Pre-Operative Adiposity and Synovial Fluid Inflammatory Biomarkers Provide a Predictive Model for Post-Operative Outcomes Following Total Joint Replacement Surgery in Osteoarthritis Patients

Background 

Osteoarthritis is one of the leading causes of chronic joint pain and is the main reason why people have to have joints replaced. Despite being very good, joint replacement operations do not always work for people. Some will have pain that continues after the joint is replaced and others might not improve their joint function. 

Study aims 

This study aimed to find out if it was possible to predict whether joint replacements worked for people with OA. The researchers measured signalling molecules in the joints of people with OA and also their weight and waist measurements. 

Study findings and conclusions 

One hundred and sixty people took part in the study. 24 different signalling molecules that are important for inflammation, arthritis and weight control were measured in the synovial fluid of the joint. Seven months after total joint replacement operations, the study participants answered questions about their quality of life. 

13% of the participants did not respond well to their joint replacement. Having a higher body mass index (being overweight or obese) and having a larger tummy (relative to the hips) were associated with worse quality of life. 

The signalling molecules also showed a link to weight control, with molecules called leptin and resitin being associated with worse quality of life. Combinations of measurements seemed to improve the prediction of joint replacement success. Body weight combined with the molecules interleukin-6 and amphiregulin showed some promise for future research. 

Recommendations to healthcare professionals and researchers 

Losing weight before joint replacement operations might help people to recover and have a better quality of life. Healthcare professionals might recommend this to their patients. 

Combined scores from multiple measurements could improve the prediction of treatment success, and seem to be better than measuring a single important factor. Predicting successful treatments is complicated and further research is needed to try and produce accurate methods to help choose therapies. 

Original article: https://www.mdpi.com/2673-4036/4/2/5