Clinically Available Biomarkers Associated with Systemic Autoimmune Rheumatic Disease Progression in Anti-Nuclear Antibody Positive Individuals
Bibliographic record
Abstract
Objectives The presence of Anti-Nuclear Antibodies (ANA) are a hallmark of Systemic Autoimmune Rheumatic Disease (SARD) and can be present years before clinical diagnosis. Although this suggests that ANAs could be used as potential biomarker for disease progression, they are also found in up to 25% of women, only a small fraction of whom (<5%) will develop a SARD. As the onset of symptomatic disease is not infrequently associated with organ damage, there is tremendous interest in identifying biomarkers associated with a high risk of progression, which could potentially enable initiation of preventative therapy. Here, we sought to determine whether any of the tests typically available to rheumatologists can be used to identify ANA positive individuals at high risk of imminent progression. Methods Study participants were recruited from the Early Autoimmune Rheumatic Disease Clinic at Toronto Western Hospital, where ANA positive (≥1:160 by IF or ≥1:80 with a specific autoantibody) individuals without a SARD diagnosis (based upon clinical classification criteria) were followed yearly, or earlier if they had new symptoms, for development of SARD symptoms. Participants either lacked SARD clinical criteria or had insufficient criteria for a SARD diagnosis (UCTD), with progression being defined as the onset of a new clinical criteria for SARD. All ANAs, complements, and specific autoantibodies were measured through the hospital laboratory, with specific ANAs being measured by Bioplex. Results 124 ANA positive individuals were followed by a minimum of 2 years, 15 of which clinically progressed within 2 years of follow-up. The mean age and proportion of female progressors did not differ significantly from that of non-progressors, however progressors were more likely to be non-Caucasian than non-progressors. Although the ANA titer and serum complement levels were similar in progressors and non-progressors, progressors had significantly more specific ANAs (1.53 ±1.41 vs 0.88 ± 0.92, p = 0.019, Student’s t test). With the exception of anti-dsDNA antibodies, all specific ANAs were more prevalent in progressors, but this difference was only significant for anti-La antibodies. The most prevalent antibody in the cohort in both progressors and non-progressors was anti-Ro (46.7% and 35.8%, respectively). Within anti-Ro positive individuals, discrimination between anti-Ro52 and -Ro60 showed that anti-Ro52 but not anti-Ro60 antibodies were significantly associated with progression, particularly when in tandem with a positive RF (Table 1). Table 1: Clinical and Serologic Associations with Progression Conclusion The presence of anti-La and -Ro52 antibodies is associated with an increased risk of imminent clinical progression in the subsequent 2 years and such individuals merit close follow-up.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".