Subdivision of the Subcutaneous Adipose Tissue Compartment and Lipid‐Lipoprotein Levels in Women
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare the relative importance of computed tomography-measured abdominal fat compartment areas, including adipose tissue located posterior to the subcutaneous Fascia, in predicting plasma lipid-lipoprotein alterations. RESEARCH METHODS AND PROCEDURES: Areas of visceral as well as subcutaneous deep and superficial abdominal adipose tissue were measured by computed tomography in a sample of 66 healthy women, ages 37 to 60 years, for whom a detailed lipid-lipoprotein profile was available. RESULTS: Strong significant associations were observed between visceral adipose tissue area and most variables of the lipid-lipoprotein profile (r = -0.25, p < 0.05 to 0.62, p < 0.0001). Measures of hepatic lipoprotein synthesis such as very-low-density lipoprotein-triglyceride and cholesterol content as well as total and very-low-density lipoprotein-apolipoprotein B levels were also strongly associated with visceral adipose tissue area (r = 0.57, 0.57, 0.61, and 0.62, respectively, p < 0.0001). Significant associations were found between these variables and the deep subcutaneous adipose tissue area or DXA-measured total body fat mass. However, the correlation coefficients were of lower magnitude compared to those with visceral adipose tissue area. Multivariate regression analyses demonstrated that visceral adipose tissue area was the strongest predictor of lipid-lipoprotein profile variables (7% to 48% explained variance, 0.02 > or = p < or = 0.0001). DISCUSSION: Although previous studies have generated controversial data as to which abdominal adipose tissue compartment was more closely associated with insulin resistance, our results suggest that visceral adipose tissue area is a stronger correlate of other obesity-related outcomes such as lipid-lipoprotein alterations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".