ABS1148 PERIODONTAL DISEASE IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS
Notice bibliographique
Résumé
Background: Periodontal disease (PD) and axial spondyloarthritis (axSpA) both are complex chronic inflammatory diseases. AxSpA patients were reported to have an elevated risk for PD compared to healthy individuals [1]. However, most studies included axSpA patients under disease modifying anti-rheumatic drugs (DMARDs), which might influence periodontal status. Objectives: To analyse the prevalence of periodontal disease in axSpA patients without DMARD therapy, and the influence of axSpA parameters and disease activity on periodontal status. Methods: Patients with axSpA fulfilling the Assessment of Spondyloarthritis International Society (ASAS) classification criteria were prospectively recruited for this study. Exclusion criteria were treatment with any DMARD and antibiotics within the last three month, and previous PD therapy. The control group consisted of healthy individuals without autoimmune disease and was recruited sex- and age-matched to the patient cohort. All patients underwent standardized rheumatological and periodontal examination. PD was defined as the presence of clinical attachment loss (CAL) of > 1mm at ≥ 2 independent interdental spaces (measurements at 6 sites per tooth) [2]. Logistic Regression analyses were used to compare axSpA patients with and without PD. Results: Table 1 shows characteristics of the 50 axSpA patients and 50 age- and sex-matched healthy individuals. Patients with axSpA showed more often PD: 34 axSpA patients (68%) presented with PD, compared to 22 (44%) of healthy individuals. Moreover, PD was exclusively mild (stage I) in healthy controls, while axSpA patients also showed more severe PD (stage II in 5 and stage III in 7 patients). In multivariable logistic regression analysis adjusted for current smoking and BMI, the presence of axSpA remained significantly associated with PD (OR 2.66, 95% CI 1.09; 6.46). We compared different periodontal measures between axSpA patients and controls: In line with the higher frequency of PD, we found the mean probing pocket depth (PPD) to be deeper in axSpA patients (2.4mm ± 0.3mm vs 2.1mm ± 0.5mm) with a probing depth of ≥3 mm in 8.4% ± 10.4% vs 4.2% ± 5.8% in healthy controls. AxSpA patients also showed higher Periodontal Inflamed Surface Areas (293.2 mm³ ± 345.5 mm³ vs 138.1 mm³ ± 146.1 mm³) and Gingival Bleeding Index (9.3 ± 8.9 vs 6.5 ± 7.4). Although Plaque Control Record (42.3 ± 19.1 vs 35.1 ± 23.2) and Bleeding on Probing (10.34% ± 9.34% vs 7.44% ± 7.35%) were higher in axSpA patients, both did not reach statistical significance. Along with these objective measurements, axSpA patients also reported reduced oral health in the Oral Health Impact Profile (OHIP)-21 questionnaire compared to healthy individuals (13.3 ± 19.9 vs 3.0 ± 4.0). We compared disease parameters of axSpA patients with and without PD (Table 2). Interestingly, both, in uni- and multivariable logistic regression analysis, only the symptom duration of back pain was significantly negatively associated with the presence of PD (OR 0.87 (0.75; 0.998)). Conclusion: Patients with axSpA showed more frequently PD compared to age- and sex-matched healthy individuals. Within axSpA patients, those with shorter symptom duration had a higher risk of PD, while there was no association of the presence of PD with disease activity parameters. Thus, periodontal inflammation might be of importance at disease onset of axSpA. REFERENCES: [1] Ratz et al, Rheumatology 2015. Doi: 10.1093/rheumatology/keu356. [2] Papapanou et al. J Periodontol. 2018. Doi: 10.1002/JPER.17-0721. Acknowledgements: This study was supported by a research grant from Novartis. Disclosure of Interests: Judith Rademacher Janssen, UCB, Katharina Anna Schildhauer: None declared, Aysegül Adam: None declared, Murat Torgutalp: None declared, Judith Kikhney BMBF and EU, Annette Moter Honoraria for lectures by BioMerieux and Chiesi, support for invited talks at meetings by BÄMI, ECCMID, SGM, ERASMUS+, REMMDI, BMBF, Volkswagenstiftung, Horizon2020, Robert Koch-Institute, Hildrun Haibel UCB, Abbvie, Novartis, Pfizer, Janssen, GSK, Sobi., Abbvie, UCB, Janssen, Sobi, Novartis, Pfizer., Sobi, Novartis, Pfizer, UCB, Alfasigma, Fabian Proft Novartis, Eli Lilly, UCB, AbbVie, AMGEN, BMS, Celgene, Janssen, Hexal, Medscape, Moonlake, MSD, Pfizer and Roche, Novartis, Eli Lilly and UCB, Mikhail Protopopov Janssen, Valeria Rios Rodriguez AbbVie, Takeda, AbbVie, Eli Lily, Janssen, Pfizer, and UCB, Denis Poddubnyy AbbVie, Canon, DKSH, Eli Lilly, Janssen, MSD, Medscape, Novartis, Peervoice, Pfizer, and UCB, AbbVie, Biocad, Bristol-Myers Squibb, Eli Lilly, Janssen, Moonlake, Novartis, Pfizer, and UCB, AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Henrik Dommisch Novartis. © 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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».