Abstract 122: Untargeted Proteomics And Carotid Artery Atherosclerosis: The Cardiovascular Health Study
Bibliographic record
Abstract
Background: Increased carotid intima-media thickness (cIMT) and moderate-severe carotid artery atherosclerosis (CAA≥50%) are associated with increased risk of stroke. We evaluated proteomic determinants of increased cIMT and CAA≥50% in the population-based Cardiovascular Health Study (CHS). Methods: Eligible participants underwent carotid artery ultrasound and aptamer-based SOMAScan platform measurement of 1298 serum proteins from the concurrent study visit. cIMT was defined as the average of the standardized values of the maximal common carotid artery IMT and maximal internal carotid artery IMT. CAA≥50% was defined as carotid plaque resulting in ≥50% luminal stenosis. The relationship between each log-normalized protein concentration with cIMT and CAA≥50% was modeled separately using multivariable linear regression and logistic regression, respectively, adjusting for demographics, estimated glomerular filtration rate (eGFR), and CAA risk factors. Bonferroni correction, based on the number of principal components that explained 95% of the protein concentration variance in CHS participants, was employed to account for multiple hypothesis testing, yielding a p -value for significance of <7.6x10 -5 (0.05/661). Results: For eligible participants (n=2783), mean age was 74.4 ± 4.9 years, 39.5% were men, and 15.8% were Black. After adjustment for demographics, eGFR, and CAA risk factors, 13 proteins were independently associated with increased cIMT (Table). The magnitude and direction of the associations were similar in subgroups defined by sex and race. Three proteins were associated with CAA≥50%, after adjusting for eGFR and demographics (Table); however, associations were no longer significant after adjusting for CAA risk factors. Conclusion: Multiple novel serum proteins - implicated in extracellular matrix remodeling, systemic inflammation, and coagulation - were independently associated with increased cIMT in a cohort of older adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".