Gut Microbiota Profile in Patients with Type 1 Diabetes Based on 16S rRNA Gene Sequencing: A Systematic Review

The gut microbiota has been presumed to have a role in the pathogenesis of type 1 diabetes (T1D). Significant changes in the microbial composition of T1D patients have been reported in several case-control studies. This study is aimed at systematically reviewing the existing literature, which has investigated the alterations of the intestinal microbiome in T1D patients compared with healthy controls (HCs) using 16S ribosomal RNA-targeted sequencing. The databases of MEDLINE, EMBASE, Web of Science, and the Cochrane Library were searched until April 2019 for case-control studies comparing the composition of the intestinal microbiome in T1D patients and HCs based on 16S rRNA gene sequencing techniques. The Newcastle-Ottawa Scale was used to assess the methodological quality. Ten articles involving 260 patients with T1D and 276 HCs were included in this systematic review. The quality scores of all included studies were 6–8 points. In summary, a decreased microbiota diversity and a significantly distinct pattern of clustering with regard to β-diversity were observed in T1D patients when compared with HCs. At the phylum level, T1D was characterised by a reduced ratio of Firmicutes/Bacteroidetes in the structure of the gut community, although no consistent conclusion was reached. At the genus or species level, T1D patients had a reduced abundance of Clostridium and Prevotella compared with HCs, whereas Bacteroides and Ruminococcus were found to be more enriched in T1D patients. This systematic review identified that there is a close association between the gut microbiota and development of T1D. Moreover, gut dysbiosis might be involved in the pathogenesis of T1D, although the causative role of gut microbiota remains to be established. Further well-controlled prospective studies are needed to better understand the role of the intestinal microbiome in the pathogenesis of T1D, which may help explore novel microbiota-based strategies to prevent and treat T1D

20(S)-Ginsenoside Rg3 Protects Kidney from Diabetic Kidney Disease via Renal Inflammation Depression in Diabetic Rats

20(S)-Ginsenoside Rg3 (20(S)-Rg3) has been shown to induce apoptosis by interfering with several signaling pathways. Furthermore, it has been reported to have anticancer and antidiabetic effects. In order to detect the protective effect of 20(S)-Rg3 on diabetic kidney disease (DKD), diabetic rat models which were established by administering high-sugar, high-fat diet combined with intraperitoneal injection of streptozotocin (STZ), and age-matched wild-type (WT) rat were given 20(S)-Rg3 for 12 weeks, with three groups: control group (normal adult rats with saline), diabetic group (diabetic rats with saline), and 20(S)-Rg3 treatment group (diabetic rats with 20(S)-Rg3 (10 mg/kg body weight/day)). The biochemical indicators and the changes in glomerular basement membrane and mesangial matrix were detected. TUNEL staining was used to detect glomerular and renal tubular cell apoptosis. Immunohistochemical staining was used to detect the expression of fibrosis factors and inflammation factors in rat kidney tissues. Through periodic acid-Schiff staining, we observed that the change in renal histology was improved and renal tubular epithelial cell apoptosis decreased significantly by treatment with 20(S)-Rg3. Plus, the urine protein decreased in the rats with the 20(S)-Rg3 treatment. Fasting blood glucose, creatinine, total cholesterol, and triglyceride levels in the 20(S)-Rg3 treatment group were all lower than those in the diabetic group. Mechanistically, 20(S)-Rg3 dramatically downregulated the expression of TGF-β1, NF-κB65, and TNF-α in the kidney. These resulted in a significant prevention of renal damage from the inflammation. The results of the current study suggest that 20(S)-Rg3 could potentially be used as a novel treatment against DKD

The Relationship between Frequently Used Glucose-Lowering Agents and Gut Microbiota in Type 2 Diabetes Mellitus

Metabolic diseases, especially diabetes mellitus, have become global health issues. The etiology of diabetes mellitus can be attributed to genetic and/or environmental factors. Current evidence suggests the association of gut microbiota with metabolic diseases. However, the effects of glucose-lowering agents on gut microbiota are poorly understood. Several studies revealed that these agents affect the composition and diversity of gut microbiota and consequently improve glucose metabolism and energy balance. Possible underlying mechanisms include affecting gene expression, lowering levels of inflammatory cytokines, and regulating the production of short-chain fatty acids. In addition, gut microbiota may alleviate adverse effects caused by glucose-lowering agents, and this can be especially beneficial in diabetic patients who experience severe gastrointestinal side effects and have to discontinue these agents. In conclusion, gut microbiota may provide a novel viewpoint for the treatment of patients with diabetes mellitus

Risk Factors for Cognitive Impairment in Patients with Type 2 Diabetes

Objectives. To investigate the risk factors for cognitive impairment in Chinese type 2 diabetes mellitus (T2DM) patients of advanced age and to identify effective biomarkers of mild cognitive impairment (MCI) in these patients. Methods. Chinese T2DM patients (n=120) aged 50–70 years were divided into groups with impaired (mild, moderate, and severe) and normal cognitive function based on Montreal Cognitive Assessment and Mini-Mental State Examination scores. Data regarding demographic characteristics, clinical features of diabetes, biochemical markers, and metabolomics were collected. Results. Age, educational level, duration of diabetes, fasting blood glucose (FBG), HbA1c, total cholesterol (TC), triglyceride (TG), and 24-hour urine protein were significantly associated with cognitive impairment in T2DM patients of advanced age. The severity of fundus retinopathy and the incidence of macrovascular disease also differed significantly among the groups (P<0.05). Metabolomics analysis suggested that increased levels of glutamate (Glu), phenylalanine (Phe), tyrosine (Tyr), proline (Pro), and homocysteine (Hcy) and a decreased level of glutamine (Gln) were significantly associated with cognitive impairment in the T2DM patients (P<0.05). Receiver operating characteristic curve analysis demonstrated that Glu, Gln, Phe, and Pro levels were significant predictors of cognitive impairment in the T2DM patients. Conclusions. Age, educational level, duration of diabetes, and the levels of FBG, HbA1c, TC, TG, and 24-hour urine protein were considered as independent risk factors for cognitive impairment in older T2DM patients. Macrovascular and microvascular diseases also were closely associated with cognitive impairment in these patients. Together, Glu and Gln levels may represent a good predictive biomarker for the early diagnosis of cognitive impairment in T2DM patients