Anti-FHL1 autoantibodies in adult patients with myositis: a longitudinal follow-up analysis
Bibliographic record
Abstract
OBJECTIVES: To determine prevalence and clinical associations of anti-Four-and-a-half-LIM-domain 1 (FHL1) autoantibodies in patients with idiopathic inflammatory myopathies (IIM) and to evaluate autoantibody levels over time. METHODS: Sera at the time of diagnosis from patients with IIM (n = 449), autoimmune disease controls (DC, n = 130), neuromuscular diseases (NMDs, n = 16) and healthy controls (HC, n = 100) were analysed for anti-FHL1 autoantibodies by enzyme-linked immunosorbent assay (ELISA). Patients with IIM FHL1+ and FHL1- were included in a longitudinal analysis. Serum levels were correlated to disease activity. RESULTS: Autoantibodies to FHL1 were more frequent in patients with IIM (122/449, 27%) compared with DC (autoimmune DC and NMD, 13/146, 9%, P < 0.001) and HC (3/100.3%, P < 0.001). Anti-FHL1 levels were higher in IIM [median (IQR)=0.62 (0.15-1.04)] in comparison with DC [0.22 (0.08-0.58)], HC [0.35 (0.23-0.47)] and NMD [0.48 (0.36-0.80)] P < 0.001. Anti-FHL1+ patients with IIM were younger at the time of diagnosis compared with the anti-FHL1- group (P = 0.05) and were seronegative for other autoantibodies in 25%.In the first follow-up, anti-FHL1+ sample 20/33 (60%) positive at baseline had turned negative for anti-FHL1 autoantibodies. Anti-FHL1 autoantibodies rarely appeared after initiating treatment. Anti-FHL1 autoantibody levels correlated with CK (r = 0.62, P= 0.01), disease activity measured using the Myositis Disease Activity Assessment Tool (MYOACT) (n = 14, P = 0.004) and inversely with Manual Muscle Test-8 (r = -0.59, P = 0.02) at baseline. CONCLUSION: Anti-FHL1 autoantibodies were present in 27% of patients with IIM; of these, 25% were negative for other autoantibodies. Other autoimmune diseases had lower frequencies and levels. Anti-FHL1 levels often decreased with immunosuppressive treatment, correlated with disease activity measures at diagnosis and rarely appeared after start of treatment.
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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 | 0.001 |
| 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".