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Record W2136070507 · doi:10.2337/db14-0746

Brown Adipose Tissue Improves Whole-Body Glucose Homeostasis and Insulin Sensitivity in Humans

2014· article· en· W2136070507 on OpenAlexfundno aff
Maria Chondronikola, Elena Volpi, Elisabet Børsheim, Craig Porter, Palam Annamalai, Sven Enerbäck, Martin E. Lidell, Manish Kumar Saraf, Sébastien M. Labbé, Nicholas M. Hurren, Christina Yfanti, Tony Chao, Clark R. Andersen, FERNANDO CESANI, Hal K. Hawkins, Labros S. Sidossis

Bibliographic record

VenueDiabetes · 2014
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute on AgingCanadian Institutes of Health Research
KeywordsInsulin sensitivityAdipose tissueGlucose homeostasisHomeostasisEndocrinologyBrown adipose tissueInternal medicineInsulinInsulin resistanceMedicineBiology

Abstract

fetched live from OpenAlex

Brown adipose tissue (BAT) has attracted scientific interest as an antidiabetic tissue owing to its ability to dissipate energy as heat. Despite a plethora of data concerning the role of BAT in glucose metabolism in rodents, the role of BAT (if any) in glucose metabolism in humans remains unclear. To investigate whether BAT activation alters whole-body glucose homeostasis and insulin sensitivity in humans, we studied seven BAT-positive (BAT(+)) men and five BAT-negative (BAT(-)) men under thermoneutral conditions and after prolonged (5-8 h) cold exposure (CE). The two groups were similar in age, BMI, and adiposity. CE significantly increased resting energy expenditure, whole-body glucose disposal, plasma glucose oxidation, and insulin sensitivity in the BAT(+) group only. These results demonstrate a physiologically significant role of BAT in whole-body energy expenditure, glucose homeostasis, and insulin sensitivity in humans, and support the notion that BAT may function as an antidiabetic tissue in humans.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.010
GPT teacher head0.250
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations787
Published2014
Admission routes1
Has abstractyes

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