Biological use influences the impact of inflammation on risk of major adverse cardiovascular events in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: Chronic inflammation promotes cardiovascular risk in rheumatoid arthritis (RA). Biological disease-modifying antirheumatic drugs (bDMARDs) improve disease activity and cardiovascular disease outcomes. We explored whether bDMARDs influence the impact of disease activity and inflammatory markers on long-term cardiovascular risk in RA. METHODS: We studied 4370 participants without cardiovascular disease in a 10-country observational cohort of patients with RA. Endpoints were (1) major adverse cardiovascular events (MACE) encompassing myocardial infarction, stroke and cardiovascular death; and (2) any ischaemic cardiovascular events (iCVE) including MACE plus revascularisation, angina, transient ischaemic attack and peripheral arterial disease. RESULTS: Over 26 534 patient-years, 239 MACE and 362 iCVE occurred. The interaction between 28-joint Disease Activity Score with C-reactive protein (DAS28-CRP) and bDMARD use was significant for MACE (p=0.017), suggesting the effect of DAS28-CRP on MACE risk differed among bDMARD users (n=515) and non-users (n=3855). DAS28-CRP (per unit increase) is associated with MACE risk in bDMARD non-users (HR 1.21 (95% CI 1.07 to 1.37)) but not users (HR 0.69 (95% CI 0.40 to 1.20)). The interaction between CRP (per log unit increase) and bDMARD use was also significant for MACE (p=0.011). CRP associated with MACE risk in bDMARD non-users (HR 1.16 (95% CI 1.04 to 1.30)), but not users (HR 0.65 (95% CI 0.36 to 1.17)). No interaction was observed between bDMARD use and DAS28-CRP (p=0.167) or CRP (p=0.237) for iCVE risk. CONCLUSIONS: RA activity and inflammatory markers associated with risk of MACE in bDMARD non-users but not users suggesting the possibility of biological-specific benefits locally on arterial wall independently of effects on systemic inflammation.
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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 | 0.001 | 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) | 0.000 | 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".