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Record W2416934161 · doi:10.1249/jes.0000000000000072

Adipose Tissue Remodeling During Endurance Training

2015· review· en· W2416934161 on OpenAlexaffabout
Emilio P. Mottillo

Bibliographic record

VenueExercise and Sport Sciences Reviews · 2015
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsMcMaster University
Fundersnot available
KeywordsThermogenesisBrown adipose tissueWhite adipose tissueBrowningThermogeninEndocrinologyInternal medicineAdipose tissueBiologyEndurance trainingObesityMedicineBiochemistry

Abstract

fetched live from OpenAlex

The role of brown adipose tissue (BAT) in defending body temperature was demonstrated initially by Foster and Frydman (1) who showed that BAT was the site of cold-induced thermogenesis. This was followed by the identification that heat production occurred via the uncoupling of BAT mitochondria through uncoupling protein 1 (UCP1) (5). Finally, work from Flier and colleagues demonstrated that BAT protected mice against the development of obesity and diabetes (2) and firmly established BAT as a thermoregulatory organ that could be targeted to reduce the risk of obesity and diabetes. Around the same time it was suggested that BAT could actively defend body weight through diet-induced thermogenesis (DIT). More recently, the discovery of BAT in adult humans has renewed interest in brown fat research and in understanding ways to remodel white adipose tissue (WAT) to a phenotype more similar to BAT. This process, termed “browning,” is marked by the appearance of “beige” adipocytes in WAT that are functionally similar to brown adipocytes. Exercise had been postulated for a while to reduce BAT thermogenesis, but, somewhat surprisingly, recent work has demonstrated that endurance training promotes the browning of WAT in rodents. At first glance, exercise-induced browning of WAT seems somewhat counterintuitive; however, in the current issue of the Journal, Sepa-Kishi and Ceddia (6) discuss novel findings that might explain the opposing effects of exercise on BAT and subcutaneous WAT (scWAT) thermogenesis. Sepa-Kishi and Ceddia (6) put forth the hypothesis that remodeling of adipose tissue during exercise training functions to shift thermogenesis away from classical BAT toward scWAT, where it would not necessarily affect core body temperature. In effect, this would allow the animal to deal with greater heat production during exercise. These ideas stem from their recent study in which endurance training reduced UCP1 content and lipid oxidation in classical BAT. In contrast, exercise promoted beige adipocytes, UCP1 content, and fatty acid oxidation in scWAT. An interesting observation was that energy expenditure after exercise remained elevated despite a reduction in BAT thermogenesis, leading the authors to suggest that exercise-induced scWAT browning can compensate partially for the reduction in BAT. Similarly, scWAT browning could limit adiposity during high-fat feeding by promoting DIT. The beneficial effects of exercise on whole-body energy homeostasis are undoubtable, but the exact contribution of scWAT browning and the reality of DIT are a matter of debate (3). Indeed, exercise does suppress BAT activity in humans; however, the evidence for browning is less apparent (7). Alternatively, the reduction in BAT activity could be a means of shifting energy away from uncoupling and toward working muscle, whereas the increase in WAT fatty acid oxidation might partly support adenosine triphosphate synthesis required for greater in situ de novo lipogenesis (4). Exercise training at thermoneutrality or in UCP1 knockout mice could be useful in teasing apart UCP1-dependent versus -independent effects on whole-body energy expenditure. In addition, as stated by the authors, understanding the cellular source of beige cells and the neural circuitry involved in the downregulation of BAT and upregulation of scWAT browning could prove useful in discovering novel therapies to combat obesity and diabetes. Emilio P. Mottillo Department of Medicine McMaster University Hamilton, Ontario, Canada

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.159
GPT teacher head0.398
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2015
Admission routes2
Has abstractyes

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