The Contribution of Visceral Adiposity and Mid‐Thigh Fat‐Rich Muscle to the Metabolic Profile in Postmenopausal Women
Bibliographic record
Abstract
This study explored the relationship between muscle fat infiltration derived from mid-thigh computed tomography (CT) scan, central fat distribution and insulin sensitivity in postmenopausal women. Mid-thigh CT scans were used to measure low attenuation muscle surface (LAMS) (0-34 Hounsfield units (HU)), which represented a specific component of fat-rich muscle. Whole-body insulin sensitivity (M/I) was evaluated by an euglycemic-hyperinsulinemic clamp. A group of 103 women aged 57.0 ± 4.4 years was studied. Women with higher levels of LAMS presented higher metabolic risk features, particularly elevated fasting, 2-h plasma glucose (2hPG) concentrations and diminished M/I (P < 0.05). To further study the contribution of muscle fat infiltration and central adiposity on metabolic parameters, we divided the whole group based on the median of LAMS and visceral adipose tissue (VAT). As expected, the best metabolic profile was found in the Low-LAMS/Low-VAT group and the worst in the High-LAMS/High-VAT group. Women with Low-LAMS/High-VAT presented similar metabolic risks to those with High-LAMS/High-VAT. There was no difference between High-LAMS/Low-VAT and Low-LAMS/Low-VAT, which presents the most healthy metabolic and glycemic profiles as reflected by the lowest levels of cardiovascular disease risk variables. This suggests that High-LAMS/Low-VAT is also at low risk of metabolic deteriorations and that High-LAMS, only in the presence of High-VAT seems associated with deteriorated risks. Although increased mid-thigh fat-rich muscle was related to a deteriorated metabolic profile, VAT appears as a more important contributor to alterations in the metabolic profile in postmenopausal women.
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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".