Aims. Femoroacetabular impingement (FAI) is a potential cause of hip osteoarthritis (OA). The purpose of this study was to investigate the expression profile of matrix metalloproteinases (MMPs) in the labral tissue with FAI pathology. Methods. In this study, labral tissues were collected from four FAI patients arthroscopically and from three normal hips of deceased donors. Proteins extracted from the FAI and normal labrums were separately applied for MMP array to screen the expression of seven MMPs and three tissue inhibitors of metalloproteinases (TIMPs). The expression of individual MMPs and TIMPs was quantified by densitometry and compared between the FAI and normal labral groups. The expression of selected MMPs and TIMPs was validated and localized in the labrum with immunohistochemistry. Results. On MMP arrays, most of the targeted MMPs and TIMPs were detected in the FAI and normal labral proteins. After data normalization, in comparison with the normal labral proteins, expression of MMP-1 and MMP-2 in the FAI group was increased and expression of TIMP-1 reduced. The histology of the FAI labrum showed disorderly cell distribution and altered composition of thick and thin collagen fibres. The labral cells expressing MMP-1 and MMP-2 were localized and their percentages were increased in the FAI labrum. Immunohistochemistry confirmed that the percentage of TIMP-1 positive cells was reduced in the FAI labrum. Conclusion. This study established an expression profile of MMPs and TIMPs in the FAI labrum. The increased expression of MMP-1 and MMP-2 and reduced expression of TIMP-1 in the FAI labrum are indicative of a pathogenic role of FAI in hip OA development. Cite this article:Bone Joint Res. 2020;9(4):173–181

Aims

This study aimed to investigate the role and mechanism of meniscal cell lysate (MCL) in fibroblast-like synoviocytes (FLSs) and osteoarthritis (OA).

Aims

The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA).