Background: The human gut microbiota (GM) is involved in the pathogenesis of hypertension (HTN), and could be affected by various factors, including sex and geography. However, available data directly linking GM to HTN based on sex differences are limited. Methods: This study investigated the GM characteristics in HTN subjects in Northwestern China, and evaluate the associations of GM with blood pressure levels based on sex differences. A total of 87 HTN subjects and 45 controls were recruited with demographic and clinical characteristics documented. Fecal samples were collected for 16S rRNA gene sequencing and metagenomic sequencing. Results: GM diversity was observed higher in females compared to males, and principal coordinate analysis showed an obvious segregation of females and males. Four predominant phyla of fecal GM included Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. LEfSe analysis indicated that phylum unidentified_Bacteria was enriched in HTN females, while Leuconostocaceae, Weissella and Weissella_cibaria were enriched in control females (P < 0.05). Functionally, ROC analysis revealed that Cellular Processes (0.796, 95% CI 0.620 ~ 0.916), Human Diseases (0.773, 95% CI 0.595 ~ 0.900), Signal transduction (0.806, 95% CI 0.631 ~ 0.922) and Two-component system (0.806, 95% CI 0.631 ~ 0.922) could differentiate HTN females as effective functional classifiers, which were also positively correlated with systolic blood pressure levels. Conclusions: This work provides evidence of fecal GM characteristics in HTN females and males in a northwestern Chinese population, further supporting the notion that GM dysbiosis may participate in the pathogenesis of HTN, and the role of sex differences should be considered. Trial registration Chinese Clinical Trial Registry, ChiCTR1800019191. Registered 30 October 2018 – Retrospectively registered, http://www.chictr.org.cn/.

Circulating exosomal miRNAs as potential biomarkers for Barrett's esophagus and esophageal adenocarcinoma

Barrett's esophagus (BE) is defined as a metaplasia condition in the distal esophagus, in which the native squamous epithelium lining is replaced by a columnar epithelium with or without intestinal metaplasia. It is commonly accepted that BE is a precancerous lesion for esophageal adenocarcinoma. The aim of this study was to investigate the aberrant microRNAs (miRNAs) and differentially expressed genes (DEGs) associated with BE based on online microarray datasets. One miRNA and five gene expression profiling datasets were retrieved from the Gene Expression Omnibus Database. Aberrant microRNAs and DEGs were obtained using R/Bioconductor statistical analysis language and software. 23 dysregulated miRNAs and 632 DEGs demonstrating consistent expression tendencies in the five gene microarrays were identified in BE. Moreover, 1962 target genes of aberrant miRNAs were predicted using three bioinformatic tools, namely TargetScan, RNA22-HSA and miRDB. Ultimately, 93 target DEGs were obtained, after which functional annotation was performed on DAVID Bioinformatics Resources. Among Gene Ontology (GO) biological processes, digestive tract development and epithelial cell differentiation have demonstrated significant associations with BE pathogenesis. In addition, analysis of the KEGG pathways has revealed associations with cancer. To enable further study, one miRNA-target DEGs regulatory network was constructed using Cytoscape. 6 target DEGs demonstrated higher-degree distributions in the network, and ROC analysis indicated that FNDC3B may be the best potential biomarker for BE diagnosis. The data presented herein may provide new perspectives for exploring BE pathogenesis and may offer hits with regard to potential biomarkers in BE diagnosis, prediction and therapeutic evaluation.

Dyslipidemia, a complex disorder characterized by systemic lipid profile abnormalities, affects more than half of adults globally and constitutes a major modifiable risk factor for atherosclerotic cardiovascular disease. Mounting evidence has established the gut microbiota (GM) as a pivotal metabolic modulator that is correlated with atherogenic lipid profiles through dietary biotransformation, immunometabolic regulation, and bioactive metabolite signaling. However, the host-microbe interactions that drive dyslipidemia pathogenesis involve complex gene-environment crosstalk spanning epigenetic modifications to circadian entrainment. Mechanistically, GM perturbations disrupt lipid homeostasis via lipopolysaccharide-triggered hepatic very low-density lipoprotein overproduction, short-chain fatty acid-G protein-coupled receptor 43/41-mediated adipocyte lipolysis, bile acid-farnesoid X receptor/Takeda G protein-coupled receptor 5 axis dysfunction altering cholesterol flux, microbial β-oxidation intermediates impairing mitochondrial energetics, and host-microbiota non-coding RNA crosstalk regulating lipogenic genes. This comprehensive review systematically examines three critical dimensions, including bidirectional GM-lipid axis interactions, molecular cascades bridging microbial ecology to metabolic dysfunction, and translational applications of GM modulation through precision probiotics, structure-specific prebiotics, and a metabolically optimized fecal microbiota transplantation protocol. Notwithstanding these advances, critical gaps persist in establishing causal microbial taxa-pathway relationships and optimal intervention timing. Future directions require longitudinal multi-omic studies, gnotobiotic models for mechanistic validation, and machine learning-driven personalized microbiota profiling. This synthesis provides a framework for developing microbiota-centric strategies targeting dyslipidemia pathophysiology, with implications for precision dyslipidemia management and next-generation cardiovascular disease prevention.

To investigate the associations between anti-Müllerian hormone (AMH) and bone mineral density (BMD) induced by ovarian insufficiency in premenopausal women. Subjects were consecutively enrolled fro...

Osteoporosis (OP), a systemic skeletal disease characterized by compromised bone strength and elevated fracture susceptibility, represents a growing global health challenge that necessitates early detection and accurate risk stratification. With the exponential growth of multidimensional biomedical data in OP research, feature selection has become an indispensable machine learning paradigm that improves model generalizability. At the same time, it preserves clinical interpretability and enhances predictive accuracy. This perspective article systematically reviews the transformative role of feature selection methodologies across 3 critical domains of OP investigation: (1) multi-omics biomarker identification, (2) diagnostic pattern recognition, and (3) fracture risk prognostication. In biomarker discovery, advanced feature selection algorithms systematically refine high-dimensional multi-omics datasets (genomic, proteomic, and metabolomic) to isolate key molecular signatures correlated with BMD trajectories and microarchitectural deterioration. For clinical diagnostics, these techniques enable efficient extraction of discriminative pattern from multimodal imaging data, including DXA, QCT, and emerging dental radiographic biomarkers. In prognostic modeling, strategic variable selection optimizes prognostic accuracy by integrating demographic, biochemical, and biomechanical predictors while mitigating overfitting in heterogeneous patient cohorts. Current challenges include heterogeneity in dataset quality and dimensionality, translational gaps between algorithmic outputs and clinical decision parameters, and limited reproducibility across diverse populations. Future directions should prioritize the development of adaptive feature selection frameworks capable of dynamic multi-omics data integration, coupled with hybrid intelligence systems that synergize machine-derived biomarkers with clinician expertise. Addressing these challenges requires coordinated interdisciplinary efforts to establish standardized validation protocols and create clinician-friendly decision support interfaces, ultimately bridging the gap between computational OP research and personalized patient care.