Association between genetic polymorphisms of cadherin 23 and noise-induced hearing loss: a meta-analysis.

BACKGROUND: NIHL is one of the most common occupational diseases induced by gene-environment interaction. The CDH23 gene is a candidate gene related to NIHL susceptibility. However, the relationship between CDH23 gene and NIHL is still inconclusive. AIM: To clarify the association between CDH23 gene and NIHL, a meta-analysis was performed. SUBJECTS AND METHODS: A search in MEDLINE, PubMed, Web of Science, EBSCO, China National Knowledge Infrastructure (CNKI), and Wanfang Data was implemented to collect data. RESULTS AND CONCLUSIONS: Six studies were eventually included and all the subjects were Chinese. The results showed that rs1227051, rs1227049, and rs3752752 were not associated with NIHL susceptibility under five genetic models. But rs3802711 reduced the risk of NIHL under the recessive model, and the BB genotype and B allele of rs3802711 were significantly linked to NIHL under recessive, super-dominant, homozygote, and allele genetic models when stratified by the HWE result. Moreover, when not conform to HWE, the BB + AB genotypes and B allele of Exon7 in dominant, super-dominant, homozygote, and allele genetic model increased the risk of NIHL. CDH23 may be a potential gene marker for the prevention and early screening of NIHL in Chinese. Further large and well-designed studies are needed to confirm this association.

Impaired renal function and dysbiosis of gut microbiota contribute to increased trimethylamine-N-oxide in chronic kidney disease patients.

Chronic kidney disease (CKD) patients have an increased risk of cardiovascular diseases (CVDs). The present study aimed to investigate the gut microbiota and blood trimethylamine-N-oxide concentration (TMAO) in Chinese CKD patients and explore the underlying explanations through the animal experiment. The median plasma TMAO level was 30.33 µmol/L in the CKD patients, which was significantly higher than the 2.08 µmol/L concentration measured in the healthy controls. Next-generation sequence revealed obvious dysbiosis of the gut microbiome in CKD patients, with reduced bacterial diversity and biased community constitutions. CKD patients had higher percentages of opportunistic pathogens from gamma-Proteobacteria and reduced percentages of beneficial microbes, such as Roseburia, Coprococcus, and Ruminococcaceae. The PICRUSt analysis demonstrated that eight genes involved in choline, betaine, L-carnitine and trimethylamine (TMA) metabolism were changed in the CKD patients. Moreover, we transferred faecal samples from CKD patients and healthy controls into antibiotic-treated C57BL/6 mice and found that the mice that received gut microbes from the CKD patients had significantly higher plasma TMAO levels and different composition of gut microbiota than did the comparative mouse group. Our present study demonstrated that CKD patients had increased plasma TMAO levels due to contributions from both impaired renal functions and dysbiosis of the gut microbiota.

[A machine learning model using gut microbiome data for predicting changes of trimethylamine-N-oxide in healthy volunteers after choline consumption].

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: The model is feasible and reliable for predicting the level of TMAO metabolism in vivo based on gut microbiota.

[Effect of intermittent fasting on physiology and gut microbiota in presenium rats].

OBJECTIVE: To investigate the effect of intermittent fasting on metabolize and gut microbiota in obese presenium rats fed with high-fat-sugar-diet. METHODS: 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. RESULTS: 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. CONCLUSION: 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.