Lab Standards

CHAPTER 1

Lab Standards: A Practical Guide for Clinicians RON HOOGEVEEN, PHD

BIOMARKERS: DEFINITION AND UTILITY IN CLINICAL PRACTICE Biomarkers have been broadly defined as biological characteristics that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention.1 Using this broad definition, biomarkers can include measurements of proteins (i.e., proteomics), metabolites (i.e., metabolomics), genetic variants such as singlenucleotide polymorphisms (SNPs) commonly identified in genome-wide association studies, and RNA (e.g., microRNAs and messenger RNAs). Furthermore, imaging techniques to identify and quantitate biological markers of pathogenic processes are also considered biomarkers. From a clinical perspective, biomarkers can be of use in risk assessment for a variety of factors related to health or disease, such as exposure to environmental factors, genetic exposure or susceptibility, markers of subclinical or clinical disease or surrogate endpoints to evaluate safety and efficacy of different therapies.2 Therefore biomarkers are generally classified according to different stages in the development of a disease. Screening biomarkers are markers used for screening of patients who have no apparent disease, diagnostic biomarkers can assist in the care of patients who are suspected to have disease, and prognostic biomarkers are used in patients with overt disease to aid in the categorization of disease severity and prediction of future disease course, including recurrence and monitoring of treatment efficacy.3 Biomarkers may be used to enhance clinical trials to support both more efficient drug development and use of new therapeutics entering the market. For example, predictive biomarkers may allow specific targeting of patients who are likely to respond positively to treatment (aka enrichment strategy), thereby potentially reducing the cost of drug development by reducing the size of the study population required to demonstrate a drug’s safety and

efficacy. Furthermore, by demonstrating that a drug will only have clinical utility for a particular subpopulation of patients, biomarker-based enrichment strategies can reduce the adverse effects and unnecessary costs associated with the administration of drugs to patients in the biomarker-negative population, who are less likely to benefit from such treatment. Novel biomarkers such as cardiac troponins (e.g., cTn-T and cTn-I) and natriuretic peptides (e.g., B-type natriuretic peptide [BNP] and amino-terminal proBNP [NT-proBNP]) have shown their efficacy in the diagnosis and risk stratification of patients with suspected acute coronary syndrome (ACS) and heart failure (http:// www.aacc.org/AACC/members/nacb). Because prevention of cardiovascular events in patients at increased risk is likely to have a significant impact on the overall public health burden, the development of novel biomarkers for screening is currently an active area of investigation. In particular, the identification of biomarkers to monitor the efficacy of new treatments for heart failure is emerging as a critical priority to enhance translational research in heart failure drug development. 

BASIC PRINCIPLES OF WHAT MAKES FOR USEFUL BIOMARKER CHARACTERISTICS Sensitivity and Specificity It is important to consider a number of issues that influence the clinical utility of potential novel biomarkers for cardiovascular risk assessment. One of the major considerations is whether a novel biomarker can improve upon the cardiovascular risk prediction that can be attained with existing well-established cardiovascular risk markers. To this end, a potential marker needs to exhibit sufficient sensitivity and specificity to allow for risk classification. A new era of high-sensitivity assays represent an important advance in the use of diagnostic and prognostic markers for cardiovascular risk stratification. As the name implies, high-sensitivity assays detect

Biomarkers in Cardiovascular Disease. https://doi.org/10.1016/B978-0-323-54835-9.00001-6 Copyright © 2019 Elsevier Inc. All rights reserved.

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Biomarkers in Cardiovascular Disease

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concentrations of the same biomarkers but at much lower concentrations. With the development of highsensitivity assays, various terms such as limit of the blank (LoB), limit of detection (LoD), and limit of quantitation (LoQ) used to describe the smallest concentration of a biomarker that can be reliably measured by an analytical procedure are becoming increasingly important as medical decision levels may approach the lower analytical limits of these tests. The Clinical and Laboratory Standards Institute has published the EP17 guideline4 to provide a standard method for determining LoB, LoD, and LoQ. EP17 defines LoB as the highest apparent analyte concentration expected to be found when replicates of a sample containing no analyte (i.e., blank sample) are tested. Note that a blank sample devoid of analyte can produce an analytical signal that might otherwise be consistent with a low concentration of analyte. LoB = meanblank + 1.645 (SDblank). LoD represents the lowest analyte concentration that can be reliably distinguished from “analytical noise” or the LoB. As defined in EP17, LoD is determined by using both the measured LoB and test replicates of a sample known to contain a low concentration of analyte. The mean and SD of the low concentration sample is then calculated. EP17 defines LoD as LoD  ­ =  LoB  +  1.645 (SDlow conc. sample). LoQ is the lowest concentration at which the analyte can not only be reliably detected but also at which limit predefined goals of bias and imprecision are met. Typically, LoQ (aka “functional

