Abdominal adiposity and insulin resistance in obese men
Bibliographic record
Abstract
We examined the independent relationships among various visceral and abdominal subcutaneous adipose tissue (AT) depots, glucose tolerance, and insulin sensitivity in 89 obese men. Measurements included an oral glucose tolerance test (OGTT), glucose disposal by euglycemic clamp, and abdominal and nonabdominal (e.g., peripheral) AT by magnetic resonance imaging (MRI). OGTT glucose and glucose disposal rates were related (P < 0.05) to visceral AT (r = 0.50 and -0.41, respectively). These observations remained significant (P < 0.05) after control for nonabdominal and abdominal subcutaneous AT, and maximal O(2) consumption (VO(2 max)). Abdominal subcutaneous AT was not a significant correlate (P > 0.05) of any metabolic variable after control for nonabdominal and visceral AT and VO(2 max). Division of abdominal subcutaneous AT into deep and superficial depots and visceral AT into intra- and extraperitoneal AT depots did not alter the observed relationships. Further analysis matched two groups of men for abdominal subcutaneous AT but also for low and high visceral AT. Men with high visceral AT had higher OGTT glucose values and lower glucose disposal rates compared with those with low visceral AT values (P < 0.05). A similar analysis performed on two groups of men matched for visceral AT but also for high and low abdominal subcutaneous AT revealed no statistically different values for any metabolic variable (P > 0.10). In conclusion, visceral AT alone is a strong correlate of insulin resistance independent of nonabdominal and abdominal subcutaneous AT and cardiovascular fitness. Subdivision of visceral and abdominal subcutaneous AT by MRI did not provide additional insight into the relationship between abdominal obesity and metabolic risk in obese men.
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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 | 0.001 |
| 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".