Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester.
NIHR Manchester Musculoskeletal Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust.
Rheumatology (Oxford). 2022 Oct 6;61(10):4136-4144. doi: 10.1093/rheumatology/keab942.
The clinical progression of JIA is unpredictable. Knowing who will develop severe disease could facilitate rapid intensification of therapies. We use genetic variants conferring susceptibility to JIA to predict disease outcome measures.
A total of 713 JIA patients with genotype data and core outcome variables (COVs) at diagnosis (baseline) and 1 year follow-up were identified from the Childhood Arthritis Prospective Study (CAPS). A weighted genetic risk score (GRS) was generated, including all single nucleotide polymorphisms (SNPs) previously associated with JIA susceptibility (P-value < 5×10-08). We used multivariable linear regression to test the GRS for association with COVS (limited joint count, active joint count, physician global assessment, parent/patient general evaluation, childhood HAQ and ESR) at baseline and change in COVS from baseline to 1 year, adjusting for baseline COV and International League of Associations of Rheumatology (ILAR) category. The GRS was split into quintiles to identify high (quintile 5) and low (quintile 1) risk groups.
Patients in the high-risk group for the GRS had a younger age at presentation (median low risk 7.79, median high risk 3.51). No association was observed between the GRS and any outcome measures at 1 year follow-up or baseline.
For the first time we have used all known JIA genetic susceptibility loci (P=<5×10-08) in a GRS to predict changes in disease outcome measured over time. Genetic susceptibility variants are poor predictors of changes in core outcome measures, it is likely that genetic factors predicting disease outcome are independent to those predicting susceptibility. The next step will be to conduct a genome-wide association analysis of JIA outcome.
幼年特发性关节炎（JIA）的临床进展难以预测。了解哪些患者会发展为重症，有助于快速强化治疗。我们使用与 JIA 易感性相关的遗传变异来预测疾病结局指标。
从儿童关节炎前瞻性研究（CAPS）中确定了 713 名 JIA 患者，这些患者具有基因型数据和诊断时（基线）和 1 年随访时的核心结局变量（COV）。生成加权遗传风险评分（GRS），包括与 JIA 易感性相关的所有单核苷酸多态性（SNP）（P 值<5×10-08）。我们使用多变量线性回归来检验 GRS 与 COV（受限关节计数、活跃关节计数、医生总体评估、父母/患者总体评估、儿童 HAQ 和 ESR）的基线值和从基线到 1 年的 COV 变化之间的关联，同时调整基线 COV 和国际风湿病联盟（ILAR）分类。将 GRS 分为五分位数，以确定高（五分位数 5）和低（五分位数 1）风险组。
GRS 高风险组患者的发病年龄更年轻（低风险中位数 7.79，高风险中位数 3.51）。在 1 年随访或基线时，未观察到 GRS 与任何结局指标之间存在关联。
我们首次使用所有已知的 JIA 遗传易感性位点（P<5×10-08）构建 GRS，以预测随时间变化的疾病结局指标的变化。遗传易感性变异是疾病结局变化的预测指标较差，预测疾病结局的遗传因素可能与预测易感性的遗传因素不同。下一步将对 JIA 结局进行全基因组关联分析。
