ABS1148 PERIODONTAL DISEASE IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".