Profiling Osteoporosis via Integrated Multi-Omics Technologies

BACKGROUND: Osteoporosis is a complex disorder involving bone loss and muscle degeneration. Multi-omics technologies provide novel insights into its molecular mechanisms and may support biomarker discovery, patient stratification, and therapeutic development. OBJECTIVE: This scoping review aimed to synthesize current evidence on the application of multi-omics approaches in osteoporosis, focusing on molecular insights, methodological diversity, and translational potential. METHODS: A literature search of PubMed, Embase, and Scopus retrieved 433 records using the keywords "osteoporosis," "osteosarcopenia," and "omics." After removing duplicates and screening titles, abstracts, and full texts, 30 studies met the inclusion criteria. Data on study populations, biological samples, multi-omics techniques, and integration methods were extracted. RESULTS: Studies employed transcriptomics, proteomics, metabolomics, lipidomics, epigenomics, and metagenomics, often combined in multi-omics analyses with computational modeling. Key pathways included osteoclast differentiation, immune regulation, ferroptosis, and microbiome-metabolite interactions. Multi-omics integration enabled the identification of molecular subtypes, candidate biomarkers, and potential therapeutic targets. Limitations included small or single-center cohorts, heterogeneous designs, and limited validation, restricting generalizability and clinical translation. CONCLUSIONS: Multi-omics approaches offer a powerful framework to uncover the molecular mechanisms underlying bone and muscle degeneration and to guide precision diagnostics and interventions. Future studies should prioritize large, multicenter, longitudinal designs integrating multi-omics data with clinical and functional validation to facilitate clinical application.