| Coverage Policy Manual | |
| Policy #: | 2012029 |
| Category: | Laboratory |
| Initiated: | July 2012 |
| Last Review: | April 2024 |
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| Biomarker Testing in Risk Assessment and Management of Cardiovascular Disease | |
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| Description: |
Numerous lipid and non-lipid biomarkers have been proposed as potential risk markers for cardiovascular disease (CVD). Biomarkers assessed herein include apolipoprotein B, apolipoprotein AI, apolipoprotein E, B-type natriuretic peptide, cystatin C, fibrinogen, high-density lipoprotein subclass, leptin, low-density lipoprotein subclass, lipoprotein(a), and lipoprotein-associated phospholipase A2 (Lp-PLA2). These biomarkers have been studied as alternatives or additions to standard lipid panels for risk stratification in CVD or as treatment targets for lipid-lowering therapy. Cardiovascular risk panels refer to different combinations of cardiac markers that are intended to evaluate the risk of CVD. There are numerous commercially available risk panels that include different combinations of lipids, noncardiac biomarkers, measures of inflammation, metabolic parameters, and/or genetic markers. Risk panels report the results of multiple individual tests, as distinguished from quantitative risk scores that combine the results of multiple markers into a single score.
Cardiovascular Disease
Cardiovascular disease (CVD) remains the single largest cause of morbidity and mortality in the developed world. Mortality from CVD has accounted for 1 in 4 deaths in the United States, and there are numerous socio-economic factors that affect CVD mortality rates (Niakouei, 2020). Lower-income, race, age, and behavioral factors all have a significant impact on health outcome disparities associated with CVD.
As a result, accurate prediction of CVD risk is a component of medical care that has the potential to focus on and direct preventive and diagnostic activities. Current methods of risk prediction in use in general clinical care are not highly accurate and, as a result, there is a potential unmet need for improved risk prediction instruments.
Risk Assessment
Although treatment for elevated coronary disease risk with statins targets cholesterol levels, selection for treatment involves estimation of future coronary artery disease (CAD) risk using well-validated prediction models that use additional variables.
Components of CVD risk include family history, cigarette smoking, hypertension, and lifestyle factors such as diet and exercise. Also, numerous laboratory tests have been associated with CVD risk, most prominently lipids such as low-density lipoprotein (LDL) and high-density lipoprotein (HDL). These clinical and lipid factors are often combined into simple risk prediction instruments, such as the Framingham Risk Score (D’Agostino, 2001). The Framingham Risk Score provides an estimate of the 10-year risk for developing cardiac disease and is currently used in clinical care to determine the aggressiveness of risk factor intervention, such as the decision to treat hyperlipidemia with statins.
Many additional biomarkers, genetic factors, and radiologic measures have been associated with an increased risk of CVD. Over 100 emerging risk factors have been proposed as useful for refining estimates of CVD risk (Helfand, 2009; Brotman, 2005; Greenland, 2010). Some general categories of these potential risk factors are as follows:
Risk Panel Testing
CVD risk panels may contain measures from 1 or all of the previous categories and may include other measures not previously listed such as radiologic markers (carotid medial thickness, coronary artery calcium score). Some CVD risk panels are relatively limited, including a few markers in addition to standard lipids. Others include a wide variety of potential risk factors from a number of different categories, often including both genetic and nongenetic risk factors. Other panels are composed entirely of genetic markers.
Some examples of commercially available CVD risk panels are as follows:
In addition to panels that are specifically focused on CVD risk, a number of commercially available panels include markers associated with cardiovascular health, along with a range of other markers that have been associated with inflammation, thyroid disorders and other hormonal deficiencies, and other disorders. An example of these panels is:
Low-density Lipoproteins and Cardiovascular Disease
Low-density lipoproteins (LDLs) have been identified as the major atherogenic lipoproteins and have long been identified by the National Cholesterol Education Project as the primary target of cholesterol-lowering therapy. An LDL particle consists of a surface coat composed of phospholipids, free cholesterol, and apolipoproteins surrounding an inner lipid core composed of cholesterol ester and triglycerides. Traditional lipid risk factors such as LDL cholesterol (LDL-C), while predictive on a population basis, are weaker markers of risk on an individual basis. Only a minority of subjects with elevated LDL and cholesterol levels will develop clinical disease, and up to 50% of cases of CAD occur in subjects with "normal” levels of total cholesterol and LDL-C. Thus, there is considerable potential to improve the accuracy of current cardiovascular risk prediction models.
Lipid Markers
Apolipoprotein B
Apolipoprotein (Apo) B is the major protein moiety of all lipoproteins, except for HDL. The most abundant form of apo B, large B or B100, constitutes the apo B found in LDL and very-low density LDL. Because LDL and very-low density LDL each contain 1 molecule of apo B, the measurement of apo B reflects the total number of these atherogenic particles, 90% of which are LDL. Because LDL particles can vary in size and in cholesterol content, for a given concentration of LDL-C, there can be a wide variety in size and numbers of LDL particles. Thus, it has been postulated that apo B is a better measure of the atherogenic potential of serum LDL than LDL concentration.
Apolipoprotein AI
HDL contains 2 associated apolipoproteins (i.e., apo AI, apo AII). HDL particles can also be classified by whether they contain apo AI only or they contain apo AI and apo AII. All lipoproteins contain apo AI, and some also contain apo AII. Because all HDL particles contain apo AI, this lipid marker can be used as an approximation for HDL number, similar to the way apo B has been proposed as an approximation of the LDL number.
Direct measurement of apo AI has been proposed as more accurate than the traditional use of HDL level in the evaluation of cardioprotective, or “good,” cholesterol. In addition, the ratio of apo B/apo AI has been proposed as a superior measure of the ratio of proatherogenic (i.e., “bad”) cholesterol to anti-atherogenic (i.e., “good”) cholesterol.
Apolipoprotein E
Apolipoprotein E is the primary apolipoprotein found in very-low density LDLs and chylomicrons. Apolipoprotein E is the primary binding protein for LDL receptors in the liver and is thought to play an important role in lipid metabolism. The apolipoprotein E (APOE) gene is polymorphic, consisting of 3 epsilon alleles (e2, e3, e4) that code for 3 protein isoforms, known as E2, E3, and E4, which differ from one another by one amino acid. These molecules mediate lipid metabolism through their different interactions with LDL receptors. The genotype of apo E alleles can be assessed by gene amplification techniques, or the APOE phenotype can be assessed by measuring plasma levels of apo E.
It has been proposed that various APOE genotypes are more atherogenic than others and that APOE measurement may provide information on the risk of CAD beyond traditional risk factor measurement. It has also been proposed that the APOE genotype may be useful in the selection of specific components of lipid-lowering therapy, such as drug selection. In the major lipid-lowering intervention trials, including trials of statin therapy, there is considerable variability in response to therapy that cannot be explained by factors such as compliance. The APOE genotype may be a factor that determines an individual’s degree of response to interventions such as statin therapy.
High-Density Lipoprotein Subclass
HDL particles exhibit considerable heterogeneity, and it has been proposed that various subclasses of HDL may have a greater role in protection from atherosclerosis. Particles of HDL can be characterized based on size or density and/or on apolipoprotein composition. Using size or density, HDL can be classified into HDL2, the larger, less dense particles that may have the greatest degree of cardioprotection, and HDL3, which are smaller, denser particles.
An alternative to measuring the concentration of subclasses of HDL (e.g., HDL2, HDL3) is a direct measurement of HDL particle size and/or number. Particle size can be measured by nuclear magnetic resonance (NMR) spectroscopy or by gradient-gel electrophoresis. HDL particle numbers can be measured by NMR spectroscopy. Several commercial labs offer these measurements of HDL particle size and number. Measurement of apo AI has used HDL particle number as a surrogate, based on the premise that each HDL particle contains a single apo AI molecule.
Low-Density Lipoprotein Subclass
Two main subclass patterns of LDL, called A and B, have been described. In subclass pattern A, particles have a diameter larger than 25 nm and are less dense, while in subclass pattern B, particles have a diameter less than 25 nm and a higher density. Subclass pattern B is a common inherited disorder associated with a more atherogenic lipoprotein profile, also termed “atherogenic dyslipidemia.” In addition to small, dense LDL, this pattern includes elevated levels of triglycerides, elevated levels of apo B, and low levels of HDL. This lipid profile is commonly seen in type 2 diabetes and is a component of the “metabolic syndrome,” defined by the Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) to also include high normal blood pressure, insulin resistance, increased levels of inflammatory markers such as C-reactive protein, and a prothrombotic state. The presence of the metabolic syndrome is considered by Adult Treatment Panel III to be a substantial risk-enhancing factor for CAD.
LDL size has also been proposed as a potentially useful measure of treatment response. Lipid-lowering treatment decreases total LDL and may also induce a shift in the type of LDL, from smaller, dense particles to larger particles. It has been proposed that this shift in lipid profile may be beneficial in reducing the risk for CAD independent of the total LDL level. Also, some drugs may cause a greater shift in lipid profiles than others. Niacin and/or fibrates may cause a greater shift from small to large LDL size than statins. Therefore, measurement of LDL size may potentially play a role in drug selection or may be useful in deciding whether to use a combination of drugs rather than a statin alone.
In addition to the size of LDL particles, interest has been shown in assessing the concentration of LDL particles as a distinct cardiac risk factor. For example, the commonly performed test for LDL-C is not a direct measure of LDL, but, chosen for its convenience, measures the amount of cholesterol incorporated into LDL particles. Because LDL particles carry much of the cholesterol in the bloodstream, the concentration of cholesterol in LDL correlates reasonably well with the number of LDL particles when examined in large populations. However, for an individual patient, the LDL level may not reflect the number of particles due to varying levels of cholesterol in different sized particles. It is proposed that the discrepancy between the number of LDL particles and the serum level of LDL represents a significant source of unrecognized atherogenic risk. The size and number of particles are interrelated. For example, all LDL particles can invade the arterial wall and initiate atherosclerosis. However, small, dense particles are thought to be more atherogenic than larger particles. Therefore, for patients with elevated numbers of LDL particles, the cardiac risk may be further enhanced when the particles are smaller versus larger.
Lipoprotein (a)
Lp (a) is a lipid-rich particle similar to LDL. The major apolipoprotein associated with LDL is Apo B; in Lp(a), however, there is an additional apo A covalently linked to apo B. The apo A molecule is structurally similar to plasminogen, suggesting that Lp(a) may contribute to the thrombotic and atherogenic basis of CVD. Levels of Lp(a) are relatively stable in individuals over time but vary up to 1000-fold between individuals, presumably on a genetic basis. The similarity between Lp(a) and fibrinogen has stimulated intense interest in Lp(a) as a link between atherosclerosis and thrombosis. In addition, approximately 20% of patients with CAD have elevated Lp(a) levels. Therefore, it has been proposed that levels of Lp(a) may be an independent risk factor for CAD.
Lipoprotein-associated Phospholipase A2
Lipoprotein-associated phospholipase A2 (Lp-PLA2), also known as platelet-activating factor acetylhydrolase, is an enzyme that hydrolyzes phospholipids and is primarily associated with LDLs. Accumulating evidence has suggested that Lp-PLA2 is a biomarker of CAD and may have a proinflammatory role in the progression of atherosclerosis. Recognition that atherosclerosis represents, in part, an inflammatory process has created considerable interest in the measurement of pro-inflammatory factors as part of cardiovascular disease risk assessment.
Interest in Lp-PLA2 as a possible causal risk factor for CAD has generated the development and testing of Lp-PLA2 inhibitors as a new class of drugs to reduce the risk of CAD. However, clinical trials of Lp-PLA2 inhibitors have not shown significant reductions in CAD endpoints (White, 2014; O’Donoghue, 2014; Nicholls, 2014). Furthermore, assessment of Lp-PLA2 levels has not been used in the selection or management of subjects in the clinical trials.
Regulatory Status
Multiple assay methods for cardiac risk marker components, such as lipid panels and other biochemical assays, have been cleared for marketing by the U.S. Food and Drug Administration (FDA) through the 510(k) process.
In December 2014, the PLAC® Test (diaDexus), a quantitative enzyme assay, was cleared for marketing by the U.S. Food and Drug Administration (FDA) through the 510(k) process for Lp-PLA2 activity. It was considered substantially equivalent to a previous version of the PLAC® Test (diaDexus), which was cleared for marketing by the FDA in July 2003. FDA product code: NOE.
Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests must meet the general regulatory standards of the Clinical Laboratory Improvement Amendments (CLIA). Components of testing panels, lipid, and non-lipid biomarker tests are available under the auspices of the CLIA. Laboratories that offer laboratory-developed tests must be licensed by the CLIA for high-complexity testing. To date, the FDA has chosen not to require any regulatory review of these tests.
Coding
There is no specific CPT code for measurement of apolipoprotein B. CPT code 82172 (apolipoprotein, each) might be used.
There is no specific code for apo E phenotyping or genotyping. For phenotyping, CPT code 84181 (Protein; Western blot) may be used.
There is no CPT code for subclassification that is specific to high-density lipoprotein (HDL). CPT code 82664 (electrophoretic technique, not otherwise specified) or 83701 (lipoprotein, blood; high resolution fractionation and quantitation of lipoproteins including lipoprotein subclasses when performed [e.g., electrophoresis, ultracentrifugation]) may be used.
There is a CPT code for lipoprotein particle number and subclass quantification by nuclear magnetic resonance spectroscopy that is also not specific to HDL: 83704: Lipoprotein, blood; quantification of lipoprotein particle numbers and lipoprotein particle subclasses (e.g., by nuclear magnetic resonance spectroscopy).
In 1999, a CPT code 83716 was introduced to describe the high-resolution quantitation of lipoprotein cholesterol levels. This code includes any of the following 3 techniques for such quantitation, i.e., gradient gel electrophoresis, nuclear magnetic resonance, or ultracentrifugation. This CPT code would apply to both measurement of small, dense lipoprotein particles and measurement of the number of lipoprotein particles.
Effective in 2006, 83716 is replaced with new CPT codes:
83700 Lipoprotein, blood; electrophoretic separation and quantitation
83701 high resolution fractionation and quantitation of lipoprotein subclasses when performed (e.g., electrophoresis, ultracentrifugation)
83704 quantitation of lipoprotein particle numbers and lipoprotein particle subclasses (e.g., by nuclear magnetic resonance spectroscopy)
There is a specific CPT code for lipoprotein (a) testing:
83695: Lipoprotein (a)
Lipoprotein associated phospholipase A2 (Lp PLA2) is reported with CPT 83698
There is no specific CPT code for cardiovascular risk panels. If there are CPT codes for the component tests in the panel and there is no algorithmic analysis used, the individual CPT codes may be reported.
Examples of possible components codes include:
82652: Vitamin D; 1, 25 dihydroxy, includes fraction(s), if performed
83090: Homocysteine
83698: Lipoprotein-associated phospholipase A2 (Lp-PLA2)
83718: Lipoprotein, direct measurement; high density cholesterol (HDL cholesterol)
83721: Lipoprotein, direct measurement; LDL cholesterol
83880: Natriuretic peptide
84478: Triglycerides
86141: C-reactive protein; high sensitivity (hsCRP)
If the testing involves multiple analytes and an algorithmic analysis, the unlisted multianalyte assay with algorithmic analysis (MAAA) code 81599 would be reported.
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Policy/ Coverage: |
Effective April 2024, coverage policies 2013041 (Cardiovascular Risk Panels), 2006010 (Measurement of Lipoprotein-Associated Phospholipase A2 [Lp-PLA2] in the Assessment of Cardiovascular Risk), and 2012029 (Novel Lipid Risk Factors in Risk Assessment and Management of Cardiovascular Disease [apolipoprotein B] [apolipoprotein A-1] [HDL subclass] [LDL subclass] [apolipoprotein E] [Lipoprotein A]) were combined into one policy. Policies 2013041 and 2006010 are archived effective April 2024.
Effective April 2024
Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
Measurement of novel lipid risk factors (apolipoprotein B, apolipoprotein AI, apolipoprotein E, low-density lipoprotein [LDL] subclass, high-density lipoprotein [HDL] subclass, and lipoprotein[a]) as an adjunct to LDL cholesterol in the risk assessment and management of cardiovascular disease does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
For members with contracts without primary coverage criteria, measurement of novel lipid risk factors (apolipoprotein B, apolipoprotein AI, apolipoprotein E, low-density lipoprotein [LDL] subclass, high-density lipoprotein [HDL] subclass, and lipoprotein[a]) as an adjunct to LDL cholesterol in the risk assessment and management of cardiovascular disease is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
The measurement of lipoprotein-associated Phospholipase A2 (Lp-PLA2) does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
For members with contracts without primary coverage criteria, the measurement of lipoprotein-associated Phospholipase A2 (Lp-PLA2) is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
Cardiovascular risk panels consisting of multiple individual biomarkers intended to assess cardiac risk (other than simple lipid panels) do not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
For members with contracts without primary coverage criteria, cardiovascular risk panels consisting of multiple individual biomarkers intended to assess cardiac risk (other than simple lipid panels) are considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
Note: A simple lipid panel is generally composed of the following lipid measures:
Effective July 2021 – March 2024
Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
Measurement of novel lipid risk factors (apolipoprotein B, apolipoprotein A-I, apolipoprotein E, LDL subclass, HDL subclass and lipoprotein[a]) as an adjunct to LDL cholesterol in the risk assessment and management of cardiovascular disease does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
For members with contracts without primary coverage criteria, measurement of novel lipid risk factors (apolipoprotein B, apolipoprotein A-I, apolipoprotein E, LDL subclass, HDL subclass and lipoprotein[a]) as an adjunct to LDL cholesterol in the risk assessment and management of cardiovascular disease is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
Effective Prior to July 2021
Measurement of novel lipid risk factors (apolipoprotein B, apolipoprotein A-I, apolipoprotein E, LDL subclass, HDL subclass and lipoprotein[a]) as an adjunct to LDL cholesterol in the risk assessment and management of cardiovascular disease does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.
For members with contracts without primary coverage criteria, measurement of novel lipid risk factors (apolipoprotein B, apolipoprotein A-I, apolipoprotein E, LDL subclass, HDL subclass and lipoprotein[a]) as an adjunct to LDL cholesterol in the risk assessment and management of cardiovascular disease is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
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| Rationale: |
Due to the detail of the rationale, the complete document is not online. If you would like a hardcopy print, please email: codespecificinquiry@arkbluecross.com.
This evidence review is regularly updated with searches of the PubMed database. The most recent literature update was performed through October 20, 2023.
Nontraditional Biomarkers
A large body of literature has accumulated on the utility of nontraditional lipid risk factors in the prediction of future cardiac events. The evidence reviewed herein consists of systematic reviews, meta-analyses, and large, prospective cohort studies that have evaluated the association between these lipid markers and cardiovascular outcomes. A smaller amount of literature is available on the utility of these markers as a marker of treatment response. Data on treatment responses are taken from randomized controlled trials (RCTs) that use one or more novel lipid markers as a target of lipid-lowering therapy.
