Objective: There is evidence that type 2 diabetes (T2DM) is affected by gut microbiota, and gut microbiota diversity modified by diet. To investigate its modifications in Uyghur patients with different glucose tolerance, we enrolled 561 subjects: newly diagnosed T2DM ( n = 145), impaired glucose regulation (IGR) patients ( n = 138) and in normal control (NC) population ( n = 278). Methods: The nutrient intake in food frequency questionnaire was calculated by R language. The regions V3-V4 of 16S ribosomal RNA were sequenced by using Illumina Miseq platform. Sequences were clustered by operational taxonomy units, gut microbiota composition, and diversity was analyzed. Correlations between bacterial composition at different level and dietary factors were evaluated. Results: The α-diversity was highest in NC, followed by T2DM and IGR; β-diversity distinguished between patients and NC. Compared to NC, Saccharibacteria was significantly increased in T2DM and IGR. Deferribacteres was significantly increased in T2DM compared to NC and IGR. Veillonella, Pasteurellaceae , and Haemophilus were over-represented in IGR. Abundance of Bacteroidetes was negatively correlated with LDL-C; Abundance of Tenericutes was negatively correlated with hip circumference and total cholesterol, positively correlated with HDL-C and cake intake; Actinobacteria was positively correlated with BMI and folic acid intake, negatively correlated with oil intake. Firmicutes was negatively correlated with beverage and alcohol intake. Spirochaetae was negatively correlated with fungus, fruits, beans, vitamin C, dietary fiber, and calcium. Fusobacteria was positively correlated with beans intake, and was negatively correlated with fat intake. Proteobacteria was positively correlated with tuber crops intake. Synergistetes was positively correlated with cholesterol, nicotinic acid, and selenium intake. Deferribacteres was negatively correlated with magnesium intake. Conclusions: At the phylum and genus level, the structure and diversity of intestinal microbiota of T2DM and IGR was altered, the number of OTUs, the relative abundance, and diversity were all decreased. The gut microbiota of the newly diagnosed T2DM, IGR, and NC were related to age, blood lipids, BMI, blood pressure, and dietary nutrient intake. Unbalanced nutrient intake in the three groups may affect the structure and abundance of the gut microbiota, which may play a role in the occurrence and development of T2DM.

Objective Gut microbiota and their metabolites play an important role in the development of type 2 diabetes mellitus (T2DM). This research was designed to study the relationship between gut microbiota and faecal metabolites of Uyghur newly onset T2DM and impaired glucose regulation (IGR) patients. Materials and Methods A total of 60 different glycemic Uyghur subjects were enrolled and divided into T2DM, IGR, and normal glucose tolerance (NGT) groups. Metagenomics and LC-MS-based untargeted faecal metabolomics were employed. Correlations between bacterial composition and faecal metabolomics were evaluated. Results We discovered that the composition and diversity of gut microbiota in newly onset T2DM and IGR were different from those in NGT. The α-diversity was higher in NGT than in T2DM and IGR; β-diversity analysis revealed apparent differences in the bacterial community structures between patients with T2DM, IGR, and NGT. LC-MS faecal metabolomics analysis discovered different metabolomics features in the three groups. Alchornoic acid, PE (14 : 0/20 : 3), PI, L-tyrosine, LysoPC (15 : 0), protorifamycin I, pimelic acid, epothilone A, 7-dehydro-desmosterol, L-lysine, LysoPC (14 : 1), and teasterone are the most significant differential enriched metabolites. Most of the differential enriched metabolites were involved in metabolic processes, including carbohydrate metabolism, starch and sucrose metabolism, phenylpropanoid biosynthesis, and biosynthesis of amino acids. Procrustes analysis and correlation analysis identified correlations between gut microbiota and faecal metabolites. Matricin was positively correlated with Bacteroides and negatively correlated with Actinobacteria; protorifamycin I was negatively correlated with Actinobacteria; epothilone A was negatively correlated with Actinobacteria and positively correlated with Firmicutes; PA was positively correlated with Bacteroides and negatively correlated with Firmicutes; and cristacarpin was positively correlated with Actinobacteria; however, this correlation relationship does not imply causality. Conclusions This study used joint metagenomics and metabolomics analyses to elucidate the relationship between gut microbiota and faecal metabolites in different glycemic groups, and the result suggested that metabolic disorders and gut microbiota dysbiosis occurred in Uyghur T2DM and IGR. The results provide a theoretical basis for studying the pathological mechanism for further research.

