POS0742 THE EFFECT OF DISEASE ACTIVITY ON CARDIOVASCULAR RISK VARIES ACCORDING TO RHEUMATOID FACTOR AND ANTICITRULLINATED PROTEIN ANTIBODY STATUS IN PATIENTS WITH RHEUMATOID ARTHRITIS
Notice bibliographique
Résumé
Background: Rheumatoid arthritis (RA) activity associates with cardiovascular (CV) risk. Anticitrullinated protein antibodies (ACPA) were linked to higher disease activity and lower remission rates. Treatment responses to immunomodulatory therapies may, likewise, vary by seropositivity status. We therefore hypothesized that the relationship between disease activity and CV risk may vary among ACPA positive and negative patients. Rheumatoid factor (RF) associated with higher RA activity independently of ACPA status. We hence posited that the effect of RA activity on CV risk may differ among RF positive and negative patients. Variance in disease activity, characteristics and outcomes have been reported in seronegative compared to single or double positive patients. Objectives: We explored whether the relationship between RA activity and CV risk varied according to ACPA and RF status. Methods: We evaluated 3952 patients free of CV disease, enrolled in An InTernation A l Consortium for Cardiovascular disease in RA (ATACC-RA). 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 association of baseline 28-joint disease activity score with C-reactive protein (DAS28CRP), ACPA positivity, RF positivity and the two- and three-way interactions of DAS28CRP with ACPA and/or RF with risk of MACE. All models adjusted for age, gender, hypertension, diabetes, smoking, family history of CV disease, total cholesterol to high-density lipoprotein ratio, and disease duration. Results: Of 3952 patients, 1007 (25.5%) were seronegative, 306 (7.7%) ACPA positive and RF negative, 557 (14.1%) ACPA negative and RF positive, and 2082 (52.7%) double positive at diagnosis. Mean (standard deviation) DAS28CRP was 3.74 (1.28). Over 22,981 patient years, 184 MACE occurred. There was a main effect of DAS28CRP (HR 1.18, 95% CI 1.03-1.36, p=0.016), but not ACPA (HR 1.24, 95% CI 0.84-1.81, p=0.279) or RF (HR 1.27, 95% CI 0.83-1.96, p=0.268) on MACE risk after multivariable adjustment. However, there were main effects of single and double seropositivity; compared to seronegative, single (HR 1.69, 95% CI 1.03-2.75, p=0.036) and double seropositivity (HR 1.72, 95% CI 1.12-2.65, p=0.014) associated with a greater risk of MACE. A significant three-way interaction between DAS28CRP, ACPA and RF positivity was observed (p-interaction=0.034) suggesting that the influence of disease activity on MACE risk varied according to both ACPA and RF status. Among RF negative patients, the ACPA × DAS28CRP interaction was significant (p=0.011) and DAS28CRP associated with MACE in ACPA negative (HR 1.58, 95% CI 1.08-2.33, p=0.020) but not positive patients (HR 1.05, 95% CI 0.67-1.64, p=0.823, Figure 1A). Among RF positive patients, the ACPA × DAS28CRP interaction (p=0.861) and ACPA main effect (HR 1.04, 95% CI 0.70-1.55, p=0.855) were not significant, though the main effect of DAS28CRP was (HR 1.18, 95% CI 1.01-1.38, p=0.044, Figure 1B). Conclusion: Single and double seropositive patients incurred higher risk of MACE compared to seronegatives. Among RF positive patients, RA activity associated with MACE risk irrespective of ACPA status. Among RF negative, disease activity associated with CV risk only in ACPA negative ones. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: George Karpouzas Scipher, Janssen, Scipher, Pfizer, Virginia Pascual: None declared, Miguel Ángel González-Gay: None declared, Elena Myasoedova: 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, Piet van Riel: 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, Durga Prasanna Misra: None declared, Patrick Durez: None declared, Brian Bridal-Logstrup: None declared, Ellen-Margrethe Hauge: None declared, George Kitas: 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 | 0,000 |
| 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 | 0,001 |
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 ».