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ABS0753 (2025)
DISEASE PHENOTYPES IN A BEHçET’S SYNDROME MONOCENTRIC COHORT - A NEURAL NETWORK ANALYSIS
Keywords: Artificial Intelligence, Real-world evidence
F. Di Cianni1,3, A. Sulis1, D. Marinello1, V. Lorenzoni2, R. Talarico1, M. Mosca1
1Azienda Ospedaliero-Universitaria Pisana, Rheumatology Unit, Pisa, Italy
2Scuola Superiore Sant’Anna, Institute of Management, Pisa, Italy
3University of Siena, Department of Medical Biotechnologies, Siena, Italy

Background: Behçet’s syndrome (BS) is a rare systemic vasculitis of unknown aetiology. BS has a peculiar geographical distribution with respect to gender prevalence, clinical features and severity of organ involvement. In addition, BS manifestations are frequently clustered rather than discrete, contributing to a highly clinical heterogeneity. Indeed, recently the concept has emerged that BS may not be a single clinical entity but a multi-system syndrome of distinct clinical phenotypes resulting from different possible combinations of involvements [1, 2]. The analysis of phenotypes might help to clarify the pathogenesis of single disease manifestations and to classify the phenotypes according to their clinical and therapeutic outcomes.


Objectives: The aim of the present work was to identify and analyze the disease phenotypes in a mono-centric cohort of BS patients, evaluating the possible associations between clinical and epidemiological variables.


Methods: Patients with BS diagnosis in regular follow-up at the Behçet Clinic of a tertiary centre were retrospectively and prospectively identified using the outclinic database and medical charts. Data on demographics, previous organ involvements and ongoing medications were collected in a total number of 202 patients. The disease subset was classified as severe in case of major organ involvement. Pairwise correlation among variables was evaluated by means of Pearson or Spearman correlation coefficient. A multiple correspondence analysis (MCA) was performed to investigate the possible phenotypes resulting from the different patterns of associations among the demographic and clinical variables.


Results: Most of the patients were females (67%), Caucasian (92%) and HLA-B51 carriers (65,5%). Mean age at disease onset was 30.06±11.39 years and oral ulcers (OU) and genital ulcers (GU) were the most common manifestations (96% and 61%, respectively). According to bivariate correlation analysis significant positive correlations were observed of male gender with older age at diagnosis (p=0.004) and the use of anti-tumour necrosis factor alpha (anti-TNFa) (p=0.017), and of lower age at onset with skin lesions (p=0.024). A positive association was found between skin lesions and both OU (p=0.005) and arthritis (p=0.014), as well as pathergy (p=0.001), gastrointestinal (GI) symptoms (p=0.001) and other involvement like fever and serositis (p=0.015). Vascular involvement correlated with both erythema nodosum (EN) (p=0.049) and orchitis/epididymitis (p=0.025). Neurological involvement was significantly and positively associated with ocular lesions (p=0.0114), GI symptoms (p=0.030), pathergy (rho=0.147, p=0.037) and vascular involvement (p=0.037). Five disease phenotypes could be recognized applying the MCA: A) male Caucasians with greater age at onset and at diagnosis than the median values, with OU and GU, skin lesions, EN, arthritis and GI symptoms; B) co-existence of benign subset and pathergy; C) orchitis/epididymitis associated with neurological involvement and ocular lesions; D) GI symptoms plus endoscopic lesions, large vessel involvement (both arterial and venous disease) and other involvement.


Conclusion: This study provides valuable insights into the possible BS clinical phenotypes, and the results partially agree with previous association studies on European and extra-European cohorts. To our knowledge, no phenotypes with major vessel disease and GI involvement was reported before. It is a common clinical perception that BS may be a syndrome with different clinical phenotypes possibly having distinct pathogenetic background, clinical outcomes and therapeutic implications. Observational comparative studies are warranted to assess the response of tailored phenotypes-based therapeutic approaches.


REFERENCES: [1] Seyahi, E. Phenotypes in Behçet’s syndrome. Intern Emerg Med 14 , 677–689 (2019).

[2] Bettiol, A. et al. Treating the Different Phenotypes of Behçet’s Syndrome. Front. Immunol. 10 , 2830 (2019).


Acknowledgements: NIL.


Disclosure of Interests: 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.


DOI: annrheumdis-2025-eular.B2839
Keywords: Artificial Intelligence, Real-world evidence
Citation: , volume 84, supplement 1, year 2025, page 1575
Session: Behcet's disease (Publication Only)