Background: Women suffer from various distress and disturbances after menopause, including osteoporosis, a risk factor associated with multiple diseases. Altered gut microbiota has been implicated in postmenopausal osteoporosis. In this study, to understand gut microbiota signatures and fecal metabolite changes in postmenopausal women with osteoporosis, 108 postmenopausal women were recruited for intestinal microbiota and fecal metabolite detection. Among these participants, 98 patients, who met the inclusion criteria, were divided into postmenopausal osteoporosis (PMO) and non-postmenopausal osteoporosis (non-PMO) groups based on bone mineral density (BMD). The compositions of gut bacteria and fungi were examined by 16 S rRNA gene sequencing and ITS sequencing, respectively. Meanwhile, fecal metabolites were analyzed using liquid chromatography coupled with mass spectrometry (LC-MS). Results: We found that bacterial α-diversity and β-diversity were significantly altered in PMO compared to non-PMO patients. Interestingly, fungi composition showed larger changes, and the differences in β-diversity were more significant between PMO and non-PMO patients. Metabolomics analysis revealed that fecal metabolites, such as levulinic acid, N-Acetylneuraminic acid, and the corresponding signaling pathways were also changed significantly, especially in the alpha-Linolenic acid metabolism and selenocompound metabolism. The screened differential bacteria, fungi, and metabolites closely correlated with clinical findings between these two groups, for example, the bacterial genus, Fusobacterium, the fungal genus, Devriesia, and the metabolite, L-pipecolic acid, were significantly associated with BMD. Conclusions: Our findings indicated that there were remarkable changes in gut bacteria, fungi, and fecal metabolites in postmenopausal women, and such changes were notably correlated with patients’ BMD ​​and clinical findings. These correlations provide novel insights into the mechanism of PMO development, potential early diagnostic indicators, and new therapeutic approaches to improve bone health in postmenopausal women.

Background: Early diagnosis and treatment of pediatric Inflammatory bowel disease (PIBD) is challenging due to the complexity of the disease and lack of disease specific biomarkers. The novel machine learning (ML) technique may be a useful tool to provide a new route for the identification of early biomarkers for the diagnosis of PIBD.Methods: In total, 66 treatment naive PIBD patients and 27 healthy controls were enrolled as an exploration cohort. Fecal microbiome profiling using 16S rRNA gene sequencing was performed. The correlation between microbiota and inflammatory and nutritional markers was evaluated using Spearman’s correlation. A random forest model was used to set up an ML approach for the diagnosis of PIBD using 1902 markers. A validation cohort including 14 PIBD and 48 irritable bowel syndrome (IBS) was enrolled to further evaluate the sensitivity and accuracy of the model.Result: Compared with healthy subjects, PIBD patients showed a significantly lower diversity of the gut microbiome. The increased Escherichia-Shigella and Enterococcus were positively correlated with inflammatory markers and negatively correlated with nutrition markers, which indicated a more severe disease. A diagnostic ML model was successfully set up for differential diagnosis of PIBD integrating the top 11 OTUs. This diagnostic model showed outstanding performance at differentiating IBD from IBS in an independent validation cohort.Conclusion: The diagnosis penal based on the ML of the gut microbiome may be a favorable tool for the precise diagnosis and treatment of PIBD. A study of the relationship between disease status and the microbiome was an effective way to clarify the pathogenesis of PIBD.