Autoantibody profile in sarcoidosis, analysis from the GRADS sarcoidosis cohort
Bibliographic record
Abstract
BACKGROUND: Sarcoidosis, a multi-systemic granulomatous disease, is a predominantly T-cell disease but evidence for a role for humoral immunity in disease pathogenesis is growing. Utilizing samples from the Genomic Research in Alpha-1 anti-trypsin Deficiency and Sarcoidosis (GRADS) study, we examined the prevalence of autoantibodies in sarcoidosis patients with pulmonary-only and extra-pulmonary organ involvement compared to normal controls. STUDY DESIGN AND METHODS: We analyzed serum samples from sarcoidosis patients who participated in the GRADS study utilizing an autoantigen microarray platform for both IgM and IgG antibodies. The cohort included sarcoidosis patients with pulmonary-only disease (POS, n = 106), sarcoidosis patients with extra-pulmonary disease (EPS, n = 120) and a normal control cohort (NC, n = 101). Organ involvement was assessed following a standardized format across all GRADS participating centers. RESULTS: Sarcoidosis patients overall had increased levels of IgM and IgG autoantibodies compared to normal controls. In addition, several autoantibodies were elevated in the POS and EPS cohorts compared to the NC cohort. Differences in autoantibody levels were also noted between the POS and the EPS cohorts. When comparing organ involvement with sarcoidosis, bone, spleen and ear, nose and throat involvement had higher IgM expression than other organs. CONCLUSION: Sarcoidosis patients have elevated IgM and IgG autoantibody levels compared to normal controls. In addition, individuals with pulmonary as well as additional organ involvement had higher IgM expression. Further research is needed focusing on specific organ-autoantibody pairs and role of autoantibodies in disease pathogenesis.
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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 | 0.001 |
| 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".