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.
Genomic and proteomic biomarkers in current clinical use for axial spondyloarthritis (axSpA) perform moderately well but there is a great need for more informative biomarkers.
Polygenic risk scores capture a greater proportion of the genetic component of risk of ankylosing spondylitis and perform better than HLA-B27 testing for the diagnosis of this disease.
Multiomic biomarkers are an underexplored area in axSpA that have the potential to be more informative than individual biomarkers.
Future biomarker development requires the availability of biological samples from large, well-characterized patient cohorts and datasets.
Biomarker development programmes should be an integral component of clinical trials, and biomarker data from those trials should be publicly available for biomarker development research.
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The authors thank Novartis for providing assistance with the preparation of this manuscript in the form of performing a comprehensive literature survey from search terms provided by the authors, collating the reference list and optimizing figures for the Supplementary Information. K.-A.L.C. was supported in part by a National Health and Medical Research Council Career Development fellowship (GNT1159458).This work was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and/or the NIHR Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
The authors declare no competing interests.
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- Polygenic risk score
(PRS). A quantitative score typically involving hundreds to hundreds of thousands of genetic variants weighted by the magnitude of their association with the disease or trait of interest.
- Prior probability
The likelihood of an event prior to the event occurring.
- Posterior probability
The updated probability of an event taking into account new related information.
- Univariate and multivariate analyses
Analyses that take into account a single variable (univariate) or multiple variables (multivariate).
- Dependent variable
A variable that is being tested in an experiment that depends on the values of independent variables.
- Linear discriminant methods
Ways of identifying sets of quantitative variables that maximize statistical separation between sample groups (or ‘classes’).
- Partial least squares discriminant analysis
A multivariate regression approach to perform dimension reduction and prediction model construction.
- Dimension reduction
The process of reducing the number of variables under consideration by obtaining a set of linearly combined variables that carry most of the available information in the dataset.
When a model contains more parameters than can be justified by the data and therefore might not fit additional data or extrapolate accurately.
- Matrix factorization
The process of decomposing a matrix into the product of two new matrices of low dimension.
- Network-based analyses
The analysis of experimental data on the basis of prior knowledge of interactive pathways or networks.
- Machine learning
A type of artificial intelligence approach whereby computational systems perform tasks on the basis of patterns and inference rather than using explicit instructions.
- Bayesian approaches
Probabilistic methods based on Bayes’ theorem to update probabilities for a hypothesis after obtaining new data.
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Brown, M.A., Li, Z. & Cao, KA.L. Biomarker development for axial spondyloarthritis. Nat Rev Rheumatol 16, 448–463 (2020). https://doi.org/10.1038/s41584-020-0450-0