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Profiling the inflammatory bowel diseases using genetics, serum biomarkers, and smoking information.
Liu, Ruize; Li, Dalin; Haritunians, Talin; Ruan, Yunfeng; Daly, Mark J; Huang, Hailiang; McGovern, Dermot P B.
Afiliação
  • Liu R; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Li D; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Haritunians T; F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
  • Ruan Y; F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
  • Daly MJ; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Huang H; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • McGovern DPB; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
iScience ; 26(10): 108053, 2023 Oct 20.
Article em En | MEDLINE | ID: mdl-37841595
Crohn's disease (CD) and ulcerative colitis (UC) are two etiologically related yet distinctive subtypes of the inflammatory bowel diseases (IBD). Differentiating CD from UC can be challenging using conventional clinical approaches in a subset of patients. We designed and evaluated a novel molecular-based prediction model aggregating genetics, serum biomarkers, and tobacco smoking information to assist the diagnosis of CD and UC in over 30,000 samples. A joint model combining genetics, serum biomarkers and smoking explains 46% (42-50%, 95% CI) of phenotypic variation. Despite modest overlaps with serum biomarkers, genetics makes unique contributions to distinguishing IBD subtypes. Smoking status only explains 1% (0-6%, 95% CI) of the phenotypic variance suggesting it may not be an effective biomarker. This study reveals that molecular-based models combining genetics, serum biomarkers, and smoking information could complement current diagnostic strategies and help classify patients based on biologic state rather than imperfect clinical parameters.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article