Intracoronary Imaging, Reverse Cholesterol Transport, and Transcriptomics: Precision Medicine in CAD?

HDL and Reverse Cholesterol Transport

Cardiovascular disease, with atherosclerosis as the major underlying factor, remains the leading cause of death worldwide. It is well established that cholesterol ester-enriched foam cells are the hallmark of atherosclerotic plaques. Multiple lines of evidence support that enhancing foam cell cholesterol efflux by HDL (high-density lipoprotein) particles, the first step of reverse cholesterol transport (RCT), is a promising antiatherogenic strategy. Yet, excitement towards the therapeutic potential of manipulating RCT for the treatment of cardiovascular disease has faded because of the lack of the association between cardiovascular disease risk and what was typically measured in intervention trials, namely HDL cholesterol, which has an inconsistent relationship to HDL function and RCT. In this review, we will summarize some of the potential reasons for this inconsistency, update the mechanisms of RCT, and highlight conditions in which impaired HDL function or RCT contributes to vascular disease. On balance, the evidence still argues for further research to better understand how HDL functionality contributes to RCT to develop prevention and treatment strategies to reduce the risk of cardiovascular disease.

A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging

Background

Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy.

Methods

FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40  mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers.

Findings

Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism.

Interpretation

In this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.