ArticleJournal of advanced research2025
Plasma proteomic profiles reveal proteins and three characteristic patterns associated with osteoporosis: A prospective cohort study.
Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Large-Scale Proteomics Uncovers Pre-Disease Inflammation-Lipid Subtypes to Refine Risk Stratification and Prediction of Type 2 Diabetes.Diabetes, obesity & metabolism · 2026Article
- Natural Polysaccharide-Mediated Nano-Delivery Systems for Osteoporosis Therapy From a Gut-Bone Axis Regulatory Perspective.Advanced healthcare materials · 2026Review
- A protein-based prediction model for fragility fracture risk in individuals with diabetes.Journal of molecular cell biology · 2026Article
- Extraction of Soybean and Pea Protein Isolates to Evaluate Therapeutic Potential Against Dexamethasone-Induced Osteoporosis: In Vivo andFood science & nutrition · 2026Article
- Meta-ERS: an exposome-based risk score using non-genetic factors to guide osteoporosis prevention.Journal of translational medicine · 2026Article
- Identification of the critical immune-related drivers shared by non-alcoholic fatty liver disease and osteoporosis.Cellular and molecular life sciences : CMLS · 2026Article
- PHEWAS, TWAS, Mendelian Randomization in Osteoporosis Research: the good, the bad, and the ugly.Current osteoporosis reports · 2026Review
- Comparative Proteomic Profiling of ClinicalJournal of extracellular biology · 2026Article
- Adherence to the EAT-Lancet Diet and Risk of Sepsis: A Prospective Cohort Study from the UK Biobank.NPJ science of food · 2026Article
- Social isolation, loneliness, genetic susceptibility, and the hazard of incident osteoporosis.International journal of surgery (London, England) · 2026Article
- Screening and validation of ZFYVE27 as a potential diagnostic biomarker for osteoporosis via integrative bioinformatics and machine learning approaches.Frontiers in immunology · 2026Article
- DNA Methylation, SERPING1 Expression, and Immune-related Traits in Osteoporosis: A Mendelian Randomization Study And SupportiveEndocrine, metabolic & immune disorders drug targets · 2026Article
- Effect of Disodium Etidronate on Bone Mineral Density in Postmenopausal Women: A Six-Month Prospective Study.International journal of women's health · 2026Article
- Integrative Single-Cell RNA Sequencing and Machine Learning Reveals Candidate Plasma Protein-Associated Gene Signatures for Osteoporosis: A Preliminary Exploratory in Silico Study.International journal of general medicine · 2026Article
- Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data.Frontiers in cell and developmental biology · 2026Review
- Proteomic signatures of type 2 diabetes predict the incidence of coronary heart disease.Cardiovascular diabetology · 2025Article
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionExploration of plasma proteins associated with osteoporosis can offer insights into its pathological development, identify novel biomarkers for screening high-risk populations, and facilitate the discovery of effective therapeutic targets.
objectivesThe present study aimed to identify potential proteins associated with osteoporosis and to explore the underlying mechanisms from a proteomic perspective.
methodsThe study included 42,325 participants without osteoporosis in the UK Biobank (UKB), of whom 1,477 developed osteoporosis during the follow-up. We used Cox regression and Mendelian randomization analysis to examine the association between plasma proteins and osteoporosis. Machine learning was utilized to explore proteins with strong predictive power for osteoporosis risk.
resultsOf 2,919 plasma proteins, we identified 134 significantly associated with osteoporosis, with sclerostin (SOST), adiponectin (ADIPOQ), and creatine kinase B-type (CKB) exhibiting strong associations. Twelve of these proteins showed significant associations with bone mineral density (BMD) T-score at the femoral neck, lumbar spine, and total body. Mendelian randomization further supported causal relationships between 17 plasma proteins and osteoporosis. Moreover, follitropin subunit beta (FSHB), SOST, and ADIPOQ demonstrated high importance in predictive modeling. Utilizing a predictive model built with 10 proteins, we achieved relatively accurate prediction of osteoporosis onset up to 5 years in advance (AUC = 0.803). Finally, we identified three osteoporosis-related protein modules associated with immunity, lipid metabolism, and follicle-stimulating hormone (FSH) regulation from a network perspective, elucidating their mediating roles between various risk factors (smoking, sleep, physical activity, polygenic risk score (PRS), and menopause) and osteoporosis.
conclusionWe identified several proteins associated with osteoporosis and highlighted the role of plasma proteins in influencing its progression through three primary pathways: immunity, lipid metabolism, and FSH regulation. This provides further insights into the distinct molecular patterns and pathogenesis of bone loss and may contribute to strengthening early diagnosis and long-term monitoring of the condition.
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