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UNLABELLED Susceptibility to develop nonalcoholic fatty liver disease (NAFLD) has genetic bases, but the associated variants are uncertain. The aim of the present study was to identify genetic variants that could help to prognose and further understand the genetics and development of NAFLD. Allele frequencies of 3,072 single-nucleotide polymorphisms (SNPs)(More)
UNLABELLED Methionine adenosyltransferase 1A (MAT1A) and glycine N-methyltransferase (GNMT) are the primary genes involved in hepatic S-adenosylmethionine (SAMe) synthesis and degradation, respectively. Mat1a ablation in mice induces a decrease in hepatic SAMe, activation of lipogenesis, inhibition of triglyceride (TG) release, and steatosis. Gnmt-deficient(More)
Our understanding of the mechanisms by which nonalcoholic fatty liver disease (NAFLD) progresses from simple steatosis to steatohepatitis (NASH) is still very limited. Despite the growing number of studies linking the disease with altered serum metabolite levels, an obstacle to the development of metabolome-based NAFLD predictors has been the lack of large(More)
UNLABELLED Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply(More)
BACKGROUND & AIMS Nonalcoholic fatty liver disease (NAFLD) is a consequence of defects in diverse metabolic pathways that involve hepatic accumulation of triglycerides. Features of these aberrations might determine whether NAFLD progresses to nonalcoholic steatohepatitis (NASH). We investigated whether the diverse defects observed in patients with NAFLD are(More)
Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this 'omic' discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics(More)
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