| First Author | Johnson R | Year | 2020 |
| Journal | Sci Rep | Volume | 10 |
| Issue | 1 | Pages | 12352 |
| PubMed ID | 32703998 | Mgi Jnum | J:294180 |
| Mgi Id | MGI:6453166 | Doi | 10.1038/s41598-020-69254-x |
| Citation | Johnson R, et al. (2020) Identification of potential biomarkers for predicting the early onset of diabetic cardiomyopathy in a mouse model. Sci Rep 10(1):12352 |
| abstractText | Type 2 diabetes (T2D) is characterized by metabolic derangements that cause a shift in substrate preference, inducing cardiac interstitial fibrosis. Interstitial fibrosis plays a key role in aggravating left ventricular diastolic dysfunction (LVDD), which has previously been associated with the asymptomatic onset of heart failure. The latter is responsible for 80% of deaths among diabetic patients and has been termed diabetic cardiomyopathy (DCM). Through in silico prediction and subsequent detection in a leptin receptor-deficient db/db mice model (db/db), we confirmed the presence of previously identified potential biomarkers to detect the early onset of DCM. Differential expression of Lysyl Oxidase Like 2 (LOXL2) and Electron Transfer Flavoprotein Beta Subunit (ETFbeta), in both serum and heart tissue of 6-16-week-old db/db mice, correlated with a reduced left-ventricular diastolic dysfunction as assessed by high-resolution Doppler echocardiography. Principal component analysis of the combined biomarkers, LOXL2 and ETFbeta, further displayed a significant difference between wild type and db/db mice from as early as 9 weeks of age. Knockdown in H9c2 cells, utilising siRNA of either LOXL2 or ETFbeta, revealed a decrease in the expression of Collagen Type I Alpha1 (COL1A1), a marker known to contribute to enhanced myocardial fibrosis. Additionally, receiver-operating curve (ROC) analysis of the proposed diagnostic profile showed that the combination of LOXL2 and ETFbeta resulted in an area under the curve (AUC) of 0.813, with a cut-off point of 0.824, thus suggesting the favorable positive predictive power of the model and further supporting the use of LOXL2 and ETFbeta as possible early predictive DCM biomarkers. |
