Visceral fat and liver fat are independent predictors of metabolic risk factors in men
Bibliographic record
Abstract
We examined the independent associations among abdominal adipose tissue (AT), liver fat, cardiorespiratory fitness (CRF), and lipid variables in 161 Caucasian men who had a wide variation in adiposity. We measured AT and liver fat by computed tomography and CRF by a maximal exercise test on a treadmill. Visceral AT remained a significant ( P ≤ 0.05) predictor of plasma triglycerides (TG), high-density-lipoprotein cholesterol (HDL-C), and total cholesterol (TC)/HDL-C ratio (TC/HDL-C) after statistical control for abdominal subcutaneous AT, CRF, and alcohol consumption. Abdominal subcutaneous AT was not a significant ( P ≥ 0.05) correlate of any lipid variable after control for visceral AT and CRF. Furthermore, subdivision of subcutaneous AT into deep and superficial depots did not alter these observations. Visceral AT was the strongest correlate of liver fat and remained so after control for abdominal subcutaneous AT, CRF, and alcohol consumption ( r = −0.34, P < 0.01). In contrast, abdominal subcutaneous AT and CRF were not significant ( P > 0.10) correlates of liver fat after control for visceral AT. Visceral AT remained a significant ( P < 0.01) correlate of TG, HDL-C, and TC/HDL-C independent of liver fat. However, liver fat was also a significant correlate ( P ≤ 0.05) of fasting glucose and TG independent of visceral AT. These observations reinforce the importance of visceral obesity in the pathogenesis of dyslipidemia in men, and they suggest that visceral AT and liver fat carry independent health 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".