CIRCULATING LIPID LEVELS AND WHOLE HEART ATHEROSCLEROTIC PLAQUE VOLUME ON CORONARY COMPUTED TOMOGRAPHY ANGIOGRAPHY
Bibliographic record
Abstract
ASCVD/CVD Risk Factors Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, with coronary artery disease (CAD) representing a major contributor. Despite circulating lipid biomarkers being widely utilized to prognosticate on the presence and severity of underlying CAD, the extent to which traditional lipid metrics correlate with coronary plaque volume remains unclear. Herein, we sought to assess the relationship between circulating lipid levels and whole heart coronary atherosclerotic plaque volume by leveraging artificial intelligence-enabled quantitative coronary computed tomography angiography (AIQCT) in statin-naïve general cardiology clinic patients referred for coronary computed tomography angiography (CCTA) due to suspected CAD. We conducted a cross-sectional study of 271 statin-naïve patients recruited from a single-center, general cardiology clinic undergoing AI-QCT for suspected CAD. Circulating lipid levels (total cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], lipoprotein(a) [Lp(a)], and apolipoprotein B [apoB]) were measured within one month of CCTA. AI-QCT was utilized to quantify total, calcified, and non-calcified plaque volumes (TPV, CPV, NCPV), as well as high-risk plaque features (remodeling index and low-attenuation plaque percent). The number of participants in each TPV category (<250, 250-750, >750 mm3) across lipid level tertiles was calculated, and the significance of between-tertile differences was assessed with Fisher’s exact test. Associations between continuous lipid levels and continuous AI-QCT features were evaluated using Spearman correlation. No significant difference was observed in clinical coronary TPV categories across TC (P=0.31), LDL-C (P=0.21), Lp(a) (P=0.57), or apoB (P=0.26) level tertiles. A significant difference in the distribution of coronary TPV categories was observed across HDL-C tertiles (P=0.034). No significant correlations were observed between continuous TC, LDL-C, or Lp(a) levels and continuous measures of coronary plaque volume or high-risk plaque features. ApoB levels were significantly, albeit weakly, positively correlated with NCPV (ρ=0.15, P=0.032), and HDL-C levels were weakly negatively correlated with TPV (ρ=-0.12, P=0.042) and NCPV (ρ=-0.16, P=0.008). Traditional lipid biomarkers may not reliably reflect coronary atherosclerotic burden in statin-naïve individuals. These findings highlight the potential value of integrating AI-QCT-based measures of coronary plaque volume to improve patient-specific diagnosis of CAD.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".