Association of BCAT2 and BCKDH polymorphisms with clinical, anthropometric and biochemical parameters in young adults.

BACKGROUND AND AIM: Circulating amino acids are modified by sex, body mass index (BMI) and insulin resistance (IR). However, whether the presence of genetic variants in branched-chain amino acid (BCAA) catabolic enzymes modifies circulating amino acids is still unknown. Thus, we determined the frequency of two genetic variants, one in the branched-chain aminotransferase 2 (BCAT2) gene (rs11548193), and one in the branched-chain ketoacid dehydrogenase (BCKDH) gene (rs45500792), and elucidated their impact on circulating amino acid levels together with clinical, anthropometric and biochemical parameters. METHODS AND RESULTS: We performed a cross-sectional comparative study in which we recruited 1612 young adults (749 women and 863 men) aged 19.7 ± 2.1 years and with a BMI of 24.9 ± 4.7 kg/m2. Participants underwent clinical evaluation and provided blood samples for DNA extraction and biochemical analysis. The single nucleotide polymorphisms (SNPs) were determined by allelic discrimination using real-time polymerase chain reaction (PCR). The frequencies of the less common alleles were 15.2 % for BCAT2 and 9.83 % for BCKDH. The subjects with either the BCAT2 or BCKDH SNPs displayed no differences in the evaluated parameters compared with subjects homozygotes for the most common allele at each SNP. However, subjects with both SNPs had higher body weight, BMI, blood pressure, glucose, and circulating levels of aspartate, isoleucine, methionine, and proline than the subjects homozygotes for the most common allele (P < 0.05, One-way ANOVA). CONCLUSION: Our findings suggest that the joint presence of both the BCAT2 rs11548193 and BCKDH rs45500792 SNPs induces metabolic alterations that are not observed in subjects without either SNP.

Long-Term Genistein Consumption Modifies Gut Microbiota, Improving Glucose Metabolism, Metabolic Endotoxemia, and Cognitive Function in Mice Fed a High-Fat Diet.

SCOPE: The aim of this study is to assess whether the long-term addition of genistein to a high-fat diet can ameliorate the metabolic and the cognitive alterations and whether the changes can be associated with modifications to the gut microbiota. METHODS AND RESULTS: C57/BL6 mice were fed either a control (C) diet, a high-fat (HF) diet, or a high-fat diet containing genistein (HFG) for 6 months. During the study, indirect calorimetry, IP glucose tolerance tests, and behavioral analyses were performed. At the end of the study, plasma, liver, brain, and fecal samples were collected. The results showed that mice fed the HFG diet gained less weight, had lower serum triglycerides, and an improvement in glucose tolerance than those fed an HF diet. Mice fed the HFG diet also modified the gut microbiota that was associated with lower circulating levels of lipopolysaccharide (LPS) and reduced expression of pro-inflammatory cytokines in the liver compared to those fed HF diet. The reduction in LPS by the consumption of genistein was accompanied by an improvement of the cognitive function. CONCLUSIONS: Genistein is able to regulate the gut microbiota, reducing metabolic endotoxemia and decreasing the neuroinflammatory response despite the consumption of a HF diet.

Metabolomics applied to the discovery of tuberculosis and diabetes mellitus biomarkers.

Tuberculosis (TB) and diabetes mellitus Type 2 (DM2) are two diseases as ancient as they are harmful to human health. The outcome for both diseases in part depends on immune and metabolic individual responses. DM2 is increasing yearly, mainly due to environmental, genetic and lifestyle habits. There are multiple evidence that DM2 is one of the most important risk factor of becoming infected with TB or reactivating latent TB. Mass spectrometry-based metabolomics is an important tool for elucidating the metabolites and metabolic pathways that influence the immune responses to M. tuberculosis infection during diabetes. We provide an up-to-date review highlighting the importance and benefit of metabolomics for identifying biomarkers as candidate molecules for diagnosis, disease activity or prognosis.