Myocardial Iron Deficiency and Mitochondrial Dysfunction in Advanced Heart Failure in Humans
Bibliographic record
Abstract
Background Myocardial iron deficiency (MID) in heart failure (HF) remains largely unexplored. We aim to establish defining criterion for MID, evaluate its pathophysiological role, and evaluate the applicability of monitoring it non-invasively in human explanted hearts. Methods and Results Biventricular tissue iron levels were measured in both failing (n=138) and non-failing control (NFC, n=46) explanted human hearts. Clinical phenotyping was complemented with comprehensive assessment of myocardial remodeling and mitochondrial functional profiles, including metabolic and oxidative stress. Myocardial iron status was further investigated by cardiac magnetic resonance imaging. Myocardial iron content in the left ventricle was lower in HF versus NFC (121.4 [88.1-150.3] versus 137.4 [109.2-165.9] μg/g dry weight), which was absent in the right ventricle. With a priori cutoff of 86.1 μg/g d.w. in left ventricle, we identified 23% of HF patients with MID (HF-MID) associated with higher NYHA class and worsened left ventricle function. Respiratory chain and Krebs cycle enzymatic activities were suppressed and strongly correlated with depleted iron stores in HF-MID hearts. Defenses against oxidative stress were severely impaired in association with worsened adverse remodeling in iron-deficient hearts. Mechanistically, iron uptake pathways were impeded in HF-MID including decreased translocation to the sarcolemma, while transmembrane fraction of ferroportin positively correlated with MID. Cardiac magnetic resonance with T2* effectively captured myocardial iron levels in failing hearts. Conclusions MID is highly prevalent in advanced human HF and exacerbates pathological remodeling in HF driven primarily by dysfunctional mitochondria and increased oxidative stress in the left ventricle. Cardiac magnetic resonance demonstrates clinical potential to non-invasively monitor MID.
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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".