Guy's and St Thomas' NHS Foundation Trust and King's College London NIHR Biomedical Research Centre, London, UK.
Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, Princess Alexandra Hospital, Brisbane, Australia.
Nat Rev Rheumatol. 2020 Aug;16(8):448-463. doi: 10.1038/s41584-020-0450-0. Epub 2020 Jun 30.
The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.
术语中轴型脊柱关节炎(axSpA)包含一组表现各异、有不同的关节外表现和临床结局、且对治疗反应不同的异质性疾病。中轴型脊柱关节炎的典型类型,即强直性脊柱炎,被认为是由遗传易感宿主免疫系统与肠道微生物群相互作用引起的。目前使用的生物标志物,如 HLA-B27 状态、C 反应蛋白和红细胞沉降率,其诊断和预测价值充其量也只是中等。需要改进 axSpA 的生物标志物,以帮助早期诊断,并更好地预测治疗反应和长期结局。一系列“组学”技术和统计方法的进步,包括基因组学方法(如多基因风险评分)、微生物组分析,以及潜在的转录组、蛋白质组和代谢组分析,使得开发更具信息量的生物标志物集用于此类临床应用成为可能。该领域的未来发展可能涉及需要新型统计方法来分析和生成易于解释的临床应用指标的生物标志物组合。需要使用广泛的生物样本进行充分特征描述的病例-队列研究的大型公共可用数据集,特别是针对早期疾病和对药物的反应,以建立成功的生物标志物发现和验证计划。