Abdominal Obesity, Muscle Composition, and Insulin Resistance in Premenopausal Women
Bibliographic record
Abstract
The independent relationships between visceral and abdominal sc adipose tissue (AT) depots, muscle composition, and insulin sensitivity were examined in 40 abdominally obese, premenopausal women. Measurements included glucose disposal by euglycemic clamp, muscle composition by computed tomography, abdominal and nonabdominal (e.g. leg) AT by magnetic resonance imaging and cardiovascular fitness. Glucose disposal rates were negatively related to visceral AT mass (r = -0.42, P < 0.01). These observations remained significant (P < 0.01) after control for nonabdominal and abdominal sc AT, muscle attenuation, and peak oxygen uptake. Total, abdominal, or leg sc AT or muscle attenuation was not significantly (P > 0.10) related to glucose disposal. Subdivision of abdominal sc AT into anterior and posterior depots did not alter the observed relationships. Further analysis matched two groups of women for abdominal sc AT but with low and high visceral AT. Women with high visceral AT had lower glucose disposal rates compared with those with low visceral AT (P < 0.05). A similar analysis performed on two groups of women matched for visceral AT but high and low abdominal sc AT revealed no statistically different values for insulin sensitivity (P > 0.10). In conclusion, visceral AT alone is a strong correlate of insulin resistance independent of nonabdominal, abdominal sc AT, muscle composition, and cardiovascular fitness. Subdivision of abdominal sc AT did not provide additional insight into the relationship between abdominal obesity and metabolic risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".