Article ID Journal Published Year Pages File Type
2773047 BBA Clinical 2016 4 Pages PDF
Abstract

BackgroundPlain radiography is the first choice for diagnosis and monitoring of knee-osteoarthritis (OA) while, Kellgren–Lawrence score (KL) is most widely used to grade OA severity. However, incompetency for reproducibility of joint space measurement in longitudinal assessment and non-linearity of KL-score system, limits radiography-based early diagnosis of the disease. Glycosaminoglycan (GAG) is direct cartilage-degradation product, which can be measured biochemically. We strived to correlate KL-score and GAG from OA patients to compliment KL-system.MethodsWe obtained 34 synovial-fluid (SF) samples from 28 OA patients (few bilateral) with different disease severity using arthrocetesis. All patients were categorised using radiographic KL-score-system. SFs were further analysed for GAG estimation using 1,2-dimethylmethylene blue (DMMB) assay.ResultsA substantial increase in GAG was noted in KL-grade-II and III, comparing grade-I patients, indicating amplified cartilage-degradation. KL-grade-IV patients revealed further rise in GAG reflecting more cartilage-loss. Another category of grade-IV patients with lower GAG were also detected, indicating close to total cartilage-loss.ConclusionsAccurate diagnosis of cartilage-loss remains a challenge with OA due to limitations of KL-system; thus no target intervention is available to arrest active cartilage-loss. We propose, GAG-estimation in OA patients, characterizes accurate biochemical depiction of cartilage degeneration. General Significance: Radiology often fails to reveal an accurate cartilage loss, associated with OA. GAG levels from the SFs of OA patients' serve as a useful marker, which parallels cartilage degeneration and strengthen radiographic grading system, ultimately

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slide

Related Topics
Life Sciences Biochemistry, Genetics and Molecular Biology Clinical Biochemistry
Authors
, , , , , ,