Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors

BMC Microbiology

Rui Jiang¹  Xin Wang²  Jianchu Li²  Ting Chen³  Hui Chen⁴  Congmin Zhu⁵  Yuqing Yang⁶ 

[1] Bioinformatics Division and Center for Synthetic & Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China;Department of Ultrasound, Peking Union Medical College Hospital, Beijing, China;Institute for Artificial Intelligence and Department of Computer Science and Technology, Tsinghua University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China;Institute for Artificial Intelligence and Department of Computer Science and Technology, Tsinghua University, Beijing, China;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China;

关键词: Gut microbiota;    Human variables;    Disease classification;    Machine learning;   

DOI  :  10.1186/s12866-021-02414-9

来源: Springer

【 摘 要 】

Lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between case-control studies for detecting disease-associated microbe existed due to limited sample size and population-wide bias in lifestyle and physiological variables. To infer gut microbiota-disease associations accurately, we propose to build machine learning models by including both human variables and gut microbiota. When the model’s performance with both gut microbiota and human variables is better than the model with just human variables, the independent gut microbiota -disease associations will be confirmed. By building models on the American Gut Project dataset, we found that gut microbiota showed distinct association strengths with different diseases. Adding gut microbiota into human variables enhanced the classification performance of IBD significantly; independent associations between occurrence information of gut microbiota and irritable bowel syndrome, C. difficile infection, and unhealthy status were found; adding gut microbiota showed no improvement on models’ performance for diabetes, small intestinal bacterial overgrowth, lactose intolerance, cardiovascular disease. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be very weak. We proposed a list of microbes as biomarkers to classify IBD and unhealthy status. Further functional investigations of these microbes will improve understanding of the molecular mechanism of human diseases.

【 授权许可】

CC BY