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Brown Adipose Tissue Volume and Distribution in Premenopausal and Postmenopausal Women

2021· article· en· W3168790955 on OpenAlexaff
Sophie Hogan‐Lamarre, Tracy Swibas, Jolan Guertin, François Haman, Kerry L. Hildreth, Yubin Miao, Wendy M. Kohrt, André C. Carpentier, Denis P. Blondin, Edward L. Melanson

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of OttawaHealth Sciences CentreCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsBrown adipose tissueEndocrinologyThermogenesisInternal medicineMenopauseEstrogenOvariectomized ratMedicineAdipose tissuePostmenopausal womenVolume of distributionPhysiology

Abstract

fetched live from OpenAlex

Introduction The loss of ovarian function and the accompanying decrease in estradiol during menopause has an impact on the metabolic activity of many tissues and organs, resulting in a reduction in resting energy expenditure. In rodents, ovariectomy or the absence of the estrogen receptor α suppresses brown adipose tissue (BAT) thermogenesis. It is therefore hypothesized that a reduction in BAT volume and thermogenesis may contribute to the reduction in resting energy expenditure in postmenopausal women. Objective The primary aim was to investigate the impact of estrogen on BAT volume and its distribution across the various depots in pre‐ and postmenopausal women. Methods A cohort of 22 premenopausal (33 years, 95% CI: 29 to 37 years) and 22 postmenopausal women (63 years, 95% CI: 60 to 67 years) took part in a 3‐hr cold exposure using a liquid‐conditioned suit perfused with 18°C water. During cold exposure, an i.v. bolus of [ 11 C]‐acetate and [ 18 F]FDG were given sequentially, with each injection followed by a 30 min list‐mode dynamic PET acquisition to quantify BAT oxidative metabolism and glucose uptake, respectively. Finally, a whole‐body static PET acquisition was performed to quantify the whole‐body biodistribution of glucose and estimate BAT volume and the distribution of this BAT volume across the various depots. BAT volume was determined according to the following criteria: [ 18 F]FDG ≥ 2 SUV mean and a radiodensity between ‐10 and ‐250 HU. Results There was significant variability in BAT volume and its distribution in both pre‐ and postmenopausal women. Premenopausal women had greater total BAT volume (53 mL, 95% CI: 34 to 72 mL) compared to postmenopausal women (21 mL, 95% CI: 8 to 34 mL, P = 0.006), although both groups included individuals with undetectable BAT. In premenopausal women, 65% of BAT was localized in the supraclavicular depot (95% CI: 57 to 73%) and 9% in the paravertebral depot (95% CI: 5 to 13%), with the remaining depots ( e.g. cervical, axillary, mediastinal, and perirenal) accounting for 25% (95% CI: 16 to 34%). In postmenopausal women, 45% of BAT was localized in the supraclavicular depot (95% CI: 24 to 66%) while the paravertebral depot represented 32% (95% CI: 12 to 52%) of total BAT. The remaining 23% (95% CI: 5 to 41 %) was distributed among the other depots. Thus, in postmenopausal women, there was a 20% reduction in the relative contribution of supraclavicular depots to the total volume and a 23% increase in the relative contribution of paravertebral depots to the total BAT volume compared to premenopausal women. Conclusion Here we show that total BAT volume is lower in postmenopausal women and results in a redistribution of BAT. However, there was tremendous variability in both BAT volume and distribution in both premenopausal and postmenopausal women. Further, it is difficult to distinguish the effects of estrogen from the effect of age or adiposity in the observed differences between our two cohorts.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.255
Teacher spread0.244 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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