Abstract P135: Hyperpolarized Carbon-13 Magnetic Resonance Reveals Early- and Late-Onset Changes to Metabolism in the Failing Heart
Bibliographic record
Abstract
Disordered metabolic substrate utilisation has been implicated in the pathogenesis of heart failure (HF). Hyperpolarised (HYP) 13C magnetic resonance, a technique in which the fate of 13C-labelled metabolites can be followed using MR imaging or spectroscopy, has enabled non-invasive assessment of metabolism. The aim of this study was to monitor carbohydrate metabolism alongside cardiac structure, function, and energetics, throughout HF progression. HF was induced in pigs (n=5) by right ventricular pacing at 188 bpm for 5 weeks. Pigs were examined at weekly time points: cine MRI assessed cardiac structure and function, HYP 13C2-pyruvate was administered intravenously and 13C MRS was used to assess 13C-glutamate production via Krebs cycle, 31P MRS assessed myocardial energetics, and HYP 13C1-pyruvate was administered to enable MRI of H13CO3- production from pyruvate dehydrogenase (PDH). At baseline, pigs had a normal left ventricular (LV) cardiac index (CI) and end diastolic volume (EDVi). The PCr/ATP was 2.3 ± 0.2. The 13C-glutamate/13C2-pyruvate was 4.3 ± 0.9%, and the H13CO3-/13C1-pyruvate ratio was 1.6 ± 0.2%. After 1–2 weeks of pacing, CI decreased to 3.3 ± 0.5 l/min/m2, PCr/ATP decreased to 1.7 ± 0.1, and 13C-glutamate/13C2-pyruvate decreased to 2.1 ± 0.6%. With the onset of HF, EDVi increased to 140.3 ± 14.1 ml/m2 and H13CO3-/13C1-pyruvate decreased to 0.5 ± 0.2%. In conclusion, we observed an early defect in Krebs' cycle that occurred alongside impaired cardiac energetics and function. Carbohydrate oxidation via PDH was maintained until the onset of HF. These results encourage use of metabolic therapies to delay/prevent the onset of heart failure in patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".