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Record W1482515139 · doi:10.1113/jphysiol.2014.283598

Contributions of white and brown adipose tissues and skeletal muscles to acute cold‐induced metabolic responses in healthy men

2014· article· en· W1482515139 on OpenAlexafffund
Denis P. Blondin, Sébastien M. Labbé, Serge Phoenix, Brigitte Guérin, Éric Turcotte, Denis Richard, André C. Carpentier, François Haman

Bibliographic record

VenueThe Journal of Physiology · 2014
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of OttawaUniversité LavalInstitut universitaire de cardiologie et de pneumologie de QuébecCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanadian Diabetes Association
KeywordsBrown adipose tissueShiveringSkeletal muscleLipolysisWhite adipose tissueAdipose tissueInternal medicineEndocrinologyThermogenesisChemistryThermoregulationMetabolismBiologyMedicinePhysiology

Abstract

fetched live from OpenAlex

KEY POINTS: Both brown adipose tissue (BAT) and skeletal muscle activation contribute to the metabolic response of acute cold exposure in healthy men even under minimal shivering. Activation of adipose tissue intracellular lipolysis is associated with BAT metabolic response upon acute cold exposure in healthy men. Although BAT glucose uptake per volume of tissue is important, the bulk of glucose turnover during cold exposure is mediated by skeletal muscle metabolic activation even when shivering is minimized. ABSTRACT: Cold exposure stimulates the sympathetic nervous system (SNS), triggering the activation of cold-defence responses and mobilizing substrates to fuel the thermogenic processes. Although these processes have been investigated independently, the physiological interaction and coordinated contribution of the tissues involved in producing heat or mobilizing substrates has never been investigated in humans. Using [U-(13)C]-palmitate and [3-(3)H]-glucose tracer methodologies coupled with positron emission tomography using (11)C-acetate and (18)F-fluorodeoxyglucose, we examined the relationship between whole body sympathetically induced white adipose tissue (WAT) lipolysis and brown adipose tissue (BAT) metabolism and mapped the skeletal muscle shivering and metabolic activation pattern during a mild, acute cold exposure designed to minimize shivering response in 12 lean healthy men. Cold-induced increase in whole-body oxygen consumption was not independently associated with BAT volume of activity, BAT oxidative metabolism, or muscle metabolism or shivering intensity, but depended on the sum of responses of these two metabolic tissues. Cold-induced increase in non-esterified fatty acid (NEFA) appearance rate was strongly associated with the volume of metabolically active BAT (r = 0.80, P = 0.005), total BAT oxidative metabolism (r = 0.70, P = 0.004) and BAT glucose uptake (r = 0.80, P = 0.005), but not muscle glucose metabolism. The total glucose uptake was more than one order of magnitude greater in skeletal muscles compared to BAT during cold exposure (674 ± 124 vs. 12 ± 8 μmol min(-1), respectively, P < 0.001). Glucose uptake demonstrated that deeper, centrally located muscles of the neck, back and inner thigh were the greatest contributors of muscle glucose uptake during cold exposure due to their more important shivering response. In summary, these results demonstrate for the first time that the increase in plasma NEFA appearance from WAT lipolysis is closely associated with BAT metabolic activation upon acute cold exposure in healthy men. In humans, muscle glucose utilization during shivering contributes to a much greater extent than BAT to systemic glucose utilization during acute cold exposure.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.317
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations243
Published2014
Admission routes2
Has abstractyes

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