Biochemical adaptations in white adipose tissue following aerobic exercise: from mitochondrial biogenesis to browning
Bibliographic record
Abstract
Our understanding of white adipose tissue (WAT) biochemistry has evolved over the last few decades and it is now clear that WAT is not simply a site of energy storage, but rather a pliable endocrine organ demonstrating dynamic responsiveness to the effects of aerobic exercise. Similar to its established effects in skeletal muscle, aerobic exercise induces many biochemical adaptations in WAT including mitochondrial biogenesis and browning. While past research has focused on the regulation of these biochemical processes, there has been renewed interest as of late given the potential of harnessing WAT mitochondrial biogenesis and browning to treat obesity and type II diabetes. Unfortunately, despite increasing evidence that innumerable factors, both exercise induced and pharmacological, can elicit these biochemical adaptations in WAT, the underlying mechanisms remain poorly defined. Here, we begin with a historical account of our understanding of WAT exercise biochemistry before presenting detailed evidence in favour of an up-to-date model by which aerobic exercise induces mitochondrial biogenesis and browning in WAT. Specifically, we discuss how aerobic exercise induces increases in WAT lipolysis and re-esterification and how this could be a trigger that activates the cellular energy sensor 5' AMP-activated protein kinase to mediate the induction of mitochondrial biogenesis and browning via the transcriptional co-activator peroxisome proliferator-activated receptor gamma co-activator-1 alpha. While this review primarily focuses on mechanistic results from rodent studies special attention is given to the translation of these results, or lack thereof, to human physiology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".