[A machine learning model based on initial gut microbiome data for predicting changes of Bifidobacterium after prebiotics consumption].

OBJECTIVE: To investigate the effects of prebiotics supplementation for 9 days on gut microbiota structure and function and establish a machine learning model based on the initial gut microbiota data for predicting the variation of Bifidobacterium after prebiotic intake. METHODS: With a randomized double-blind self-controlled design, 35 healthy volunteers were asked to consume fructo-oligosaccharides (FOS) or galacto-oligosaccharides (GOS) for 9 days (16 g per day). 16S rRNA gene high-throughput sequencing was performed to investigate the changes of gut microbiota after prebiotics intake. PICRUSt was used to infer the differences between the functional modules of the bacterial communities. Random forest model based on the initial gut microbiota data was used to identify the changes in Bifidobacterium after 5 days of prebiotic intake and then to build a continuous index to predict the changes of Bifidobacterium. The data of fecal samples collected after 9 days of GOS intervention were used to validate the model. RESULTS: Fecal samples analysis with QIIME revealed that FOS intervention for 5 days reduced the intestinal flora alpha diversity, which rebounded on day 9; in GOS group, gut microbiota alpha diversity decreased progressively during the intervention. Neither FOS nor GOS supplement caused significant changes in ß diversity of gut microbiota. The area under the curve (AUC) of the prediction model was 89.6%. The continuous index could successfully predict the changes in Bifidobacterium (R=0.45, P=0.01), and the prediction accuracy was verified by the validation model (R=0.62, P=0.01). CONCLUSION: Short-term prebiotics intervention can significantly decrease α-diversity of the intestinal flora. The machine learning model based on initial gut microbiota data can accurately predict the changes in Bifidobacterium, which sheds light on personalized nutrition intervention and precise modulation of the intestinal flora.

Dysbiosis of Gut Microbiota With Reduced Trimethylamine-N-Oxide Level in Patients With Large-Artery Atherosclerotic Stroke or Transient Ischemic Attack.

BACKGROUND: Gut microbiota has been suggested to play a role in almost all major diseases including cardio- and cerebrovascular diseases. A possible mechanism is the transformation of dietary choline and l-carnitine into trimethylamine by gut bacteria. This metabolite is further oxidized into trimethylamine-N-oxide (TMAO) in liver and promotes atherogenesis. Nevertheless, little is known about gut microbial diversity and blood TMAO levels in stroke patients. METHODS AND RESULTS: We performed a case-control study of patients with large-artery atherosclerotic ischemic stroke and transient ischemic attack. TMAO was determined with liquid chromatography tandem mass spectrometry. Gut microbiome was profiled using Illumina sequencing of the 16S rRNA V4 tag. Within the asymptomatic control group, participants with and without carotid atherosclerotic plaques showed similar levels of TMAO without a significant difference in gut microbiota; however, the gut microbiome of stroke and transient ischemic attack patients was clearly different from that of the asymptomatic group. Stroke and transient ischemic attack patients had more opportunistic pathogens, such as Enterobacter, Megasphaera, Oscillibacter, and Desulfovibrio, and fewer commensal or beneficial genera including Bacteroides, Prevotella, and Faecalibacterium. This dysbiosis was correlated with the severity of the disease. The TMAO level in the stroke and transient ischemic attack patients was significantly lower, rather than higher, than that of the asymptomatic group. CONCLUSIONS: Participants with asymptomatic atherosclerosis did not exhibit an obvious change in gut microbiota and blood TMAO levels; however, stroke and transient ischemic attack patients showed significant dysbiosis of the gut microbiota, and their blood TMAO levels were decreased.