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. 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.Aims
Methods
This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes.Aims
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