Unraveling the Brain-Heart Axis: More Than Inflammation
Bibliographic record
Abstract
Cardiovascular disease remains the leading cause of death worldwide, with a rising burden projected over the coming decades. Although traditional risk factors such as diabetes, hypertension, and dyslipidemia form the main targets of prevention strategies, many individuals carry a "residual" risk secondary to systemic inflammation which remains underrecognized. Landmark trials have established inflammation as a causal and modifiable driver of atherosclerotic cardiovascular disease. This review focuses on how inflammation can modulate interconnected diseases of the brain and heart. Growing evidence suggests that inflammation mediates a "brain-heart" axis, linking psychological stress, neuroinflammation, and cardiovascular pathology. Observational and mechanistic studies demonstrate that stress-induced neural activity, particularly within the amygdala, is associated with hematopoietic activation, arterial inflammation, and increased cardiovascular events. Models such as Takotsubo's cardiomyopathy and stroke-heart syndrome illustrate how acute brain injury can precipitate cardiovascular dysfunction via autonomic and inflammatory pathways. More recently, molecular imaging studies have provided direct evidence of stress-associated amygdalar activity being associated with both cardiovascular outcomes and cancer prognosis. This emerging framework reframes the brain and heart as interdependent organs connected through inflammatory and neurobiological processes, highlighting the potential for novel therapeutic targets, including modulation of stress-related neural pathways, alongside established anti-inflammatory strategies. Future directions include refinement of targeted therapies, use of advanced molecular imaging, and mechanistic studies to better delineate the pathways linking stress, inflammation, and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".