Pancreas-enriched miRNAs are altered in the circulation of subjects with diabetes: a pilot cross-sectional study

• Published in: Scientific reports Vol. 6; no. 1; p. 31479
• Main Authors: Seyhan, Attila A., Nunez Lopez, Yury O., Xie, Hui, Yi, Fanchao, Mathews, Clayton, Pasarica, Magdalena, Pratley, Richard E.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.08.2016; Nature Publishing Group
• Subjects: 38; 38/89; 45/90; 692/53; Autoimmune diseases; Biomarkers; Cell injury; Cross-sectional studies; Diabetes; Diabetes mellitus; Glucose tolerance; Humanities and Social Sciences; Immunological tolerance; Insulin; miRNA; multidisciplinary; Pancreas; Plasma levels; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep31479

The clinical presentation of diabetes sometimes overlaps, contributing to ambiguity in the diagnosis. Thus, circulating pancreatic islet-enriched microRNAs (miRNAs) might be useful biomarkers of β-cell injury/dysfunction that would allow more accurate subtyping of diabetes. We measured plasma levels of selected miRNAs in subjects with prediabetes (n = 12), type 2 diabetes (T2D, n = 31), latent autoimmune diabetes of adults (LADA, n = 6) and type 1 diabetes (T1D, n = 16) and compared them to levels in healthy control subjects (n = 27). The study was conducted at the Translational Research Institute for Metabolism and Diabetes (TRI-MD), Florida Hospital. MiRNAs including miR-375 (linked to β-cell injury), miR-21 (associated with islet inflammation), miR-24.1, miR-30d, miR-34a, miR-126, miR-146, and miR-148a were significantly elevated in subjects with various forms of diabetes compared to healthy controls. Levels of several miRNAs were significantly correlated with glucose responses during oral glucose tolerance testing, HbA 1c , β-cell function, and insulin resistance in healthy controls, prediabetes, and T2D. These data suggest that miRNAs linked to β-cell injury and islet inflammation might be useful biomarkers to distinguish between subtypes of diabetes. This information could be used to predict progression of the disease, guide selection of optimal therapy and monitor responses to interventions, thus improving outcomes in patients with diabetes.