The genetic backbone of ankylosing spondylitis: how knowledge of genetic susceptibility informs our understanding and management of disease
Bibliographic record
Abstract
Ankylosing spondylitis (AS) is a seronegative, chronic inflammatory arthritis with high genetic burden. A strong association with HLA-B27 has long been established, but to date its contribution to disease aetiology remains unresolved. Recent insights through genome wide studies reveal an increasing array of immunogenetic risk variants extraneous to the HLA complex in AS cohorts. These genetic traits build a complex profile of disease causality, highlighting several molecular pathways associated with the condition. This and other evidence strongly implicates T-cell-driven pathology, revolving around the T helper 17 cell subset as an important contributor to disease. This prominence of the T helper 17 cell subset has presented the opportunity for therapeutic intervention through inhibition of interleukins 17 and 23 which drive T helper 17 activity. While targeting of interleukin 17 has proven effective, this success has not been replicated with interleukin 23 inhibition in AS patients. Evidence points to significant genetic diversity between AS patients which may, in part, explain the observed refractoriness among a proportion of patients. In this review we discuss the impact of genetics on our understanding of AS and its relationship with closely linked pathologies. We further explore how genetics can be used in the development of therapeutics and as a tool to assist in the diagnosis and management of patients. This evidence indicates that genetic profiling should play a role in the clinician's choice of therapy as part of a precision medicine strategy towards disease management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".