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A machine learning model using gut microbiome data for predicting changes of trimethylamine-N-oxide in healthy volunteers after choline consumption / 南方医科大学学报
Journal of Southern Medical University ; (12): 290-295, 2017.
Artigo em Chinês | WPRIM | ID: wpr-273772
ABSTRACT
<p><b>OBJECTIVE</b>To establish a machine learning model based on gut microbiota for predicting the level of trimethylamine N-oxide (TMAO) metabolism in vivo after choline intake to provide guidance of individualized precision diet and evidence for screening population at high risks of cardiovascular disease.</p><p><b>METHODS</b>We quantified plasma levels of TMAO in 18 healthy volunteers before and 8 h after a choline challenge (ingestion of two boiled eggs). The volunteers were divided into two groups with increased or decreased TMAO level following choline challenge. Fresh fecal samples were collected before taking fasting blood samples for amplifying 16S rRNA V4 tags, and the PCR products were sequenced using the platform of Illumina HiSeq 2000. The differences in gut microbiata between subjects with increased and decreased plasma TMAO were analyzed using QIIME. Based on the gut microbiota data and TMAO levels in the two groups, the prediction model was established using the machine learning random forest algorithm, and the validity of the model was tested using a verified dataset.</p><p><b>RESULTS</b>An obvious difference was found in beta diversity of the gut microbota between the subjects with increased and decreased plasma TMAO level following choline challenge. The area under the curve (AUC) of the model was 86.39% (95% CI 72.7%-100%). Using the verified dataset, the model showed a much higher probability for correctly predicting TMAO variation following choline challenge.</p><p><b>CONCLUSION</b>The model is feasible and reliable for predicting the level of TMAO metabolism in vivo based on gut microbiota.</p>
Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Tipo de estudo: Estudo prognóstico Idioma: Chinês Revista: Journal of Southern Medical University Ano de publicação: 2017 Tipo de documento: Artigo

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Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Tipo de estudo: Estudo prognóstico Idioma: Chinês Revista: Journal of Southern Medical University Ano de publicação: 2017 Tipo de documento: Artigo