Abstract 21261: Circulating Extracellular Vesicles From Mouse and Rat Models of Diabetes Reveal Specific Microrna Signatures as Biomarkers of Diabetic Cardiomyopathy
Bibliographic record
Abstract
Background: Type-2 diabetes (T2D) is associated with both reduced and preserved ejection fraction heart failure. Obese db/db mice and Goto Kakizaki (GK) rats represents animals models of T2D that develop cardiac dysfunction similar to human diabetic cardiomyopathy, in which dominant early findings are of diastolic (and not systolic) dysfunction. Circulating extracellular vesicles (EV) contain microRNAs (miR) that can be transferred to recipient cells to modulate their function. We explored whether analysis of EV content from animals models of T2D would inform on the pathophysiology, diagnosis and therapeutic targets of cardiac dysfunction. Hypothesis: EV from animal models of T2D will have altered miR content that contributes to the pathophysiology of diabetic cardiomyopathy. Methods & Results: miR qPCR arrays on circulating EV isolated from plasma of db/db mice reveal several miR (-7, -15, -25, -30e, -148a, -150, -195) modulated during disease progression. These changes in miR content occur prior to echocardiographic evidence of diastolic dysfunction, including global longitudinal strain and strain rate. Among circulating EV miR from the GK rat model, miR-30 was also upregulated (1.42 fold, p=0.03) compared to Wistar rat. In GK rat left ventricle, and in H9C2 rat cardiac myoblast cultured in 25 mM high glucose media, mass spectrometry revealed proteins that were overexpressed in the diabetic heart including oxidative phosphorylation, glycolysis, fatty acid degredation and the citrate cycle. Using a bioinformatics approach, we next identified metabolic pathways affected by miR-30. Based on these findings, in vivo therapy with antagomiR and mimics of miR-30 are underway to test causality and reversibility of the observed cardiomyopathy. Conclusion: EV from animal models of T2D have altered miR content, including miR-30. We also identify alterations in the expression of a network of metabolism genes in the heart, which are implicated in diabetic cardiomyopathy. If causality is supported by experiments that enhance or block miR-30 expression in these models of disease, we will have identified a novel biomarker and therapeutic target for diabetic cardiomyopathy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".