Thermogenic Capacity Is Antagonistically Regulated in Classical Brown and White Subcutaneous Fat Depots by High Fat Diet and Endurance Training in Rats
Bibliographic record
Abstract
This study investigated the regulation of thermogenic capacity in classical brown adipose tissue (BAT) and subcutaneous inguinal (SC Ing) white adipose tissue (WAT) and how it affects whole-body energy expenditure in sedentary and endurance-trained rats fed ad libitum either low fat or high fat (HF) diets. Analysis of tissue mass, PGC-1α and UCP-1 content, the presence of multilocular adipocytes, and palmitate oxidation revealed that a HF diet increased the thermogenic capacity of the interscapular and aortic brown adipose tissues, whereas exercise markedly suppressed it. Conversely, exercise induced browning of the SC Ing WAT. This effect was attenuated by a HF diet. Endurance training neither affected skeletal muscle FNDC5 content nor circulating irisin, but it increased FNDC5 content in SC Ing WAT. This suggests that locally produced FNDC5 rather than circulating irisin mediated the exercise-induced browning effect on this fat tissue. Importantly, despite reducing the thermogenic capacity of classical BAT, exercise increased whole-body energy expenditure during the dark cycle. Therefore, browning of subcutaneous WAT likely exerted a compensatory effect and raised whole-body energy expenditure in endurance-trained rats. Based on these novel findings, we propose that exercise-induced browning of the subcutaneous WAT provides an alternative mechanism that reduces thermogenic capacity in core areas and increases it in peripheral body regions. This could allow the organism to adjust its metabolic rate to accommodate diet-induced thermogenesis while simultaneously coping with the stress of chronically increased heat production through exercise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".