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Abstract Background Obesity and related co-morbidities represent a major health challenge nowadays, with a rapidly increasing incidence worldwide. The gut microbiome has recently emerged as a key modifier of human health that can affect the development and progression of obesity, largely due to its involvement in the regulation of food intake and metabolism. However, there are still few studies that have in-depth explored the functionality of the human gut microbiome in obesity and even fewer that have examined its relationship to eating behaviors. Methods In an attempt to advance our knowledge of the gut-microbiome-brain axis in the obese phenotype, we thoroughly characterized the gut microbiome signatures of obesity in a well-phenotyped Italian female cohort from the NeuroFAST and MyNewGut EU FP7 projects. Fecal samples were collected from 63 overweight/obese and 37 normal-weight women and analyzed via a multi-omics approach combining 16S rRNA amplicon sequencing, metagenomics, metatranscriptomics, and lipidomics. Associations with anthropometric, clinical, biochemical, and nutritional data were then sought, with particular attention to cognitive and behavioral domains of eating. Results We identified four compositional clusters of the gut microbiome in our cohort that, although not distinctly associated with weight status, correlated differently with eating habits and behaviors. These clusters also differed in functional features, i.e., transcriptional activity and fecal metabolites. In particular, obese women with uncontrolled eating behavior were mostly characterized by low-diversity microbial steady states, with few and poorly interconnected species (e.g., Ruminococcus torques and Bifidobacterium spp.), which exhibited low transcriptional activity, especially of genes involved in secondary bile acid biosynthesis and neuroendocrine signaling (i.e., production of neurotransmitters, indoles and ligands for cannabinoid receptors). Consistently, high amounts of primary bile acids as well as sterols were found in their feces. Conclusions By finding peculiar gut microbiome profiles associated with eating patterns, we laid the foundation for elucidating gut-brain axis communication in the obese phenotype. Subject to confirmation of the hypotheses herein generated, our work could help guide the design of microbiome-based precision interventions, aimed at rewiring microbial networks to support a healthy diet-microbiome-gut-brain axis, thus counteracting obesity and related complications.

Background. Stimulated thyroglobulin levels measured at the time of remnant ablation (A-hTg) and BRAFV600E mutation had shown prognostic value in predicting persistent disease in differentiated thyroid cancer (DTC). The aim of this study was to evaluate the prognostic role of A-hTg combined with the BRAFV600E status in association with the revised American Thyroid Association (ATA) risk stratification. Material and Methods. 620 patients treated for a DTC were included in this study with a median follow-up duration of 6.1 years. All patients underwent total thyroidectomy followed by radioiodine ablation. Patients with positive anti-thyroglobulin antibodies were excluded. The predictive value of A-hTg was calculated by receiver operating characteristic curve (ROC curve) analysis. The Cox proportional hazard regression model, including the BRAF status, A-hTg, and ATA classification system, was assessed to evaluate the existing persistent disease risk. Results. Taken together, the BRAF status and A-hTg levels improve the ATA risk classification in all categories. In particular, in the low-risk ATA classification, only the combination of BRAFV600E+A-hTg>8.9ng/ml was associated with persistent disease (P=0.001, HR 60.2, CI 95% 5.28-687). In the intermediate-risk ATA classification, BRAFWT+A-hTg>8.9ng/ml was associated with persistent disease (P=0.029, HR 2.71, CI 95% 1.106-6.670) and BRAFV600E+A-hTg>8.9ng/ml was also associated with persistent disease (P8.9 ng/ml were associated with persistent disease (P=0.042, HR 5.963, CI 95% 1.069-33.255 and P=0.002, HR 11.564, CI 95% 2.543-52.576, respectively). Conclusions. The BRAF status and stimulated thyroglobulin levels at ablation time improve the ATA risk stratification of differentiated thyroid cancer; therefore, even A-hTg could be included in risk classification factors.