Advancing Chrononutrition for Cardiometabolic Health: A 2023 National Heart, Lung, and Blood Institute Workshop Report
Bibliographic record
Abstract
The circadian system maintains optimal biological functions at the appropriate time of day, and the disruption of this organization can contribute to the pathogenesis of cardiometabolic disorders. The timing of eating is a prominent external time cue that influences the circadian system. "Chrononutrition" is an emerging dimension of nutrition and active area of research that examines how timing-related aspects of eating and nutrition impact circadian rhythms, biological processes, and disease pathogenesis. There is evidence to support chrononutrition as a form of chronotherapy, such that optimizing the timing of eating may serve as an actionable strategy to improve cardiometabolic health. This report summarizes key information from the National Heart, Lung, and Blood Institute's virtual workshop entitled "Chrononutrition: Elucidating the Role of Circadian Biology and Meal Timing in Cardiometabolic Health," which convened on May 2 to 3, 2023, to review current literature and identify critical knowledge gaps and research opportunities. The speakers presented evidence highlighting the impact on cardiometabolic health of earlier and shorter eating windows and more consistent day-to-day eating patterns. The multidimensionality of chrononutrition was a common theme, as it encompasses multiple facets of eating along with the timing of other behaviors including sleep and physical activity. Advancing the emerging field of chrononutrition will require: (1) standardization of terminology and metrics; (2) scalable and precise tools for real-world settings; (3) consideration of individual differences that may act as effect modifiers; and (4) deeper understanding of social, behavioral, and cultural influences. Ultimately, there is great potential for circadian-based dietary interventions to improve cardiometabolic health.
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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 | 0.021 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".