Abstract P621: Visceral Adipose Tissue Attenuation: A Marker of Liver Fat Content Beyond the Body Mass Index and Visceral Adipose Tissue Area
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".