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Record W4205586196 · doi:10.1111/obr.13393

Sex differences in regional adipose tissue depots pose different threats for the development of Type 2 diabetes in males and females

2022· review· en· W4205586196 on OpenAlexafffund
Kerri Z. Delaney, Sylvia Santosa

Bibliographic record

VenueObesity Reviews · 2022
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsConcordia UniversityHôpital du Sacré-Cœur de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdipose tissueAdipokineAdiponectinObesityInternal medicineEndocrinologyDiabetes mellitusLeptinType 2 Diabetes MellitusType 2 diabetesSexual dimorphismHyperplasiaMedicineSex characteristicsBiologyInsulin resistance

Abstract

fetched live from OpenAlex

Type 2 diabetes mellitus (T2DM) affects males and females disproportionately. In midlife, more males have T2DM than females. The sex difference in T2DM prevalence is, in part, explained by differences in regional adipose tissue characteristics. With obesity, changes to regional adipokine and cytokine release increases the risk of T2DM in both males and females with males having greater levels of TNFα and females having greater levels of leptin, CRP, and adiponectin. Regional immune cell infiltration appears to be pathogenic in both sexes via different routes as males with obesity have greater VAT ATM and a decrease in the protective Treg cells, whereas females have greater SAT ATM and T cells. Lastly, the ability of female adipose tissue to expand all regions through hyperplasia, rather than hypertrophy, protects them against the development of large insulin-resistant adipocytes that dominate male adipose tissue. The objective of this review is to discuss how sex may affect regional differences in adipose tissue characteristics and how these differences may distinguish the development of T2DM in males and females. In doing so, we will show that the origins of T2DM development differ between males and females.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.142
GPT teacher head0.354
Teacher spread0.212 · 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 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

Citations45
Published2022
Admission routes2
Has abstractyes

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