Metabolic syndrome: from epidemiology to systems biology

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Abstract
Metabolic syndrome (MetSyn) is a group of metabolic conditions that occur together and promote the development of type 2 diabetes and cardiovascular disease. These include abdominal obesity, insulin resistance, low levels of high-density lipoprotein (HDL), elevated levels of triglycerides, elevated blood pressure, and a pro-inflammatory, pro-thrombotic milieu. MetSyn traits are determined by the interaction of environmental factors, particularly excess calorific intake and a sedentary lifestyle, and genetic factors. Genome-wide association studies have revealed a number of novel genes contributing to MetSyn traits. These include genes affecting obesity, lipoprotein levels, type 2 diabetes, and fasting glucose levels. As yet, only a small fraction of the genetic component of MetSyn traits can be explained by known genes and loci. It is clear that the overall genetic architecture of MetSyn traits must be complex, involving hundreds of genes, with both common and rare variants. Two somewhat unexplored areas that are important in MetSyn are sex differences and maternal nutrition. Studies in rodent models have elucidated some fundamental mechanisms contributing to MetSyn and its relationship to diabetes and cardiovascular disease. These include abnormalities in fuel partitioning and mitochondrial function, inflammation related to obesity, and endoplasmic reticulum stress. It seems unlikely that the complex molecular networks that underlie MetSyn can be fully addressed by traditional genetic and biochemical approaches. Recent studies suggest that systems-based approaches, that look beyond individual components, may be able to model gene–gene and gene–environment interactions in MetSyn. One systems-based approach that is proving particularly useful is the integration of common DNA variation, global expression array analysis and clinical phenotypes. When applied to genetically randomized populations, it has the potential to model causal interactions as well as gene co-expression networks.