sensitivity”) is defined as the concentration that results in a coefficient of variance (CV = [SD/mean] * 100%) of 20% and is thus a measure of an assay’s precision at low analyte concentrations. The LoQ may be equivalent to the LoD, or it could be at a much higher concentration, depending on whether the estimated bias and imprecision at the LoD meet the requirements for total error for the analyte (i.e., LoD = LoQ) or not (i.e., LoQ > LoD). In particular, high-sensitivity assays for cardiac troponins have shown added sensitivity for cardiac myocyte necrosis,5–7 but there remains a need for careful interpretation of these tests by the practicing clinician.8 According to expert consensus, high-sensitivity troponin assays should have a CV of <10% at the 99th percentile value of the population of interest.5 Furthermore, to be classified as a high-sensitivity assay, concentrations below the 99th percentile should be detectable above the assay’s LoD for >50% of healthy individuals in the population of interest. A number of manufacturers are currently producing high-sensitivity troponin assays, but there is wide variability in assay characteristics (Table 1.1, adapted from Sherwood et al.9), which prevents direct comparisons between the assays and poses a challenge with the advent of high-sensitivity troponin testing. Establishing the 99th percentile value in the general population, for each assay, will be critical in optimizing the sensitivity and specificity of high-sensitivity troponins and can serve to minimize false-positive testing.10

TABLE 1.1

Analytic Comparisons of Contemporary High-Sensitivity Cardiac Troponin Assays Limit of Detection (ng/L)

99% CV (ng/L)

10% CV (ng/L)

5.0

14 (13%)

13

Abbott ARCHITECT

1.2

16 (5.6%)

3.0

Beckman Access

2–3

8.6 (10%)

8.6

Mitsubishi Pathfast

8.0

29 (5%)

14

Nanosphere

0.2

2.8 (9.5%)

0.5

Radiometer AQT90

9.5

23 (17.7%)

39

Singulex Erenna

0.09

10.1 (9.0%)

0.88

Siemens Vista

0.5

9 (5.0%)

3

Siemens Centaur

6.0

40 (10%)

30

Hs-cTn-T Roche Elecsys Hs-cTn-I

CV, coefficient of variance; Hs-cTn-I, high-sensitivity cardiac troponin I; Hs-cTn-T, high-sensitivity cardiac troponin T. Adapted from Sherwood MW, Kristin Newby L. High-sensitivity troponin assays: evidence, indications, and reasonable use. J Am Heart Assoc. 2014;3:e000403, with permission.

CHAPTER 1  Lab Standards: A Practical Guide for Clinicians The receiver operating characteristic (ROC) curve is typically used to evaluate the clinical utility of a biomarker for both diagnostic and prognostic purposes.11 More specifically, evaluation of a novel biomarker is generally based on its capability to improve the area under the ROC curve (AUC).12 However, even strong independent risk predictors can have a very limited impact on the AUC. Therefore calibration, i.e., measuring how well the predicted probability agrees with the observed proportions in a population, is essential in the assessment of the accuracy of prediction models.13 Reclassification can directly compare the clinical impact of two prediction models by determining how many individuals would be reclassified into clinically relevant risk strata (e.g., low, intermediate, or high), which may form the basis for treatment decisions. The percent reclassified can thus be used as a measure of the clinical impact of a new marker when added to an existing prediction model. 

Variability A number of aspects related to the biophysical and/ or structural features of a specific biomarker can also greatly influence its utility. For example, it is important to understand how circulating levels of a particular biomarker are influenced by factors such as diet, diurnal variation, day-to-day variation within an individual, half-life in circulation, and dynamic range within a population. Information regarding the intraindividual variation in biomarker levels as well as the analytical variation of biomarker assays at medical decision levels can be particularly useful to the practicing clinician in interpreting test results. Biomarkers that have a relatively long half-life in circulation (i.e., at least several hours) and a relatively small intraindividual variation in circulating levels compared with the dynamic range within a population are better suited as potential markers for risk prediction. 

