Postmenopausal osteoporosis is a prevalent metabolic bone disorder characterized by a decrease in bone mineral density and deterioration of bone microstructure. Despite the high prevalence of this disease, no effective treatment for osteoporosis has been developed. Exercise has long been considered a potent anabolic factor that promotes bone mass via upregulation of myokines secreted by skeletal muscle, exerting long‐term osteoprotective effects and few side effects. Irisin was recently identified as a novel myokine that is significantly upregulated by exercise and could increase bone mass. However, the mechanisms underlying exercise‐induced muscle‐bone crosstalk remain unclear. Here, we identified that polyunsaturated fatty acids (arachidonic acid and docosahexaenoic acid) are increased in skeletal muscles following a 10‐week treadmill exercise programme, which then promotes the expression and release of FNDC5/irisin. In osteoblasts, irisin binds directly to Cav1, which recruits and interacts with AMP‐activated protein kinase α (AMPKα) to activate the AMPK pathway. Nrf2 is the downstream target of the AMPK pathway and increases the transcription of HMOX1 and Fpn. HMOX1 is involved in regulating the cell cycle and promotes the proliferation of osteoblasts. Moreover, upregulation of Fpn in osteoblasts enhanced iron removal, thereby suppressing ferroptosis in osteoblasts. Additionally, we confirmed that myotube‐derived exosomes are involved in the transportation of irisin and enter osteoblasts through caveolae‐mediated endocytosis. In conclusion, our findings highlight the crucial role of irisin, present in myotube‐derived exosomes, as a crucial regulator of exercise‐induced protective effects on bone, which provides novel insights into the mechanisms underlying exercise‐dependent treatment of osteoporosis.

Background. With the COVID‐19 epidemic breakout in China, up to 25% of diagnosed cases are considered to be severe. To effectively predict the progression of COVID‐19 via patients’ clinical features at an early stage, the prevalence of these clinical factors and their relationships with severe illness were assessed. Methods. In this study, electronic databases (PubMed, Embase, Web of Science, and Chinese database) were searched to obtain relevant studies, including information on severe patients. Publication bias analysis, sensitivity analysis, prevalence, sensitivity, specificity, likelihood ratio, diagnosis odds ratio calculation, and visualization graphics were achieved through software Review Manager 5.3, Stata 15, Meta‐DiSc 1.4, and R. Results. Data of 3.547 patients from 24 studies were included in this study. The results revealed that patients with chronic respiratory system diseases (pooled positive likelihood 6.07, 95% CI: 3.12‐11.82), chronic renal disease (4.79, 2.04‐11.25), cardiovascular disease (3.45, 2.19‐5.44), and symptoms of the onset of chest tightness (3.8, 1.44‐10.05), shortness of breath (3.18, 2.24‐4.51), and diarrhea (2.04, 1.38‐3.04) exhibited increased probability of progressing to severe illness. C‐reactive protein, ratio of neutrophils to lymphocytes, and erythrocyte sedimentation rate increased a lot in severe patients compared to nonsevere. Yet, it was found that clinical features including fever, cough, and headache, as well as some comorbidities, have little warning value. Conclusions. The clinical features and laboratory examination could be used to estimate the process of infection in COVID‐19 patients. The findings contribute to the more efficient prediction of serious illness for patients with COVID‐19 to reduce mortality.

Aims: This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms.Methods: We analyzed microarray data from the Gene Expression Omnibus (GEO) database using weighted gene co-expression network analysis (WGCNA), machine learning, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify common genetic factors between POMP and sarcopenia. Further validation was done via differential gene expression in a new cohort. Single-cell analysis identified high expression cell subsets, with mononuclear macrophages in osteoporosis and muscle stem cells in sarcopenia, among others. A competitive endogenous RNA network suggested regulatory elements for these genes.Results: Signal transducer and activator of transcription 3 (STAT3) was notably expressed in both conditions. Single-cell analysis pinpointed specific cells with high STAT3 expression, and microRNA (miRNA)-125a-5p emerged as a potential regulator. Experiments confirmed the crucial role of STAT3 in osteoclast differentiation and muscle proliferation.Conclusion: STAT3 has emerged as a key gene in both POMP and sarcopenia. This insight positions STAT3 as a potential common therapeutic target, possibly improving management strategies for these age-related diseases.Cite this article: Bone Joint Res 2024;13(8):411–426.

Aims: We aimed to develop and validate a novel prediction model for osteoporosis based on serotonin, fat-soluble vitamins, and bone turnover markers to improve prediction accuracy of osteoporosis.Methods: Postmenopausal women aged 55 to 65 years were recruited and divided into three groups based on DXA (normal, osteopenia, and osteoporosis). A total of 109 participants were included in this study and split into healthy (39/109, 35.8%), osteopenia (35/109, 32.1%), and osteoporosis groups (35/109, 32.1%). Serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers of participants were measured. Stepwise discriminant analysis was performed to identify efficient predictors for osteoporosis. The prediction model was developed based on Bayes and Fisher’s discriminant functions, and validated via leave-one-out cross-validation. Normal and empirical volume under the receiver operating characteristic (ROC) surface (VUS) tests were used to evaluate predictive effects of variables in the prediction model.Results: Significant variables including oestrogen (E2), total procollagen type 1 amino-terminal propeptide (TP1NP), parathyroid hormone (PTH), BMI, vitamin K, serotonin, osteocalcin (OSTEOC), vitamin A, and vitamin D3 were used for the development of the prediction model. The training accuracy for normal, osteopenia, and osteoporosis is 74.4% (29/39), 80.0% (28/35), and 85.7% (30/35), respectively, while the total training accuracy is 79.8% (87/109). The internal validation showed excellent performance with 72.5% testing accuracy (72/109). Among these variables, serotonin and vitamin K exert important roles in the prediction of osteoporosis.Conclusion: We successfully developed and validated a novel prediction model for osteoporosis based on serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers. In addition, interactive communication between serotonin and fat-soluble vitamins was observed to be critical for bone health in this study.Cite this article: Bone Joint Res 2025;14(2):111–123.