Department of Paediatrics and Adolescent Medicine, Aarhus University Hospital, Aarhus, Denmark
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
RMD Open. 2024 Sep 5;10(3):e004317. doi: 10.1136/rmdopen-2024-004317.
To assess the ability of baseline serum biomarkers to predict disease activity and remission status in juvenile idiopathic arthritis (JIA) at 18-year follow-up (FU) in a population-based setting.
Clinical data and serum levels of inflammatory biomarkers were assessed in the longitudinal population-based Nordic JIA cohort study at baseline and at 18-year FU. A panel of 16 inflammatory biomarkers was determined by multiplexed bead array assay. We estimated both univariate and multivariate logistic regression models on binary outcomes of disease activity and remission with baseline variables as explanatory variables.
Out of 349 patients eligible for the Nordic JIA cohort study, 236 (68%) had available serum samples at baseline. We measured significantly higher serum levels of interleukin 1β (IL-1β), IL-6, IL-12p70, IL-13, MMP-3, S100A9 and S100A12 at baseline in patients with active disease at 18-year FU than in patients with inactive disease. Computing receiver operating characteristics illustrating the area under the curve (AUC), we compared a conventional prediction model (gender, age, joint counts, erythrocyte sedimentation rate, C reactive protein) with an extended model that also incorporated the 16 baseline biomarkers. Biomarker addition significantly improved the ability of the model to predict activity/inactivity at the 18-year FU, as evidenced by an increase in the AUC from 0.59 to 0.80 (p=0.02). Multiple regression analysis revealed that S100A9 was the strongest predictor of inactive disease 18 years after disease onset.
Biomarkers indicating inflammation at baseline have the potential to improve evaluation of disease activity and prediction of long-term outcomes.
在基于人群的背景下，评估基线血清生物标志物在少年特发性关节炎（JIA）18 年随访（FU）时预测疾病活动度和缓解状态的能力。
在基于人群的北欧 JIA 队列研究中，在基线和 18 年 FU 时评估临床数据和炎症生物标志物的血清水平。通过多重珠阵列测定法确定了一组 16 种炎症生物标志物。我们使用基线变量作为解释变量，对疾病活动度和缓解的二项结局进行了单变量和多变量逻辑回归模型的估计。
在符合北欧 JIA 队列研究条件的 349 名患者中，有 236 名（68%）患者有基线时的血清样本。我们发现，在 18 年 FU 时疾病活跃的患者的血清白细胞介素 1β（IL-1β）、IL-6、IL-12p70、IL-13、MMP-3、S100A9 和 S100A12 水平显著高于疾病不活跃的患者。通过计算描绘曲线下面积（AUC）的接收者操作特征，我们将常规预测模型（性别、年龄、关节计数、红细胞沉降率、C 反应蛋白）与也纳入 16 个基线生物标志物的扩展模型进行了比较。生物标志物的添加显著提高了模型在 18 年 FU 时预测疾病活动/不活动的能力，这表现为 AUC 从 0.59 增加到 0.80（p=0.02）。多元回归分析显示，S100A9 是疾病发病 18 年后疾病不活跃的最强预测因子。
基线时提示炎症的生物标志物有可能改善疾病活动度的评估和长期结局的预测。
