Background Single Nucleotide Polymorphisms (SNPs) are inherited genetic variations that can predispose or protect individuals against clinical events. Osteoarthritis (OA) has a multifactorial etiology with a strong genetic component. Genetic factors influence not only knee OA onset, but also disease progression.
Objectives The aim of this study was to develop a genetic prognostic tool to predict radiologic progression towards severe disease in primary knee OA (KOA) patients.
Methods Cross-sectional, retrospective, multicentric, association study with Spanish KOA patients. 595 patients from 31 sites were selected. Inclusion criteria: Caucasian patients aged ≥40 years at the time of diagnosis of primary KOA, for whom two anteroposterior X-rays were available, one corresponding to the time of OA diagnosis with Kellgren-Lawrence grade 2 or 3 and the other to the end of the follow-up period. Patients who progressed to KL score 4 or were referred for total knee replacement in ≤8 years since the diagnosis were classified as progressors to severe disease. A unique expert viewer measured radiologic progression from all X-rays.
A candidate gene study analyzing 774 SNPs was conducted. SNP genotyping was performed with Illumina Golden gate technology or KASPar chemistry. 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.
Univariate analysis was done to identify associations between the baseline clinical variables or SNPs and KOA progression. SNPs and clinical variables with an association of p<0.05 were included on the multivariate analysis using forward logistic regression.
Results 282 patients fulfilled DNA and X-ray quality control criteria (220 in the exploratory cohort and 62 in the validation cohort). The univariate association analysis showed that one of the clinical variables and 23 SNPs were significantly associated to KOA severe progression in the exploratory cohort (p<0.05). The predictive accuracy of the clinical variable was limited, as indicated by the area under the ROC curve (AUC=0.66). When genetic variables were added to the clinical model (full model) the prediction of KOA progression was improved and the AUC increased to 0.82. Combining only genetic variables, a predictive model with a good accuracy (AUC=0.78) was also obtained. The predictive ability for KOA progression of the full model was confirmed on the validation cohort (two-sample Z-test p=0.190).
Conclusions Genetic polymorphisms predict radiologic progression more accurately than clinical variables. An accurate prognostic tool to predict primary KOA progression has been developed, based on genetic and clinical information from OA patients. This model could help clinicians optimize patients' preventive and therapeutic care, according to their OA progression rate, and personalize disease management.
Disclosure of Interest : F. J. Blanco: None declared, I. Möller: None declared, N. Bartolome Employee of: Progenika Biopharma, M. Artieda Employee of: Progenika Biopharma, D. Tejedor Employee of: Progenika Biopharma, A. Martinez Employee of: Progenika Biopharma, E. Montell Employee of: Bioiberica, H. Martinez Employee of: Bioiberica, M. Herrero Employee of: Bioiberica, J. Verges Employee of: Bioiberica
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