P691 Influence of ethnicity on the phenotype and clinical outcomes in Inflammatory Bowel Disease; comparison of a South American with a North American population. (21st January 2022)

• Record Type: Journal Article
• Authors: Pérez, T; Pizarro, B; Ascui, G; Tobar, F; Cerda-Villablanca, M; Alvares, D; Orellana, M; De la Vega, A; Canistra, M; Cornejo, B; Baez, P; Silva, V; Arriagada, E; Estela, R; Hernandez-Rocha, C; Alvarez-Lobos, M; Rivera-Nieves, J
• Is Part Of: Journal of Crohn's and colitis. Volume 16(2022)Supplement 1
• Page Start: i591
• Page End: i591
• Publication Date: 2022-01-21
• Subjects: Inflammatory bowel diseases -- Periodicals; 616.344005
• DOI: 10.1093/ecco-jcc/jjab232.812
• Languages: English
• ISSNs: 1873-9946

Abstract

Abstract: Background: Inflammatory Bowel Diseases (IBD) are chronic systemic inflammatory disorders, which affect the gastrointestinal tract. An increased incidence of IBD has been vastly reported in Europe, and North America, however, IBD has now emerged as a global disease affecting people of different genetic backgrounds and geographic locations, with increasing incidence in developing and newly industrialized regions such as South America, offering the opportunity to explore differences and similarities in disease presentation and outcomes across these variables. Methods: Our study includes 265 patients with a diagnosis of Ulcerative Colitis (UC) (n=173) or Crohn′s Disease (CD) (n=92). We carried out an exploratory analysis of databases of Chilean and North American IBD patients to compare the clinical phenotype between both cohorts. We used an unsupervised machine learning approach using Principal Component Analysis (PCA), t- Stochastic Neighbor Embedding (t-SNE), among others, for each disease (CD and UC). Finally, we predicted the center (North American versus Chilean) using random forest, among other supervised machine learning methods to confirm these results. Results: Several unsupervised machine learning methods separated two main groups supporting differences between North American and Chilean patients in each disease. The variables that explained the loadings of the clinical metadata on the principal components were related to therapies and disease extension/location at diagnose. Our Random Forest (RF) models were trained for cohort classification (Chilean or North American) based on clinical characteristics, obtaining a high accuracy of 0.86 (UC) and 0.89 (CD). Similarly, variables related to therapies and disease extension/location had a higher Gini index. In the same line, univariate analysis showed a later CD age at diagnoses in Chilean IBD patients (37 vs 24; p=0.005) Conclusion: Our study suggests a clinical difference between North American and Chilean IBD patients; later CD age at diagnoses with a predominate less aggressive phenotype (39% vs. 54% B1), a more localized disease despite less biological therapies used in Chile for both diseases. The next research steps to confirm these results with more patients and explore more factors between different populations that could determine a less aggressive phenotype. Understanding the causes of these differences could unravel new pathways or protective factors, which could improve treatments.