Childhood obesity is a matter of great concern for public health. Efforts have been made to understand its impact on health through advanced imaging techniques. An increasing number of studies focus on fat distribution and its associations with metabolic risk, in interaction with genetics, environment and ethnicity, in children. The present review is a qualitative synthesis of the existing literature on visceral and subcutaneous abdominal, intrahepatic and intramuscular fat. Our search revealed 80 original articles. Abdominal as well as ectopic fat depots are prevalent already in childhood and contribute to abnormal metabolic parameters, starting early in life. Visceral, hepatic and intramuscular fat seem to be interrelated but their patterns as well as their independent contribution on metabolic risk are not clear. Some ethnic-specific characteristics are also prevalent. These results encourage further research in childhood obesity by using imaging techniques such as magnetic resonance imaging and computed tomography. These imaging methods can provide a better understanding of fat distribution and its relationships with metabolic risk, compared to less detailed fat and obesity assessment. However, studies on bigger samples and with a prospective character are warranted.

Background and aims: Antimicrobial peptides (AMPs) are components of the innate immune system. In addition, evidence suggests that these peptides are associated with various inflammatory diseases. We examined whether expression of the catheticidin LL-37 in peripheral blood mononuclear cells (PBMCs) is associated with cardiovascular risk factors.Methods and results: A total of 90 men and 87 women selected from STANISLAS cohort were studied. Expression of LL-37 mRNA isolated from PBMCs of these subjects was quantified by quantitative RTPCR. Anthropometric measurements and biochemical profiles were assessed for each individual. In women, LL-37 mRNA expression was significantly and positively correlated with body mass index (BMI) (p <= 0.001); waist circumference (WC) (p <= 0.01); systolic blood pressure (SBP) (p <= 0.05) and triglycerides (TG) level (p <= 0.05) and negatively with plasma levels of HDL-C (p <= 0.05). In men however, LL-37 was positively associated with waist to hip ratio (WHR) (p <= 0.05); SBP (p <= 0.901); TG (p <= 0.05); fasting glucose levels (p <= 0.01); alanine aminotransferase (ALT) activity (p <= 0.61); neutrophils counts (p <= 0.01) and negatively with Lymphocyte counts (p <= 0.001); serum HDL-C (p <= 0.001) and apoA-I (p <= 0.05) levels. After adjustment for WC and BMI, multiple regression analysis showed that LL-37 remained significantly associated with SBP; HDL-C; fasting glucose level; ALT activity; neutrophil and lymphocyte counts (p <= 0.001 to p <= 0.05) in men.Conclusion: Our results suggest that LL-37 gene expression may be closely associated with cardiovascular risk factors independently of BMI and WC. However, functional studies are required to confirm these data. (C) 2009 Published by Elsevier B.V.

Objective: The purpose of the present longitudinal study was to describe the associations between the 5-year changes in body mass index (BMI) and alterations in the clusters of metabolic syndrome (MS)-related factors. Methods: The study population comprised 1099 middle-aged adults drawn from the Stanislas study. Individuals were stratified into four groups according to the 5-year changes in BMI (weight loss (<0 kg/m 2 ), and weight gain (0–1, 1–2 and >2 kg/m 2 )). Changes in various MS-related variables and clusters were compared between groups: anthropometric indices, blood pressure, lipid and inflammatory markers, liver enzymes, uric acid and the five summary factors extracted by using factor analysis (‘risk lipids’, ‘liver enzymes’, ‘inflammation’, ‘protective lipids’ and ‘blood pressure’). Results: There was a strong linear trend between increasing BMI and worsening of risk lipids and blood pressure factors for both men and women ( P ⩽0.001). In men only, liver enzymes and protective lipids factors were significantly related to the 5-year gain of BMI ( P ⩽0.001), whereas inflammation factor positively increased across the four BMI-change groups, in women only. Interaction terms for sex were statistically significant for inflammation and liver enzymes clusters. Conclusion: In our population, there was a strong linear trend between increasing BMI and worsening of various MS-related variables. More interestingly, the identification of five factors associated with BMI changes dependent to gender, support the hypothesis that weight gain, and probably obesity, trigger metabolic mechanisms that differ between men and women.

La thérapeutique personnalisée est basée sur une meilleure connaissance de la variabilité biologique, en prenant en compte l’importance de la génétique. Afin d’identifier les gènes ainsi que leurs produits, impliqués dans la réponse différentielle aux médicaments à visée cardio-vasculaire nous proposons une stratégie en cinq étapes qui considère : 1) les gènes et phénotypes liés à la pharmacocinétique ; 2) les gènes et produits (cibles thérapeutiques) liés à la pharmacodynamie ; 3) les maladies et risques cardio-vasculaires vus sous l’angle des cycles métaboliques spécifiques en cause ; 4) les variations physiologiques des gènes et protéines précédemment identifiées ; 5) et l’influence de l’environnement. Après avoir pris comme exemple des gènes impliqués dans le métabolisme des médicaments, nous nous intéresserons aux statines, considérés comme étant des médicaments très importants d’un point de vue de santé publique. Il existe une grande variabilité de la réponse à ces médicaments notamment à cause de plusieurs polymorphismes dans les gènes cibles de ces hypolipémiants. Par ailleurs, à chacune des cinq étapes de la stratégie pharmacogénomique, nous avons en plus de l’information génétique, besoin d’utiliser les informations disponibles au sujet des peptides, protéines et métabolites, qui sont généralement les produits des gènes. Une approche de type profil est nécessaire et utile en génomique mais aussi en protéomique. En conclusion, le nombre important de données va plus que jamais rendre nécessaire une interprétation intégrée des variations de l’ADN, de l’ARN messager ainsi que des protéines au niveau individuel et de la population générale pour espérer d’une façon cliniquement simple, adapter un médicament à chaque individu.

