Osteoporosis is a systemic bone disorder characterized by decreased bone mass and deteriorated microarchitecture, leading to an increased risk of fractures. Recent studies have revealed that its pathogenesis involves complex biological processes beyond bone remodeling, including oxidative stress, chronic inflammation, cellular senescence, osteoimmunology, gut microbiota alterations, and epigenetic modifications. Oxidative stress disrupts bone homeostasis by promoting excessive free radical production and osteoclast activity. Chronic inflammation and the accumulation of senescent cells impair skeletal repair mechanisms. Advances in osteoimmunology have highlighted the critical role of immune–bone crosstalk in regulating bone resorption and formation. Moreover, the gut–bone axis, mediated by microbial metabolites, influences bone metabolism through immune and endocrine pathways. Epigenetic changes, such as DNA methylation and histone modification, contribute to gene–environment interactions, affecting disease progression. Multi-omics approaches (genomics, proteomics, and metabolomics) systematically identify molecular networks and comorbid links with diabetes/cardiovascular diseases, revealing pathological feedback loops that exacerbate bone loss. In conclusion, osteoporosis pathogenesis extends beyond bone remodeling to encompass systemic inflammation, immunometabolic dysregulation, and gut microbiota–host interactions. Future research should focus on integrating multi-omics biomarkers with targeted therapies to advance precision medicine strategies for osteoporosis prevention and treatment.

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...

This study explored the proteomic landscape of inflammatory protein dysregulation in ankylosing spondylitis (AS), a chronic inflammatory disorder primarily affecting the axial skeleton and strongly associated with the HLA-B27 allele, particularly the HLA-B2705 and HLA-B2704 subtypes prevalent in Chinese populations. Blood samples from HLA-B27-positive AS patients and normal controls (NC) were analyzed using the Olink Target 96 inflammation panel to profile 92 inflammatory proteins. HLA-B27 subtyping was performed via PCR-SSP. To identify key proteins and stratify AS subtypes, we employed machine learning classifiers, including LightGBM models coupled with SHAP value interpretation, alongside traditional statistical analyses. The proteomic analysis revealed significant dysregulation of pro-inflammatory cytokines, such as IL-6 and IL-17A, in AS patients compared to NC, with CXCL9 and NRTN identified as potential biomarkers associated with disease activity. The combination of LightGBM classifiers and traditional statistical methods demonstrated high accuracy in distinguishing AS from NC and effectively stratifying subtypes. These findings provide valuable insights into the inflammatory mechanisms underlying AS pathogenesis and highlight potential biomarkers and therapeutic targets for improving diagnosis and treatment strategies. Future studies with larger and more diverse cohorts, as well as longitudinal designs, are warranted to validate these biomarkers and elucidate their dynamic changes during disease progression.

Objectives: Abnormal lipid metabolism is increasingly recognized as a contributing factor to the development of osteonecrosis of the femoral head (ONFH). This study aimed to explore the lipidomic profiles of ONFH patients, focusing on distinguishing between traumatic ONFH (TONFH) and non-traumatic ONFH (NONFH) subtypes and identifying potential biomarkers for diagnosis and understanding pathogenesis. Methods: Plasma samples were collected from 92 ONFH patients (divided into TONFH and NONFH subtypes) and 33 healthy normal control (NC) participants. Lipidomic profiling was performed using ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS). Data analysis incorporated a machine learning-based feature selection method, least absolute shrinkage and selection operator (LASSO) regression, to identify significant lipid biomarkers. Results: Distinct lipidomic signatures were observed in both TONFH and NONFH groups compared to the NC group. LASSO regression identified 11 common lipid biomarkers that signify shared metabolic disruptions in both ONFH subtypes, several of which exhibited strong diagnostic performance with areas under the curve (AUCs) > 0.7. Additionally, subtype-specific lipid markers unique to TONFH and NONFH were identified, providing insights into the differential pathophysiological mechanisms underlying these subtypes. Conclusions: This study highlights the importance of lipidomic profiling in understanding ONFH-associated metabolic disorders and demonstrates the utility of machine learning approaches, such as LASSO regression, in high-dimensional data analysis. These findings not only improve disease characterization but also facilitate the discovery of diagnostic and mechanistic biomarkers, paving the way for more personalized therapeutic strategies in ONFH.

Anti-Müllerian hormone (AMH), a member of the transforming growth factor-β (TGF-β) superfamily, has been widely recognized for its role in reproductive endocrinology and is regarded as one of the “gold standards” for evaluating ovarian age and fertility potential. In recent years, the focus of research on AMH has gradually expanded from the reproductive system to the skeletal system. Although the specific mechanism of its action in bone-metabolism-related diseases and associated signaling pathways still requires in-depth exploration, existing studies have confirmed—through cell experiments, animal models, and clinical data—the important role of AMH in maintaining bone health. Here, the significance of AMH in research on female osteoporosis is reviewed, the current signaling pathway mechanisms by which AMH regulates bone metabolism are summarized, and the relevant clinical research results are discussed. This work features three unique contributions: first, the logical progression of AMH research from reproductive regulation to bone metabolism is explicitly clarified; second, multi-level evidence is integrated to form a complete regulatory network, avoiding fragmented discussions of individual findings; and third, concrete clinical translation pathways and targeted solutions for existing limitations are proposed, rather than merely outlining general directions. This review aims to identify new biomarkers for the early screening of osteoporosis and therapeutic targets, ultimately promoting the formulation of personalized prevention and treatment strategies. Additionally, as a key factor linking ovarian function and bone health, the AMH research concepts and methods summarized herein can be extended to other hormone-related bone metabolism disorders.