The Rheumatoid Arthritis Gene Expression Signature Among Women Who Improve or Worsen During Pregnancy: A Pilot Study
Bibliographic record
Abstract
Objective. To assess whether gene expression signatures associated with rheumatoid arthritis (RA) before pregnancy differ between women who improve or worsen during pregnancy, and to determine whether these expression signatures are altered during pregnancy when RA improves or worsens. Methods. Clinical data and blood samples were collected before pregnancy (T0) and at the third trimester (T3) from 11 women with RA and 5 healthy women. RA disease activity was assessed using the Clinical Disease Activity Index (CDAI). At each timepoint, RA-associated gene expression signatures were identified using differential expression analysis of RNA sequencing profiles between women with RA and healthy women. Results. Of the women with RA, 6 improved by T3 (RAimproved), 3 worsened (RAworsened),and 2 were excluded. At T0, mean CDAI scores were similar in both groups (RAimproved 11.2 ± 9.8; RAworsened 13.8 ± 6.7; Wilcoxon rank-sum test: P = 0.6). In the RAimproved group, 89 genes were differentially expressed at T0 (q < 0.05 and fold change ≥ 2) compared to healthy women. When RA improved at T3, 65 of 89 (73%) of these genes no longer displayed RA-associated expression. In the RAworsened group, a largely different RA gene expression signature (429 genes) was identified at T0. When RA disease activity worsened at T3, 207 of 429 (48%) genes lost their differential expression, while an additional 151 genes became newly differentially expressed. Conclusion. In our pilot dataset, pre-pregnancy RA expression signatures differed between women who subsequently improved or worsened during pregnancy, suggesting that inherent genomic differences may influence how pregnancy affects disease activity. Further, these RA signatures were altered during pregnancy as disease activity changed.
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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 | 0.000 | 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) | 0.001 | 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".