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Record W4324139839 · doi:10.1161/circ.147.suppl_1.p621

Abstract P621: Visceral Adipose Tissue Attenuation: A Marker of Liver Fat Content Beyond the Body Mass Index and Visceral Adipose Tissue Area

2023· article· en· W4324139839 on OpenAlexaff
Dominic Chartrand, Natalie Alméras, Isabelle Lemieux, Jean‐Pierre Després

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentres Intégré Universitaires de Santé et de Services SociauxInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsAdipose tissueMedicineBody mass indexFatty liverAttenuationInternal medicineAdipocyteEndocrinologyIntra-Abdominal FatWhite adipose tissueObesityPathologyInsulin resistanceVisceral fat

Abstract

fetched live from OpenAlex

Introduction: Body mass index (BMI) and visceral adiposity cannot fully discriminate individuals at risk of presenting non-alcoholic fatty liver. Discordant visceral adiposity/liver fat phenotypes highlight the need for a better understanding of mechanisms involved in liver fat (LF) accumulation independent of BMI and visceral adipose tissue (VAT) quantity. Hypothesis: As VAT attenuation is a marker of VAT quality, we hypothesized that this marker would be an important determinant of LF accumulation and that a lower VAT attenuation would be associated with an increased LF accumulation irrespective of BMI and VAT area. Methods: Analyses included 3002 participants (51% men) aged 40 to 70 years with BMI values between 18.5 and 40.0 kg/m 2 of INSPIRE ME-IAA, a prospective observational study conducted in 29 countries in America, Asia, and Europe. Computed tomography was used to measure subcutaneous adipose tissue (SAT) and VAT area and attenuation (a marker of adipocyte size) and liver attenuation (a marker of LF content). Participants’ cardiometabolic health profile was assessed in the fasting state. Partial Pearson correlation coefficients were computed to document the associations between VAT area and attenuation and liver attenuation. Multivariable regression analyses were performed to quantify the contribution of SAT and VAT area and attenuation to LF attenuation. A mixed-model ANOVA was used to compare liver attenuation according to VAT area and attenuation, and to compare VAT attenuation according to LF level and VAT area. A generalized linear mixed model was used to compare the prevalence of type 2 diabetes according to LF content and VAT area. Results: VAT attenuation was associated with liver attenuation in women (r=0.34, p<0.0001; r=0.38, p<0.0001; r=0.36, p<0.0001) and men (r=0.42, p<0.0001; r=0.30, p<0.0001; r=0.24, p<0.0001) within each BMI category (normal weight, overweight, obesity), respectively. VAT attenuation better explained LF attenuation than SAT and VAT area. A low VAT attenuation was associated with a lower liver attenuation within each VAT area tertile in all BMI categories (p<0.05). Furthermore, an increased LF content was associated with a lower VAT attenuation (p<0.05) independent of SAT and VAT area in both sexes in all BMI categories. An increased LF content was also associated with a higher prevalence of type 2 diabetes (p<0.05) in individuals with normal weight and overweight beyond VAT area. Conclusion: A low VAT attenuation reflecting larger adipocytes is associated with an increased LF content and risk of type 2 diabetes independent of BMI and VAT area. These results suggest that VAT attenuation as a marker of adipose tissue quality might be involved in the development of non-alcoholic fatty liver disease.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.034
GPT teacher head0.277
Teacher spread0.243 · 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
Published2023
Admission routes1
Has abstractyes

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