RESUMO
<p><b>OBJECTIVE</b>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.</p><p><b>METHODS</b>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.</p><p><b>RESULTS</b>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 β 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).</p><p><b>CONCLUSION</b>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.</p>
RESUMO
<p><b>OBJECTIVE</b>To investigate the effect of intermittent fasting on metabolize and gut microbiota in obese presenium rats fed with high-fat-sugar-diet.</p><p><b>METHODS</b>We fed the Wistar rats with high-fat and high-sugar diet to induce adiposity, and the rats for intermittent fasting were selected base on their body weight. The rats were subjected to fasting for 72 h every 2 weeks for 18 weeks. OGTT test was performed and fasting blood samples and fecal samples were collected for measurement of TC, TG, HDL-C and LDL-C and sequence analysis of fecal 16S rRNA V4 tags using Illumina. Gut microbial community structure was analyzed with QIIME and LEfSe.</p><p><b>RESULTS</b>After the intervention, the body weight of the fasting rats was significantly lower than that in high-fat diet group (P<0.01). OGTT results suggested impairment of sugar tolerance in the fasting group, which showed a significantly larger AUC than compared with the high-fat diet group (P<0.05). Intermittent fasting significantly reduced blood HDL-C and LDL-C levels (P<0.05) and partially restored liver steatosis, and improved the gut microbiota by increasing the abundance of YS2, RF32 and Helicobacteraceae and reducing Lactobacillus, Roseburia, Erysipelotrichaceae and Ralstonia. Bradyrhizobiaceae was found to be positively correlated with CHOL and HDL-C, and RF39 was inversely correlated with the weight of the rats.</p><p><b>CONCLUSION</b>Intermittent fasting can decrease the body weight and blood lipid levels and restore normal gut microbiota but can cause impairment of glucose metabolism in obese presenium rats.</p>