasize the role of exercise intensity in promoting physiological adaptations to exercise. The trial was registered on October 5, 2017, at the German Clinical Trials Register under the Registration Number DRKS00009743 (Universal Trial Number of WHO: U1111-1200-2530).