Article Text
Abstract
Background Osteoarthritis (OA) is a progressive degenerative disease of all joint structures, affecting over 50% of the population over the age of 65 in the US. Joint injury, a common outcome of sport participation, and high impact loading, such as that experienced in landing and running, are associated with an increased risk of cartilage degeneration and OA development. However, joint loading is also important to cartilage matrix health. The relationship between mechanical loading and joint degeneration is not clear.
Objective To investigate the response of biological markers of cartilage metabolism to moderate cyclical and high impact loading activities.
Design Comparative cohort study.
Setting High level athletes.
Participants 10 impact athletes (five women and five men) and 14 adult non-athletic controls (seven women and seven men).
Interventions Data were collected and compared between three experimental conditions: complete rest; moderate cyclical loading (slow jog); and high impact loading (landing). The duration of intervention was 30 min and subjects were supervised throughout the intervention.
Main outcome measurements Blood samples were collected from each subject at baseline and 30, 60, 120 and 240 min after three interventions. Blood samples were allowed to clot, centrifuged to separate the serum and aliquots were stored at −80° until testing. Serum concentrations of glucosaminoglycans, C-propeptide of collagen II (CP2), cleavage of collagen II (C2C) and cartilage oligomeric matrix protein (COMP) were evaluated using commercially available Enzyme Linked Immunosorbent Assay kits.
Results A significant change in serum biomarker concentration in response to the loading intervention was observed for COMP.
Conclusion The results of this study suggest that serum CP2 and C2C concentrations were not influenced by the loading interventions at the analysed time points. However, COMP increased immediately after the loading interventions. Future studies should investigate the role of biomarkers as a diagnostic tool for evidence based workload assessment.