Epidemiology of Obesity.

Obesity is in theory defined on the basis of the excess health risk caused by adiposity exceeding the size normally found in the population, but for practical reasons, the World Health Organization (WHO) has defined obesity as a body mass index (weight (kg)/height (m)2) of 30 or above for adults. WHO considers the steep increases in prevalence of obesity in all age groups, especially since the 1970s as a global obesity epidemic. Today, approximately 650 million adult people and approximately 340 million children and adolescence (5-19 years) suffer from obesity. It is generally more prevalent among women and older age groups than among men and younger age groups. Beyond the necessity of availability of food, evidence about causes of obesity is still very limited. However, studies have shown that obesity 'runs in families', where both genetics and environmental, and especially social, factors play important roles. Obesity is associated with an increased risk of many adverse medical, mental and social consequences, including a strong relation to type 2 diabetes. Type 2 diabetes and related metabolic syndrome and diseases are major contributors to the excess morbidity and mortality associated with obesity.

Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series.

BACKGROUND: The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits. METHODS: We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualize the shortest paths between metabolites and genes significantly associated with each genomic block. RESULTS: Despite strong genomic similarities (95-99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing. CONCLUSIONS: These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism.