The Crosstalk Between Immune Activation and Metabolism in Heart Failure. A Scientific Statement of the Heart Failure Association of the ESC
Bibliographic record
Abstract
A better understanding of additional mechanisms of heart failure (HF) progression may allow a different and more complete phenotyping of the disease and identification of novel therapeutic targets. Persistent latent myocardial inflammation/immune activation in HF may represent an attempt to restore tissue homeostasis in the failing heart, where cardiomyocytes and immune cells undergo metabolic reprogramming, which allows them to deal with decreased availability of nutrients and oxygen. This status can trigger a metabolic crosstalk between immune cells and cardiomyocytes which, depending on the outcome, can either perpetuate the maladaptive remodelling of the heart, or determine an adaptive response. Therefore, the interplay between immune activation and metabolism is gaining recognition as a potential therapeutic framework. On these premises, future studies addressing novel HF treatments should attempt to evaluate the potential therapeutic role of direct metabolic and immunological crosstalk modulation. The aim of the present scientific statement from the Heart Failure Association of the ESC is to summarize the current evidence for the connection between inflammatory and immune activation and metabolic adaptation in the onset and progression of HF, in order to promote future strategies for the development of targeted-disease preventive and therapeutic measures.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".