Abstract P227: Cardiovascular Disease (CVD) Risk Factors Have Opposite Associations with Abdominal Aortic Calcium (AAC) Density and Volume: The Multi-Ethnic Study of Atherosclerosis (MESA)
Bibliographic record
Abstract
Background: AAC predicts future CVD events independently of CVD risk factors. The standard AAC score, the Agatston method, up-weights for greater calcium density, thus models higher calcium density as associated with increased CVD risk. We evaluated this model by investigating associations of CVD risk factors with AAC density and volume separately. Methods: Multivariable linear regression was used to investigate the independent cross-sectional associations of CVD risk factors, markers of inflammation, and potential promoters of calcification with AAC density and ln (AAC volume). AAC density was calculated as: Density = Agatston / (volume, mm 3 / CT scan slice thickness, mm). Agatston = area, mm 2 X density. Results: In 1413 MESA participants with prevalent AAC, mean age was 65 ± 9 years, mean AAC density was 3.0 ± 0.6, 52% were men, 44% were European-, 24% were Hispanic-, 18% were African-, and 14% were Chinese Americans (EA, HA, AA, and CA respectively). In fully adjusted models, older age was non-significantly associated with lower AAC density, but significantly associated with higher ln (AAC volume) (Figure). Compared to EA, we observed AAC density was significantly higher in CA, non-significantly higher HA and AA, but ln (AAC volume) was significantly lower in CA, HA, and AA. Also, smoking, alcohol use, family history of myocardial infarction, higher systolic blood pressure, elevated total cholesterol, and interluken-6 were significantly associated with higher ln (AAC volume), but not AAC density. Lastly, lower body mass index, higher serum HDL and calcium were significantly associated with higher AAC density, but associations with ln (AAC volume) were in the opposite direction. Conclusion: A greater burden of CVD risk factors was associated with higher AAC volume, but not AAC density. Thus, the Agatston method of up-weighting AAC scores for greater density in CVD risk prediction may be inappropriate. Investigators should evaluate separately associations of AAC density and volume with CVD events.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".