Browsing by Author "Álvarez Lobos, Manuel Marcelo"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Impact of Amerindian ancestry on clinical outcomes in Crohn’s disease and ulcerative colitis in a Latino population
    (2025) Pérez Jeldres, Tamara De Lourdes; Bustamante, María Leonor; Álvares, Danilo; Álvarez Lobos, Manuel Marcelo; Kalmer, Lajos; Azócar López, Lorena Karina; Segovia Melero, Roberto; Ascui, Gabriel; Aguilar, Nataly; Estela, Ricardo; Hernández Rocha, Cristian Antonio; Candia Balboa, Roberto Andrés; González, Mauricio; Silva, Verónica; De La Vega, Andrés; Arriagada, Elizabeth; Serrano, Carolina A.; Pávez Ovalle, Carolina Denisse; Quinteros Moraga, Carol; Miquel Poblete, Juan Francisco; Alex, Di Genova
    Research in Inflammatory Bowel Disease (IBD) assessing the genetic structure and its association with IBD phenotypes is needed, especially in IBD-underrepresented populations such as the South American IBD population. Aim. We examine the correlation between Amerindian ancestry and IBD phenotypes within a South American cohort and investigate the association between previously identified IBD risk variants and phenotypes. We assessed the ancestral structure (IBD = 291, Controls = 51) to examine the association between Amerindian ancestry (AMR) and IBD variables. Additionally, we analyzed the influence of known IBD genetic risk factors on disease outcomes. We used Chi-square and Fisher’s tests to analyze the relationship between phenotypes and ancestry proportions, calculating odds ratios (OR) and confidence intervals (CI). Logistic regression examined genetic variants associations with IBD outcomes, and classification models for predicting prolonged remission were developed using decision tree and random forest techniques. The median distribution of global ancestry was 58% European, 39% Amerindian, and 3% African. There were no significant differences in IBD risk based on ancestry proportion between cases and controls. In Ulcerative colitis (UC), patients with a high Amerindian Ancestry Proportion (HAAP) were significantly linked to increased chances of resective surgery (OR = 4.27, CI = 1.41–12.94, p = 0.01), pouch formation (OR = 7.47, CI = 1.86–30.1, p = 0.003), and IBD reactivation during COVID-19 infection (OR = 5.16, CI = 1.61–6.53, p = 0.005). Whereas, in the Crohn’s Disease (CD) group, the median Amerindian ancestry proportion was lower in the group with perianal disease (33.5% versus 39.5%, P value = 0.03). CD patients with High Amerindian Ancestry proportion had lower risk for surgery (OR = 0.17, CI = 0.03–0.83, P value = 0.02). Our study highlights the impact of Amerindian ancestry on IBD phenotypes, suggesting a role for genetic and ancestral factors in disease phenotype. Further investigation is needed to unravel the underlying mechanisms driving these associations.
  • Thumbnail Image
    Item
    Prediction of Extraintestinal Manifestations in Inflammatory Bowel Disease Using Clinical and Genetic Variables with Machine Learning in a Latin IBD Group
    (2025) Pérez Jeldres, Tamara de Lourdes; Reyes Pérez, Paula; González Hormazábal, Patricio; Avendaño Soriano, Cristóbal Raimundo; Segovia Melero, Roberto; Azócar, Lorena; Verónica Silva; Andrés de la Vega; Arriagada, Elizabeth; Hernández, Elisa; Aguilar, Nataly; Pavez Ovalle, Carolina Denisse; Hernández Rocha, Cristián Antonio; Candia Balboa, Roberto Andrés; Miquel Poblete, Juan Francisco; Álvarez Lobos, Manuel Marcelo; Valdés, Ivania; Medina Rivera, Alejandra; Bustamante, María Leonor
    Extraintestinal manifestations (EIMs) significantly increase morbidity in inflammatory bowel disease (IBD) patients. In this study, we examined clinical and genetic factors associated with EIMs in 414 Latin IBD patients, utilizing machine learning for predictive modeling. In our IBD group (314 ulcerative colitis (UC) and 100 Crohn’s disease (CD) patients), EIM presence was assessed. Clinical differences between patients with and without EIMs were analyzed using Chi-square and Mann–Whitney U tests. Based on the genetic data of 232 patients, we identified variants linked to EIMs, and the polygenic risk score (PRS) was calculated. A machine learning approach based on logistic regression (LR), random forest (RF), and gradient boosting (GB) models was employed for predicting EIMs. EIMs were present in 29% (120/414) of patients. EIM patients were older (52 vs. 45 years, p = 0.01) and were more likely to have a family history of IBD (p = 0.02) or use anti-TNF therapy (p = 0.01). EIMs were more common in patients with CD than in those with UC without reaching statistical significance (p = 0.06). Four genetic variants were associated with EIM risk (rs9936833, rs4410871, rs3132680, and rs3823417). While the PRS showed limited predictive power (AUC = 0.69), the LR, GB, and RF models demonstrated good predictive capabilities. Approximately one-third of IBD patients experienced EIMs. Significant risk factors included genetic variants, family history, age, and anti-TNF therapy, with predictive models effectively identifying EIM risk.