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https://www.selleckchem.com/products/KU-60019.html 708 (0.618-0.799) and 0.654 (0.559-0.749), respectively to predict rKOA in the replication cohort. The inclusion of ITIH1 in the clinical model (age, gender, BMI, previous knee injury and WOMAC pain) improved the predictive capacity of the clinical covariates (AUC=0.754 [0.670-0.838]) producing the model with the highest AUC (0.786 [0.705-0.867]) and the highest IDI index (9%). High levels of ITIH1 were also associated with an earlier onset of the disease. A clinical model including protein biomarkers that predicts incident rKOA has been developed. Among the tested biomarkers, ITIH1 showed potential to improve the capacity to predict rKOA incidence in clinical practice. A clinical model including protein biomarkers that predicts incident rKOA has been developed. Among the tested biomarkers, ITIH1 showed potential to improve the capacity to predict rKOA incidence in clinical practice. To identify proximate causes ('triggers') of flares in adults with, or at risk of, knee osteoarthritis (OA), estimate their course and consequences, and determine higher risk individuals. In this 13-week web-based case-crossover study adults aged ≥40 years, with or without a recorded diagnosis of knee OA, and no inflammatory arthropathy who self-reported a knee flare completed a questionnaire capturing information on exposure to 21 putative activity-related, psychosocial and environmental triggers (hazard period, ≤72h prior). Comparisons were made with identical exposure measurements at four 4-weekly scheduled time points (non-flare control period) using conditional logistic regression. Flare was defined as a sudden onset of worsening signs and symptoms, sustained for ≥24h. Flare characteristics, course and consequence were analysed descriptively. Associations between flare frequency and baseline characteristics were estimated using Poisson regression. Of 744 recruited participants (mean age [SD] 62.1 [10.2] years; 61% female), 376 repo

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