Bridging the gap for lipid lowering therapy: plaque regression, coronary computed tomographic angiography, and imaging-guided personalized medicine

ABSTRACT

Introduction: Lipid-lowering therapy effectively decreases cardiovascular risk on a population level, but it remains difficult to identify an individual patient’s personal risk reduction while following guideline directed medical therapy, leading to overtreatment in some patients and cardiovascular events in others. Recent improvements in cardiac CT technology provide the ability to directly assess an individual’s atherosclerotic disease burden, which has the potential to personalize risk assessment for lipid-lowering therapy.

Areas covered: We review the current unmet need in identifying patients at elevated residual risk despite guideline directed medical therapy, the evidence behind plaque regression as a potential marker of therapeutic response, and highlight state-of-the-art advances in coronary computed tomographic angiography (CCTA) for measurement of quantitative and qualitative changes in coronary atherosclerosis over time. Literature search was performed using PubMed and Google Scholar for literature relevant to statin therapy and residual risk, coronary plaque regression measurement, and CCTA assessment of quantitative and qualitative change in coronary atherosclerosis.

Expert commentary: We discuss the potential ability of CCTA to guide lipid-lowering therapy as a bridge between population and personalized medicine in the future, as well as the potential barriers to its use.

KEYWORDS:

• Cardiac computed tomography
• coronary atherosclerosis
• coronary computed tomographic angiography
• lipid-lowering therapy
• PCSK-9
• personalized medicine
• plaque regression
• statin

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

This paper was funded by the National Institutes of Health, grant ID: EB000072.