Transcriptome profiling of microRNAs associated with latent autoimmune diabetes in adults (LADA)

Human pancreatic islet miRNA-mRNA networks of altered miRNAs due to glycemic status

MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression via mRNA targeting, playing important roles in the pancreatic islets. We aimed to identify molecular pathways and genomic regulatory regions associated with altered miRNA expression due to glycemic status, which could contribute to the development of type 2 diabetes (T2D). To this end, miRNAs were identified by a combination of differential miRNA expression and correlation analysis in human islet samples from donors with normal and elevated blood glucose levels. Analysis and clustering of highly correlated, experimentally validated gene targets of these miRNAs revealed two islet-specific clusters, which were associated with key aspects of islet functions and included a high number of T2D-related genes. Finally, cis-eQTLs and public GWAS data integration uncovered suggestive genomic signals of association with insulin secretion and T2D. The miRNA-driven network-based approach presented in this study contributes to a better understanding of impaired insulin secretion in T2D pathogenesis.

Development and Validation of a Prevalence Model for Latent Autoimmune Diabetes in Adults (LADA) Among Patients First Diagnosed with Type 2 Diabetes Mellitus (T2DM)

Background

We designed this study to develop and validate a prevalence model for latent autoimmune diabetes in adults (LADA) among people initially diagnosed with type 2 diabetes mellitus (T2DM).

Material/Methods

The study recruited 930 patients aged ≥18 years who were diagnosed with T2DM within the past year. Demographic information, medical history, and clinical biochemistry records were collected. Logistic regression was used to develop a regression model to distinguish LADA from T2DM. Predictors of LADA were identified in a subgroup of patients (n=632) by univariate logistic regression analysis. From this we developed a prediction model using multivariate logistic regression analysis and tested its sensitivity and specificity among the remaining patients (n=298).

Results

Among 930 recruited patients, 880 had T2DM (96.4%) and 50 had LADA (5.4%). Compared to T2DM patients, LADA patients had fewer surviving β cells and reduced insulin production. We identified age, ketosis, history of tobacco smoking, 1-hour plasma glucose (1hPG-AUC), and 2-hour C-peptide (2hCP-AUC) as the main predictive factors for LADA (P<0.05). Based on this, we developed a multivariable logistic regression model: Y=−8.249−0.035(X1)+1.755(X2)+1.008(X3)+0.321(X4)−0.126(X5), where Y is diabetes status (0=T2DM, 1=LADA), X1 is age, X2 is ketosis (1=no, 2=yes), X3 is history of tobacco smoking (1=no, 2=yes), X4 is 1hPG-AUC, and X5 is 2hCP-AUC. The model has high sensitivity (78.57%) and selectivity (67.96%).

Conclusions

This model can be applied to people newly diagnosed with T2DM. When Y ≥0.0472, total autoantibody screening is recommended to assess LADA.

Loss of Znt8 function in diabetes mellitus: risk or benefit?

The zinc transporter 8 (ZnT8) plays an essential role in zinc homeostasis inside pancreatic β cells, its function is related to the stabilization of insulin hexameric form. Genome-wide association studies (GWAS) have established a positive and negative relationship of ZnT8 variants with type 2 diabetes mellitus (T2DM), exposing a dual and controversial role. The first hypotheses about its role in T2DM indicated a higher risk of developing T2DM for loss of function; nevertheless, recent GWAS of ZnT8 loss-of-function mutations in humans have shown protection against T2DM. With regard to the ZnT8 role in T2DM, most studies have focused on rodent models and common high-risk variants; however, considerable differences between human and rodent models have been found and the new approaches have included lower-frequency variants as a tool to clarify gene functions, allowing a better understanding of the disease and offering possible therapeutic targets. Therefore, this review will discuss the physiological effects of the ZnT8 variants associated with a major and lower risk of T2DM, emphasizing the low- and rare-frequency variants.

MicroRNA Signatures as Future Biomarkers for Diagnosis of Diabetes States

Diabetes results from the inability of pancreatic islets to maintain blood glucose concentrations within a normal physiological range. Clinical features are usually not observed until islets begin to fail and irreversible damage has occurred. Diabetes is generally diagnosed based on elevated glucose, which does not distinguish between type 1 and 2 diabetes. Thus, new diagnostic approaches are needed to detect different modes of diabetes before manifestation of disease. During prediabetes (pre-DM), islets undergo stress and release micro (mi) RNAs. Here, we review studies that have measured and tracked miRNAs in the blood for those with recent-onset or longstanding type 1 diabetes, obesity, pre-diabetes, type 2 diabetes, and gestational diabetes. We summarize the findings on miRNA signatures with the potential to stage progression of different modes of diabetes. Advances in identifying selective biomarker signatures may aid in early detection and classification of diabetic conditions and treatments to prevent and reverse diabetes.