Personalized MedicineVol. 4, No. 1 Conference SceneFrom human genetic variations to prediction of risks and responses to drugs and the environmentGérard Siest, B Bastien, H Benachour, B Herbeth, E Jeannesson, D Lambert, A Samara & S Visvikis-SiestGérard Siest† Author for correspondenceINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.fr, B BastienINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.fr, H BenachourINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.fr, B HerbethINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.fr, E JeannessonINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.fr, D LambertINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.fr, A SamaraINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.fr & S Visvikis-SiestINSERM US25 Faculte de Pharmacie, Universite Henri Poincare Nancy I, 30 Rue Lionnois, 54000 Nancy, France. Search for more papers by this authorEmail the corresponding author at gerard.siest@pharma.uhp-nancy.frPublished Online:7 Feb 2007https://doi.org/10.2217/17410541.4.1.95AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail View articleBibliography1 Kirchheiner J, Fuhr U, Brockmoller J: Pharmacogenetics-based therapeutic recommendations – ready for clinical practice? Nat. Rev. Drug Discov.4(8),639–647 (2005).Crossref, Medline, CAS, Google Scholar101 Third “Biologie Prospective” Santorini Conference homepage http://biol.prospective-conf.u-nancy.fr/Google Scholar102 National Academy of Clinical Chemistry www.aacc.org/NR/rdonlyres/AE772314– 08C0–4F75–9895-D40B7181E4F8/0/LMPG_ Pharmacogenetics.pdfGoogle Scholar103 The Innovative Medicines Initiative (IMI) Strategic Research Agenda: Creating Biomedical R&D Leadership for Europe to Benefit Patients and Society www.efpia.org/4_pos/SRA.pdfGoogle Scholar104 Institute for Prospective Technological Studies publications www.jrc.es/home/pages/detail. cfm?prs=1387Google ScholarFiguresReferencesRelatedDetailsCited ByPersonalized therapy and pharmacogenomics: future perspectiveGérard Siest, Jean-Brice Marteau & Sophie Visvikis-Siest17 June 2009 | Pharmacogenomics, Vol. 10, No. 6Systems biology and personalized preventionGérard Siest19 May 2009 | Personalized Medicine, Vol. 6, No. 3 Vol. 4, No. 1 STAY CONNECTED Metrics Downloaded 239 times History Published online 7 February 2007 Published in print February 2007 Information© Future Medicine LtdPDF download

The development of personalized medicine will require improved knowledge of biological variability, particularly concerning the important impact of each individual's genetic makeup. A five-step strategy can be followed when trying to identify genes and gene products involved in differential responses to cardiovascular drugs: 1) Pharmacokinetic-related genes and phenotypes; (2) Pharmacodynamic targets, genes and products; (3) Cardiovascular diseases and risks depending on specific or large metabolic cycles; (4) Physiological variations of previously identified genes and proteins; (5) Environmental influences on them. After summarizing the most well known genes involved in drug metabolism, we used statins as an example. In addition to their economic impact, statins are generally considered to be of significant importance in terms of public health. Individuals respond differently to these drugs depending on multiple polymorphisms. Applying a pharmacoproteomic strategy, it is important to use available information on peptides, proteins and metabolites, generally gene products, in each of the five steps. A profiling approach dealing with genomics as well as proteomics is useful. In conclusion, the ever growing volume of available data will require an organized interpretation of variations in DNA and mRNA as well as proteins, both on the individual and population level.

Personalized medicine is based on a better knowledge of biological variability, considering the important part due to genetics. When trying to identify involved genes and their products in differential cardiovascular drug responses, a five-step strategy is to be followed: (1) Pharmacokinetic-related genes and phenotypes (2) Pharmacodynamic targets, genes and products (3) Cardiovascular diseases and risks depending on specific or large metabolic cycles (4) Physiological variations of previously identified genes and proteins (5) Environment influences on them After summarizing the most well-known genes involved in drug metabolism, we will take as example of drugs, the statins, considered as very important drugs from a Public-Health standpoint, but also for economical reasons. These drugs respond differently in human depending on multiple polymorphisms. We will give examples with common ApoE polymorphisms influencing the hypolipemic effects of statins. These drugs also have pleiotropic effects and decrease inflammatory markers. This illustrates the need to separate clinical diseases phenotypes in specific metabolic pathways, which could propose other classifications, of diseases and related genes. Hypertension is also a good example of clinical phenotype which should be followed after various therapeutic approaches by genes polymorphisms and proteins markers. Gene products are under clear environmental expression variations such as age, body mass index and obesity, alcohol, tobacco and dietary interventions which are the first therapeutical actions taken in cardiovascular diseases. But at each of the five steps, within a pharmacoproteomic strategy, we also need to use available information from peptides, proteins and metabolites, which usually are the gene products. A profiling approach, i.e., dealing with genomics, but now also with protcomics, is to be used. In conclusion, the profiling, as well as the large amount of data, will more than before render necessary an organized interpretation of DNA, RNA as well as proteins variations, both at individual and population level. - Cluster analyses; - Multidimensional approaches; - Pathways and metabolomics; - Biological systems analyses. (c) 2005 Elsevier B.V All rights reserved.