CIRCULATING LIPID LEVELS AND WHOLE HEART ATHEROSCLEROTIC PLAQUE VOLUME ON CORONARY COMPUTED TOMOGRAPHY ANGIOGRAPHY

Abstract

Therapeutic Area

ASCVD/CVD Risk Factors

Background

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.

Methods

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.

Results

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).

Conclusions

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.