| [Background]As the living standard improves, the rate of type2diabetes (T2DM) grows fast and type2diabetes will become the leading killer of the health. The pathophysiology of T2DM involves both environmental and genetic factors. Recently, the gut microflora has emerged as another parameter at the crossroad of these interactions. Several animal and human studies have demonstrated that the gut microflora composition differs between T2DM and controls, which may play a role in the development of insulin resistance and type2diabetes. In addition, it has been shown that modification of the gut microbiota by environmental factors may alter body weight and energy metabolism regulation, which may lead to the development of obesity, the major risk factor for T2DM. More than1012microorganisms can be found in the human colon. Some microflora which are helpful to the healthy of the body takes part in anti-inflammatory effects, improves nutrient digestion, absorption and regulation of lipid metabolism. Lactobacillus belong to the Firmicutes phyla. In addition to the fact that these species are highly prevalent in the human gut, they can also easily be added or removed from probiotic food preparations, making them therefore ideal candidate for potential clinical interventions. Effects of the various strains of bacteria on health can be very divergent, and it still remains unclear which species are responsible for specific metabolic effects. In mice, administration of Lactobacillus Casei improves diet-induced obesity and insulin resistance. However, it has also been shown that presence of Lactobacillus may increase inflammation, which may be related to obesity and T2DM.[Objective]Therefore, the primary aim of this study was to assess whether specific species of Lactobacillus differed between T2DM and controls in Southern China. The secondary aim was to determine to which metabolic parameters these specific sets of bacteria most strongly related. The gut microflora may be an effective approach to prevent and cure T2DM.[Methods]This analysis included50T2DM patients and30control subjects of similar age, gender and BMI. Participants were excluded if there is any evidence of diarrh, constiputation, significant cardiovascular complications, other significant renal, hepatic, cardiovascular, or neurological disease; cancer; pregnancy. Fasting blood samples were taken and fasting blood Glucose, serum total cholesterol, HDL-cholesterol, LDL-cholestero, triglycerides and1Glycated hemoglobin (HbAlc), C-peptide, C-reactive protein were measured. Glucose and C-peptide were also measured by collecting the blood samples after OGTT. Total bacterial DNA was extracted from the fecal samples using DNA stool kit according to the manufacture’s protocol. DNA concentration and quality in the extracts was determined by agarose gel electrophoresis. Bacterial copy numbers in fecal samples from50subjects with type2diabetes and30controls were quantified by qPCR with primers specifically targeting V3region of the16S rRNA using the7500Fast Real-time PCR system. Each fecal sample was performed double. Standard curves were constructed using10fold serial dilutions of fecal bacterial DNA of known concentration. Copy numbers of bacteria in fecal samples were calculated from the threshold cycle values (Ct) and expressed as quantity of bacteria per gram feces. All data are means±SD. Statistical analyses were performed using SPSS17.0. P-values<0.05was considered statistically significant. Values for bacterial content were log-transformed in order to reach a normal distribution. Comparisons were done using independent samples t-tests. Bacterial content patterns were evaluated using cluster analysis, which separates participants into mutually exclusive groups and maximizes differences in the content of a number of bacteria. The cluster procedure in SPSS17.0was used based on the K-means method. The relationship between the2clusters identified and the presence of diabetes was tested using the chi-squared test. Differences among clusters were investigated using the independent samples t-tests. To examine which were the main predictors of the gut microflora, we performed a correlation analysis including blood Glucose, cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides and1Glycated hemoglobin (HbA1c), C-peptide, C-reactive protein as independent variables.[Results]Type2diabetic patients had significantly greater amount of the total amount of Lactobacillus (7.771±0.874)(P<0.001), as well as greater amounts of Lactobacillus bulgaricus (5.188±0.681)(P<0.001), L. casei (5.016±1.047)(P=0.031), L. Rhamnosus (3.744±0.995)(P<0.001) and L. acidophillus (4.308±1.428)(P<0.001),and the controls had significantly less amount of the total amount of Lactobacillus (6.606±0.586)(P<0.001), as well as less amounts of Lactobacillus bulgaricus (4.550±0.674)(P<0.001), L. casei (4.504±0.938)(P=0.031), L. Rhamnosus (2.730±0.459)(P<0.001) and L. acidophillus (2.904±0.614)(P<0.001). Cluster analysis identified two mutually exclusive clusters characterized as follows:Cluster1had greater number of Lactobacillus (7.8±0.9P<0.001) as well as L. acidophilus (5.2±1.0P<0.001), L. bugaricus (5.2±0.6P=0.038), L. cacei5.2±1.2P=0.007)and L. Rahmnosumand(4.0±1.1P<0.001).Cluster2had the less number of Lactobacillus (7.0±0.9P<0.001) as well as L. acidophilus (2.9±0.6P<0.001), L. bugaricus (4.8±0.8P=0.038), L. cacei(4.6±0.8P=0.007)and L. Rahmnosumand(2.9±0.6P<0.001). Interestingly, there was a strong association between the clusters and the presence of diabetes (chi-squared=25.368, P-value of chi-squared test:P<0.001), suggesting that the presence of diabetes may be characterized by a specific gut microflora pattern. Sensitivity and specificity calculations of this cluster analysis showed that for such bacterial analysis and prediction of type2diabetes, the sensitivity=0.600, specificity=0.967. In correlation analysis, the main factor that was consistently significantly related to the various bacteria species was cholesterol level. the total amount of Lactobacillus showed a trend for a negative association with LDL-cholesterol (Rp=-0.252P=0.039)[Conclusion]The results of this study indicate that T2DM patients can be characterized by an increased quantity of total Lactobacillus and specific species of Lactobacillus including Lactobacillus bulgaricus, L. casei, L. Rhamnosuand L. acidophilus in the gust compared to the controls. We showed that T2DM patients can be characterized by a specific gut microbiota pattern. And this may provide new direction to identify individuals at high risk for T2DM. The results also suggest that Lactobacillus in the gust play a role in influencing cholesterol metabolism in T2DM patients. The total amount of Lactobacillus showed a trend for a negative association with LDL-cholesterol. Modification of the gut microflora by the diet may possibly help controlling metabolic diseases such as dyslipidemia and diabetes. |
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