Circulating Interleukin 17A and Other Inflammatory Proteins May Predict Cardiovascular Disease in Early Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate the impact of 92 inflammatory proteins on the risk of cardiovascular disease (CVD) in patients with early rheumatoid arthritis (RA). METHODS: This study included consecutive patients with early RA recruited between 1995 and 2002. Stored plasma samples were analyzed for 92 inflammatory proteins. CVD diagnoses were retrieved from national in-patient and cause-of-death registries. Statistical analyses were predesignated as hypothesis-driven or exploratory. For the latter, proteins were selected based on principal component analysis (ie, factor loading > 0.5 within main components). Potential predictors of CVD and coronary artery disease (CAD) were assessed using Cox regression. RESULTS: Data on baseline levels of proteins and CVD were available for 163 patients. As hypothesized, levels of interleukin 17A (IL-17A) were associated with CVD (hazard ratio 1.35, 95% CI 1.02-1.78, adjusted for age, sex, hypertension, diabetes, smoking, and erythrocyte sedimentation rate [ESR]), although not significantly with CAD. Osteoprotegerin (OPG) levels were significantly associated with both outcomes, but only in crude models. No associations were observed for IL-6, tumor necrosis factor, monocyte chemotactic protein-1, or IL-8. In the exploratory analyses, MCP-3 in particular had significant associations with both outcomes in crude models. CONCLUSION: Circulating IL-17A at RA diagnosis predicted future CVD, although we cannot exclude the possibility that this finding is due to multiple testing. The association was independent of traditional CVD risk factors, and of ESR at the time of diagnosis. Further, OPG may be a predictor of CVD. We also identified some novel potential biomarkers for CVD in RA.
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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 | 0.003 |
| 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 | 0.001 |
| 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".