Impact of nutrients on circadian rhythmicity
Bibliographic record
Abstract
The suprachiasmatic nucleus (SCN) in the mammalian hypothalamus functions as an endogenous pacemaker that generates and maintains circadian rhythms throughout the body. Next to this central clock, peripheral oscillators exist in almost all mammalian tissues. Whereas the SCN is mainly entrained to the environment by light, peripheral clocks are entrained by various factors, of which feeding/fasting is the most important. Desynchronization between the central and peripheral clocks by, for instance, altered timing of food intake can lead to uncoupling of peripheral clocks from the central pacemaker and is, in humans, related to the development of metabolic disorders, including obesity and Type 2 diabetes. Diets high in fat or sugar have been shown to alter circadian clock function. This review discusses the recent findings concerning the influence of nutrients, in particular fatty acids and glucose, on behavioral and molecular circadian rhythms and will summarize critical studies describing putative mechanisms by which these nutrients are able to alter normal circadian rhythmicity, in the SCN, in non-SCN brain areas, as well as in peripheral organs. As the effects of fat and sugar on the clock could be through alterations in energy status, the role of specific nutrient sensors will be outlined, as well as the molecular studies linking these components to metabolism. Understanding the impact of specific macronutrients on the circadian clock will allow for guidance toward the composition and timing of meals optimal for physiological health, as well as putative therapeutic targets to regulate the molecular clock.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".