Periaortic Adipose Tissue and Aortic Dimensions in the Framingham Heart Study
Bibliographic record
Abstract
BACKGROUND: Periaortic fat, because of its contiguity with the aorta, may promote vascular remodeling and aortic dilatation. However, the relations between perioartic fat depots and aortic dimensions have not been previously described. METHODS AND RESULTS: A total of 3001 individuals (mean age 50 ± 10 years, 49% women) from the Framingham Offspring and Third Generation cohorts underwent computed tomography for quantification of periaortic fat and aortic dimensions. We estimated the association between quantitative periaortic and visceral adipose tissue volumes (per standard deviation [SD] increment of volume) with aortic dimensions in both the thorax and abdomen. Thoracic periaortic fat was associated with higher thoracic aortic dimensions (β coefficient per SD of fat volume 0.67 mm, 95% confidence interval 0.58 to 0.76 mm; P<0.001). The association persisted after adjustment for age, sex, and cardiovascular risk factors including body mass index and visceral adipose tissue volume. Results for the association of periaortic fat and abdominal aortic dimensions were similar. Further adjustment for adipokines (resistin and adiponectin) had no significant impact on these associations. CONCLUSIONS: Periaortic fat volume was associated with aortic dimensions in both the thorax and abdomen, supporting the notion that local fat depots may contribute to aortic remodeling. Further work to understand the mechanisms underlying this association is warranted.
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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 | 0.000 | 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".