The Adult Treatment Panel III (ATP III) guidelines noted that, to determine their clinical significance, emerging risk factors should be evaluated against the following criteria (The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001):
It is preferable, but not necessary, that modification of the risk factor in clinical trials will have shown a reduction in risk.
A 2015 health technology assessment conducted for the National Institute for Health Research assessed strategies for monitoring lipid levels in patients at risk or with cardiovascular disease (CVD) (Perera, 2015). The assessment included a systematic review of predictive associations for CVD events. Studies were included if they had at least 12 months of follow-up and 1000 participants. Results were stratified by the use of statins and primary versus secondary prevention. For populations not taking statins, 90 publications reporting 110 cohorts were included and, for populations taking statins, 25 publications reporting 28 cohorts were included. In populations not taking statins, the ratio of apolipoprotein B (apo B) to apolipoprotein AI (apo AI) was most strongly associated with the outcome of CVD events (hazard ratio [HR], 1.35; 95% confidence interval [CI], 1.22 to 1.5) although the HRs for apo B, total cholesterol (TC)/high-density lipoprotein (HDL), and low-density lipoprotein (LDL)/HDL all had overlapping CIs with the HR for apo B/apo AI. In populations taking statins, insufficient data were available to estimate the association between apo B or apo AI and CVD events.
Thanassoulis et al reported on a meta-analysis of 7 placebo-controlled statin trials evaluating the relation between statin-induced reductions in lipid levels and reduction of coronary heart disease (CHD) risk (Thanassoulis, 2014). Each trial included LDL cholesterol (LDL-C), non-HDL cholesterol (HDL-C), and apo B values assessed at baseline and 1-year follow-up. In both frequentist and Bayesian meta-analyses, reductions in apo B were more closely related to CHD risk reduction from statins than LDL-C or non-HDL-C.
Van Holten et al reported on a systematic review of 85 articles with 214 meta-analyses to compare serologic biomarkers for risk of CVD (van Holten, 2013). Predictive potential for primary CVD events was strongest with lipids, with a ranking from high to low found with: C-reactive protein (CRP), fibrinogen, cholesterol, apo B, the apo A/apo B ratio, HDL, and vitamin D. Markers associated with ischemia were more predictive of secondary cardiovascular events and included from high to low result: cardiac troponins I and T, CRP, serum creatinine, and cystatin C. A strong predictor for stroke was fibrinogen.
Tzoulaki et al reported on meta-analyses of biomarkers for CVD risk to examine potential evidence of bias and inflation of results in the literature (Tzoulaki, 2013). Included in the evaluation were 56 meta-analyses, with 49 reporting statistically significant results. Very large heterogeneity was seen in 9 meta-analyses, and small study effects were seen in 13 meta-analyses. Significant excess of studies with statistically significant results was found in 29 (52%) meta-analyses. Reviewers reported only 13 meta-analyses with statistically significant results that had more than 1000 cases and no evidence of large heterogeneity, small-study effects, or excess significance.
In a systematic review, Willis et al evaluated whether validated CVD risk scores could identify patients at risk for CVD for participation in more intensive intervention programs for primary prevention (Willis, 2012). Sixteen articles reporting on 5 studies were selected. Reviewers were unable to perform a meta-analysis due to the heterogeneity of studies. The evidence was not considered strong enough to draw definitive conclusions, but reviewers noted that lifestyle interventions with higher intensity might have the potential for lowering CVD risk.
Asymptomatic Individuals with Risk of Cardiovascular Disease
The purpose of nontraditional cardiac biomarker testing in individuals who are asymptomatic with risk of CVD is to inform a decision whether nontraditional cardiac biomarker testing improves CVD diagnosis and treatment decisions.
Apolipoprotein B
Robinson et al published results of a Bayesian random-effects meta-analysis of RCTs to compare the effectiveness of lowering apo B versus LDL-C and non-HDL-C for reducing CVD, CHD, and stroke risk (Robinson, 2012). Selected for analysis were 131,134 patients from 25 RCTs including 12 trials on statins, 5 on niacin, 4 on fibrates, 1 on simvastatin plus ezetimibe, 1 on aggressive versus standard LDL and blood pressure targets, and 1 on ileal bypass surgery. In the analysis of all trials, each apo B decrease of 10 mg/dL resulted in a 6% decrease in major CVD risk and a 9% decrease in CHD risk prediction, but stroke risk was not decreased. Decreased apo B levels were not superior to decreased non-HDL levels in decreasing CVD (Bayes factor [BF], 2.07) and CHD risk (BF, 1.45) prediction. When non-HDL-C plus LDL-C decrease were added to apo B decrease, CVD risk prediction improved slightly (BF, 1.13) but not CHD risk prediction (BF, 1.03) and stroke risk prediction worsened (BF, 0.83). In summary, any apo B decrease did not consistently add information to LDL, non-HDL, or LDL/non-HDL decreases to improve CVD risk prediction when analyzed across lipid-modifying treatments of all types.
The Emerging Risk Factors Collaboration published a patient-level meta-analysis of 37 prospective cohort studies enrolling 154,544 patients (Di Angelantonio, 2012). Risk prediction was examined for a variety of traditional and nontraditional lipid markers. For apo B, evidence from 26 studies (n=139,581) reported that apo B was an independent risk factor for cardiovascular events. On reclassification analysis, when apo B and apo AI were substituted for traditional lipids, there was no improvement in risk prediction. In fact, there was a slight worsening in the predictive ability, as evidenced by a -0.0028 decrease in the C statistic (p<.001), and a -1.08% decrease in the net reclassification improvement (p<.01).
The Quebec Cardiovascular Study evaluated the ability of levels of apo B and other lipid parameters to predict subsequent coronary artery disease (CAD) events in a prospective cohort study of 2155 men followed for 5 years (Lamarche, 1996). Elevated levels of apo B were found to be an independent risk factor for ischemic heart disease after adjustment for other lipid parameters. In patients with an apo B level of greater than 120 mg/dL, there was a 6.2-fold increase in the risk of cardiovascular events.
The Apolipoprotein Mortality Risk Study was another prospective cohort study that followed 175,000 Swedish men and women presenting for routine outpatient care over a mean of 5.5 years (Walldius, 2001). This study found that apo B was an independent predictor of CAD events and was superior to LDL-C levels in predicting risk, not only for the entire cohort but also for all subgroups examined. Relative risks (RR) for the highest quartile of apo B levels were 1.76 in men (p<.001) and 1.69 in women (p<.001).
A cohort study of 15,632 participants from the Women’s Health Initiative provided similar information in women (Ridker, 2005). In this analysis, the HR for developing CHD in the highest versus the lowest quintiles was greater for apo B (2.50; 95% CI, 1.68 to 3.72) than LDL-C (1.62; 95% CI, 1.17 to 2.25), after adjusting for traditional cardiovascular risk factors.
The Copenhagen City Heart Study prospectively evaluated a cohort of 9,231 asymptomatic persons from the Danish general population followed for 8 years (Benn, 2007). Subjects with total apo B levels in the top one-third (top tertile) had a significantly increased RR of cardiovascular events than patients in the lowest one-third, after controlling for LDL-C and other traditional cardiovascular risk factors. This study also compared the discriminatory ability of apo B with that of traditional lipid measures, by using the area under the curve (AUC) for classifying cardiovascular events. Total apo B levels had a slightly higher AUC (0.58) than LDL-C (0.57); however, this difference in AUC was not statistically significant.
Kappelle et al used data from the prospective Prevention of Renal and Vascular End-stage Disease trial (PREVEND) cohort to evaluate the predictive value of the apo B/apo AI ratio independent of other traditional risk factors, including albuminuria and CRP (Kappelle, 2011). Among 6948 subjects without previous heart disease and who were not on lipid-lowering drugs, the adjusted HR (aHR) for a high apo B/apo AI ratio did not differ significantly from the TC/HDL-C ratio of 1.24 (95% CI, 1.18 to 1.29), and did not change significantly after further adjustment for triglycerides.
Pencina et al used data from 2966 participants of the Framingham Offspring Study cohort who were 40 to 75 years of age in the fourth examination cycle and did not have CVD, triglyceride levels greater than 400 mg/dL, or missing data on model covariates (Pencina, 2015). They calculated the differences between observed apo B and expected apo B based on linear regression models of LDL-C and non-HDL-C levels. These differences were added to a Cox model to predict new-onset CHD, adjusting for standard risk factors (age, sex, systolic blood pressure, antihypertensive treatment, smoking, diabetes, HDL-C, and LDL-C or non-HDL-C). The difference between observed and expected apo B was associated with future CHD events. The aHR for the difference based on the apo B and LDL-C model was 1.26 (95% CI, 1.15 to 1.37) for each standard deviation (SD) increase beyond expected apo B levels. For the difference based on the apo B and non-HDL-C model, the HR was 1.20 (95% CI, 1.11 to 1.29). The discrimination C statistic for predicting new-onset CHD from a model with standard risk factors was 0.72 (95% CI, 0.70 to 0.75). The C statistic improved very slightly but with overlapping CIs to 0.73 (95% CI, 0.71 to 0.76) after adding the difference based on the apo B and LDL-C model to the standard risk factors and increased to 0.73 (95% CI, 0.71 to 0.75) after adding the difference based on the apo B and non-HDL-C model.
The Atherosclerosis Risk in Communities (ARIC) study concluded that apo B did not add additional predictive information above standard lipid measures (Sharrett, 2001). The ARIC study followed 12,000 middle-aged adults free of CAD at baseline for 10 years. While apo B was a strong univariate predictor of risk, it did not add independent predictive value above traditional lipid measures in multivariate models.
