Ethnicity influences phenotype and clinical outcomes: Comparing a South American with a North American inflammatory bowel disease cohort

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dc.contributor.authorPerez-Jeldres, Tamara
dc.contributor.authorPizarro, Benjamin
dc.contributor.authorAscui, Gabriel
dc.contributor.authorOrellana, Matias
dc.contributor.authorCerda-Villablanca, Mauricio
dc.contributor.authorAlvares, Danilo
dc.contributor.authorde la Vega, Andres
dc.contributor.authorCannistra, Macarena
dc.contributor.authorCornejo, Barbara
dc.contributor.authorBaez, Pablo
dc.contributor.authorSilva, Veronica
dc.contributor.authorArriagada, Elizabeth
dc.contributor.authorRivera-Nieves, Jesus
dc.contributor.authorEstela, Ricardo
dc.contributor.authorHernandez-Rocha, Cristian
dc.contributor.authoralvarez-Lobos, Manuel
dc.contributor.authorTobar, Felipe
dc.date.accessioned2025-01-20T21:02:42Z
dc.date.available2025-01-20T21:02:42Z
dc.date.issued2022
dc.description.abstractInflammatory 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 (North American vs Chilean) using a random forest. Several unsupervised machine learning methods have separated the 2 main groups, supporting the differences between North American and Chilean patients with each disease. The variables that explained the loadings of the clinical metadata on the principal components were related to the therapies and disease extension/location at diagnosis. Our random forest models were trained for cohort classification based on clinical characteristics, obtaining high accuracy (0.86 = UC; 0.79 = CD). Similarly, variables related to therapy and disease extension/location had a high Gini index. Similarly, univariate analysis showed a later CD age at diagnosis in Chilean IBD patients (37 vs 24; P = .005). Our study suggests a clinical difference between North American and Chilean IBD patients: later CD age at diagnosis with a predominantly less aggressive phenotype (39% vs 54% B1) and more limited disease, despite fewer biological therapies being used in Chile for both diseases.
dc.fuente.origenWOS
dc.identifier.doi10.1097/MD.0000000000030216
dc.identifier.eissn1536-5964
dc.identifier.issn0025-7974
dc.identifier.urihttps://doi.org/10.1097/MD.0000000000030216
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/93073
dc.identifier.wosidWOS:000851993100037
dc.issue.numero36
dc.language.isoen
dc.revistaMedicine
dc.rightsacceso restringido
dc.subjectethnicity
dc.subjectIBD
dc.subjectmachine learning
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleEthnicity influences phenotype and clinical outcomes: Comparing a South American with a North American inflammatory bowel disease cohort
dc.typeartículo
dc.volumen101
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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