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Improved prediction of knee osteoarthritis progression by genetic polymorphisms: the Arthrotest Study

2015; Oxford University Press; Volume: 54; Issue: 7 Linguagem: Inglês

10.1093/rheumatology/keu478

1462-0332

Francisco J. Blanco, Ingrid Möller, Montserrat Romera Baurés, A Rozadilla, J. Sánchez-Lázaro, Ά de Cossío Rodríguez, José Fortes Gálvez, Joaquím Forés, Jordi Monfort, Soledad Ojeda, Carme Moragues, Miguel A. Caracuel, Teresa Clavaguera, Carmen Ayuso Valdés, Josep Soler, C. Orellana, Miguel Ángel Belmonte, Florentina Martín, Sergio Giménez, Eduardo Úcar, Josep A. Pous, Nerea Bartolomé, Marta Artieda, Magdalena Szczypiorska, Diego Tejedor, Antonio Martínez, E. Montell, H. Martínez, Marta Herrero, Josep Vergés,

Tendon Structure and Treatment

Objective.The aim of this study was to develop a genetic prognostic tool to predict radiographic progression towards severe disease in primary knee OA (KOA) patients.Methods.This investigation was a cross-sectional, retrospective, multicentric association study in 595 Spanish KOA patients.Caucasian patients aged 540 years at the time of diagnosis of primary KOA of KellgrenLawrence grade 2 or 3 were included.Patients who progressed to KellgrenLawrence score 4 or who were referred for total knee replacement within 8 years after diagnosis were classified as progressors to severe disease.Clinical variables of the initial stages of the disease (gender, BMI, age at diagnosis, OA in the contralateral knee, and OA in other joints) were registered as potential predictors.Single nucleotide polymorphisms and clinical variables with an association of P < 0.05 were included in the multivariate analysis using forward logistic regression.Results.A total of 23 single nucleotide polymorphisms and the time of primary KOA diagnosis were significantly associated with KOA severe progression in the exploratory cohort (n = 220; P < 0.05).The predictive accuracy of the clinical variables was limited: area under the curve (AUC) = 0.66.When genetic variables were added to the clinical model (full model), the prediction of KOA progression was significantly improved (AUC = 0.82).Combining only genetic variables (rs2073508, rs10845493, rs2206593, rs10519263, rs874692, rs7342880, rs780094 and rs12009), a predictive model with good accuracy was also obtained (AUC = 0.78).The predictive ability for KOA progression of the full model was confirmed on the replication cohort (two-sample Z-test; n = 62; P = 0.190).