Journal article
Noninvasive, microbiome-based diagnosis of inflammatory bowel disease
J Zheng, Q Sun, M Zhang, C Liu, Q Su, L Zhang, Z Xu, W Lu, J Ching, W Tang, CP Cheung, AL Hamilton, AL Wilson O’Brien, SC Wei, CN Bernstein, DT Rubin, EB Chang, M Morrison, MA Kamm, FKL Chan Show all
Nature Medicine | NATURE PORTFOLIO | Published : 2024
Open access
Abstract
Despite recent progress in our understanding of the association between the gut microbiome and inflammatory bowel disease (IBD), the role of microbiome biomarkers in IBD diagnosis remains underexplored. Here we developed a microbiome-based diagnostic test for IBD. By utilization of metagenomic data from 5,979 fecal samples with and without IBD from different geographies and ethnicities, we identified microbiota alterations in IBD and selected ten and nine bacterial species for construction of diagnostic models for ulcerative colitis and Crohn’s disease, respectively. These diagnostic models achieved areas under the curve >0.90 for distinguishing IBD from controls in the discovery cohort, and..
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Awarded by Research Grants Council, University Grants Committee
Funding Acknowledgements
We thank J. Mak and A. Chan (Prince of Wales Hospital), C. Ng (Princess Margaret Hospital), M. Li (Tuen Mun Hospital), C. W. Chow (Tseung Kwan O Hospital), E. Shan (Caritas Medical Centre), C. Y. Lam (Kwong Wah Hospital), K. H. Chan (North District Hospital), F. H. Lo (United Christian Hospital), S. F. Sze (Queen Elizabeth Hospital), C. M. Leung (Pamela Youde Nethersole Eastern Hospital), A. J. Hui (Alice Ho Miu Ling Nethersole Hospital) and W. H. Chow (Yan Chai Hospital) for IBD patient recruitment and sample collection. We thank clinical research staff and students for their technical contribution to this study, including clinical data and sample collection, inventory and processing. We thank S. Lin for her assistance in coordinating the shipment of samples from other countries/regions and for helping with sample processing and testing. We thank H. Min Tun and J. Zhu for their assistance in bioinformatic analysis. This research has been conducted using the CU-Med Biobank Resource under Request ID R20221009 and R20231025. S.C.N. has received research funding from the Research Grants Council-Research Impact Fund (RGC-RIF, grant no. R4030-22), New Cornerstone Science Foundation (grant no. NCI202346), InnoHK, The Government of Hong Kong, Special Administrative Region of the People's Republic of China and the Leona M. and Harry B. Helmsley Charitable Trust (grant no. 2017PG-IBD003). J. Zhang has received research funding from the Health and Medical Research Fund (grant no. 10210816), Research Grants Council-General Research Fund (RGC-GRF, grant no. 14121322) and the National Natural Science Foundation of China (grant no. 82100573). Those authors affiliated with MagIC are partially supported by InnoHK, The Government of Hong Kong, Special Administrative Region of the People's Republic of China. The funder had no role in design of the study, data collection, data interpretation and analysis or writing of the manuscript.