Prevalence, Distribution, and Risk Factor Correlates of High Pericardial and Intrathoracic Fat Depots in the Framingham Heart Study
Bibliographic record
Abstract
BACKGROUND: Pericardial and intrathoracic fat depots may represent novel risk factors for obesity-related cardiovascular disease. We sought to determine the prevalence, distribution, and risk factor correlates of high pericardial and intrathoracic fat deposits. METHODS AND RESULTS: Participants from the Framingham Heart Study (n=3312; mean age, 52 years; 48% women) underwent multidetector CT imaging in 2002 to 2005; high pericardial and high intrathoracic fat were defined on the basis of the sex-specific 90th percentile for these fat depots in a healthy reference sample. For men and women, the prevalence of high pericardial fat was 29.3% and 26.3%, respectively, and high intrathoracic fat was 31.4% and 35.3%, respectively. Overall, 22.1% of the sample was discordant for pericardial and intrathoracic fat depots: 8.3% had high pericardial but normal intrathoracic fat and 13.8% had high intrathoracic but normal pericardial fat. Higher body mass index, higher waist circumference, and increased prevalence of metabolic syndrome were more prevalent in participants with high intrathoracic fat depots than with high pericardial fat (P<0.05 for all comparisons). High abdominal visceral adipose tissue was more frequent in participants with high intrathoracic adipose tissue compared with those with high pericardial fat (P<0.001). Intrathoracic fat but not waist circumference was more highly correlated with visceral adipose tissue (r=0.76 and 0.78 in men and women, respectively; P<0.0001) than with subcutaneous adipose tissue (SAT) (r=0.46 and 0.54 in men and women, respectively; P<0.0001). CONCLUSIONS: Although prevalence of pericardial fat and intrathoracic fat were comparable at 30%, intrathoracic fat correlated more closely with metabolic risk and visceral fat. Intrathoracic fat may be a potential marker of metabolic risk and visceral fat on thoracic imaging.
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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) | 0.000 | 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 | 0.001 | 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".