POS0467 Methotrexate use influenced the effect of inflammation on cardiovascular risk differently in anticitrullinated protein antibody negative and positive patients with rheumatoid arthritis
Bibliographic record
Abstract
Background: Disease activity was linked to cardiovascular risk in rheumatoid arthritis (RA). Anticitrullinated protein antibody (ACPA) positivity associated with greater disease activity, lower remission rates and greater cardiovascular risk. We therefore hypothesized that the effect of disease activity on cardiovascular risk may vary between ACPA positive and negative patients. Methotrexate is generally the initial treatment prescribed upon RA diagnosis, is shown to decrease proinflammatory cytokines driving disease activity and may lower cardiovascular risk. We hence postulated that the impact of disease activity on cardiovascular risk may differ among methotrexate users and nonusers. Moreover, methotrexate was reported to be less effective in seronegative compared to seropositive RA. Objectives: We here explored whether the relationship between RA activity and cardiovascular risk varied according to ACPA status and methotrexate use. Methods: We evaluated 3958 patients free of cardiovascular disease upon enrollment to an international consortium. Main outcome was major adverse cardiovascular events (MACE) defined as non-fatal myocardial infarction, non-fatal stroke, or CV death. Missing data were imputed using multiple imputation by chained equations with 10 iterations. Multivariable Cox models stratified by center risk explored the impact of disease activity based on 28-joint counts and C-reactive protein (DAS28CRP), ACPA positivity, methotrexate use and the two- and three-way interactions of DAS28CRP with ACPA positivity and/or methotrexate use on risk of MACE. All models adjusted for age, gender, hypertension, diabetes, smoking, family history of cardiovascular disease, total cholesterol to high-density lipoprotein ratio, and disease duration. Results: Of 3958 patients, 2373 (59.95%) were ACPA positive, 1323 (33.43%) were methotrexate users, and mean (standard deviation) DAS28CRP was 3.74 (1.28). Throughout 22,749 patient years, 185 first MACE were recorded. There was a main effect of DAS28CRP (HR 1.18, 95% CI 1.03-1.30, p=0.019) and ACPA positivity (HR 1.44, 95% CI 1.04-1.99, p=0.027) but not methotrexate use (HR 0.78, 95% CI 0.47-1.30, p=0.341) on risk of MACE overall after multivariable adjustment. A three-way interaction between DAS28CRP, ACPA positivity and methotrexate use on the risk of MACE was observed (p-interaction=0.011) indicating that the influence of methotrexate use on the association between RA activity and MACE differed among ACPA negative and positive patients. Among ACPA negative patients (Figure 1A), the methotrexate × DAS28CRP interaction was significant (p=0.038) such that higher disease activity associated with increased risk of MACE in methotrexate nonusers (HR 1.35, 95% CI 1.02-1.77, p=0.033) but not users (HR 0.40, 95% CI 0.15-1.09, p=0.073). Among ACPA positive patients (Figure 1B), the methotrexate × DAS28CRP interaction (p=0.237), main effect of DAS28CRP (HR 1.15, 95% CI 0.98-1.36, p=0.093), and main effect of methotrexate use (HR 0.80, 95% CI 0.43-1.47, p=0.464) were not significant. Conclusion: Among ACPA negative patients, RA activity associated with MACE risk in methotrexate nonusers but not in users. In contrast, there were no interaction or main effects of RA activity and methotrexate in ACPA positive patients, perhaps due to competing risk conferred by ACPA positivity. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: George Karpouzas Scipher, Janssen, Scipher, Pfizer, Durga Prasanna Misra: None declared, George Kitas: None declared, Piet van Riel: None declared, Elena Myasoedova: None declared, Miguel Ángel González-Gay: None declared, Alfonso Corrales Martinez: None declared, Solbritt Rantapää Dahlqvist: None declared, Petros P. Sfikakis: None declared, Patrick Dessein: None declared, Carol Hitchon: None declared, Virginia Pascual: None declared, Irazú Contreras-Yáñez: None declared, Iris J. Colunga-Pedraza: None declared, Dionicio A. Galarza-Delgado: None declared, Jose R. Azpiri-Lopez: None declared, Anne Grete Semb: None declared, Patrick Durez: None declared, Brian Bridal-Logstrup: None declared, Ellen-Margrethe Hauge: None declared, Sarah Ormseth: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".