Lale Tokgozoglu; David A. Morrow; Stephen J. Nicholls
Eur Heart J. 2023;44(25):2292-2304.
Advantages of tailoring lipid lowering therapy with biomarkers versus imaging.
Dyslipidemia is an important modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD) and lipid lowering an integral component of cardiovascular prevention. Apolipoprotein B containing lipoproteins—mainly low-density lipoprotein cholesterol (LDL-C)—have indubitably been shown to be causal for atherosclerosis, and interventions that lower LDL-C can change the trajectory of the disease to improve cardiovascular outcomes.[1] With the ever-expanding armamentarium of lipid lowering therapies, it becomes important to decide to whom, when, and how to administer these therapies optimally. In the current guidelines, the decision to initiate and intensify lipid lowering therapy is guided by the untreated LDL-C levels of the individual as well as the total cardiovascular risk level.[2] As the risk becomes higher, the actions are intensified in a graded manner. The risk of developing a cardiovascular (CV) event depends on the extent of atherosclerosis, which is the result of the complex interplay of genetics, lifestyle, and cumulative LDL-C levels over time. Current risk estimation systems consider major causal risk factors at a single time point to classify individuals into different risk categories. The most recent prevention guidelines in Europe use the contemporary and improved SCORE2 risk estimation system to determine the 10-year risk of CV events.[3] However, this approach where risk estimation is based on group averages and applied to individual patients may not reflect genetic vulnerability or resilience, the cumulative exposure of risk factors over time, and interaction with other risk factors. While lifestyle should be recommended for all, there is a large group of patients in the moderate- or low-risk category where treatment decision needs more accurate assessment. Identifying novel risk markers may improve the selection of individuals for preventive strategies. Current risk estimation systems are limited to predicting 10-year risk therefore may underestimate the risk especially in women and younger individuals and overestimate risk in the elderly.[4] A recent study applied the 2021 European guideline treatment criteria to a low-risk population and found that <1% of women met eligibility for class I recommendation to statins.[5]
In attempts to refine risk prediction further and tailor therapy in an optimal and cost-effective way, imaging and biomarkers have been utilized. The following debate will focus on whether biomarkers or imaging can help us answer the following questions better:
How can we better identify the seemingly low–moderate risk patient who will benefit from lipid lowering therapy and the high risk patient who needs treatment intensification?
Which tool will aid our decision to intensify or deescalate lipid lowering therapy?
Can imaging or a biomarker help us choose the ideal lipid lowering regimen in a given individual?
The 2021 European Prevention Guidelines base their treatment recommendations on plasma levels of LDL-C, apolipoprotein B, and non-HDL-C.[6] Will adding any other biomarker help tailor therapy? Although not recommended in these guidelines, several other biomarkers have been utilized in an attempt to further define risk and personalize therapy. Recent evidence has shown that increased lipoprotein(a) [Lp(a)] leads to an incremental and continuous increase in absolute CV disease (CVD) risk.[7] As Lp(a) levels increase, the LDL-C reduction needed to mitigate the increased risk of major CV events becomes higher, and elevated Lp(a) levels may justify more intensive LDL-C lowering therapy. Biomarkers of inflammation may also help guide therapy.[8] High-sensitivity C-reactive protein (hs-CRP) >2 mg/L is considered a risk enhancer in the US and Canadian guidelines especially for intermediate risk patients.[9] Newer lipid lowering therapies such as icosapent ethyl and bempedoic acid lower hs-CRP substantially, but whether this can be used to personalize therapy is not known. Other biomarkers, such as N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin I, have also been shown to predict increased hazard of incident CVD and modestly improve discrimination and reclassification.[10] Although biomarkers may help personalize therapy in selected patients, whether or when to use which biomarker in which patient is still debated.
On the other hand, imaging can give us the memory of lifetime exposure to risk factors. Non-invasive imaging can detect the presence, extent, and composition of the atherosclerotic plaque; all of which are determinants of CV events. Detection of coronary artery calcification with computed tomography (CT) improves both discrimination and reclassification for CV risk.[11] The US multisociety guidelines on the management of blood cholesterol recommend to use coronary artery calcium (CAC) for guiding treatment decisions for primary prevention of ASCVD in individuals at borderline or intermediate risk.[12] The European prevention guidelines consider CAC score as a risk modifier to reclassify CVD risk upwards or downwards in addition to conventional risk factors but exert caution about presence of noncalcified plaques that are not detected by CAC.[6] Assessment of carotid or femoral plaque burden with ultrasound can also predict CV events and may be considered as a risk modifier in patients at intermediate risk when a CAC score is not feasible.[13] Because of the cost, low-dose radiation, and need for specialized centers for some of these techniques, who will benefit most and at what stage of life from imaging need to be determined. The decision to utilize imaging or biomarkers should be personalized by carefully weighing the risk of testing—especially low dose radiation—against potential benefit of the intervention. Despite the supportive epidemiology, no randomized trial has yet directly tested the benefit of imaging-guided interventions on top of risk stratification using clinical characteristics and biomarkers. In the future, integrating a large number of patient-related variables over time with artificial intelligence including genetics, omics, biomarkers, imaging, and data from wearables can truly personalize lifetime risk prediction and management.[14] Till then, we should utilize the tools we have to identify those who will benefit from lipid lowering meanwhile avoiding unnecessary overtreatment. The following debate will focus on whether imaging or biomarkers can better guide therapy today.
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