Ontology type: schema:ScholarlyArticle
2018-08-27
AUTHORSYan He, Wei Wu, Hui-Min Zheng, Pan Li, Daniel McDonald, Hua-Fang Sheng, Mu-Xuan Chen, Zi-Hui Chen, Gui-Yuan Ji, Zhong-Dai-Xi Zheng, Prabhakar Mujagond, Xiao-Jiao Chen, Zu-Hua Rong, Peng Chen, Li-Yi Lyu, Xian Wang, Chong-Bin Wu, Nan Yu, Yan-Jun Xu, Jia Yin, Jeroen Raes, Rob Knight, Wen-Jun Ma, Hong-Wei Zhou
ABSTRACTDysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression1–3. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis4, colorectal cancer prescreening5 and therapeutic choices in melanoma6. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic7 and cardiovascular diseases8. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks. More... »
PAGES1532-1535
http://scigraph.springernature.com/pub.10.1038/s41591-018-0164-x
DOIhttp://dx.doi.org/10.1038/s41591-018-0164-x
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30150716
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]Download the RDF metadata as:Â json-ld nt turtle xml License info
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This table displays all metadata directly associated to this object as RDF triples.
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