The ratio of apo B/apo AI has also been proposed as a superior measure of the ratio of proatherogenic (i.e., “bad”) cholesterol to anti-atherogenic (i.e., “good”) cholesterol. This ratio may be a more accurate measure of this concept, compared with the more common TC/HDL ratio. A number of epidemiologic studies have reported that the apo B/apo AI ratio is superior to other ratios, such as TC/HDL-C and non-HDL-C/HDL-C (Rasouli, 2006; Walldius, 2004). Other representative studies of the apo B/apo AI ratio are discussed next.
Some studies have tested the use of apo B in a multivariate risk prediction model with both traditional risk factors and apolipoprotein measures included as potential predictors. Ridker et al published the Reynolds Risk Score, based on data from 24,558 initially healthy women enrolled in the Women’s Health Study and followed for a median of 10.2 years (Ridker, 2007). Thirty-five potential predictors of CVD were considered as potential predictors, and 2 final prediction models were derived. The first was the best-fitting model statistically and included both apo B and the apo B/apo AI ratio as 2 of 9 final predictors. The second called the “clinically simplified model” substituted LDL-C for apo B and TC/HDL-C for apo B/apo AI. The authors developed this simplified model “for the purpose of clinical application and efficiency” and justified replacing the apo B and apo B/apo AI measures as a result of their high correlation with traditional lipid measures (r=0.87 and 0.80, respectively). The predictor has not been evaluated in clinical care.
Ingelsson et al used data from 3322 subjects in the Framingham Offspring Study to compare prediction models using traditional lipid measures with models using apolipoprotein and other nontraditional lipid measures (Ingelsson, 2007). This study reported that the apo B/apo AI ratio had a similar predictive ability as traditional lipid ratios with respect to model discrimination, calibration, and reclassification. The authors also reported that the apo B/apo AI ratio did not provide any incremental predictive value over traditional measures.
Sniderman et al reported on 9345 acute myocardial infarction (MI) patients who were compared with 12,120 controls in the standardized case-control INTERHEART study (Sniderman, 2012). The authors reported discordance in the levels of cholesterol contained in apo B and non-HDL-C. Unlike the 2012 Robinson et al study, apo B was found to be more accurate than non-HDL-C as a marker for cardiovascular risk.
Apolipoprotein AI
In the Emerging Risk Factors Collaboration meta-analysis described above, apo AI was also examined as an independent risk factor (Di Angelantonio, 2012). For apo AI, evidence from 26 studies (n=139,581 subjects) reported that apo AI was an independent risk factor for reduced cardiovascular risk. However, as with apo B, when apo AI was substituted for traditional lipids, there was no improvement in risk prediction. In fact, there was a slight worsening in the predictive ability, evidenced by a -0.0028 decrease in the C statistic (p<.001) and a -1.08% decrease in the net reclassification improvement (p<.01).
Clarke et al published a prospective cohort study of 7044 elderly men enrolled in the Whitehall Cardiovascular Cohort from England (Clark, 2007). Measurements of apolipoprotein levels were performed on 5344 of these men, and they were followed for a mean of 6.8 years. The authors reported that the apo B/apo AI ratio was a significant independent predictor with similar predictive ability as the TC/HDL ratio (HR, 1.57; 95% CI, 1.32 to 1.86).
Ridker et al compared the predictive ability of apo AI and the apo B/apo AI ratio with standard lipid measurements (Ridker, 2007). Both ratios had similar predictive ability to standard lipid measurements but were no better. The HR for future cardiovascular events was 1.75 (95% CI, 1.30 to 2.38) for apo AI compared with 2.32 (95% CI, 1.64 to 3.33) for HDL-C. The HR for the apo B/apo AI ratio was 3.01 (95% CI, 2.01 to 4.50) compared with 3.18 (95% CI, 2.12 to 4.75) for the LDL-C/HDL-C ratio.
A nested case-control study, performed within the larger European Prospective Investigation into Cancer and Nutrition-Norfolk cohort study, evaluated the predictive ability of the apo B/apo AI ratio in relation to traditional lipid measures in 25,663 patients (van der Steeg, 2007). The case-control subgroup study enrolled 869 patients who had developed CAD during a mean follow-up of 6 years and 1511 control patients without CAD. The authors reported that the apo B/apo AI ratio was an independent predictor of cardiovascular events after controlling for traditional lipid risk factors and the Framingham Risk Score. However, the authors also reported that this ratio was no better than the TC/HDL ratio in discriminating between cases (AUC, 0.673) and controls (AUC, 0.670; p=.38).
The Apolipoprotein Mortality Risk Study followed 175,000 Swedish men and women for 5.5 years and reported that decreased apo AI was an independent predictor of CAD events (Walldius, 2001). The Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS) investigated lipid parameters among 6605 men and women with average LDL-C and low HDL-C levels who were randomized to lovastatin or placebo (Gotto, 2000). This study reported that apo AI levels and the apo B/apo AI ratio were strong predictors of CAD events.
The Copenhagen City Heart Study was a prospective cohort study of 9231 asymptomatic persons from the Danish general population (Benn, 2007). The apo B/apo AI ratio was reported as an independent predictor of cardiovascular events, with an HR similar to that for TC/HDL-C. This study also compared the discriminatory ability of the apo B/apo AI ratio with that of traditional lipid measures, using the AUC for classifying cardiovascular events. The apo B/apo AI ratio had a slightly higher AUC (0.59) than the TC/HDL-C ratio (0.58), but this difference was not statistically significant.
Apolipoprotein E
A large body of research has established a correlation between lipid levels and the underlying APOE genotype. For example, in population studies, the presence of an apo e2 allele is associated with the lowest cholesterol levels and the apo e4 allele is associated with the highest levels (Kastelein, 2008; Mora, 2012).
A meta-analysis published by Bennet et al summarized the evidence from 147 studies on the association between APOE genotypes using lipid levels and cardiac risk (Bennet, 2007). Eighty-two studies with a total of 86,067 participants included data on the association between apo E and lipid levels and 121 studies reported on the association with clinical outcomes. The authors estimated that patients with the apo e2 allele had LDL levels that were approximately 31% lower than those in patients with the apo e4 allele. Compared with patients with the apo e3 allele, patients with apo e2 had an approximately 20% lower risk for coronary events (odds ratio [OR], 0.80; 95% CI, 0.70 to 0.90). Patients with the apo e4 had an estimated 6% higher risk of coronary events, which was of marginal statistical significance (OR, 1.06; 95% CI, 0.99 to 1.13).
Sofat et al published a meta-analysis of 3 studies of circulating apo E and CVD events (Sofat, 2016). The method for selecting the studies was not described. The 3 studies included 9587 participants and 1413 CVD events. In a pooled analysis, there was no association between apo E and CVD events. The unadjusted OR for CVD events for each SD increase in apo E concentration was 1.02 (95% CI, 0.96 to 1.09). After adjustment for other cardiovascular risk factors, the OR for CVD for each SD increase in apo E concentration was 0.97 (95% CI, 0.82 to 1.15).
Numerous studies have focused on the relation between genotype and physiologic markers of atherosclerotic disease. A number of small- to medium-sized cross-sectional and case-control studies have correlated apo E with surrogate outcomes such as cholesterol levels, markers of inflammation, or carotid intima-media thickness (Koch, 2008; Kulminski, 2008; Schmitz, 2007; Vaisi-Raygani, 2007; Ciftdogan, 2012; Vasunilashorn, 2011). These studies have generally shown a relationship between apo E and these surrogate outcomes. Other studies have suggested that carriers of apo e4 are more likely to develop signs of atherosclerosis independent of TC and LDL-C levels (de Andrade, 1995; Eichner, 1993; Wilson, 1994; Wilson, 1996).
Some larger observational studies have correlated APOE genotype with clinical disease. The ARIC study followed 12,000 middle-aged subjects free of CAD at baseline for 10 years (Sharrett, 2001). This study reported that the apo e3/2 genotype was associated with carotid artery atherosclerosis after controlling for other atherosclerotic risk factors. Volcik et al, also analyzing ARIC study data, reported that APOE polymorphisms were associated with LDL levels and carotid intima-media thickness but were not predictive of incident CAD (Volcik, 2006).
High-Density Lipoprotein Particle Size and Concentration
Singh et al reported the results for a pooled analysis examining the association between HDL particle concentration and stroke and MI in patients without baseline atherosclerotic disease (Singh, 2020). The analysis included 15,784 patients from 4 prospective cohort studies, which included the ARIC study. A significant inverse association was reported between HDL particle concentration and stroke and MI, when comparing patients with HDL particle concentration in the fourth quartile and the first quartile (HR, 0.64; 95% CI, 0.52 to 0.78). When comparing quartile 4 with quartile 1 with regard to the individual components of the primary endpoint, a significant reduction in both MI (HR, 0.63; 95%, 0.49 to 0.81) and stroke (HR, 0.66; 95% CI, 0.48 to 0.93) was reported. There was significant heterogeneity between studies with regard to patient ethnicity and geographic location. Sub-analysis by race revealed that the significant inverse association between HDL particle concentration and stroke and MI was not seen in black populations. When comparing quartile 4 with quartile 1 among black patients, HDL particle concentration did not have an inverse association with MI (HR, 1.22; 95% CI, 0.76 to 1.98). However, the heterogeneity and uneven distribution of patients may have contributed to subgroup analyses being underpowered and the possibility of type 2 error.
