Diabetes mellitus (DM), a complex metabolic disease, has become a global threat to human health worldwide. Over the past decades, an enormous amount of effort has been devoted to understand how microRNAs (miRNAs), a class of small non-coding RNA regulators of gene expression at the post-transcriptional level, are implicated in DM pathology. Growing evidence suggests that the expression signature of a specific set of miRNAs has been altered in the progression of DM. In the present review, we summarize the recent investigations on the miRNA profiles as novel DM biomarkers in clinical studies and in animal models, and highlight recent discoveries on the complex regulatory effect and functional role of miRNAs in DM.
Aim: The increased morbidity and mortality due to type 2 diabetes can be partly due to its delayed diagnosis. In developing countries, the cost and unavailability of conventional screening methods can be a setback. Use of random blood glucose (RBG) may be beneficial in testing large numbers at a low cost and in a short time in identifying persons at risk of developing diabetes. In this analysis, we aim to derive the values of RBG corresponding to the cut-off values of glycosylated hemoglobin (HbA1c) used to define prediabetes and diabetes.
Methods: Based on their risk profile of developing diabetes, a total of 2835 individuals were screened for a large diabetes prevention study. They were subjected to HbA1c testing to diagnose prediabetes and diabetes. Random capillary blood glucose was also performed. Correlation of RBG with HbA1c was computed using multiple linear regression equation. The optimal cut-off value for RBG corresponding to HbA1c value of 5.7% (39 mmol/mol), and ≥ 6.5% (48 mmol/mol) were computed using the receiver operating curve (ROC). Diagnostic accuracy was assessed from the area under the curve (AUC) and by using the Youden's index.
Results: RBG showed significant correlation with HbA1c (r=0.40, p<0.0001). Using the ROC analysis, a RBG cut-off value of 140.5 mg/dl (7.8 mmol/L) corresponding to an HbA1c value of 6.5% (48mmol/mol) was derived. A cut-off value could not be derived for HbA1c of 5.7% (39 mmol/mol) since the specificity and sensitivity for identifying prediabetes were low.
Conclusion: Use of a capillary RBG value was found to be a simple procedure. The derived RBG cut-off value will aid in identifying people with undiagnosed diabetes. This preliminary screening will reduce the number to undergo more cumbersome and invasive diagnostic testing.
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