Measurement Site and the Association Between Visceral and Abdominal Subcutaneous Adipose Tissue With Metabolic Risk in Women
Bibliographic record
Abstract
The associations between visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) and metabolic risk may be influenced by measurement site. The aim of this study was to compare the strength of the associations between VAT and ASAT, as assessed by a cross-sectional image (area) or total volume, and prevalent metabolic syndrome (MetS). We also examined the association between changes in abdominal AT area and volume with concomitant changes in metabolic risk. Abdominal AT volume and areas were derived using ~35 continuous computed tomography (CT) images from T10-T11 to L5-S1 in overweight or obese postmenopausal women before (n = 67) and after (n = 39) a 6-month exercise intervention. At baseline, measurement site did not influence the inter-relationship between ASAT area and total volume, and between ASAT and MetS. Conversely, VAT areas at L1-L2 and L2-L3 were stronger correlates of VAT volume at baseline (L1-L2 (r = 0.94), L2-L3 (r = 0.95), L4-L5 (r = 0.89)) and changes therein (L1-L2 (r = 0.77), L2-L3 (r = 0.75), L4-L5 (r = 0.55)) as compared to L4-L5, but were not significantly better predictors of MetS as compared to L4-L5 or the total volume (L2-L3: odds ratio (OR) = 2.68 (1.6-4.4), L1-L2: OR = 1.88 (1.2-3.0), L4-L5: OR = 2.56 (1.6-4.1), volume: OR = 2.07 (1.1-3.8)). Changes in VAT and ASAT were not associated with changes in MetS (P > 0.10). Although measurement site has an impact on the prediction of VAT volume, this does not translate into an improved prediction for the MetS. Thus, there is not enough evidence to support changing the current research practice of assessing VAT volume or at L4-L5 for the prediction of metabolic risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".