Type 2 diabetes mellitus (T2DM) is a multifactorial disorder that leads to alterations in gene regulation. ncRNAs have the characteristics of tissue specificity, disease specificity, timing specificity, high stability and post transcriptional regulation effect. These preconditions are more conducive to promote ncRNA to become a new biomarker for clinical diagnosis. Our study aims to explore the relationship between circRNA, lncRNA, miRNA and T2DM, and to evaluate their diagnostic value for T2DM. A total of 101 pairs of T2DM and controls were conducted in the study. QRT-PCR was used to study the differential expression of circRNAs, miRNAs and lncRNAs. ROC curve was used to estimate their diagnostic value in T2DM. Compared with healthy controls, the expression levels of hsa_circ_0071106, hsa_circ_0000284, hsa_circ_0071271, hsa-miR-29a-5p, hsa-miR-3690, hsa-miR-607, lncRNA MEG3 and lncRNA TUG1were higher in T2DM (all P < 0.05). The AUCs of hsa_circ_0071106, hsa-miR-607 and lncRNA TUG1 for diagnosis of T2DM were 0.563,0.645 and 0.642, respectively. The combined AUC of hsa-miR-607, lncRNA TUG1 and hsa_circ_0071106 was 0.798 ([0.720~0.875], P < 0.001). Moreover, the sensitivity of combined diagnosis was 75.2% and the specificity was 100.0%. The levels of lncRNA TUG1, hsa-miR-607 and hsa_circ_0071106 in peripheral blood have potential clinical diagnostic value for T2DM.

We explored has_circ_0071106 as a diagnostic marker for type 2 diabetes (T2DM) in south China, and predicted the functional mechanism of the target circRNA. A total of 107 T2DM patients and 107 healthy reference persons were included as the research objects. In the first stage, the circRNA microarray was used to detect the peripheral blood of 4 T2DM and 4 control groups to screen the differential expression profile of circRNA. In the second stage, four circRNAs were screened from the differential expression profiles of circRNA, and real-time polymerase chain reaction (Q-PCR) technology was used to verify the blood samples of 103 T2DM and 103 controls. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis in bioinformatics were used to predict the functional mechanism of target circRNA. Lastly,we found that has_circ_0071106 increase the risk of T2DM (OR=2.819 (95% CI: 1.415~5.616)). Thearea under the ROCcurve has_circ_0071106 was 0.690, the sensitivity was 62.1%, and the specificity was 69.9%. The function prediction results showed that has_circ_0071106 was involved in biological processes such as protein binding, gene transcription, and may be involved in the pathway of hsa-miR-29a-5p regulating diabetes, has_circ_0071106 may be used as a diagnostic marker for T2DM.

Xinjiang affiliated Chinese Medicine Hospital medical research, design and data processing center, China

Background Pre-diabetes mellitus (PDM) is considered an early warning signal of type 2 diabetes mellitus (T2DM). However, most studies only analyze the risk factors of diabetes, ignore the exploration of PDM. The aim of this study was to investigate the independent and combined impacts of overweight obesity and smoking on the risk of PDM and T2DM. Methods 28,208 patients with T2DM were selected from 5 cities in the Pearl River Delta, Guangdong Province, China. According to the same region, gender and age difference less than 5 years old, 28208 patients with PDM and 28208 patients with normal glucose tolerance (NGT) were randomly selected. We analyzed the influential factors of PDM,T2DM and the interaction between smoking and overweight and obesity on using ordered multi-class logistic regression and multi-class results non-conditional logistic regression. Results Overweight and obesity (OR = 1.427,95%CI:1.388 ~ 1.468; OR = 1.829,95%CI:1.753 ~ 1.908) and smoking (OR = 1.161,95%CI:1.113 ~ 1.212) were risk factors for the onset of T2DM by ordered multiple Logistic regression. Furthermore, both in the comparison of NGT, PDM and NGT, T2DM, the results showed that overweight, obesity and smoking were risk factors for both PDM and T2DM too. And we found there was an additive interaction between overweight obesity and smoking in the developing of T2DM. Moreover, there would be 0.196(0.051 ~ 0.341) relative excess risk due to the additive interaction, 9.1% (2.0%-16.1%) of T2DM exposed to both risk factors was attributable to the additive interaction, and the risk of T2DM in overweight and obese smokers was 1.203(1.004–1.402) times as high as the sum of risks in the participants exposed to a single risk factor alone too. Conclusions Overweight obesity and smoking are the risk factors for the onset of T2DM. The risk of the coexistence of both factors is greater than that of single factors. Early weight control and positive smoking control are beneficial to prevent and delay the occurrence of T2DM.