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QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

ABSTRACT: Metabolic Syndrome (MetS) is a phenotype cluster predisposing to type 2 diabetes and cardiovascular disease. We conducted a study to elucidate the genetic basis underlying linkage signals for multiple representative traits of MetS that we had previously identified at two significant QTLs on chromosomes 3q27 and 17p12.We performed QTL-specific genomic and transcriptomic analyses in 1,137 individuals from 85 extended families that contributed to the original linkage. We tested in SOLAR association of MetS phenotypes with QTL-specific haplotype-tagging SNPs as well as transcriptional profiles of peripheral blood mononuclear cells (PBMCs).SNPs significantly associated with MetS phenotypes under the prior hypothesis of linkage mapped to seven genes at 3q27 and seven at 17p12. Prioritization based on biologic relevance, SNP association, and expression analyses identified two genes: insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) at 3q27 and tumor necrosis factor receptor 13B (TNFRSF13B) at 17p12. Prioritized genes could influence cell-cell adhesion and adipocyte differentiation, insulin/glucose responsiveness, cytokine effectiveness, plasma lipid levels, and lipoprotein densities.Using an approach combining genomic, transcriptomic, and bioinformatic data we identified novel candidate genes for MetS.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC3769476 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

Zhang Y Y Kent J W JW Olivier M M Ali O O Broeckel U U Abdou R M RM Dyer T D TD Comuzzie A A Curran J E JE Carless M A MA Rainwater D L DL Göring H H H HH Blangero J J Kissebah A H AH

Obesity (Silver Spring, Md.) 20130529 10

<h4>Objective</h4>Metabolic Syndrome (MetS) is a phenotype cluster predisposing to type 2 diabetes and cardiovascular disease. We conducted a study to elucidate the genetic basis underlying linkage signals for multiple representative traits of MetS that we had previously identified at two significant QTLs on chromosomes 3q27 and 17p12.<h4>Design and methods</h4>We performed QTL-specific genomic and transcriptomic analyses in 1,137 individuals from 85 extended families that contributed to the ori ...[more]

PMID: 23418049

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Project description:(1) Background. Visceral adiposity index (VAI) has been recently identified as a new cardiometabolic risk marker reflecting abdominal fat distribution and dyslipidaemia. The aim of the present paper was to evaluate the relationship between VAI, daily energy intake and metabolic syndrome (MetS) in a cohort of obese Caucasian children and adolescents, aged 8 to 15 years. (2) Methods. Consecutive Italian children and adolescents with obesity, according to World Health Organization were enrolled. Anthropometric parameters and blood pressure were measured. Fasting blood samples have been analyzed for lipids, insulin and glucose levels. MetS was diagnosed using identification and prevention of dietary- and lifestyle-induced health effects in children and infants (IDEFICS) or International Diabetes Federation (IDF) criteria according to age. Homeostatic model assessment index (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), A body shape index (ABSI) and VAI were calculated. Multivariable logistic regression analyses with sex, age and each anthropometric parameter (body mass index (BMI) z-score, ABSI, waist-to-height ratio (WHR)) or VAI was performed to predict MetS. Receiver operation curve (ROC) analysis was used to define the optimal VAI cut-off to identify MetS. Multiple regression was performed to predict the BMI z-score and VAI from daily energy intake after adjusting for age and sex. (3) Results. Six hundred and thirty-seven (313 boys and 324 girls) children and adolescents with obesity with median age 11 (interquartile range 10-13) years were included in the analysis. MetS was diagnosed in 79 patients. VAI correlated with BMI, WHR, ABSI, HOMA-IR, QUICKI, systolic blood pressure, low- and high-density lipoprotein cholesterol, triglycerides and triglycerides-to-HDL ratio (p < 0.050). Optimal VAI cut-off (AUC) values to identify MetS were 1.775 (0.774), 1.685 (0.776) and 1.875 (0.797) in the whole population, boys and girls, respectively. Energy intake was positively associated with BMI z-score but no association was found with VAI. (4) Conclusion. VAI is a promising tool to identify MetS in children and adolescents with obesity and should be used in the management of abdominal obesity together with dietary assessment.

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QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

Publications

QTL-based association analyses reveal novel genes influencing pleiotropy of metabolic syndrome (MetS).

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