Photoperiod Alters Visceral Adiposity and Molecular Circadian Rhythms in White‐footed Mice at Thermoneutrality
Bibliographic record
Abstract
The length of daylight (photoperiod) and environmental temperature fluctuate seasonally and act as cues for physiology. How photoperiod per se impacts adipose physiology is essential to consider as misalignment of circadian rhythms is suggested to promote obesity. Photoperiod alters fat deposition when temperature simultaneously acts as a cue. However, whether photoperiod regulates adipose physiology independent of temperature is unknown, as ambient temperatures below thermoneutrality are typically used. We compared the effects of 4 weeks of long (LD ‐ 16hrs light:8hrs dark) and short (SD – 8hrs light:16hrs dark) photoperiod exposure at thermoneutrality (27°C) to isolate photoperiod from temperature cues in photosensitive F1 generation, male white‐footed mice ( Peromyscus leucopus ). Short photoperiod selectively lowered visceral white adipose tissue (WAT) (vWAT) mass (p<0.05 vs LD), with no effect on subcutaneous WAT or interscapular brown adipose tissue mass or bodyweight. Calorie consumption and 24‐hour feeding behaviour were not affected by photoperiod, although short photoperiod mice ate almost exclusively in the dark (SD ate 8.5% of daily intake in 8hr light phase vs. LD ate 26% of intake in 16hr light phase (p<0.0001)). In vWAT, short photoperiod increased Adrβ3, Lpl and Nr1d1 mRNA expression (p<0.05 vs LD), with no change in Ucp1, Pgc1a or Hsl . Circadian Per1 and Per2 gene expression aligned with the onset of dark and thus were misaligned between photoperiods (p<0.05), while Bmal1 and Clock were also misaligned. Thus, photoperiod alone is a strong enough cue to alter visceral fat accumulation and alter clock gene misalignment, without temperature‐induced thermogenesis. These findings have important implications for understanding obesity in humans in whom day light is artificially extended while environmental temperatures are kept consistent.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".