In the Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER RCT), 10,886 patients without CVD were randomized to rosuvastatin or placebo and followed for a median of 2 years (Mora, 2013). Before randomization and 1 year after, levels of LDL-C, HDL-C, apo AI, and nuclear magnetic resonance (NMR)-measured HDL size and HDL particle numbers were evaluated. Statistically significant changes in the median and 25th and 75th percentile values of HDL levels between baseline and year 1 values occurred in the rosuvastatin and placebo groups for all levels (p<.001), except for apo AI and HDL particle size in the placebo group, which did not differ significantly (p=.09 and.74, respectively). Changes in the rosuvastatin group were also statistically significant compared with placebo for LDL-C, HDL-C, apo AI, and HDL particle size and number (all p<.001). In the placebo group, inverse associations with CVD and HDL-C, apo AI, and HDL particles were reported. HDL particle number in the rosuvastatin group had a greater association with CVD (HR, 0.73; 95% CI, 0.57 to 0.93; p=.01) than HDL-C (HR, 0.82; 95% CI, 0.63 to 1.08; p=.16) or apo AI (HR, 0.86; 95% CI, 0.67 to 1.10; p=.22). This association remained after adjusting for HDL-C (HR, 0.72; 95% CI, 0.53 to 0.97; p=.03). Size of HDL was not significantly associated with CVD in risk factor-adjusted models.
Low-Density Lipoprotein Subclass and Low-Density Lipoprotein Particle Size and Concentration
A nested case-control study from the Physician’s Health Study, a prospective cohort study of approximately 15,000 men, investigated whether LDL particle size is an independent predictor of CAD risk, particularly compared to triglyceride levels (Stampfer, 1996). The authors concluded that while LDL particle diameter was associated with the risk of MI, this association was not present after adjustment for triglyceride level. Only the triglyceride level was independently significant.
The Quebec Cardiovascular Study evaluated the ability of “nontraditional” lipid risk factors, including LDL size, to predict subsequent CAD events in a prospective cohort of 2155 men followed for 5 years (Lamarche, 1996; Lamarche, 1997). The presence of small LDL particles was associated with a 2.5-fold increased risk for ischemic heart disease after adjustment for traditional lipid values, indicating a level of risk similar to total LDL. This study also suggested an interaction in atherogenic risk between LDL size and apo B levels. In the presence of small LDL particles, elevated apo B levels were associated with a 6-fold increased risk of CAD, whereas when small LDL particles were not present, elevated apo B levels were associated with only a 2-fold increase in risk.
Tzou et al examined the clinical value of “advanced lipoprotein testing” in 311 randomly selected adults participating in the Bogalusa Heart Study (Tzou, 2005). Advanced lipoprotein testing consisted of subclass patterns of LDL (i.e., presence of large buoyant particles, intermediate particles, or small dense particles). These measurements were used to predict the presence of subclinical atherosclerosis, as measured ultrasonographically by carotid intimal-media thickness. In multivariate logistic regression models, substituting advanced lipoprotein testing for corresponding traditional lipoprotein values did not improve prediction of the highest quartile of carotid intimal-media thickness.
Low-Density Lipoprotein Particle Size and Concentration Measured by Nuclear Magnetic Resonance
Similar to small dense lipoprotein particles, several epidemiologic studies have shown that the lipoprotein particle size and concentration measured by NMR are also associated with cardiac risk. For example, data derived from the Women’s Health Study, Cardiovascular Health Study, and Pravastatin Limitation of Atherosclerosis in the Coronary Arteries (PLAC-1) trial have suggested that the number of LDL particles is an independent predictor of cardiac risk (Blake, 2002; Kuller, 2002; Rosenson, 2002). Translating these findings into clinical practice requires setting target values for lipoprotein numbers. Proposed target values have been derived from the same data set (i.e., Framingham study) used to set the ATP III target goals for LDL-C. For example, the ATP III targets for LDL-C correspond to the 20th, 50th, and 80th percentile values in the Framingham Offspring Study, depending on the number of risk factors present. Proposed target goals for lipoprotein numbers correspond to the same percentile values, and LDL particle concentrations corresponding to the 20th, 50th, and 80th percentile are 1100, 1400, and 1800 nmol/L, respectively (Otvos, 2002).
Rosenson and Underberg conducted a systematic review of studies on lipid-lowering pharmacotherapies to evaluate changes in LDL particles pre- and post-treatment (Rosenson, 2013). Reductions in mean LDL particles occurred in 34 of the 36 studies evaluated. Percentage reductions of LDL particles in several statin studies were smaller than reductions in LDL-C. LDL particles and apo B changes were comparable. Reviewers suggested the differences in LDL particle reductions with different lipid-lowering therapies demonstrated potential areas of residual cardiovascular risk that could be addressed with LDL particle monitoring.
Mora et al evaluated the predictive ability of LDL particle size and number measured by NMR in participants of the Women’s Health Study, a prospective cohort trial of 27,673 women followed over an 11-year period (Mora, 2009). After controlling for non-lipid factors, LDL particle number was a significant predictor of incident CVD, with an HR of 2.51 (95% CI, 1.91 to 3.30) for the highest compared with the lowest quintile. LDL particle size was similarly predictive of cardiovascular risk, with an HR of 0.64 (95% CI, 0.52 to 0.79). Compared with standard lipid measures and apolipoproteins, LDL particle size and number showed similar predictive ability but were not superior in predicting cardiovascular events.
Toth et al analyzed LDL-C and LDL particle levels and cardiovascular risk using commercial insurance and Medicare claims data on 15,569 high-risk patients from the HealthCore Integrated Research Database (Toth, 2014). For each 100 nmol/L increase in LDL particle level, there was a 4% increase in the risk of a CHD event (HR, 1.04; 95% CI, 1.02 to 1.05; p<.0001). A comparative analysis, using 1:1 propensity score matching of 2094 patients from the LDL-C target cohort (LDL-C level <100 mg/dL without a LDL particle level) and a LDL particle target cohort (LDL particle <1000 nmol/L and LDL-C of any level) found a lower risk of CHD or stroke in patients who received LDL-C measurement and were presumed to have received more intensive lipid-lowering therapy (HR, 0.76; 95% CI, 0.61 to 0.96; at 12 months). A comparison of smaller LDL particle target groups at 24 (n=1242) and 36 (n=705) months showed similar reductions in CHD (HR, 0.78; 95% CI, 0.62 to 0.97) and stroke (HR, 0.75; 95% CI, 0.58 to 0.97).
Lipoprotein(a)
Numerous prospective RCTs, cohort studies, and systematic reviews have evaluated lipoprotein(a) [Lp(a)] as a cardiovascular risk factor. The following are representative prospective trials drawn from the relevant literature.
The Emerging Risk Factors Collaboration published a patient-level meta-analysis assessing 37 prospective cohort studies enrolling 154,544 individuals (Di Angelantonio). Risk prediction was examined for a variety of traditional and nontraditional lipid markers. For Lp(a), evidence from 24 studies on 133,502 subjects reported that Lp(a) was an independent risk factor for reduced cardiovascular risk. The addition of Lp(a) to traditional risk factors resulted in a small improvement in risk prediction, with a 0.002 increase in the C statistic. A reclassification analysis found no significant improvement in the net reclassification index (0.05%; 95% CI, -0.59% to 0.70%).
Several meta-analyses have also examined the relation between Lp(a) levels and cardiovascular risk. Bennet et al synthesized the results of 31 prospective studies with at least 1 year of follow-up and that reported data on cardiovascular death and nonfatal MI (Bennet, 2008). The combined results revealed a significant positive relationship between Lp(a) and cardiovascular risk . This analysis reported a moderately high degree of heterogeneity in selected studies (I2=43%), reflecting the fact that not all reported a significant positive association.
Smolders et al summarized evidence from observational studies on the relation between Lp(a) and stroke (Smolders, 2007). Five prospective cohort studies and 23 case-control studies were included in this meta-analysis. Results from prospective cohort studies showed that Lp(a) level added only incremental predictive information (combined RR for the highest one-third of Lp[a], 1.22; 95% CI, 1.04 to 1.43). Results from case-control studies showed an elevated Lp(a) level was associated with an increased risk of stroke.
Several RCTs on lipid-lowering therapies have found Lp(a) is associated with residual cardiovascular risk. In a subgroup analysis of 7746 white patients from the JUPITER study, median Lp(a) levels did not change in either group of patients randomized to treatment with rosuvastatin or placebo during a median 2-year follow-up (Khera, 2014). Lp(a) was independently associated with a residual risk of CVD despite statin treatment. In the Atherothrombosis Intervention in Metabolic Syndrome with Low HDL/High Triglyceride and Impact on Global Health Outcomes study (2013), Lp(a) levels in 1440 patients at baseline and on simvastatin plus placebo or simvastatin plus extended-release niacin were significantly predictive of cardiovascular events (Albers, 2013).
Kamstrup et al analyzed data from the Copenhagen City Heart Study, which followed 9330 subjects from the Copenhagen general population over 10 years (Kamstrup, 2008). This study reported on a graded increase in the risk of cardiac events with increasing Lp(a) levels. At extreme levels of Lp(a) above the 95th percentile, the aHR for MI was slightly higher for women than for men. Tzoulaki et al reported on data from the Edinburgh Artery Study, a population cohort study that followed 1592 subjects for a mean of 17 years (Tzoulaki, 2007). They reported that Lp(a) was an independent predictor of MI.
Zakai et al evaluated 13 potential biomarkers for independent predictive ability compared with established risk factors, using data from 4510 subjects followed for 9 years in the Cardiovascular Health Study (Zakai, 2007). Lipoprotein (a) was 1 of 7 biomarkers that had incremental predictive ability above the established risk factors.
