Personalized Therapy Using Negative Risk Markers for Subclinical Cardiovascular Disease

Current Practice
The pooled cohort equation (PCE) is an important instrument in estimating an adult's risk of cardiovascular disease. Age has the greatest impact on estimated cardiovascular risk by the PCE. As a result, essentially all adults over the age of 65 qualify for a clinician-patient risk discussion regarding initiation of a statin.1 As the general population ages, an even greater number of older adults will qualify for preventive therapy. Much of recent research has focused on risk enhancers to identify patients who have elevated risk not fully captured by the PCE. However, it is also important to identify who may be at lower risk to prevent overtreatment and to minimize medication burden.

Aspirin is among the most commonly prescribed medications.2 A majority of the population uses aspirin for primary prevention, despite prior evidence suggesting that risk may outweigh benefit in older populations.3 The year 2018 marked a critical shift in thinking about the use of aspirin in primary prevention. Three randomized trials studied the use of aspirin therapy, ASCEND (A Study of Cardiovascular Events in Diabetes) in adults with diabetes, ARRIVE (Aspirin to Reduce Risk of Initial Vascular Events) in individuals with moderate cardiovascular risk, and ASPREE (Aspirin in Reducing Events in the Elderly) in adults over age 70. ASCEND and ARRIVE showed no net benefit in using aspirin, while ASPREE demonstrated a small increase in risk of mortality.

Moving beyond Aspirin
Similar to use of aspirin in primary prevention, statin therapy also warrants further study aimed at more precisely understanding the patient population that will benefit most. It does not seem reasonable to assume that all older patients who qualify for statin therapy based on the PCE will derive benefit.

In a recent study, Mortensen and colleagues identified negative risk markers (NRM) to downgrade cardiovascular risk in older adults. In their cohort of 5,805 participants of older age, 86% qualified for consideration of a statin based on the PCE risk estimation.4 The highly sensitive PCE successfully identified most older adults who would benefit from statin therapy. However, the PCE incorrectly places considerable numbers of very low risk older individuals in the same risk category as high risk older individual due to their age.

Negative Risk Markers
The 2019 ACC AHA Primary Prevention Guideline identify risk-enhancing factors that should be used to further guide treatment in borderline or intermediate risk individuals.5 In a similar manner, patient history, imaging, and biomarkers have potential to be used as NRMs to downgrade one's estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk.

Mortensen and colleagues studied 13 candidate NRMs in their recent study. The markers ranged from lack of family history, to tests used to identify subclinical atherosclerosis (coronary artery calcium score [CAC], carotid plaque, carotid intima media thickness, ankle brachial index), to circulating biomarkers (galectin-3, hsCRP, NT-proBNP, transferrin, ApoB, Lp(a), and ApoA1). They showed that a CAC score of 0 or ≤10 were the strongest NRMs, followed by galectin-3 below the 25th percentile and absence of carotid plaque.

In 2016, Dr. Blaha and colleagues studied the role of a CAC score of zero and other NRMs for cardiovascular disease (CVD).6 They showed that CAC score of zero resulted in the greatest downward shift in estimated ASCVD risk; generally the observed event rate was at least 60% lower than predicted in persons with no coronary artery calcium.

A Novel Statistical Approach
The statistical approach of calculating diagnostics likelihood ratios (DLR) utilized in both of these studies was described by Janssens et al. and Gu and Pepe in 2005 and 2009, respectively.7,8 DLRs determine the importance of performing a diagnostic test and can be used to upgrade or downgrade traditional predicted ASCVD risk. In this manner, use of DLRs is a Bayesian approach for risk prediction. The quantifiable difference in pretest (without factoring in NRM) and post-test risk (accounting for NRM) is the DLR.

In the context of cohort studies, DLRs <1 indicate that the test results in a lower post-test risk. A DLR of >1 indicates that post-test risk increases after the results of the test. For example, a NRM such as CAC score of 0 has a DLR <1 since data shows that in those individuals cardiovascular events are uncommon. Utilizing NRMs, the DLR can help estimate post-test risk in individuals who are less likely to have cardiovascular events, taking in account all of their other risk factors.

Evidence for use of Negative Risk Markers
Mortensen and colleagues showed that CAC=0, CAC ≤10, absence of carotid plaque, and elevated galectin-3 indicated a substantially downgraded predicted cardiovascular risk. A limitation of the study is the 2.7 year median follow-up whereas preventive treatment decisions are based on a longer time horizon. Nevertheless, the predictive power of CAC in detecting subclinical atherosclerosis has been well established in a variety of cohorts.9,10

The 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease note that the CAC score can be utilized to guide decisions in preventive interventions.5 The absence of carotid plaque has been well studied and noted to improve accuracy of coronary artery heart disease risk prediction.11 However, there have been no studies focusing on older adults, especially with the idea of downgrading estimated ASCVD risk.

Galectin-3 is a produced by activated macrophages and is a biomarker of tissue fibrosis and inflammation. Prior research has focused on its role in prediction of onset of heart failure as well as adverse outcomes in heart failure.12 Galectin-3 has also been studied in post-ACS patients where it is thought to be a marker of adverse remodeling due to ischemic heart disease.13 Low levels of galectin-3 in older adults may indicate a low likelihood of subclinical ASCVD and therefore less of a benefit from statin therapy. Further research is needed in this since data is otherwise sparse.

