Visceral Fat Is an Independent Predictor of All‐cause Mortality in Men
Bibliographic record
Abstract
OBJECTIVE: To examine the independent associations of abdominal fat (visceral and subcutaneous) and liver fat with all-cause mortality. RESEARCH METHODS AND PROCEDURES: Participants included 291 men [97 decedents and 194 controls; mean age, 56.4 +/- 12.0 (SD) years] who received a computed tomography (CT) examination at the preventive medicine clinic in Dallas, TX, between 1995 and 1999, with a mean mortality follow-up of 2.2 +/- 1.3 years. Abdominal fat was determined using contiguous CT images from the L3-L4 to L4-L5 intervertebral space. Liver fat was assessed using the CT-determined liver attenuation value, which is inversely related to liver fat. Logistic regression was used to determine the independent association between the fat depots and all-cause mortality. RESULTS: During the study, there were 97 deaths. Visceral fat [odds ratio (OR) per SD: 1.83; 95% CI: 1.23 to 2.73], abdominal subcutaneous fat (1.44; 1.02 to 2.03), liver fat (0.64; 0.46 to 0.87), and waist circumference (1.41; 1.01 to 1.98) were significant individual predictors of mortality after controlling for age and length of follow-up. In a model including all three fat measures (subcutaneous, visceral, and liver fat), age, and length of follow-up, only visceral fat (1.93; 1.15 to 3.23) was a significant predictor of mortality. DISCUSSION: Visceral fat is a strong, independent predictor of all-cause mortality in 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 | 0.001 | 0.003 |
| 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".