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Bone & Joint Research
Vol. 7, Issue 7 | Pages 494 - 500
1 Jul 2018
Jiang L Zhu X Rong J Xing B Wang S Liu A Chu M Huang G

Objectives

Given the function of adiponectin (ADIPOQ) on the inflammatory condition of obesity and osteoarthritis (OA), we hypothesized that the ADIPOQ gene might be a candidate gene for a marker of susceptibility to OA.

Methods

We systematically screened three tagging polymorphisms (rs182052, rs2082940 and rs6773957) in the ADIPOQ gene, and evaluated the association between the genetic variants and OA risk in a case-controlled study that included 196 OA patients and 442 controls in a northern Chinese population. Genotyping was performed using the Sequenom MassARRAY iPLEX platform.


Bone & Joint Research
Vol. 6, Issue 12 | Pages 640 - 648
1 Dec 2017
Xia B Li Y Zhou J Tian B Feng L

Objectives

Osteoporosis is a chronic disease. The aim of this study was to identify key genes in osteoporosis.

Methods

Microarray data sets GSE56815 and GSE56814, comprising 67 osteoporosis blood samples and 62 control blood samples, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in osteoporosis using Limma package (3.2.1) and Meta-MA packages. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to identify biological functions. Furthermore, the transcriptional regulatory network was established between the top 20 DEGs and transcriptional factors using the UCSC ENCODE Genome Browser. Receiver operating characteristic (ROC) analysis was applied to investigate the diagnostic value of several DEGs.


Bone & Joint Research
Vol. 6, Issue 10 | Pages 572 - 576
1 Oct 2017
Wang W Huang S Hou W Liu Y Fan Q He A Wen Y Hao J Guo X Zhang F

Objectives

Several genome-wide association studies (GWAS) of bone mineral density (BMD) have successfully identified multiple susceptibility genes, yet isolated susceptibility genes are often difficult to interpret biologically. The aim of this study was to unravel the genetic background of BMD at pathway level, by integrating BMD GWAS data with genome-wide expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (meQTLs) data

Method

We employed the GWAS datasets of BMD from the Genetic Factors for Osteoporosis Consortium (GEFOS), analysing patients’ BMD. The areas studied included 32 735 femoral necks, 28 498 lumbar spines, and 8143 forearms. Genome-wide eQTLs (containing 923 021 eQTLs) and meQTLs (containing 683 152 unique methylation sites with local meQTLs) data sets were collected from recently published studies. Gene scores were first calculated by summary data-based Mendelian randomisation (SMR) software and meQTL-aligned GWAS results. Gene set enrichment analysis (GSEA) was then applied to identify BMD-associated gene sets with a predefined significance level of 0.05.


Bone & Joint Research
Vol. 6, Issue 4 | Pages 231 - 244
1 Apr 2017
Zhang J Yuan T Zheng N Zhou Y Hogan MV Wang JH

Objectives

After an injury, the biological reattachment of tendon to bone is a challenge because healing takes place between a soft (tendon) and a hard (bone) tissue. Even after healing, the transition zone in the enthesis is not completely regenerated, making it susceptible to re-injury. In this study, we aimed to regenerate Achilles tendon entheses (ATEs) in wounded rats using a combination of kartogenin (KGN) and platelet-rich plasma (PRP).

Methods

Wounds created in rat ATEs were given three different treatments: kartogenin platelet-rich plasma (KGN-PRP); PRP; or saline (control), followed by histological and immunochemical analyses, and mechanical testing of the rat ATEs after three months of healing.


Bone & Joint Research
Vol. 1, Issue 5 | Pages 71 - 77
1 May 2012
Keurentjes JC Van Tol FR Fiocco M Schoones JW Nelissen RG

Objectives

We aimed first to summarise minimal clinically important differences (MCIDs) after total hip (THR) or knee replacement (TKR) in health-related quality of life (HRQoL), measured using the Short-Form 36 (SF-36). Secondly, we aimed to improve the precision of MCID estimates by means of meta-analysis.

Methods

We conducted a systematic review of English and non-English articles using MEDLINE, the Cochrane Controlled Trials Register (1960–2011), EMBASE (1991–2011), Web of Science, Academic Search Premier and Science Direct. Bibliographies of included studies were searched in order to find additional studies. Search terms included MCID or minimal clinically important change, THR or TKR and Short-Form 36. We included longitudinal studies that estimated MCID of SF-36 after THR or TKR.