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

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

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Author(s)
Nunez Lopez, Yury O.
•
Xie, Hui
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Yi, Fanchao
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Mathews, Clayton
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Pasarica, Magdalena
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Pratley, Richard E.
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Seyhan, Attila A
Date Issued
August 2016
Journal
Scientific Reports
Publisher
Springer Nature
Citation
Seyhan, Attila A., Yury O. Nunez Lopez, Hui Xie, Fanchao Yi, Clayton Mathews, Magdalena Pasarica, and Richard E. Pratley. “Pancreas-Enriched miRNAs Are Altered in the Circulation of Subjects with Diabetes: a Pilot Cross-Sectional Study.” Scientific Reports 6 (August 25, 2016): 31479.
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Final published version
Abstract
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[subscript 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
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Creative Commons Attribution 4.0 International License
http://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
http://hdl.handle.net/1721.1/109380
DOI of Published Version
https://doi.org/10.1038/srep31479
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