Time-restricted Eating to Improve Metabolic Health: Investigating the Mechanisms Underlying Glycolytic and Lipolytic actions
Bibliographic record
Abstract
Time-restricted eating (TRE) is a behavioral intervention approach motivated by the emerging role of circadian rhythms in physiology and metabolism. In this approach, all caloric intake is restricted within a regular interval of less than 12 hours, with no overt attempt to reduce calories. We present a whole-body computational model of TRE that incorporates enzyme and substrate reactions as well as hormonal actions during fasting and postprandial states. The model is divided into seven compartments: brain, heart, skeletal muscle, gastrointestinal tract, liver, adipose tissue, and other tissues. We conducted simulations to test the hypothesis that TRE modulates whole-body glucose balance by improving postprandial hyperglycemia and hypertriglyceridemia, keeping these excursions to a minimum. We simulated different TRE schedules with meals varying in fat and carbohydrate content. Our results highlight the importance of chrono-pharmacological considerations in glycemic control: TRE modulates the interplay between glycogenolysis in the liver, lipolysis in adipose tissue and fatty acid oxidation in other organs to improve glucose control. The alternation of eating and fasting, in particular, could be manipulated to balance blood sugar levels. TRE simulations have also shown beneficial effects on the lipid front, such as lower triglyceride levels and improved lipolytic effciency, meaning the body is better able to use fat stores for fuel. This work was supported by the Canadian Institutes of Health Research (CIHR) and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery award. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".