Noninvasive In Vivo Assessment of Cardiac Metabolism in the Healthy and Diabetic Human Heart Using Hyperpolarized <sup>13</sup> C MRI
Bibliographic record
Abstract
Rationale: The recent development of hyperpolarized 13 C magnetic resonance spectroscopy has made it possible to measure cellular metabolism in vivo, in real time. Objective: By comparing participants with and without type 2 diabetes mellitus (T2DM), we report the first case-control study to use this technique to record changes in cardiac metabolism in the healthy and diseased human heart. Methods and Results: Thirteen people with T2DM (glycated hemoglobin, 6.9±1.0%) and 12 age-matched healthy controls underwent assessment of cardiac systolic and diastolic function, myocardial energetics ( 31 P-magnetic resonance spectroscopy), and lipid content ( 1 H-magnetic resonance spectroscopy) in the fasted state. In a subset (5 T2DM, 5 control), hyperpolarized [1- 13 C]pyruvate magnetic resonance spectra were also acquired and in 5 of these participants (3 T2DM, 2 controls), this was successfully repeated 45 minutes after a 75 g oral glucose challenge. Downstream metabolism of [1- 13 C]pyruvate via PDH (pyruvate dehydrogenase, [ 13 C]bicarbonate), lactate dehydrogenase ([1- 13 C]lactate), and alanine transaminase ([1- 13 C]alanine) was assessed. Metabolic flux through cardiac PDH was significantly reduced in the people with T2DM (Fasted: 0.0084±0.0067 [Control] versus 0.0016±0.0014 [T2DM], Fed: 0.0184±0.0109 versus 0.0053±0.0041; P =0.013). In addition, a significant increase in metabolic flux through PDH was observed after the oral glucose challenge ( P <0.001). As is characteristic of diabetes mellitus, impaired myocardial energetics, myocardial lipid content, and diastolic function were also demonstrated in the wider study cohort. Conclusions: This work represents the first demonstration of the ability of hyperpolarized 13 C magnetic resonance spectroscopy to noninvasively assess physiological and pathological changes in cardiac metabolism in the human heart. In doing so, we highlight the potential of the technique to detect and quantify metabolic alterations in the setting of cardiovascular disease.
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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 | 0.000 | 0.000 |
| 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".