Serum Cytokine Profile of Unaffected First-degree Relatives of Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Various cytokines have been implicated in the pathogenesis of rheumatoid arthritis (RA) and it is known that elevations in multiple cytokines occur prior to disease onset. The objective of our study is to determine whether the cytokine profile in unaffected first-degree relatives (FDR) of patients with RA is distinct from healthy controls. METHODS: The sera of patients with RA, their unaffected FDR, and healthy controls were measured for 27 cytokines using a luminex-based multiplexed immunoassay. Subjects were also tested for rheumatoid factor and 6 different anticitrullinated protein/peptide antibodies (ACPA). Discriminant analysis was performed to define the cytokine profile of the 3 study groups. RESULTS: Fourteen patients with RA, 37 unaffected FDR, and 27 healthy controls were enrolled. All patients with RA and 49% of FDR were ACPA-positive. Patients with RA had elevated levels of inflammatory cytokines compared to FDR and controls. Lower interleukin 13 (IL-13) levels were independently associated with RA. IL-4 was significantly lower in FDR compared to controls. Using discriminant analysis based on the levels of all cytokines measured, RA, FDR, and healthy controls could be distinguished with 91% accuracy. ACPA-positive subjects had higher levels of IL-9, but lower levels of IL-12p70 and macrophage inflammatory protein-1β. No individual cytokine was associated with ACPA-positivity in FDR, but the entire cytokine profile accurately distinguished ACPA-positive from ACPA-negative FDR. CONCLUSION: The cytokine profiles of patients with RA, healthy controls, unaffected ACPA-positive FDR, and ACPA-negative FDR appear to be distinct.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".