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The gene expression of 182 cardiovascular candidate genes was measured in high quality groups of individuals (n = 20) by microarrays to determine whether a subset of genes would discriminate obese and hypertensive individuals, in spite of the existence of a close link between these two cardiovascular risk factors. Materials & methods: The results were validated on the 20 subjects used for microarray analysis and on 62 additional individuals by real-time PCR. Results: The first analysis, where patient groups were compared with healthy subjects, revealed 15 out of 182 genes that differed in hypertensive, obese or obesity-related hypertensive individuals. These genes were ALOX5, APOA2, SELL, RGS2, CD14, FPR1, CAMP, DEFA3, DEFA4, CBS, CHRM1, ICAM1, NR1H2, SCNN1B and TGFB1. A second analysis was carried out in which patient groups were compared with each other, demonstrating FPR1 and DEFA3 as being significant genes discriminating patient groups. Furthermore, an analysis stratified by sex revealed that, with the exception of DEFA3, there are no other common genes between men and women. Discussion: We were able to indentify a number of interesting genes that distinguish patient and healthy subject groups as well as patient groups between them. Conclusion: In addition, it seems that gender plays an important role, at least for some of the genes we tested. These findings may have important implications in the screening and etiology of hypertension or obesity, and could further help to focus on these specific mRNAs as antisense therapy targets. [ABSTRACT FROM AUTHOR]

Familial history of cardiovascular disease is acknowledged as a risk indicator in offspring. The aim of this study was to assess whether the cardiovascular risk factors in parents predicted the risk of their children developing cardiovascular disease in a French population: the STANISLAS Cohort, in which Caucasian biparental families with at least two siblings were followed for 5 years. Silent risk factors (blood pressure, lipid traits, glycemia, BMI and waist circumference) of children were compared according to their parents' risk status in a subsample of 693 families. All of these traits, with the exception of glucose, were significantly higher in children who had parents at a high risk than in children with parents at a low risk at the first health examination, and these results were confirmed again 5 years later at the second health examination. Thus, silent cardio--metabolic risk factors can be screened in children according to the risk status of their parents for early prevention. The influence of parents' variants on their offspring underlined the need to initiate familial prevention strategies, with a particular follow-up of young individuals between childhood and adolescence. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of the 4th Biologie Prospective Santorini Conference held in Santorini Island, Greece from September 21 to 23, 2008. Some of the sponsors of the event are GlaxoSmithKline and Applied Biosystems. The evolutions between the genome scan of APOE polymorphisms and Alzheimer's disease were tackled by Allen D. Roses of Duke University School of Medicine in North Carolina. Steve Humphries of Royal Free described a study on genome-wide scan findings of novel loci involved in determining coronary heart disease (CHD) risk.

Pharmacogenomics focuses on genes and the transcriptome and proteome. It has the potential to enhance healthcare management by improving disease diagnosis and implementing treatments adapted to each patient. Previously, pharmacogenetics of candidate genes focused on clinical research. It is now extended by using genome-wide approaches to elucidate the inherited basis of differences between individuals in their response to drugs. We summarize relevant polymorphisms of genes involved in the pharmacokinetics and pharmacodynamics of antihypertensive drugs and we give an overview of the state of pharmacogenomic research in hypertension medicine. Even if things are getting better, current pharmacogenetic studies still lack power, adequate selection of candidate genes and knowledge of their functions at the physiological level. Finally, some specific end point phenotypes (i.e., peptides or proteins related to the metabolic cycle targeted by the drug) should be integrated to propose data that are easily applicable to personalized medicine. [ABSTRACT FROM AUTHOR]