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

Abstract

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 (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. Abbreviations: AI = artificial intelligence, CD = Crohn disease, CF = creeping fat, E1 = UC proctitis, E2 = UC left colitis, E3 = UC extensive colitis, HLA = human leukocyte antigen, IBD = inflammatory bowel disease, IL-23R = interleukin 23 receptor, L1 = ileal CD, L2 = colonic CD, L3 = ileocolonic CD, L4 = upper digestive compromise CD, MHC = major histocompatibility complex, ML = machine learning, MLP = multilayer perceptron, PC1 = first principal component, PC2 = second principal component, PCA = principal component analysis, RF = random forest, UCSD = University California San Diego, UMAP = uniform manifold approximation.