Anti-mitochondrial antibodies as markers of disease activity in childhood-onset systemic lupus erythematosus: a longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVES: Mitochondria are prominent antigenic sources, capable of triggering IFN-induced inflammatory pathways in SLE. Recent studies suggest presence of AMA in LN patients with adult-onset SLE. Whether AMA reflect disease activity in childhood-onset SLE (cSLE) remains unexplored. Here, we present inaugural data on the potential utility of AMA as a biomarker in cSLE. METHODS: Human mitochondria were incubated with serum, and IgG, IgM and IgA binding assessed by flow cytometry. Sera were obtained prospectively from 29 cSLE patients, at two different timepoints (TP), the first (TP-1) representing a state of active disease (SLEDAI ≥4), and the second (TP-2), of clinically quiescent disease (SLEDAI ≤4). IFNα2 serum levels were assessed by single-molecule array. RESULTS: Median age at disease diagnosis was 14.9 ± 3.6 years. Median SLEDAI and prednisone dose were 9.5 and 0.43 mg/kg/day at TP-1, and 2 and 0.14 mg/kg/day at TP-2. Median time interval between TP-1 and TP-2 was of 6 ± 3.7 months. AMA-IgG decreased between TP-1 and TP-2 (P = 0.0426), and correlated positively with SLEDAI (ρ = 0.41, P = 0.026) and anti-dsDNA (ρ = 0.66, P = 0.0001) at TP-1. AMA-IgM oscillated according to disease activity (P = 0.0065), while AMA-IgA remained stable. AMA-IgG and -IgM correlated with IFNα2 levels (ρ = 0.729, P < 0.0001, and ρ = 0.631, P = 0.001, respectively) during active disease. CONCLUSION: AMA-IgG emerges as a promising biomarker of disease activity in cSLE, and its increase is in alignment with elevated IFNα2 expression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".