OBJECTIVE: To determine whether the currently known genetic risk factors for rheumatoid arthritis (RA) improve the prediction of the development of RA compared to prediction using clinical risk factors alone in patients with undifferentiated arthritis (UA). METHODS: Five hundred and seventy early UA-patients included in the Leiden Early Arthritis Clinic cohort, previously used to derive a clinical prediction rule, were used to explore the additional value of genetic variants. The following genetic variants were assessed HLA-DRB1 shared epitope (SE) alleles, rs2476601 (PTPN22), rs108184088 (TRAF1-C5), rs7574865 (STAT4), rs3087243 (CTLA4), rs4810485 (CD40), rs1678542 (KIF5A-PIP4K2C), rs2812378 (CCL21), rs42041 (CDK6), rs4750316 (PRKCQ), rs6684865 (MMEL1-TNFRSF14), rs2004640 (IRF5), rs6920220 and rs10499194 (TNFAIP3-OLIG3), interactions between HLA-SE alleles and rs2476601 (PTPN22) and between HLA-SE alleles and smoking. The area under the receiver operator curve (AUC) was used as measure of the discriminative ability of the models. RESULTS: The AUC of a model consisting of genetic variants only was low, 0.536 (95% CI 0.48 to 0.59). The AUC of the model including genetic and clinical risk factors was not superior over the AUC of the clinical prediction rule (0.889, 95% CI 0.86 to 0.95 and 0.884, 95% CI 0.86 to 0.92). CONCLUSION: In a population at risk, information on currently known genetic risk factors for RA does not improve prediction of risk for RA compared to a prediction rule based on common clinical risk factors alone.
OBJECTIVE: To determine whether the currently known genetic risk factors for rheumatoid arthritis (RA) improve the prediction of the development of RA compared to prediction using clinical risk factors alone in patients with undifferentiated arthritis (UA). METHODS: Five hundred and seventy early UA-patients included in the Leiden Early Arthritis Clinic cohort, previously used to derive a clinical prediction rule, were used to explore the additional value of genetic variants. The following genetic variants were assessed HLA-DRB1 shared epitope (SE) alleles, rs2476601 (PTPN22), rs108184088 (TRAF1-C5), rs7574865 (STAT4), rs3087243 (CTLA4), rs4810485 (CD40), rs1678542 (KIF5A-PIP4K2C), rs2812378 (CCL21), rs42041 (CDK6), rs4750316 (PRKCQ), rs6684865 (MMEL1-TNFRSF14), rs2004640 (IRF5), rs6920220 and rs10499194 (TNFAIP3-OLIG3), interactions between HLA-SE alleles and rs2476601 (PTPN22) and between HLA-SE alleles and smoking. The area under the receiver operator curve (AUC) was used as measure of the discriminative ability of the models. RESULTS: The AUC of a model consisting of genetic variants only was low, 0.536 (95% CI 0.48 to 0.59). The AUC of the model including genetic and clinical risk factors was not superior over the AUC of the clinical prediction rule (0.889, 95% CI 0.86 to 0.95 and 0.884, 95% CI 0.86 to 0.92). CONCLUSION: In a population at risk, information on currently known genetic risk factors for RA does not improve prediction of risk for RA compared to a prediction rule based on common clinical risk factors alone.
Authors: Jeffrey A Sparks; Chia-Yen Chen; Xia Jiang; Johan Askling; Linda T Hiraki; Susan Malspeis; Lars Klareskog; Lars Alfredsson; Karen H Costenbader; Elizabeth W Karlson Journal: Ann Rheum Dis Date: 2014-03-31 Impact factor: 19.103
Authors: A Paradowska-Gorycka; K Romanowska-Próchnicka; E Haladyj; M Manczak; S Maslinski; M Olesinska Journal: Clin Exp Immunol Date: 2015-03 Impact factor: 4.330
Authors: Zoltán Szekanecz; Bertalan Meskó; Szilard Poliska; Andrea Váncsa; Szilvia Szamosi; Edit Végh; Enikö Simkovics; Judit Laki; Júlia Kurkó; Timea Besenyei; Katalin Mikecz; Tibor T Glant; László Nagy Journal: Immunol Res Date: 2013-07 Impact factor: 2.829