Assay Standardization It is highly desirable that a proposed marker can be measured accurately using standardized and cost-effective methods in a routine clinical laboratory setting. For most well-established risk factors, methods are eventually adapted so that these biomarkers can be measured using standardized assays on specialized automated chemistry analyzers in routine clinical laboratories. The development of standardized reference methods and standard reference materials (SRMs) containing known amounts of the analyte of interest is a crucial component of assay standardization. Immunoaffinity assays are most commonly used to evaluate emerging risk factors. Immunoassays use

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monoclonal or polyclonal antibodies to capture targeted proteins through establishment of diverse noncovalent bonds (e.g., electrostatic or hydrophobic interactions, van der Waals forces, or hydrogen bonds), specific to the individual antigen-antibody pair. Target proteins are then indirectly quantified based on the signal intensity of luminescent (e.g., electrochemiluminescence immunoassay [ECLIA]), fluorescent (fluorescent immunoassay [FIA]), enzymatic (e.g., enzyme-linked immunosorbent assay [ELISA]), or radioactive (radioimmunoassay [RIA]) antibodylabeled reporter molecules or via light scattering of antigen-antibody complexes (e.g., immunoturbidimetry and nephelometry). ELISA methods in particular are considered the “work horse” of novel biomarker research. ELISA methodology generally provides sufficient sensitivity, is relatively cost-effective, does not require advanced instrumentation, and can be performed in routine laboratories.14 However, ELISA methods are notoriously difficult to standardize, even when monoclonal antibodies are used, and can be subject to interfering factors such as antibody-specific cross-reactivity, complexity of the sample matrix, autoantibodies, and genetic mutations or polymorphisms which can alter epitope recognition by monoclonal antibodies. Mass spectrometry (MS) is the most common technology used in proteomics biomarker research because of its unique ability to identify proteins in a nonbiased manner. The mass-to-charge (m/z) ratio of a molecule is the principal measurement obtained from MS analysis, and data from a specific sample are usually displayed as a “spectrum,” which is a plot of the m/z ratio on the x-axis versus the level of intensity on the y-axis. Currently a number of different instruments are being manufactured which use MS sometimes combined with a chromatographic technique to aid in the separation of molecules in a complex sample matrix. The two most common chromatographic techniques used in combination with MS are liquid chromatography (LC) and gas chromatography (GC). Using LC combined with tandem mass spectrometry, captured ions are subjected to sequential ionization, and fragmented products of these ions can be analyzed, which allows for the identification of different peptides. Methods based on the combination of chromatography and MS techniques (e.g., gas chromatography-mass spectrometry [GC–MS] and liquid chromatography/tandem mass spectroscopy [LC-MS/MS]) are generally considered “the gold standard” because they are not affected by the same interfering factors as ELISA methods. However, GC–MS and LC-MS/MS are generally less cost-efficient and, because

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Biomarkers in Cardiovascular Disease

they require a higher level of operator expertise, are generally not well suited for routine clinical laboratories. The recent advancements in GC–MS and LC-MS/ MS technologies will almost certainly improve their clinical utility in the near future. 

disease but fail to predict risk for disease have been described. 

Biomarker Stability

The human proteome is far more complex than the human genome and has been estimated to include anywhere from 20,000 (based on the number of proteincoding genes) to millions of proteins resulting from alternative splicing, posttranslational modification, and proteolytic processing (Fig. 1.1). Although a number of different proteomic approaches have been applied to identify new biomarkers for cardiovascular diseases, the principle underlying all proteomic approaches is the comparative analysis of protein expression profiles in normal versus diseased tissues. A growing number of studies have used proteomic analysis of different types of tissues including vascular cells, atherosclerotic plaques, heart, and blood to identify potential novel protein biomarkers associated with the pathogenesis of cardiovascular disease.17–20 Furthermore, the Human Proteome Organization (www.HUPO.org) ­initiatives have been created to aid in the development of a human protein reference database derived from different biological compartments, including plasma, urine, brain, heart, and liver. Proteomic approaches using plasma have gained in popularity because plasma samples, unlike other biospecimens, are usually collected and stored as part of clinical examinations in large epidemiologic studies and clinical trials. Furthermore, the plasma proteome is believed to constitute a complex mixture of proteins

Specialized sample processing and storage requirements can also affect the clinical utility of a biomarker. In particular, the effect of long-term storage on biomarker stability varies greatly among analytes and is dependent on storage conditions, sample matrix, and the addition of specific preservatives. Large prospective epidemiological studies are ideally suited to assess the predictive value of novel risk factors. Because most epidemiological studies measure biomarkers in biological samples that have been stored for long time periods, it is important to investigate what impact long-term storage may have on biomarker levels. The Atherosclerosis Risk in Communities study recently reported that the impact of long-term biospecimen storage (up to 25 years) on circulating levels of established cardiovascular lipid risk factors may be minimal.15 However, there is a paucity of data regarding the potential impact of long-term biospecimen storage on novel biomarkers. 

