Liver disease encompasses pathologies as non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, alcohol liver disease, hepatocellular carcinoma, viral hepatitis, and autoimmune hepatitis. Nowadays, underlying mechanisms associating gut permeability and liver disease development are not well understood, although evidence points to the involvement of intestinal microbiota and their metabolites. Animal studies have shown alterations in Toll-like receptor signaling related to the leaky gut syndrome by the action of bacterial lipopolysaccharide. In humans, modifications of the intestinal microbiota in intestinal permeability have also been related to liver disease. Some of these changes were observed in bacterial species belonging Roseburia, Streptococcus, and Rothia. Currently, numerous strategies to treat liver disease are being assessed. This review summarizes and discusses studies addressed to determine mechanisms associated with the microbiota able to alter the intestinal barrier complementing the progress and advancement of liver disease, as well as the main strategies under development to manage these pathologies. We highlight those approaches that have shown improvement in intestinal microbiota and barrier function, namely lifestyle changes (diet and physical activity) and probiotics intervention. Nevertheless, knowledge about how such modifications are beneficial is still limited and specific mechanisms involved are not clear. Thus, further in-vitro, animal, and human studies are needed.

New microbiome sequencing technologies provide novel information about the potential interactions among intestinal microorganisms and the host in some neuropathologies as autism spectrum disorders (ASD). The microbiota–gut–brain axis is an emerging aspect in the generation of autistic behaviors; evidence from animal models suggests that intestinal microbial shifts may produce changes fitting the clinical picture of autism. The aim of the present study was to evaluate the fecal metagenomic profiles in children with ASD and compare them with healthy participants. This comparison allows us to ascertain how mental regression (an important variable in ASD) could influence the intestinal microbiota profile. For this reason, a subclassification in children with ASD by mental regression (AMR) and no mental regression (ANMR) phenotype was performed. The present report was a descriptive observational study. Forty-eight children aged 2–6 years with ASD were included: 30 with ANMR and 18 with AMR. In addition, a control group of 57 normally developing children was selected and matched to the ASD group by sex and age. Fecal samples were analyzed with a metagenomic approach using a next-generation sequencing platform. Several differences between children with ASD, compared with the healthy group, were detected. Namely, Actinobacteria and Proteobacteria at phylum level, as well as, Actinobacteria, Bacilli, Erysipelotrichi, and Gammaproteobacteria at class level were found at higher proportions in children with ASD. Additionally, Proteobacteria levels showed to be augmented exclusively in AMR children. Preliminary results, using a principal component analysis, showed differential patterns in children with ASD, ANMR and AMR, compared to healthy group, both for intestinal microbiota and food patterns. In this study, we report, higher levels of Actinobacteria, Proteobacteria and Bacilli, aside from Erysipelotrichi, and Gammaproteobacteria in children with ASD compared to healthy group. Furthermore, AMR children exhibited higher levels of Proteobacteria. Further analysis using these preliminary results and mixing metagenomic and other “omic” technologies are needed in larger cohorts of children with ASD to confirm these intestinal microbiota changes.

Physical activity, exercise, or physical fitness are being studied as helpful nonpharmacological therapies to reduce signaling pathways related to inflammation. Studies describing changes in intestinal microbiota have stated that physical activity could increase the microbial variance and enhance the ratio of Firmicutes/Bacteroidetes, and both actions could neutralize the obesity progression and diminish body weight. The aim of this review is to provide an overview of the literature describing the relationship between physical activity profiles and gut microbiota and in obesity and some associated comorbidities. Promoting physical activity could support as a treatment to maintain the gut microbiota composition or to restore the balance toward an improvement of dysbiosis in obesity; however, these mechanisms need to be studied in more detail. The opportunity to control the microbiota by physical activity to improve health results and decrease obesity and related comorbidities is very attractive. Nevertheless, several incompletely answered questions need to be addressed before this strategy can be implemented.

We investigated the associations of different sedentary behaviors (SB) with gray matter volume and we tested whether SB related to gray matter volume is associated with intelligence. Methods: 99 children with overweight or obesity aged 8–11 years participated in this cross-sectional study. SB was measured using the Youth Activity Profile-Spain questionnaire. T1-weighted images were acquired with a 3.0 T Magnetom Tim Trio system. Intelligence was assessed with the Kaufman Brief Test. Whole-brain voxel-wise multiple regression models were used to test the associations of each SB with gray matter volume. Results: Watching TV was associated with lower gray matter volume in six brain regions (β ranging −0.314 to −0.489 and cluster size 106 to 323 voxels; p < 0.001), playing video games in three brain regions (β ranging −0.391 to −0.359, and cluster size 96 to 461 voxels; p < 0.001) and total sedentary time in two brain regions (β ranging −0.341 to −0.352, and cluster size 897 to 2455 voxels; p < 0.001). No brain regions showed a significant positive association (all p > 0.05). Two brain regions were related, or borderline related, to intelligence. Conclusions: SB could have the potential to negatively influence brain structure and, in turn, intelligence in children with overweight/obesity.