The recent CVD prevention guidelines highlight risk enhancing factors that are crucial to decision making. Given the cumulative evidence of CAC=0 and CAC≤10 in predicting low likelihood of ASCVD, these may be considered potent NRMs in older adults. The absence of carotid plaque and low levels of galectin-3 NRMs warrant further study at this time. When considering statin initiation in older adults or patients who are intolerant to a statin or reluctant to try one, a NRM such as the absence of coronary artery calcium can be used to guide decision making. Alternatively, an expanded risk score such as the MESA 10-Year CHD Risk which incorporates a coronary artery calcification14 can be utilized. Additive risk prediction information provided by other NRMs would also be supportive of the decision to reclassify a patient to a low risk category but confirmatory date are needed (Table 1). A MESA CHD 10-year risk estimate (which includes angina leading to revascularization) score of < 7.5% or an Astro-CHARM ASCVD risk estimate of < 7.5% would support not starting a statin and focusing simply on other risk factors and lifestyle improvements over the next 5 years.15

Table 1: Risk enhancers and Negative risk markers with prior evidence of upgrading or downgrading predicted ASCVD risk

ASCVD Risk Enhancers*

Negative Risk Markers**

Family history of premature ASCVD

CAC=0 or CAC ≤10

Primary hypercholesterolemia

Absence of carotid plaque

Metabolic syndrome

Low galectin-3

Chronic kidney disease

 

Chronic inflammatory conditions, such as psoriasis, RA, lupus, or HIV/AIDS

 

History of premature menopause and history of pregnancy-associated conditions, such as pre-eclampsia

 

High-risk race/ethnicity (e.g., South Asian ancestry)

 

Persistently elevated primary hypertriglyceridemia

 

Elevated high-sensitivity C-reactive protein

 

Elevated Lp(a)

 

Elevated apoB

 

ABI <0.9

 

*Adapted from 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease5
**Derived from Blaha et al.6 and Mortensen et al.4

Conclusion
Aside from the decision to initiate statins in older adults, de-escalation of therapies also becomes an important question. As the population ages, adults with optimal risk factors and NRMs suggesting low risk of disease, foregoing statin therapy to reduce medication burden will become an important conversation. Health care providers must consider, in select patients, at what point the benefit of statin therapy will diminish. Risk enhancers and negative risk markers will be very helpful in assisting with shared-decision making.

References

  1. Karmali KN, Goff DC, Ning H, Lloyd-Jones DM. A systematic examination of the 2013 ACC/AHA pooled cohort risk assessment tool for atherosclerotic cardiovascular disease. J Am Coll Cardiol 2014;64:959-68.
  2. Stuntz M, Bernstein B. Recent trends in the prevalence of low-dose aspirin use for primary and secondary prevention of cardiovascular disease in the United States, 2012–2015. Prev Med Rep 2017;5:183-86.
  3. Bibbins-Domingo K. Aspirin use for the primary prevention of cardiovascular disease and colorectal cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 2016;164:836-45.
  4. Mortensen MB, Fuster V, Muntendam P et al. Negative Risk Markers for Cardiovascular Events in the Elderly. J Am Coll Cardiol 2019;74:1-11.
  5. Arnett DK, Blumenthal RS, Albert MA et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease. J Am Coll Cardiol 2019:26029.
  6. Blaha MJ, Cainzos-Achirica M, Greenland P et al. Role of coronary artery calcium score of zero and other negative risk markers for cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2016;133:849-58.
  7. Janssens AC, Deng Y, Borsboom GJ, Eijkemans MJ, Habbema JDF, Steyerberg EW. A new logistic regression approach for the evaluation of diagnostic test results. Med Decis Making 2005;25:168-77.
  8. Gu W, Pepe MS. Estimating the capacity for improvement in risk prediction with a marker. Biostatistics 2009;10:172-86.
  9. Greenland P, Blaha MJ, Budoff MJ, Erbel R, Watson KE. Coronary calcium score and cardiovascular risk. J Am Coll Cardiol 2018;72:434-47.
  10. Hecht H, Blaha MJ, Berman DS et al. Clinical indications for coronary artery calcium scoring in asymptomatic patients: Expert consensus statement from the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr 2017;11:157-68.
  11. Nambi V, Chambless L, Folsom AR et al. Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol 2010;55:1600-7.
  12. Shah RV, Januzzi JL. Soluble ST2 and galectin-3 in heart failure. Clin Lab Med 2014;34:87-97.
  13. Grandin EW, Jarolim P, Murphy SA et al. Galectin-3 and the development of heart failure after acute coronary syndrome: pilot experience from PROVE IT-TIMI 22. Clin Chem 2012;58:267-73.
  14. McClelland RL, Jorgensen NW, Budoff M et al. 10-year coronary heart disease risk prediction using coronary artery calcium and traditional risk factors: Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) with validation in the HNR (Heinz Nixdorf Recall) study and the DHS (Dallas Heart Study). J Am Coll Cardiol 2015;66:1643-53.
  15. Khera A, Budoff MJ, O'Donnell CJ et al. Astronaut Cardiovascular Health and Risk Modification (Astro-CHARM) coronary calcium atherosclerotic cardiovascular disease risk calculator. Circulation 2018;138:1819-27.

Clinical Topics: Diabetes and Cardiometabolic Disease, Dyslipidemia, Heart Failure and Cardiomyopathies, Noninvasive Imaging, Prevention, Atherosclerotic Disease (CAD/PAD), Lipid Metabolism, Nonstatins, Novel Agents, Statins, Acute Heart Failure, Heart Failure and Cardiac Biomarkers, Echocardiography/Ultrasound

Keywords: Dyslipidemias, Aspirin, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Carotid Intima-Media Thickness, Apolipoproteins B, Risk Factors, Calcium, Galectin 3, Ankle Brachial Index, Cardiovascular Diseases, Bayes Theorem, Transferrin, Diagnostic Tests, Routine, Follow-Up Studies, Coronary Artery Disease, Apolipoprotein A-I, Angina Pectoris, Atherosclerosis, Primary Prevention, Heart Failure, Diabetes Mellitus, Decision Making, Inflammation, Life Style, Macrophages, Biological Markers


< Back to Listings