Causality Issues related to the need for a causal relationship between a marker and the pathogenesis of a certain disease remain a controversial topic among biomarker researchers. Markers that have proven to be valuable in risk assessment without a clear causal relationship as well as markers that are established mediators of

Genome

Transcriptome

Genes (2 × 104)

mRNAs (> 106) Transcription

Proteins (> 106) Translation

PROTEOMICS-BASED DEVELOPMENT OF CARDIOVASCULAR BIOMARKERS

Proteome

Cellular processing

Modified proteins (> 107)

Phenotype Enzymatic reactions

Metabolome Metabolites (3 × 103) FIG. 1.1  Increasing complexity of the human proteome compared with the human genome. (Adapted

from Gerszten RE, Wang TJ. The search for new cardiovascular biomarkers. Nature. 2008;451:949–952.)

CHAPTER 1  Lab Standards: A Practical Guide for Clinicians derived from all tissues, which makes plasma an attractive medium for clinical analysis as it represents the molecular states of diverse systems.21 The measurement of circulating biomarkers has long been central in decision-making in cardiovascular medicine, most prominently for the diagnosis of myocardial infarction (e.g., creatine kinase and troponins) and heart failure (e.g., natriuretic peptides) and for cardiovascular risk stratification (e.g., lipids and lipoproteins). However, it is clear that the complexity of cardiovascular disease requires a more complete and systematic assessment of the entire range of proteins measurable in plasma (the plasma proteome) to further our understanding of the underlying pathogenesis of cardiovascular disease. The systematic investigation of the plasma proteome may lead to unbiased discovery of novel biomarkers to improve diagnostic and predictive accuracy and identify therapeutic targets. The recent advent of novel multiplexing methods, including nucleotide-labeled immunoassays and aptamer reagents, and other proteomic approaches allows for a more systematic investigation of complex human diseases and provides new tools for biomarker development.

Tools Used in Proteomics The diversity in protein abundance of the plasma proteome, which spans more than 11 orders of magnitude, can greatly influence the identification of potential biomarkers, particularly those that are present at extremely low concentrations.22 MS is the most powerful tool for systematic and unbiased investigation of proteins present in tissues and cells, including posttranslational protein modifications.23–25 However, the complexity of the plasma proteome requires multiple sample preparation stages, including depletion of high-abundance proteins, concentration of low-abundance proteins, protein separation (e.g., LC or GC), and protein identification (e.g., trypsin digestion).26 A large number of different methods for sample preparation and protein separation and identification have been described,27 Sample processing usually include protocols to solubilize proteins in chaotropic agents (e.g., urea) or nonionic detergents (e.g., 3[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate [CHAPS]). These types of reagents do not substantially alter protein charge, which affects the chromatographic properties of proteins during fractionation. The most widely used technique for proteomic protein separation is two-dimensional electrophoresis (2-DE).28,29 Proteins are amphoteric molecules that carry

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a negative, positive, or zero net charge, which is determined by their amino acid composition and the pH of their environment (molecular charge). In 2-DE proteins are first separated based on their differences in molecular charge, a process known as isoelectric focusing. The proteins are then separated according to their molecular weight by sodium dodecyl sulfate-polyacrylamide gel electrophoresis in the second dimension. After separation by 2-DE, the proteins can be visualized via a number of different techniques, such as staining, radiolabeling, and immunodetection. The final step in the proteomic analysis is protein identification, which almost always involves some form of MS as mentioned previously. Although 2-DE and MS have been the methods of choice for proteomic analysis, other methodologies that use multiplexing of immunoaffinity assays are currently being developed. Protein microarray technology generally uses a platform for capturing proteins by immobilizing potential ligands (binding partners) on a solid surface, followed by detection and identification of the bound protein.30 Potential ligands include antibodies and nucleic acids among other molecules. A specific example of microarray technology has been developed by the Luminex Corporation (Austin, TX). The Luminex xMAP system is a multiplexed microsphere-based suspension array platform for highthroughput protein and nucleic acid detection.31 This platform is unique in that it combines flow cytometry with conventional ELISA technology, which makes it relatively cost-effective and accessible to routine laboratories. Several studies have demonstrated its use as an efficient tool for both genetic and proteomic biomarker discovery.32–34 Multiplex analysis can be very useful to examine the effects of an intervention on numerous biomarkers in clinical research studies, particularly when there may be small sample volume of stored plasma or serum.33 However, some multiplex assays may require validation before selection in a clinical research protocol because low assay sensitivity and relatively poor correlation to conventional ELISA methods may be problematic, particularly for some analytes with very low plasma concentrations.33 Furthermore, a major obstacle for affinity proteomic technologies is that multiplexing is limited because of cross-reactivity of affinity reagents.35 Other proteomic approaches currently under development include systems for (1) ultrasensitive detection of single molecules, (2) nucleotide-labeled immunoassays, (3) aptamer reagents for efficient multiplexing at high sample throughput, and (4) coupling of affinity capture methods to MS for improved specificity (see Fig. 1.2).

