Genetic Markers of Endothelial Dysfunction

The rate of endothelial dysfunction is influenced by genetic variation and thus inherited in families. Genetic disorders, such as familial hypercholesterolemia and homocystinuria, are at risk for premature atherosclerosis, and exhibit early endothelial dysfunction. The known spectrum of mutations in LDL receptor, APOB and PCSK9 gene represent the monogenic dominant hypercholesterolemia. An autosomal recessive form of hypercholesterolaemia in the caused by homozygous mutations in the LDL-R adaptor protein. The polygenic hypercholesterolaemia for patients with a clinical diagnosis of FH is based on the cumulative effect of LDL-C-raising alleles with a cumulative effect, in a complex interaction with the environment that leads to an increase in LDL-C, producing an FH-like phenotype and presenting this type of hypercholesterolaemia as a typical complex disease. The various causes of homocysteinaemia like genetic causes include mutations and enzyme deficiencies such as the most frequently mentioned 5, 10-methylenetetrahydrofolate reductase (MTHFR), but also methionine synthase (MS) and cystathionine β-synthase (CβS) but also by deficiencies of folate, vitamin B12 and, to a lesser extent, deficiencies of vitamin B6, which affects methionine metabolism, and leads also to endothelial disfunction in different mechanismms. Mutations in genes coding enzymes in homocysteine metabolism and also in nitric oxide (NO) synthesis, the main vasodilatator is also presented in this chapter. The crucial importance of microRNAs in endothelial physiology following EC-specific inactivation of the enzyme Dicer which is involved in altered expression of key regulators of endothelial function, including endothelial nitric oxide synthase (eNOS), vascular endothelial growth factor receptor 2 (VEGF), interleukin-8, Tie-1 and Tie-2. The new discoveries based on genome-wide screening (GWAS) complement the knowledge of the topic

Gene–nutrient interactions on metabolic diseases: findings from the GeNuIne Collaboration

This article describes how the British Nutrition Foundation Drummond Pump Priming award was used to initiate a large scale collaborative project called the Gene-Nutrient Interactions (GeNuIne) Collaboration, the main objective of which is to investigate the effect of gene-nutrient interactions (nutrigenetics) on metabolic disease outcomes using population based studies from various ethnic groups. The article also provides a summary of gene–diet interaction studies, performed in developing countries as part of this collaborative project, and gives an overview of how nutrigenetic findings can be translated into personalised and public health initiatives for the prevention of metabolic diseases such as obesity and type 2 diabetes

Association of the tumor necrosis factor-alpha promoter polymorphism with change in triacylglycerol response to sequential meals

Background: Reported associations between Tumor Necrosis Factor-alpha (TNFA) and the postprandial triacylglycerol (TAG) response have been inconsistent, which could be due to variations in the TNFA gene, meal fat composition or participant’s body weight. Hence, we investigated the association of TNFA polymorphism (−308G → A) with body mass index (BMI) and postprandial lipaemia and also determined the impact of BMI on the association of the polymorphism with postprandial lipaemia. Methods: The study participants (n = 230) underwent a sequential meal postprandial study. Blood samples were taken at regular intervals after a test breakfast (t = 0, 49 g fat) and lunch (t =330 min, 29 g fat) to measure fasting and postprandial lipids, glucose and insulin. The Metabolic Challenge Study (MECHE) comprising 67 Irish participants who underwent a 54 g fat oral lipid tolerance test was used as a replication cohort. The impact of genotype on postprandial responses was determined using general linear model with adjustment for potential confounders. Results: The -308G → A polymorphism showed a significant association with BMI (P = 0.03) and fasting glucose (P = 0.006), where the polymorphism explained 13 % of the variation in the fasting glucose. A 30 % higher incremental area under the curve (IAUC) was observed for the postprandial TAG response in the GG homozygotes than A-allele carriers (P = 0.004) and the genotype explained 19 % of the variation in the IAUC. There was a non-significant trend in the impact of BMI on the association of the genotype with TAG IAUC (P = 0.09). These results were not statistically significant in the MECHE cohort, which could be due to the differences in the sample size, meal composition, baseline lipid profile, allelic diversity and postprandial characterisation of participants across the two cohorts. Conclusions: Our findings suggest that TNFA -308G → A polymorphism may be an important candidate for BMI, fasting glucose and postprandial TAG response. Further studies are required to investigate the mechanistic effects of the polymorphism on glucose and TAG metabolism, and determine whether BMI is an important variable which should be considered in the design of future studies. Trial registration: NCT01172951

Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500 ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6 h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation

Nutrigenomic approaches for benefit-risk analysis of foods and food components:defining markers of health

To be able to perform a comprehensive and rigorous benefit-risk analysis of individual food components, and of foods, a number of fundamental questions need to be addressed first. These include whether it is feasible to detect all relevant biological effects of foods and individual food components, how such effects can confidently be categorised into benefits and risks in relation to health and, for that matter, how health can be quantified. This article examines the last of these issues, focusing upon concepts for the development of new biomarkers of health. Clearly, there is scope for refinement of classical biomarkers so that they may be used to detect even earlier signs of disease, but this approach defines health solely as the absence of detectable disease or disease risk. We suggest that the health of a biological system may better be reflected by its ability to withstand and manage relevant physiological challenges so that homeostasis is maintained. We discuss the potential for expanding the range of current challenge tests for use in conjunction with functional genomic technologies to develop new types of biomarkers of health

Nutrient-gene interactions in benefit-risk analysis

Individuals respond differently to nutrients and foods. This is reflected in different levels of benefits and risks at the same intake of a nutrient and, consequently, different `windows of benefit' in terms of nutrient intake. This has led recently to the concept of `personalised nutrition'. Genetic factors such as single nucleotide polymorphisms may be one source of this inter-individual variation in benefit¿risk response to nutrients. In 2004 a European Union-funded network of excellence in the area of nutrigenomics (European Nutrigenomics Organisation; NuGO) organised a workshop on the role of nutrient¿gene interactions in determining benefit¿risk of nutrients and diet. The major issues discussed at the workshop are presented in the present paper and highlighted with examples from the presentations. The overall consensus was that although genetics provides a new vision where genetic information could in the future be used to provide knowledge on disease predisposition and nutritional requirements, such a goal is still far off and much more research is required before we can reliably include genetic factors in the risk¿benefit assessment of nutrients and diet