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Bone & Joint Research
Vol. 10, Issue 12 | Pages 820 - 829
15 Dec 2021
Schmidutz F Schopf C Yan SG Ahrend M Ihle C Sprecher C

Aims. The distal radius is a major site of osteoporotic bone loss resulting in a high risk of fragility fracture. This study evaluated the capability of a cortical index (CI) at the distal radius to predict the local bone mineral density (BMD). Methods. A total of 54 human cadaver forearms (ten singles, 22 pairs) (19 to 90 years) were systematically assessed by clinical radiograph (XR), dual-energy X-ray absorptiometry (DXA), CT, as well as high-resolution peripheral quantitative CT (HR-pQCT). Cortical bone thickness (CBT) of the distal radius was measured on XR and CT scans, and two cortical indices mean average (CBTavg) and gauge (CBTg) were determined. These cortical indices were compared to the BMD of the distal radius determined by DXA (areal BMD (aBMD)) and HR-pQCT (volumetric BMD (vBMD)). Pearson correlation coefficient (r) and intraclass correlation coefficient (ICC) were used to compare the results and degree of reliability. Results. The CBT could accurately be determined on XRs and highly correlated to those determined on CT scans (r = 0.87 to 0.93). The CBTavg index of the XRs significantly correlated with the BMD measured by DXA (r = 0.78) and HR-pQCT (r = 0.63), as did the CBTg index with the DXA (r = 0.55) and HR-pQCT (r = 0.64) (all p < 0.001). A high correlation of the BMD and CBT was observed between paired specimens (r = 0.79 to 0.96). The intra- and inter-rater reliability was excellent (ICC 0.79 to 0.92). Conclusion. The cortical index (CBTavg) at the distal radius shows a close correlation to the local BMD. It thus can serve as an initial screening tool to estimate the local bone quality if quantitative BMD measurements are unavailable, and enhance decision-making in acute settings on fracture management or further osteoporosis screening. Cite this article: Bone Joint Res 2021;10(12):820–829


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.