Interferon and interferon-induced cytokines as markers of impending clinical progression in ANA+ individuals without a systemic autoimmune rheumatic disease diagnosis
Bibliographic record
Abstract
Abstract Background Elevated levels of interferons (IFNs) are a characteristic feature of systemic autoimmune rheumatic diseases (SARDs) and may be useful in predicting impending symptomatic progression in anti-nuclear antibody-positive (ANA+) individuals lacking a SARD diagnosis. Typically, these are measured by their effect on gene expression in the blood, which has limited their utility in clinical settings. Here, we assessed whether the measurement of serum IFN-α or selected IFN-induced cytokines accurately mirrors IFN-induced gene expression in ANA+ individuals and investigated their utility as biomarkers of clinical progression. Methods A total of 280 subjects were studied, including 50 ANA− healthy controls, 160 ANA+ individuals without a SARD diagnosis (96 asymptomatic, 64 with undifferentiated connective tissue disease), and 70 SARD patients. IFN-induced gene expression was measured by nanoString and cytokine levels by ELISA or Simoa. ANA+ individuals lacking a SARD diagnosis who had the new onset of SARD criteria over the subsequent 2 years were defined as progressors. Results Measurement of IFN-α levels by high-sensitivity ELISA or Simoa correlated much better with IFN-induced gene expression than measurement of CXCL-10 or Galectin-9 levels. Despite this, high CXCL-10 and Galectin-9 levels were better predictors of subsequent progression in ANA+ individuals than measures of IFN-α or IFN-induced gene expression with the optimal combination of predictive cytokines (CXCL-10 and IFN-α as measured by ELISA), resulting in a specificity and positive predictive value of 100%. Conclusion Easily performed ELISA assays for CXCL-10 and IFN-α can be used to predict ANA+ individuals at high risk of imminent symptomatic progression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".