POS0467 Methotrexate use influenced the effect of inflammation on cardiovascular risk differently in anticitrullinated protein antibody negative and positive patients with rheumatoid arthritis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».