Waldeyer et al analyzed data of 56,084 participants from Biomarkers for Cardiovascular Risk Assessment in Europe project, which followed 7 prospective population-based cohorts across Europe, with a maximum follow-up of 24 years, to characterize the association of Lp(a) concentration with major coronary events, incident CVD, and total mortality (Waldeyer, 2017). The highest event rate of major coronary events and CVD was observed for Lp(a) levels at the 90th percentile or higher (p<.001 for major coronary events and CVD). Adjusting for age, sex, and cardiovascular risk factors, compared with Lp(a) levels in the lowest third in the 67th to 89th percentile, there were significant associations between Lp(a) levels and major coronary events (HR, 1.3; 95% CI, 1.15 to 1.46) and CVD (HR, 1.25; 95% CI, 1.12 to 1.39). For Lp(a) levels at the 90th percentile or higher, the aHR for the association between Lp(a) and major coronary events was 1.49 (95% CI, 1.29 to 1.73) and for the association between Lp(a) and CVD, it was 1.44 (95% CI, 1.25 to 1.65) compared with Lp(a) levels in the lowest third. There was no significant association between Lp(a) levels and total mortality.
Lee et al investigated whether elevated circulating Lp(a) level was a key determinant in predicting the incidence of major adverse cardiovascular events (MACE) among the participants of the Dallas Health Study, a multiethnic prospective cohort with a median follow-up of 9.5 years (N =3419 patients) (Lee, 2017). Quartiles 4 of Lp(a) and oxidized phospholipid on apo B-100 were associated with HRs for time to MACE of 2.35 (95% CI, 1.50 to 3.69) and 1.89 (95% CI, 1.26 to 2.84), respectively, adjusting for age, sex, body mass index (BMI), diabetes, smoking, LDL, HDL-C, and triglycerides. The addition of major apolipoprotein(a) isoform and 3 LPA single nucleotide variants prevalent among White, Black, and Hispanic subjects in the model attenuated the risk, but significance was maintained for both Lp(a) and oxidized phospholipid on apo B-100.
Some researchers have hypothesized that there is a stronger relation between Lp(a) and stroke than CHD. Similar to the situation with cardiac disease, most prospective studies have indicated that Lp(a) level is an independent risk factor for stroke. In a prospective cohort study, Rigal et al reported that an elevated Lp(a) level was an independent predictor of ischemic stroke in men (Rigal, 2007).
There also may be a link between Lp(a) level as a cardiovascular risk factor and hormone status in women. Suk Danik et al reported on the risk of a first cardiovascular event over a 10-year period in 27,736 women enrolled in the Women’s Health Study (Suk, 2008). After controlling for standard cardiovascular risk factors, Lp(a) levels were an independent predictor of risk in women not taking hormone replacement therapy. However, for women who were taking hormone replacement therapy, Lp(a) levels were not a significant independent predictor of cardiovascular risk (HR, 1.13; 95% CI, 0.84 to 1.53; p=.18).
Additional Studies
Additional key studies have examined the relation between Lp(a) and CVD risk, which are summarized below.
A systematic review by Genser et al included 67 prospective studies (N=181,683) that evaluated the risk of CVD associated with Lp(a) (Genser, 2011). Pooled analysis was performed on 37 studies that reported the endpoints of cardiovascular events. When grouped by design and populations, the RRs for these studies, comparing the uppermost and lowest strata of Lp(a), ranged from 1.64 to 2.37. The RR for cardiovascular events was higher in patients with previous CVD than with patients without the previous disease. There were no significant associations found between Lp(a) levels, overall mortality, or stroke.
A patient-level meta-analysis of 36 prospective studies published between 1970 and 2009 included 126,634 participants (Erqou, 2009). Overall, the independent association between Lp(a) level and vascular disease was consistent across studies but modest in size. The combined RR, adjusted for age, sex, and traditional lipid risk factor, was 1.13 (95% CI, 1.09 to 1.18) for CHD and 1.10 (95% CI, 1.02 to 1.18) for ischemic stroke. There was no association between Lp(a) levels and mortality.
The Lipid Research Clinics Coronary Primary Prevention Trial, one of the first large-scale RCTs of cholesterol-lowering therapy, measured initial Lp(a) levels and reported that Lp(a) was an independent risk factor for CAD when controlling for other lipid and non-lipid risk factors (Schaefer, 1994).
The LIPID RCT randomized 7863 patients to pravastatin or placebo (Nestel, 2013). Patients were followed for a median of 6 years. Lipoprotein (a) concentrations did not change significantly at 1 year. Baseline Lp(a) concentration was associated with total CHD events (p<.001), total CVD events (p=.002), and coronary events (p=.03).
As part of the Framingham Offspring Study, Lp(a) levels were measured in 2191 asymptomatic men between the ages of 20 and 54 years (Bostom, 1996). After a mean follow-up of 15 years, there were 129 CHD events, including MI, coronary insufficiency, angina, or sudden cardiac death. Comparing the Lp(a) levels of these patients with the other participants, the authors concluded that elevated Lp(a) was an independent risk factor for the development of premature CHD (i.e., before age 55 years). The ARIC study evaluated the predictive ability of Lp(a) in 12,000 middle-aged subjects free of CAD at baseline who were followed for 10 years (Sharrett, 2001). Lipoprotein (a) levels were significantly higher among patients who developed CAD than among those who did not, and Lp(a) levels were an independent predictor of CAD above traditional lipid measures.
In the ARIC prospective cohort study of 14,221 participants, elevated Lp(a) was a significant independent predictor of stroke in Black women (RR, 1.84; 95% CI, 1.05 to 3.07) and White women (RR, 2.42; 95% CI, 1.30 to 4.53) but not in Black men (RR, 1.72; 95% CI, 0.86 to 3.48) or White men (RR, 1.18; 95% CI, 0.47 to 2.90) (Ohira, 2006).
Fogacci et al examined whether serum Lp(a) levels could predict long-term survival in 1215 adults with no CVD at enrollment and similar general cardiovascular risk profiles from Brisighella Heart Study cohort in Italy (Fogacci, 2017). Subjects were stratified into low (n=865), intermediate (n=275), and high (n=75) cardiovascular risk groups using an Italian-specific risk chart. Subjects at high and intermediate cardiovascular risk aged 56 to 69 years (regardless of sex) and women aged 40 to 55 years with a low cardiovascular risk profile who had lower Lp(a) levels showed statistically significant lower cardiovascular mortality (p<.05) and longer survival time (p<.05) during the 25-year follow-up. The authors constructed a receiver operating characteristic curve for each cardiovascular risk group using Lp(a) as a test variable and death as a state variable and identified serum Lp(a) as an independent long-term cardiovascular mortality prognostic indicator for subjects at high cardiovascular risk (AUC, 0.63; 95% CI, 0.50 to 0.76; p=.049) and for women at intermediate cardiovascular risk (AUC, 0.7; 95% CI, 0.52 to 0.79; p=.034).
Some studies, however, have failed to demonstrate such predictive ability. In the Physicians’ Health Study, initial Lp(a) levels in the 296 participants who subsequently experienced MI were compared with Lp(a) levels in matched controls who remained free from CAD (Ridker, 1993). Authors found that the distribution of Lp(a) levels between the groups was identical. The European Concerted Action on Thrombosis and Disabilities study, a trial of secondary prevention, evaluated Lp(a) as a risk factor for coronary events in 2800 patients with known angina pectoris (Bolibar, 2000). In this study, Lp(a) levels did not differ significantly among patients who did and did not have subsequent events, suggesting that Lp(a) levels were not useful risk markers in this population.
Genetic studies have examined the association between various genetic loci and Lp(a) levels, and Mendelian randomization studies have examined whether Lp(a) level is likely to be causative for CAD. In a 2009 study, 3 separate loci were identified for increased Lp(a) levels (Clarke, 2009). Genetic variants identified at 2 of these loci were independently associated with coronary disease (OR, 1.70; 95% CI, 1.49 to 1.95; OR, 1.92; 95% CI, 1.48 to 2.49). This finding strongly implies that elevated Lp(a) levels are causative of coronary disease, as opposed to simply being associated.
Individuals with Hyperlipidemia Managed with Lipid-Lowering Therapy
The purpose of nontraditional cardiac biomarker testing in individuals with hyperlipidemia managed with lipid-lowering therapy is to inform a decision to proceed with appropriate treatment.
Apolipoprotein B
A number of RCTs of statin therapy have examined the change in apo B on-treatment in relation to clinical CAD outcomes and assessed whether apo B predicted outcomes better than LDL-C.
Boekholdt et al published a patient-level meta-analysis of on-treatment levels of traditional and nontraditional lipids as a measure of residual risk (Boekholdt, 2012). Eight studies enrolling 62,154 participants were included. The aHR for each 1 SD increase in apo B was 1.14 (95% CI, 1.11 to 1.18), which did not differ significantly from LDL-C (aHR, 1.13; 95% CI, 1.10 to 1.17; p=.21). The aHR for HDL-C was 1.16 (95% CI, 1.12 to 1.19), which was significantly greater than LDL-C or apo B (p=.002). In a subsequent report from this meta-analysis, Boekholdt et al evaluated the LDL-C, non-HDL-C, and apo B levels of 38,153 patients allocated to the statin therapy groups (Boekholdt, 2014). Despite statin therapy, reductions in levels of LDL-C, non-HDL-C, and apo B from baseline to 1 year showed large interindividual variations.
