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Bone & Joint Research
Vol. 12, Issue 7 | Pages 423 - 432
6 Jul 2023
Xie H Wang N He H Yang Z Wu J Yang T Wang Y

Aims. Previous studies have suggested that selenium as a trace element is involved in bone health, but findings related to the specific effect of selenium on bone health remain inconclusive. Thus, we performed a meta-analysis by including all the relevant studies to elucidate the association between selenium status (dietary intake or serum selenium) and bone health indicators (bone mineral density (BMD), osteoporosis (OP), or fracture). Methods. PubMed, Embase, and Cochrane Library were systematically searched to retrieve relevant articles published before 15 November 2022. Studies focusing on the correlation between selenium and BMD, OP, or fracture were included. Effect sizes included regression coefficient (β), weighted mean difference (WMD), and odds ratio (OR). According to heterogeneity, the fixed-effect or random-effect model was used to assess the association between selenium and bone health. Results. From 748 non-duplicate publications, 19 studies were included. We found a significantly positive association between dietary selenium intake (β = 0.04, 95% confidence interval (CI) 0.00 to 0.07, p = 0.029) as well as serum selenium (β = 0.13, 95% CI 0.00 to 0.26, p = 0.046) and BMD. Consistently, those with higher selenium intake had a lower risk of OP (OR = 0.47, 95% CI 0.31 to 0.72, p = 0.001), and patients with OP had a significantly lower level of serum selenium than healthy controls (WMD = -2.01, 95% CI -3.91 to -0.12, p = 0.037). High dietary selenium intake was associated with a lower risk of hip fracture (OR = 0.44, 95% CI 0.37 to 0.52, p < 0.001). Conclusion. Selenium was positively associated with BMD and inversely associated with OP; dietary selenium intake was negatively associated with hip fracture. The causality and therapeutic effect of selenium on OP needs to be investigated in future studies. Cite this article: Bone Joint Res 2023;12(7):423–432


Aims

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.

Methods

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.