A brief history of circadian time in the heart
Bibliographic record
Abstract
This review tracks the discovery of circadian biology in cardiovascular science, starting with early clinical observations of daily changes in heart rate, blood pressure, and cardiovascular events. These patterns suggested that time of day matters, but it was not until the past two decades that the mechanisms and knowledge translation of these rhythms were uncovered. We describe the heart's intrinsic circadian properties and importantly how this leads to regulation of cardiac gene and protein expression, neuroendocrine and vascular rhythms, metabolism, cellular electrophysiology, and cell signaling pathways. Next, we explore emerging themes, including the impact of circadian timing on ischemic injury, cardiac aging, and trends in circadian desynchrony, sex, and interorgan crosstalk. Building on these discoveries, circadian medicine is beginning to reshape clinical care including timing of surgery, chronotherapies, biomarkers, ICU design, novel molecular drugs targeting the circadian clock, the role of the microbiome and time restricted eating, the new field of rest, and the concept of One Health and applications to veterinary medicine. Looking ahead we address new frontiers such as epigenetics, gene editing, and spaceflight. Together, these advances offer a roadmap for how circadian rhythms can be harnessed to improve cardiovascular health and disease outcomes, supporting longer and healthier lives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".