Bartolomé Nerea, Szczypiorska Magdalena, Sánchez Alejandra, Sanz Jesús, Juanola-Roura Xavier, Gratacós Jordi, Zarco-Montejo Pedro, Collantes Eduardo, Martínez Antonio, Tejedor Diego, Artieda Marta, Mulero Juan
Diagnostic Department, Progenika Biopharma SA, Parque Tecnológico de Bizkaia, Edificio 504, Derio 48160, Bizkaia, Spain.
Rheumatology (Oxford). 2012 Aug;51(8):1471-8. doi: 10.1093/rheumatology/kes056. Epub 2012 Apr 11.
The aim of this study was to analyse if single nucleotide polymorphisms (SNPs) inside and outside the MHC region might improve the prediction of radiographic severity in AS.
A cross-sectional multi-centre study was performed including 473 Spanish AS patients previously diagnosed with AS following the Modified New York Criteria and with at least 10 years of follow-up from the first symptoms of AS. Clinical variables and 384 SNPs were analysed to predict radiographic severity [BASRI-total (BASRI-t) corrected for the duration of AS since first symptoms] using multivariate forward logistic regression. Predictive power was measured by the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV).
The model with the best fit measured radiographic severity as the BASRI-t 60th percentile and combined eight variables: male gender, older age at disease onset and six SNPs at ADRB1 (rs1801253), NELL1 (rs8176785) and MHC (rs1634747, rs9270986, rs7451962 and rs241453) genes. The model predictive power was defined by AUC = 0.76 (95% CI 0.71, 0.80), being significantly better than the model with only clinical variables, AUC = 0.68 (95% CI 0.63, 0.73), P = 0.0004. Internal split-sample analysis proved the validation of the model. Patient genotype for SNPs outside the MHC region, inside the MHC region and clinical variables account for 26, 38 and 36%, respectively, of the explained variability on radiographic severity prediction.
Prediction of radiographic severity in AS based on clinical variables can be significantly improved by including SNPs both inside and outside the MHC region.
本研究旨在分析MHC区域内外的单核苷酸多态性（SNP）是否能改善强直性脊柱炎（AS）影像学严重程度的预测。
进行了一项横断面多中心研究，纳入473例先前根据改良纽约标准确诊为AS且自AS首次出现症状起至少有10年随访的西班牙AS患者。分析临床变量和384个SNP，使用多变量向前逻辑回归预测影像学严重程度[自首次症状起校正AS病程后的BASRI总分（BASRI-t）]。通过受试者工作特征曲线下面积（AUC）、特异性、敏感性、阳性预测值（PPV）和阴性预测值（NPV）来衡量预测能力。
拟合度最佳的模型将影像学严重程度测量为BASRI-t第60百分位数，并组合了八个变量：男性、发病年龄较大以及ADRB1（rs1801253）、NELL1（rs8176785）和MHC（rs1634747、rs9270986、rs7451962和rs241453）基因的六个SNP。该模型的预测能力由AUC = 0.76（95%CI 0.71，0.80）定义，显著优于仅包含临床变量的模型，后者的AUC = 0.68（95%CI 0.63，0.73），P = 0.0004。内部拆分样本分析证实了该模型的有效性。MHC区域外的SNP、MHC区域内的SNP和临床变量的患者基因型分别占影像学严重程度预测中可解释变异性的26%、38%和36%。
纳入MHC区域内外的SNP可显著改善基于临床变量对AS影像学严重程度的预测。
