Fatigue severity in anti-nuclear antibody-positive individuals does not correlate with pro-inflammatory cytokine levels or predict imminent progression to symptomatic disease
Bibliographic record
Abstract
Abstract Background Fatigue is a common symptom of systemic autoimmune rheumatic disease (SARD). Patients with SARD have a protracted pre-clinical phase during which progressive immunologic derangements occur culminating in disease. In this study, we sought to determine when fatigue develops and whether its presence correlates with inflammatory factors or predicts disease progression. Methods Anti-nuclear antibody (ANA)-negative healthy controls (HCs) and ANA-positive participants with no criteria, at least one clinical criteria (undifferentiated connective tissue disease, UCTD), or meeting SARD classification criteria were recruited. Fatigue was assessed using a modified version of the FACIT-F questionnaire and the presence of fibromyalgia determined using a questionnaire based on the modified 2010 ACR criteria. Peripheral blood expression of five IFN-induced genes was quantified by NanoString and the levels of IL-1β, IL-6, or TNF-α by ELISA. Results Fatigue was as prevalent and severe in individuals lacking SARD criteria as it was in UCTD and SARD. Overall, ~ 1/3 of ANA+ subjects met fibromyalgia criteria, with no differences between sub-groups. Although fatigue was more severe in these individuals, those lacking fibromyalgia remained significantly more fatigued than ANA− HC. However, even in these subjects, fatigue correlated with the widespread pain index and symptom severity scores on the fibromyalgia questionnaire. Fatigue was not associated with elevated cytokine levels in any of the ANA+ sub-groups and did not predict imminent disease progression. Conclusions Fatigue is common in ANA+ individuals lacking sufficient criteria for a SARD diagnosis, correlates with fibromyalgia-related symptoms, and is not associated with inflammation or predictive of disease 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 | 0.000 | 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) | 0.002 | 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".