Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved]
Background: Patients with Crohn’s disease (CD) have an altered intestinal microbiome, which may facilitate novel diagnostic testing. However, accuracy of microbiome classification models across geographic regions may be limited. Therefore, we sought to examine geographic variation in the microbiome...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2023-01-01
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| Series: | F1000Research |
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| Online Access: | https://f1000research.com/articles/11-156/v2 |
| _version_ | 1797946369382547456 |
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| author | Richard Kellermayer Subramaniam Kugathasan Rajesh Shah Lee Denson Kristi Hoffman |
| author_facet | Richard Kellermayer Subramaniam Kugathasan Rajesh Shah Lee Denson Kristi Hoffman |
| author_sort | Richard Kellermayer |
| collection | DOAJ |
| description | Background: Patients with Crohn’s disease (CD) have an altered intestinal microbiome, which may facilitate novel diagnostic testing. However, accuracy of microbiome classification models across geographic regions may be limited. Therefore, we sought to examine geographic variation in the microbiome of patients with CD from North America and test the performance of a machine learning classification model across geographic regions. Methods: The RISK cohort included 447 pediatric patients with CD and 221 non-inflammatory bowel disease controls from across North America. Terminal ileum, rectal and fecal samples were obtained prior to treatment for microbiome analysis. We divided study sites into 3 geographic regions to examine regional microbiome differences. We trained and tested the performance of a machine learning classification model across these regions. Results: No differences were seen in the mucosal microbiome of patients with CD across regions or in either the fecal or mucosal microbiomes of controls. Machine learning classification algorithms for patients with CD performed well across regions (area under the receiver operating characteristic curve [AUROC] range of 0.85-0.91) with the best results from terminal ileum. Conclusions: This study demonstrated the feasibility of microbiome based diagnostic testing in pediatric patients with CD within North America, independently from regional influences. |
| first_indexed | 2024-04-10T21:09:56Z |
| format | Article |
| id | doaj.art-71c4a5eff7274fab8f1f8a181b9264cb |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-04-10T21:09:56Z |
| publishDate | 2023-01-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-71c4a5eff7274fab8f1f8a181b9264cb2023-01-21T01:00:00ZengF1000 Research LtdF1000Research2046-14022023-01-0111142172Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved]Richard Kellermayer0https://orcid.org/0000-0002-4146-1335Subramaniam Kugathasan1Rajesh Shah2https://orcid.org/0000-0002-2318-3714Lee Denson3Kristi Hoffman4Baylor College of Medicine, Houston, USAEmory University, Atlanta, USASuite 200, Baylor Health Care System, Austin, Texas, 78735, USACincinnati Children's Hospital Medical Center, Cincinnati, USABaylor College of Medicine, Houston, USABackground: Patients with Crohn’s disease (CD) have an altered intestinal microbiome, which may facilitate novel diagnostic testing. However, accuracy of microbiome classification models across geographic regions may be limited. Therefore, we sought to examine geographic variation in the microbiome of patients with CD from North America and test the performance of a machine learning classification model across geographic regions. Methods: The RISK cohort included 447 pediatric patients with CD and 221 non-inflammatory bowel disease controls from across North America. Terminal ileum, rectal and fecal samples were obtained prior to treatment for microbiome analysis. We divided study sites into 3 geographic regions to examine regional microbiome differences. We trained and tested the performance of a machine learning classification model across these regions. Results: No differences were seen in the mucosal microbiome of patients with CD across regions or in either the fecal or mucosal microbiomes of controls. Machine learning classification algorithms for patients with CD performed well across regions (area under the receiver operating characteristic curve [AUROC] range of 0.85-0.91) with the best results from terminal ileum. Conclusions: This study demonstrated the feasibility of microbiome based diagnostic testing in pediatric patients with CD within North America, independently from regional influences.https://f1000research.com/articles/11-156/v2Crohn’s disease microbiome inflammatory bowel disease machine learningeng |
| spellingShingle | Richard Kellermayer Subramaniam Kugathasan Rajesh Shah Lee Denson Kristi Hoffman Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved] F1000Research Crohn’s disease microbiome inflammatory bowel disease machine learning eng |
| title | Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved] |
| title_full | Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved] |
| title_fullStr | Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved] |
| title_full_unstemmed | Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved] |
| title_short | Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America [version 2; peer review: 2 approved] |
| title_sort | mucosal microbiome is predictive of pediatric crohn s disease across geographic regions in north america version 2 peer review 2 approved |
| topic | Crohn’s disease microbiome inflammatory bowel disease machine learning eng |
| url | https://f1000research.com/articles/11-156/v2 |
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