Purpose This study aimed to characterize dietary patterns in the Uyghur population and examined the relationship between dietary pattern, TCF7L2 single-nucleotide polymorphisms (SNPs), and the risk of type 2 diabetes mellitus (T2DM). Patients and methods Dietary patterns were defined using factor analysis, and associations between dietary patterns were evaluated using multivariate logistic regression analyses. Genotyping of seven SNPs of TCF7L2 (rs11196205, rs12255372, rs12573128, rs4506565, rs7895340, rs7901695, and rs7903146) was conducted, and the association between these seven SNPs and the risk of T2DM was evaluated. Interactions between SNPs, homeostasis model assessment-insulin resistance, and dietary patterns were also analyzed. Results A total of 828 participants were enrolled in this study, including 491 people with T2DM and 337 healthy controls. Five dietary patterns were defined, and the results indicated that the “fruit” and “vegetables” dietary patterns were associated with a significant decrease in the risk of T2DM, whereas the “meats” and “grains” dietary patterns were associated with an increased risk of T2DM. Moreover, the “dairy product” dietary pattern showed no association with the risk of T2DM. Furthermore, our results revealed that the TCF7L2 SNP, rs12573128, is associated with an increased risk of T2DM. SNPs rs4506565 and rs7903146 significantly interacted with dietary pattern. Conclusion Our studies suggest that dietary pattern and genetic polymorphisms of TCF7L2 are associated with the development of T2DM in the Uyghur population of China.

Observational and interventional studies have showed associations between dietary patterns and the risk of type 2 diabetes (T2DM). The aim of the present study was to identify dietary patterns and examine their association with incident of T2DM and physiopathologic mechanism in the Uygur population of Xinjiang, China. A community-based case control study in Urumqi, Xinjiang Uygur Autonomous Region, China. We enrolled 836 Uygur adults (345 newly diagnosed T2DM and 491 healthy controls) receiving physical examination in community health service centers. Dietary patterns were evaluated using a validated 121 items semi-quantitative food frequency questionnaire (SQFFQ). Dietary patterns were identified by exploratory factor analysis. Data on demographic, anthropometric, socioeconomic characteristics were collected. Homeostasis model assessment index for insulin resistance (HOMA-IR) and for b cell function (HOMA-b) were calculated. Multivariate logistic regression were used to estimate the associations between dietary patterns and the risk of T2DM and multiple linear regression were used to evaluate the association between dietary patterns and fasting plasm glucose (FPG), fasting insulin (FINS), HOMA-IR and HOMA-b, adjusting for confounding factors. Five major dietary patterns were identified: (1) the "vegetable and tubes" pattern; (2) the "fruit and nut" pattern; (3) the "refined grains and meat" pattern; (4) the "dairy, legume and egg" pattern; and (5) the "oil and salt" pattern. Logistic regression results demonstrated that the "vegetable and tubes" dietary pattern and the "fruit and nut" dietary pattern were related to T2DM, OR values were 0.223 (95% CI: 0.135-0.371), 0.160 (95% CI: 0.093-0.275), respectively. The "refined grains and meat" dietary pattern and the "oil and salt" dietary pattern were related to T2DM, OR values were 6.146 (95% CI: 3.217-11.739), 9.554 (95% CI: 5.668-16.104), respectively. Multiple linear regression analysis showed that the above five dietary patterns were related to log (FPG), b values were 20.040, 20.039, 0.028, 20.010 and 0.036, respectively, all p,0.05. The "vegetable and tubes" pattern, the "fruit and nut" pattern, the "refined grains and meat" pattern and the "oil and salt" pattern were related to log (HOMA-IR), b values were 20.061, 20.060, 0.045, and 0.042, and were related to log (HOMA-b), b values were 0.071, 0.063, 20.035 and 20.070, respectively, all p,0.05. The "refined grains and meat" dietary pattern and the "oil and salt" dietary pattern may be the important reason for the rapid increase of T2DM incidence among Chinese Uygur residents. Our findings suggest that modifying dietary patterns could reduce T2DM incidence in the adult Uygur population.

Background : To investigate the effects of air pollutants on the incidence of diabetes, and to provide reliable evidence for the prevention and control of diabetes. Method : Electronic databases of PubMed, Embase, Web of Science, Wanfang Digital Periodicals (WANFANG), WeiPu, China National Knowledge Infrastructure (CNKI), Springer, and Ovid were searched. RevMan5.3 software was used for meta analysis of the literature included in this study, I 2 was used to measure the heterogeneity of the research contents of the included literature, and fixed effect model or random effect model was used for combined analysis of the overall incidence of diabetes and the incidence of diabetes under different air pollutants. Result : A total of 22 articles with high overall quality were included in this study, including 2703882 subjects and 535190 patients with diabetes.Pooled effect of the studies was determined using relative risk(RR). PM 10 ( RR =1.00; 95% CI :0.99-1.02, P =0.61), PM 2.5 ( RR =1.02 ; 95% C I:1.01-1.04, P =0.01), NO X ( RR =1.00; 95% CI :1.00-1.01, P =0.006), NO 2 ( RR =1.00; 95% CI :1.00-1.01, P =0.01), O 3 ( RR =1.00; 95% CI :0.99-1.01, P =0.99). No correlation was found between PM 10 , NO 2 , O 3 and the risk of type 2 diabetes. The risk of diabetes increased by 1% in people exposed to total air pollutants, increased by 2% in people exposed to PM 2.5 , and increased by 1% in people exposed to NO X . Conclusion : Results of this paper showed that air pollutants are weakly positively correlated with the risk of diabetes. As to the other influencing factors should be further studied.