Carotid plaque score is associated with 10-year major adverse cardiovascular events in low-intermediate risk patients referred to a general cardiology community clinic

Lipoprotein subfractions and carotid plaque: NMR analysis of triglyceride-rich vs LDL particle size concentrations (ELSA-Brasil study)

BACKGROUND

This study analyzed the cross-sectional association of elevated concentrations of low-density lipoprotein particles (LDLp) and triglyceride-rich lipoprotein particles (TRLp) with carotid artery plaque (CAP) in the ELSA-Brasil cohort.

METHODS

Data from 3801 participants (median age: 50.0 years [interquartile range 44.0-57.0], 54.3% women) with no prior history of cardiovascular disease nor use of lipid-lowering medications were analyzed. CAP was assessed by ultrasonography, while nuclear magnetic resonance (NMR) spectroscopy was used to measure LDLp and TRLp concentrations according to size. Poisson regression models characterized the association of elevated lipid concentrations (≥1 SD above the mean) with CAP, adjusted for sociodemographic variables, cardiovascular risk factors, and for the concentration of high-density lipoprotein particles (HDLp), LDLp, and TRLp.

RESULTS

The frequency of CAP was 33.9% (n = 1,287). Elevated concentrations of total TRLp (prevalence ratio [PR]:1.05 [95% CI:1.01-1.10]) and small-sized TRLp (PR:1.23 [95% CI:1.11-1.36]) were associated with CAP, but lost significance after adjustment for LDLp. Elevated LDLp concentration was associated with CAP in total (PR:1.10 [95% CI:1.05-1.15]) and in all the different sizes (large [PR:1.09], medium [PR:1.11] and small [PR:1.09]), regardless of TRLp. When both LDLp and TRLp were simultaneously included in a dedicated model, only LDLp remained associated with CAP (PR:1.11 [95% CI: 1.06-1.16]). By particle size, elevated small TRLp and elevated LDLp in all sizes were associated with CAP even when mutually adjusted.

CONCLUSION

The elevated concentration of small TRLp seems to portend an incremental residual likelihood of prevalent CAP beyond LDLp, whereas the association of LDLp with CAP remained consistent beyond classical risk factors and NMR-assessed HDLp and TRLp concentration.

Deep Learning-Based Detection of Carotid Plaques Informs Cardiovascular Risk Prediction and Reveals Genetic Drivers of Atherosclerosis

Atherosclerotic cardiovascular disease, the leading cause of global mortality, is driven by lipid accumulation and plaque formation within arterial walls. Carotid plaques, detectable via ultrasound, are a well-established marker of subclinical atherosclerosis. In this study, we trained a deep learning model to detect plaques in 177,757 carotid ultrasound images from 19,499 UK Biobank (UKB) participants (aged 47-83 years) to assess the prevalence, risk factors, prognostic significance, and genetic architecture of carotid atherosclerosis in a large population-based cohort. The model demonstrated high performance metrics with accuracy, sensitivity, specificity, and positive predictive value of 89.3%, 89.5%, 89.2%, and 82.9%, respectively, identifying carotid plaques in 45% of the population. Plaque presence and count were significantly associated with future cardiovascular events over a median follow-up period of up to 7 years, leading to improved risk reclassification beyond established clinical prediction models. A genome-wide association study (GWAS) meta-analysis of carotid plaques (29,790 cases, 36,847 controls) uncovered two novel genomic loci (p < 5×10-8) with downstream analyses implicating lipoprotein(a) and interleukin-6 signaling, both targets of investigational drugs in advanced clinical development. Observational and Mendelian randomization analyses showed associations between smoking, low-density-lipoprotein (LDL) cholesterol, and high blood pressure and the odds of carotid plaque presence. Our study underscores the potential of carotid plaque assessment for improving cardiovascular risk prediction, provides novel insights into the genetic basis of subclinical atherosclerosis, and offers a valuable resource for advancing atherosclerosis research at the population scale.