Ballantyne et al reported on a post hoc analysis of 682 patients with acute coronary syndrome from the randomized, phase 3 Limiting Undertreatment of Lipids in Acute coronary syndrome with Rosuvastatin trial (Ballantyne, 2013). The Limiting Undertreatment of Lipids in Acute coronary syndrome with Rosuvastatin subgroup analysis examined apo B in relation to LDL-C and non-HDL-C under intensive statin therapy with rosuvastatin or atorvastatin. The treatment target level for apo B of 80 mg/dL correlated with an LDL-C level of 90 mg/dL and a non-HDL-C level of 110 mg/dL at baseline and with an LDL-C of 74 mg/dL and a non-HDL-C of 92 mg/dL with statin therapy. Independent of triglyceride status, non-HDL-C was found to have a stronger correlation with apo B than with LDL-C and could be an adequate surrogate for apo B during statin therapy.
The AFCAPS/TexCAPS trial evaluated lipid parameters among 6605 men and women with average LDL-C and low HDL-C levels who were randomized to lovastatin or placebo (Gotto, 2000). Baseline LDL-C, HDL-C, and apo B levels were predictive of future coronary events. However, in the treatment group, posttreatment levels of LDL-C and HDL-C were not predictive of subsequent risk, while posttreatment apo B levels were.
In the Long-term Intervention with Pravastatin in Ischemic Disease trial, the relation between on-treatment apo B levels and clinical outcomes was examined in 9140 patients randomized to pravastatin or placebo and followed for a mean of 6.1 years (Simes, 2002).,The aHR for apo B levels (2.10; 95% CI, 1.21 to 3.64; p=.008) was higher than that for LDL-C (1.20; 95% CI, 1.00 to 1.45; p=.05). Also, the proportion of the treatment effect explained by on-treatment apo B levels (67%) was higher than that for LDL-C levels (52%).
Kastelein et al combined data from 2 RCTs, the Treating to New Targets (TNT) and Incremental Decrease in End Points Through Aggressive Lipid Lowering trials, to compare the relation between response to lipids, apo B levels, and other lipid measures (Kastelein, 2008). The analysis included 18,889 patients with established coronary disease randomized to low- or high-dose statin treatment. In pairwise comparisons, the on-treatment apo B level was a significant predictor of cardiovascular events (HR, 1.24; 95% CI, 1.13 to 1.36; p<.001), while LDL level was not. Similarly, the ratio of apo B/apo AI was a significant predictor of events (HR, 1.24; 95% CI, 1.17 to 1.32), while the TC/HDL-C ratio was not. In another publication that reported on the TNT study (2012), the on-treatment apo B level was also a significant predictor of future events (aHR, 1.19; 95% CI, 1.11 to 1.28) (Mora, 2012). In this study, the known baseline variables performed well in discriminating future cases from non-cases, and the addition of apo B was not associated with additional risk.
Mora et al measured on-treatment lipid levels to assess the prediction of residual risk while on statin therapy (Mora, 2012). Using data from the JUPITER trial, on-treatment levels of LDL-C, non-HDL-C, high-sensitivity CRP, apo B, and apo AI were used to predict subsequent cardiovascular events. The HRs for cardiovascular events were similar among the lipid measures, ranging from 1.22 to 1.31, with no significant differences between them. The residual risk declined overall with a decreasing level of LDL-C, with the lowest risk seen in subjects achieving an LDL-C level of less than 70 mg/dL.
Apolipoprotein AI
A number of studies have evaluated the utility of the apo B/apo AI ratio as a marker of treatment response in RCTs of statin treatment. For example, in the Kastelein et al study, authors combined data from 2 RCTs, the TNT, and the Incremental Decrease in End Points Through Aggressive Lipid Lowering trials, to compare the relation between response to lipids, apo B/apo AI ratio, and other lipid measures (Kastalein, 2008). The apo B/apo AI ratio was a significant predictor of events (HR, 1.24; 95% CI, 1.17 to 1.32) while the TC/HDL-C was not.
The Pravastatin or Atorvastatin Evaluation and Infection Therapy-Thrombolysis in MI (PROVE-IT TIMI) study randomized 4162 patients with an acute coronary syndrome to standard statin therapy or intensive statin therapy (Ray, 2009). While the on-treatment apo B/apo AI ratio was a significant predictor of cardiac events (HR for each SD increment, 1.10; 95% CI, 1.01 to 1.20); it was not superior to LDL-C (HR, 1.20; 95% CI, 1.07 to 1.35) or the TC/HDL ratio (HR, 1.12; 95% CI, 1.01 to 1.24) as a predictor of cardiac events.
Preliminary studies of infusions of reconstituted apo AI have demonstrated plaque regression in a small number of patients with the acute coronary syndrome (Osei-Hwedieh, 2011). Based on this research, there has been an interest in developing synthetic apo AI mimetic proteins, and such agents are in the drug development stage. These types of agents would likely target patients with residual cardiac risk following maximal statin therapy, especially patients with low HDL levels.
Apolipoprotein E
Apolipoprotein E has been investigated as a predictor of response to therapy by examining apo E alleles in the intervention arm(s) of lipid-lowering trials. Some data have suggested that patients with an apo e4 allele may respond better to diet-modification strategies (Ordovas, 2002; Sarkkinen, 1998). Other studies have suggested that response to statin therapy may vary by APOE genotype and that the e2 allele indicates greater responsiveness to statins (Ordovas, 2002; Carmena, 1993).
Chiodini et al examined the differential response to statin therapy by APOE genotype in a reanalysis of data from the Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto-Prevenzione (GISSI-P) study (Chiodini, 2007). The GISSI-P study was an RCT comparing pravastatin with placebo in 3304 Italian patients with previous MI. Patients with the apo e4 allele treated with statins had a better treatment response as evidenced by lower overall mortality rates (1.85% vs. 5.28%, respectively; p=.023), while there was no difference in mortality rates for patients not treated with statins (2.81% vs. 3.67%, respectively; p=.21). This study corroborated results reported previously but did not provide evidence that changes in treatment should be made as a result of the APOE genotype.
Other studies have evaluated APOE genetic status as a predictor of response to lipid-lowering therapy. Donnelly et al reported on 1383 patients treated with statins from the Genetics of Diabetes Audit and Research in Tayside, Scotland (Go-DARTS) database (Donnelly, 2008). Researchers reported on final LDL levels and percentages of patients achieving target LDL by APOE genetic status. LDL levels following treatment were lower for patients who were homozygous for apo e2 (0.6 mmol/L) than for patients homozygous for apo e4 (1.7 mmol/L; p<.001). All patients who were homozygous for apo e2 reached their target LDL level compared with 68% of patients homozygous for apo e4 (p<.001).
Vossen et al evaluated response to diet and statin therapy by apo E status in 981 patients with CAD who were enrolled in a cardiac rehabilitation program (Vossen, 2008). They reported that patients with an apo e4 allele were more responsive to diet and statin therapy than were patients with an apo e2 allele. The overall response to treatment was more dependent on baseline LDL levels than APOE genetic status, with 30% to 47% of the variation in response to treatment explained by baseline LDL, compared with only 1% of the variation explained by APOE status.
Low-Density Lipoprotein Subclass and Low-Density Lipoprotein Particle Size and Concentration
Patients with subclass pattern B have been reported to respond more favorably to diet therapy than those with subclass pattern A (Kwiterovich, 2002). Subclass pattern B has also been shown to respond more favorably to gemfibrozil and niacin, with a shift from small, dense LDL particles to larger LDL particles. While statin drugs lower the overall concentration of LDL-C, there is no shift to the larger LDL particles.
Superko et al (2005) reported that the response to gemfibrozil differed in patients who had LDL subclass A compared with those who had LDL subclass B (Superko, 2005). There was a greater reduction in the small, LDL levels for patients with subclass B, but this did not correlate with clinical outcomes. Another study has reported that atorvastatin treatment led to an increase in mean LDL size, while pravastatin treatment led to a decrease in LDL size (Sirtori, 2005).
Various studies have generally confirmed that small, dense LDL is impacted preferentially by fibrate treatment and possibly also by statin therapy (Arca, 2007; Rosenson, 2007; Tokuno, 2007). However, none demonstrated that preferentially targeting small, dense LDL leads to improved outcomes, compared with standard LDL targets widely used in clinical care.
Several trials with angiographic outcomes have examined the change in LDL particle size in relation to the angiographic progression of CAD. The 1996 Stanford Coronary Risk Intervention Project trial studied the relation between small, dense LDL and the benefit of diet, counseling, and drug therapy in patients with CAD, as identified by initial coronary angiogram (Miller, 1996). Patients with subclass pattern B showed a significantly greater reduction in CAD progression than those with subclass pattern A. The 1990 Familial Atherosclerosis Treatment Study randomized patients from families with premature CAD and elevated apo B levels (Brown, 1990). Change in LDL particle size correlated significantly with the angiographic progression of CAD in this study.
Fewer studies have evaluated clinical outcomes in relation to LDL particle size. In the 2001 Cholesterol and Recurrent Events trial, survivors of MI with normal cholesterol levels were randomized to lipid-lowering therapy or placebo (Campos, 2001). A post hoc analysis from this trial failed to demonstrate a correlation between change in particle size and treatment benefit.
Lipoprotein(a)
There is a lack of evidence to determine whether Lp(a) can be used as a target of treatment. Several randomized studies of lipid-lowering therapy have included Lp(a) measurements as an intermediate outcome. While these studies have demonstrated that Lp(a) levels are reduced in patients receiving statin therapy, the data are inadequate to demonstrate how this laboratory test can be used to improve patient management (Bays, 2003; van Wissen, 2003).
Lipoprotein-Associated Phospholipase A2 and Cardiovascular Risk
A large body of literature has accumulated on the utility of risk factors in the prediction of future cardiac events. The evidence assessed for this review consists of several systematic reviews, of prospective cohort studies that have evaluated the association between lipoprotein-associated phospholipase A2 (Lp-PLA2) and cardiovascular outcomes.
