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A set of miRNAs predicts T2DM remission in patients with coronary heart disease: from the CORDIOPREV study
- Source :
- Molecular Therapy: Nucleic Acids, Vol 23, Iss , Pp 255-263 (2021)
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
Abstract
- MicroRNAs (miRNAs) regulate the expression of genes associated with the development of diseases, including type 2 diabetes mellitus (T2DM). However, the use of miRNAs to predict T2DM remission has been poorly studied. Therefore, we aimed to investigate whether circulating miRNAs could be used to predict the probability of T2DM remission in patients with coronary heart disease. We included the newly diagnosed T2DM (n = 190) of the 1,002 patients from the CORDIOPREV study. Seventy-three patients reverted from T2DM after 5 years of dietary intervention with a low-fat or Mediterranean diet. Plasma levels of 56 miRNAs were measured by OpenArray. Generalized linear model, receiver operating characteristic (ROC), Cox regression, and pathway analyses were performed. ROC analysis based on clinical variables showed an area under the curve (AUC) of 0.66. After a linear regression analysis, seven miRNAs were identified as the most important variables in the group’s differentiation. The addition of these miRNAs to clinical variables showed an AUC of 0.79. Cox regression analysis using a T2DM remission score including miRNAs showed that high-score patients have a higher probability of T2DM remission (hazard ratio [HR]low versus high, 4.44). Finally, 26 genes involved in 10 pathways were related to the miRNAs. We have identified miRNAs (hsa-let-7b, hsa-miR-101, hsa-miR-130b-3p, hsa-miR-27a, hsa-miR-30a-5p, hsa-miR-375, and hsa-miR-486) that contribute to the prediction of T2DM remission in patients with coronary heart disease.
- Subjects :
- Therapeutics. Pharmacology
RM1-950
Subjects
Details
- Language :
- English
- ISSN :
- 21622531
- Volume :
- 23
- Issue :
- 255-263
- Database :
- Directory of Open Access Journals
- Journal :
- Molecular Therapy: Nucleic Acids
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.9679fdc9ca3d49198cc39b2434a928d3
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.omtn.2020.11.001