Abstract P1156: Associations Between Absolute Blood Eosinophil Count and Subclinical Atherosclerotic Plaque in the Multi-Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
Background: Prior studies have demonstrated associations between eosinophil activation products and incident stroke. We sought to investigate associations between blood eosinophil count and imaging markers of atherosclerosis (carotid artery plaque [CAP] and coronary artery calcium [CAC]) in the Multi-Ethnic Study of Atherosclerosis (MESA). Methods: The MESA enrolled adults aged 45-84 years, free of atherosclerotic cardiovascular disease (ASCVD) at baseline. ASCVD risk factors, blood eosinophils, CAP presence and score (0-12) and CAC presence and Agatston score were measured at exam 5. Logistic and linear regression models were employed to test the association of blood eosinophils, CAP and CAC presence and score (log[score+1]) adjusted for biologic confounders. Results: The 2,166 participants were a mean (standard deviation [SD]) 69.6 (9.3) years old, 53% female, 29% Hispanic, 28% Black, 1% Chinese. The median (interquartile range) eosinophil count was 0.1 (0.1, 0.2)x10E3/uL, CAP score= 2 (0,4) and CAC score= 45 (0, 292) Agatston units. In risk-factor adjusted models (Table 1; model 5), increased blood eosinophil count (per 1 SD [0.15 x10E3/uL]) was associated with CAP (β = 0.05 [95% CI 0.02-0.08, p = 0.001) and CAC (β = 0.11, [95% CI: 0.01-0.21], p = 0.03) score. Similar associations were seen with eosinophils and CAP (Odds ratio [OR] = 1.12, [95% CI: 1.01-1.25], p = 0.03) and CAC (OR = 1.15, [95% CI: 1.02-1.30], p = 0.02) presence. Conclusions: In a large, contemporary, multiethnic, U.S. cohort, blood eosinophils were strongly associated with imaging measures of atherosclerosis even after adjustment for ASCVD risk factors. These data suggest potential roles of T2/eosinophilic inflammation in atherosclerosis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".