Blood-Pool Stress MRI as a Tool for Identifying Early Biomarkers of Diabetic Heart Failure with Preserved Ejection Fraction
Bibliographic record
Abstract
Microvascular dysfunction (mvD) has been implicated as the primary hallmark of several cardiac and inflammatory diseases (e.g., diabetes, heart failure), affecting millions of people worldwide. The onset and progression of microvascular disease is driven by vascular inflammation and is characterized by vascular smooth muscle cell thickening and impaired vasomodulation, resulting in reduced baseline perfusion and potentially leading to tissue damage. The ability to noninvasively assess microvascular dilation and constriction is essential to assessing intact microvascular function and dysfunction. Yet, conventional measurements based on catheterization and MRI blood oxygenation are invasive and not specific to changes in blood volume, respectively. While cardiac and skeletal muscle mvD have been predicted to be the origin of diabetic heart failure and heart failure with a preserved ejection fraction (HFpEF), current diagnostics are onlyutilized when the patient suffers from cardiac symptoms, at which point the microvascular system is already compromised. This requires a paradigm shift in diagnostics, focusing on the early detection of mvD prior to the onset of heart failure signs and symptoms. In this body of work, we propose that MRI can serve as a non-invasive imaging modality to diagnose mvD within the heart and skeletal muscle, which can offer an early diagnostic platform for diabetics who are at risk of developing HFpEF and other diabetic cardiomyopathies. By combining the use of an MRI blood-pool contrast agent, T1 – weighted imaging, and mild carbon dioxide as a vasodilator, we showcase a technology that can assess microvascular reactivity in bothcardiac and skeletal muscle. Evaluation in a non-genetically modified rat model of type II diabetesiii revealed that cardiac and skeletal muscle reactivity are compromised prior to any classical signs and symptoms of heart failure (e.g., diastolic dysfunction, hypertension, fibrosis). These findings were shortly followed by exercise intolerance and a reduction in oxygen saturation in skeletal muscle. Additionally, we show that sex-dependent differences exist from a young age and must be considered when developing diagnostic criteria for HFpEF. The studies presented in this thesis will pave the way for translating this diagnostic platform into a clinical setting, where patients suffering from type II diabetes and are prone to developing HFpEF can be diagnosed early and begin treatment, prior to exhibiting heart failure symptoms.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".