BACKGROUND: We conducted an exploratory analysis of osteoarthritis progression among medication users in the Osteoarthritis Initiative to identify interventions or pathways that may be associated with disease modification and therefore of interest for future clinical trials. METHODS: We used participants from the Osteoarthritis Initiative with annual medication inventory data between the baseline and 36-month follow-up visit (n = 2938). Consistent medication users were defined for each medication classification as a participant reporting at all four annual visits that they were regularly using an oral prescription medication at the time of the visit. The exploratory analysis focused on medication classes with 40 or more users. The primary outcome measures were medial tibiofemoral joint space width change and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) knee pain score change (12-36-month visits). Within each knee, we explored eight comparisons between users and matched or unmatched nonusers (defined two ways). An effect size of each comparison was calculated. Medication classes had potential signals if (a) both knees had less progression among users compared with nonusers, or (b) there was less progression based on structure and symptoms in one knee. RESULTS: We screened 28 medication classes. Six medication classes had signals for fewer structural changes and better knee pain changes: alpha-adrenergic blockers, antilipemic (excluding statins and fibric acid), anticoagulants, selective serotonin reuptake inhibitors, antihistamines, and antineoplastic agents. Four medication classes had signals for structural changes alone: anti-estrogen (median effect size = 0.28; range = -0.41-0.64), angiotensin-converting enzyme inhibitors (median effect size = 0.13; range = -0.08-0.28), beta-adrenergic blockers (median effect size = 0.09; range = 0.01-0.30), and thyroid agents (median effect size = 0.04; range = -0.05-0.14). Thiazide diuretics had evidence for symptom modification (median effect size = -0.12; range = -0.24-0.04). CONCLUSIONS: Users of neurovascular, antilipemic, or hormonal interventions may have less disease progression compared with nonusers.

OBJECTIVE: Cartilage health is thought to be dependent on the relationship between mechanics, structure, and metabolism, rather than these individual components in isolation. Due to sex differences in cartilage health, there is need to determine if the relationships between these cartilage components separately for males and females. Therefore, we sought to determine the sex-specific associations between cartilage structure and metabolism at rest and their acute response following walking and drop-landing in healthy individuals. DESIGN: A cartilage ultrasound assessment and an ante-cubital blood draw were performed before and after walking and drop-landing conditions in 20 males and 20 females. Cartilage structure was assessed via medial and lateral femoral cartilage cross-sectional area. Cartilage metabolism was quantified with serum cartilage oligomeric matrix protein (COMP) concentration. Percent change scores from pre- to postloading were used to calculate acute alterations in cross-sectional area and COMP. Correlational analyses were used to assess the association between cartilage structure and metabolism measures separately for males and females. RESULTS: In females, greater resting COMP concentration was associated with less cartilage cross-sectional area in the medial(rho = -0.50, P = 0.03) and lateral (rho = -0.69, P = 0.001) femur. Resting cartilage measures were not associated among males. Following walking and drop-landing, percent change scores in cartilage structure and metabolism were not associated. CONCLUSIONS: This study highlights that, in females, thinner anterior femoral cartilage is associated with greater resting serum COMP concentrations, a biomarker often linked to cartilage breakdown. Future studies into the relationships between various cartilage components should consider sex-specific analyses as these relationships are sex dependent.

Artificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic evaluation of knee pathology. Experimental algorithms have already been developed that can assess the severity of knee osteoarthritis from radiographs, detect and classify cartilage lesions, meniscal tears, and ligament tears on magnetic resonance imaging, provide automatic quantitative assessment of tendon healing, detect fractures on radiographs, and predict those at highest risk for recurrent bone tumors. This article reviews and summarizes the most current literature.