MétaCan
Menu
← Back to cohort

Adipose Tissue – Skeletal Muscle Crosstalk: Obese Human Subcutaneous and Visceral Adipose Tissues Both Supress Muscle Insulin Signalling in Men and Women

2017· article· en· W4389028665 on OpenAlexafffundabout
Ousseynou Sarr, Rachel J. Strohm, Tara MacDonald, John K. Reed, Jules Foute‐Nelong, David J. Dyck, David M. Mutch

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsGuelph General HospitalUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdipose tissueAdipokineInternal medicineEndocrinologyAdiponectinSkeletal muscleMyocyteSecretionWhite adipose tissueInsulinMedicineBiologyInsulin resistance

Abstract

fetched live from OpenAlex

Adipose tissue is an important endocrine organ that communicates with other peripheral tissues via the secretion of proteins, termed adipokines. In particular, altered secretion of specific adipokines (e.g., TNF‐α and adiponectin) has previously been shown to regulate skeletal muscle insulin sensitivity. For the current study, we established a human adipose tissue – muscle crosstalk model to investigate the impact of different adipose tissue depots on skeletal myocyte insulin signaling, and if this differs between men and women. Visceral (VAT) and subcutaneous (SAT) adipose tissue samples were collected from obese men and women during laparoscopic bariatric surgery. Adipose tissue samples were subsequently cultured for 48h and secretion media was collected. Secretion media was then transferred on to human skeletal myocytes derived from healthy, non‐diabetic male and female donors for 24h. Content of insulin signaling proteins (phosphorylated and total AKT) in mature myocytes incubated with either control M199 media or secretion media from VAT or SAT was assessed using Western Blotting in basal and insulin‐stimulated conditions (100uU/well). Adipokines in secretion media were measured with a multiplex immunoassay. In men, VAT had higher interleukin‐6 gene expression compared to SAT (p=0.004), while no differences were seen in women (p=0.271). Adiponectin and TNF‐α gene expression were not different between VAT or SAT for men or women. Secretion media collected from male SAT and VAT reduced p‐AKT Thr308 activation in insulin‐stimulated myocytes compared to controls (p <0.01). Interestingly, myocytes incubated with VAT secretion media, compared to SAT secretion media, showed a significant reduction in p‐AKT Ser473 in men after insulin stimulation (p<0.01). Secretion media collected from female VAT and SAT caused significant reductions in p‐AKT Thr308 and p‐AKT Ser473 activation following insulin stimulation, compared to controls (p<0.01). Secretion media from VAT or SAT for men or women did not affect total‐AKT levels. Independent of sex, 14 out of 18 detected adipokines (i.e., a panel consisting of cytokines, chemokines and growth factors) were more abundant in VAT secretion media compared to SAT secretion media (p<0.01). Only interleukin‐21 levels showed a sex difference, with higher levels detected in secretion media from female adipose tissue (p=0.003). In conclusion, SAT and VAT secretion media from obese men and women suppress insulin signaling in myocytes to a similar extent. These results reveal the power of this human adipose tissue – muscle model to investigate crosstalk between these two tissues. Support or Funding Information This work was supported by the Natural Sciences and Engineering Research Council of 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 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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

Citations0
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicAdipokines, Inflammation, and Metabolic Diseases→French-language works237,207→