biostudies-literatureUnknown101(36)Perez-Jeldres TInflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn disease (CD), has emerged as a global disease with an increasing incidence in developing and newly industrialized regions such as South America. This global rise offers the opportunity to explore the differences and similarities in disease presentation and outcomes across different genetic backgrounds and geographic locations. Our study includes 265 IBD patients. We performed an exploratory analysis of the databases of Chilean and North American IBD patients to compare the clinical phenotypes between the cohorts. We employed an unsupervised machine-learning approach using principal component analysis, uniform manifold approximation, and projection, among others, for each disease. Finally, we predicted the cohort Medicinee30216https://www.ebi.ac.uk/biostudies/studies/S-EPMC10980497biostudies-literatureEthnicity influences phenotype and clinical outcomes: Comparing a South American with a North American inflammatory bowel disease cohort.PMC10980497Baez PRivera-Nieves JPerez-Jeldres TPizarro BCornejo BSilva VOrellana MAlvarez-Lobos MAscui GCerda-Villablanca Mde la Vega ACannistra MAlvares DArriagada EHernandez-Rocha CTobar FEstela RfalseEthnicity influences phenotype and clinical outcomes: Comparing a South American with a North American inflammatory bowel disease cohort.Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn disease (CD), has emerged as a global disease with an increasing incidence in developing and newly industrialized regions such as South America. This global rise offers the opportunity to explore the differences and similarities in disease presentation and outcomes across different genetic backgrounds and geographic locations. Our study includes 265 IBD patients. We performed an exploratory analysis of the databases of Chilean and North American IBD patients to compare the clinical phenotypes between the cohorts. We employed an unsupervised machine-learning approach using principal component analysis, uniform manifold approximation, and projection, among others, for each disease. Finally, we predicted the cohort 2022-01-01T00:00:00Z2022 Sep2025-04-26T11:24:02.032Z2025-04-06T13:39:41.575ZS-EPMC109804973608678210.1097/MD.0000000000030216