The National Cholesterol Education Program ATP-III guidelines have indicated that to determine the clinical significance of Lp-PLA2, the emerging risk factors should be evaluated against the following criteria (NIH NHLBI, 2001):
The purpose of Lp-PLA2 testing in patients who have a risk of CVD is to inform, improve patient stratification using risk prediction models that alter management decisions and improve health outcomes.
Results of numerous, large-scale observational studies have examined whether Lp-PLA2 is an independent risk factor for CAD. These observational studies have been analyzed in several systematic reviews (Di Angelantonio, 2012; Thompson, 2010; Garza, 2007).The largest, conducted by The Emerging Risk Factors Collaboration, included 37 cohort studies and performed a patient-level meta-analysis of the association between novel lipid risk factors and cardiovascular risk over a median follow-up of 10.4 years in patients without CVD (Di Angelantonio, 2012). The review found Lp-PLA2 was an independent risk factor for cardiovascular events with an HR of 1.12 (95% CI, 1.09 to 1.21) for each 1 standard deviation increase in Lp-PLA2 activity based on 11 studies (N=32,075). However, there was no significant improvement in risk reclassification following the addition of Lp-PLA2 to the reclassification model, with a net reclassification change of 0.21 (95% CI, -0.45 to 0.86).
Two other systematic reviews reported similar results. One review of 32 studies (N=79,036) found for every 1 standard deviation increase in Lp-PLA2 levels, the relative risk was 1.10 (95% CI, 1.04 to 1.17) for CAD, 1.08 (95% CI, 0.97 to 1.20) for stroke, and 1.16 (95% CI, 1.09 to 1.24) for vascular death, following adjustment for traditional risk factors. There was also a significant association between Lp-PLA2 levels and nonvascular deaths (RR, 1.10; 95% CI, 1.04 to 1.17) (Thompson, 2010). The second, smaller review (14 studies, N=20,549) reported a pooled OR of 1.60 (95% CI, 1.36 to 1.89), adjusted for traditional cardiac risk factors, for the development of future cardiac events with elevated Lp-PLA2 levels (Garza, 2007).
Cardiovascular Disease Risk Testing Panels
The purpose of CVD risk panel testing in individuals who have risk factors for CVD is to inform management and treatment decisions.
There is a large evidence base on the association between individual risk markers and CVD risk. Many observational studies have established that individual risk markers are independent predictors of cardiac risk (Helfand, 2009; Greenland, 2010).
Antonopoulos et al conducted a meta-analysis to evaluate biomarkers of vascular inflammation for cardiovascular risk prognosis in stable patients without known CHD (Antonopoulos, 2022). Various biomarkers of vascular inflammation (such as CRP, interleukin-6 and tumor necrosis factor-alpha) were evaluated in the 39 studies (N=175,778) that were included. The primary composite endpoint was the difference in c-index with the use of inflammatory biomarkers for MACE and mortality. Vascular inflammation biomarkers provided added prognostic value for the composite endpoint and for MACE only. However, limitations in the published literature included a lack of reporting on the net clinical benefit, cost-effectiveness of such biomarkers in clinical practice, and other metrics of improvement of risk stratification.
Van Holten et al conducted a systematic review of meta-analyses of prospective studies evaluating the association between serologic biomarkers and primary cardiovascular events (i.e., cardiovascular events and stroke in CVD-naive populations) and secondary cardiovascular events (i.e., cardiovascular events and stroke in populations with a history of CVD) (van Holten, 2013). The final data synthesis included 85 studies published from 1988 to 2011. Sixty-five meta-analyses reported biomarkers’ association with primary cardiovascular events and 43 reported associations with secondary cardiovascular events. Eighteen meta-analyses reported biomarkers’ association with ischemic stroke in patients with a history of CVD. Only 2 meta-analyses that reported associations with ischemic stroke in patients with no history of CVD were identified, and results were not reported.
Since the publication of the van Holten et al review, multiple studies have reported on the associations between various risk markers and CVD outcomes. Representative examples of reported associations include: endothelin-1 in predicting mortality in patients with heart failure with reduced ejection fraction; troponin and NT-proBNP in predicting CVD-related death; growth differentiation factor and interleukin 6 with CVD- and non-CVD-related death, mid-regional pro-atrial natriuretic peptide and C-terminal pro-endothelin-1 with morbidity and mortality after cardiac surgery, and triglyceride-glucose index with the incidence of acute coronary syndrome (Gottlieb, 2015; Patterson, 2015; Malachias, 2020; Schoe, 2014; Saylik, 2023).
Mohebi et al conducted a review of data from the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) cohort study to identify a panel of biomarkers to help stratify patient risk for CV events within 2 years of coronary angiography (Mohebi, 2023). All patients in the study (n=446) had chronic kidney disease (stage 1 to 2, 84.8%; stage 3 to 5, 15.2%). Monte Carlo simulation was used to identify a prognostic panel of biomarkers, which consisted of NT-proBNP, kidney injury molecule-1, osteopontin, and tissue inhibitor of matrix metalloproteinase-1. The panel had a C-statistic for predicting CV events of 0.77 (95% CI, 0.72 to 0.82). Among patients with stage 1 to 2 chronic kidney disease, the HR for CV events was 2.82 (95% CI, 1.53 to 5.22) in patients with higher cardiovascular risk compared to lower cardiovascular risk. In patients with stage 3 to 5 chronic kidney disease, the HR was 8.32 (95% CI, 1.12 to 61.76) in patients with higher CV risk compared to lower CV risk.
Safo et al derived a protein biomarker risk score to predict CVD in patients with HIV (Safo, 2023). The risk score was derived from 4 trials conducted by the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT) and included the following 8 proteins: FAM3B, integrin α11, interleukin-6, hepatocyte growth factor, C-C motif chemokine 25, gastrotropin, platelet-activating factor acetylhydrolase, and secretoglobin family 3A member. After adjusting for CVD at baseline and HIV-related factors, the protein score was associated with an increased risk of CVD (OR, 2.17; 95% CI, 1.58 to 2.99).
Wallentin et al analyzed data in a subset of patients with chronic CHD from the Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy (STABILITY) trial to assess the association between various CV and inflammatory biomarkers and CV death; patients in the STABILITY trial had a median follow-up time of 3.7 years (Wallentin, 2021). Biomarkers were compared between patients who experienced CV death (n=605) and those who did not experience CV death (n=2788). Another prospective observational study (the Ludwigshafen Risk and Cardiovascular Health [LURIC] study) was used for replication. This study followed a cohort of 3316 patients scheduled for coronary angiography over a period of 12 years to assess cardiovascular mortality. Both studies included patients with a median age of 65 years and 20% smokers; the STABILITY trial included 82% males, 70% with hypertension, and 39% with diabetes while the LURIC trial had 76% males, 78% with hypertension, and 30% with diabetes. Unadjusted and adjusted Cox regression analyses showed that NT-proBNP (HR for 1 standard deviation [SD] increase of the log scale of the distribution of the biomarker in the replication cohort, 2.079; 95% CI, 1.799 to 2.402) and high-sensitivity troponin T (HR, 1.715; 95% CI, 1.491 to 1.973) had the highest prognostic values for CV death.
Wuopio et al analyzed 10-year data from the CLARICOR trial in Denmark to investigate the association between serum levels of cathepsin B and S and CV risk and mortality in patients with stable CHD (Wuopio, 2018). The researchers used the placebo group (n=1998) as a discovery sample and the treatment group (n=1979) as a replication sample. A multivariable Cox regression model was used to adjust for risk factors and other variables. Analysis showed that cathepsin B was associated with an increased risk of CV events and mortality (p<.001 for both groups), but cathepsin S was not (p>.45). Limitations included unknown generalizability to patients with acute symptoms, other ethnic groups, and those unlikely to volunteer for such trials. In another evaluation involving the placebo group of the CLARICOR trial (n=1998), Winkel et al evaluated whether 12 novel circulating biomarkers (NT-proBNP, high-sensitive assay cardiac troponin T, YKL40, osteoprotegerin, pregnancy-associated plasma protein A, cathepsin B, cathepsin S, endostatin, soluble tumor necrosis factors 1 and 2, calprotectin, and neutrophil gelatins-associated lipocalin) when added to "standard predictors" (e.g., age, smoking, plasma lipids) improved the 10-year prediction of CV events and mortality in patients with stable CHD (Winkel, 2020). Results of the analysis revealed that the overall contribution of these novel biomarkers to all-cause death and composite CV outcome predictions was minimal. Two of the 12 biomarkers (calprotectin and cathepsin S) were not associated with the outcomes, not even as single predictors. The addition of the 10 remaining biomarkers to the "standard predictors" only increased the correct all-cause death predictions from 83.4% to 84.7% and the composite outcome predictions from 68.4% to 69.7%.
Welsh et al analyzed data from the Reduction of Events by Darbepoetin Alfa in Heart Failure (RED-HF) drug trial to assess the prognostic value of emerging biomarkers in CVD screening (Welsh, 2017). A panel of several biomarkers was measured at randomization in 1853 participants with complete data, and the relation between these biomarkers and a primary composite endpoint of heart failure hospitalization or CV death over 28 months of follow-up (n=834) was evaluated using Cox proportional hazards regression. Analysis showed that NT-proBNP (HR, 3.96) and high-sensitivity troponin T (HR, 3.09) far outperformed other emerging biomarkers studied for predicting adverse CV outcomes. Limitations included the homogenous sample from the trial cohort and regional differences.
Harari et al conducted a prospective cohort study analyzing the association between non-HDL-C levels and CVD mortality in a long-term follow-up of 4832 men drawn from the Cardiovascular Occupational Risk Factor Determination in
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