Serum cytokine profiles of adults with idiopathic inflammatory myopathies
Bibliographic record
Abstract
OBJECTIVES: There is a paucity of available biomarkers of disease activity in idiopathic inflammatory myopathies (IIM), and serum cytokines/chemokines hold potential as candidate biomarkers. We aimed to determine serum cytokine profiles of IIM patients with active disease as compared to patients in remission and healthy controls. METHODS: The IIM patients with active disease (included patients enrolled in repository corticotropin injection trial), in remission, and healthy controls were enrolled in this cross-sectional observational study. Serum concentrations of 51 cytokines/chemokines were obtained by utilising a bead-based multiplex cytokine assay (Luminex®). The myositis core set measures were obtained for all the patients. Cytokines with the best predictive ability to differentiate these clinical groups were assessed with three methods: 1) Least Absolute Shrinkage and Selection Operator modelling, 2) stepwise approach, and 3) logistic regression model. RESULTS: Twenty-one IIM patients with active disease, 11 IIM patients in remission and 10 healthy controls were enrolled. Myositis patients had elevated levels of chemokines that attract eosinophils (eotaxin) and dendritic cells, NK cells, cytotoxic T-cells and monocytes/macrophages (CXCL-9, IP-10), cytokines that drive T-helper 1 responses (TNF-a, lymphotoxin-a), matrix degrading enzymes (MMP-3 and -9), and IGFBP-2 compared to healthy controls. Myositis patients with active disease had higher levels of lymphotoxin-a, CXCL-9, MIP-1a, MIP-1b and MMP-3 than patients in remission. CONCLUSIONS: This study demonstrated differences in cytokine profiles of IIM patients (active and inactive disease) compared to healthy controls and identified some cytokines that could potentially be used as biomarkers. Larger longitudinal studies are needed to validate our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".