Regional variation limits applications of healthy gut microbiome reference ranges and disease models

He, Y; Wu, W; Zheng, HM; Li, P; McDonald, D; Sheng, HF; Chen, MX; Chen, ZH; Ji, GY; Zheng, ZDX; Mujagond, P; Chen, XJ; Rong, ZH; Chen, P; Lyu, LY; Wang, X; Wu, CB; Yu, N; Xu, YJ; Yin, J; Raes, J; Knight, R; Ma, WJ; Zhou, HW

Zhou, HW (reprint author), Southern Med Univ, Zhujiang Hosp, Div Lab Med, Guangzhou, Guangdong, Peoples R China.; Zhou, HW (reprint author), Southern Med Univ, Sch Publ Hlth, Dept Environm Hlth, Guangzhou, Guangdong, Peoples R China.; Ma, WJ (reprint auth

NATURE MEDICINE, 2018; 24 (10): 1532

Abstract

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression(1-3). Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis(4), colorectal cancer prescreening(5) and therapeutic choices in melanoma(6). Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic(7) and cardiovascular diseases(8). 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.

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