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Biomarkers in Cardiovascular Disease

1. Ultrasensitive detection of single molecules by immunoaffinity assays is typically accomplished by using highly sensitive detectors, antibodies labeled with fluorescent or luminescent reporters for signal amplification, and preanalytical enrichment steps.37,38 2. Labeling of antibodies with nucleic acids can significantly improve assay sensitivity because nucleic acids can be amplified and quantified by WatsonCrick base pairing to fluorescently labeled primers based on the polymerase chain reaction (PCR).39 One specific development of this principle technique, referred to as a proximity extension assay, has shown particularly useful in multiplexing by significantly reducing the problem of cross-reactivity.40,41 3.  An alternative strategy to address the limitations of current immunoassays is the development of affinity reagents other than antibodies, such as synthetic molecules or aptamers. Aptamers can include engineered antibody fragments, synthetic peptides,

or oligonucleotides.42,43 Oligonucleotide-based aptamers are particularly attractive because they are simple and cost-efficient to design and have the major advantage that they can be amplified for improved sensitivity and easy detection using PCR and hybridization arrays. Large libraries of random oligonucleotides are mixed with target proteins in an iterative process to test for binding, whereas other oligonucleotides that bind to other targets are depleted from the pool. The nucleotide aptamers will physically interact with the protein surface and display high-affinity binding that can be altered via sequence modifications.44 A commercial platform using this aptamer technology has been developed by SomaLogic Inc.45 4. The target specificity of affinity-based assays can be improved by coupling them to MS methods. The affinity reagents are used to capture protein targets in a pull-down assay which are then eluted from the

Ultrasenstitive flow-based immunoassay

A

Capture on plate or bead

Binding of labeled antibody

Elution

Fluorescent tag counting in flow cell

Proximity extension assay

B

Capture with labeled antibodies

Extension of oligonucleotides with DNA polymerase

Real-time qPCR quantification

Aptamer array

C

Capture with bead- Protein biotinylation immobilized and photocleavage aptamers release

Capture on streptavidin beads, elution of aptamers

Aptamer quantification on DNA microarrays

Intensity

Affinity pulldown for mass spectrometry

Protease digestion

D

Antibody pull-down Mass spectrometry and spike-in of isotopically labeled peptide standards

m/z Quantification from m/z spectra

FIG. 1.2  Use of emerging affinity-based proteomics methods. (Adapted from Smith JG, Gerszten RE.

Emerging affinity-based proteomic technologies for large-scale plasma profiling in cardiovascular disease. Circulation. 2017;135:1651–1664.)

CHAPTER 1  Lab Standards: A Practical Guide for Clinicians

(see Rayner and Moore52 for review and Fig. 1.3). Particularly, miR-33a and miR-33b have been shown to repress the expression of ABCA1, ABCG1, and NPC1, thereby inhibiting cellular cholesterol efflux.50,53 Interestingly, both miR-33a and miR-33b are embedded in the intronic regions of the genes that encode the nuclear transcription factors sterol regulatory element– binding proteins SREBP1 and SREBP2, which control the expression of a number of genes involved in cholesterol and fatty acid synthesis. Inhibition of miR-33 results in increased cholesterol efflux from hepatocytes to apo A-I in vitro and raises high-density lipoprotein cholesterol (HDL-C) levels in mice.50,53 Furthermore, miR-33a/b inhibition also has been shown to raise HDL-C and lower very-low-density lipoprotein triglycerides in nonhuman primates.54 Although miRNAs act intracellularly, extracellular miRNAs can be detected in the circulation bound to proteins,55 on lipoproteins,56 or within vesicles such as microparticles, exosomes, and apoptotic bodies.57–59 Interestingly, miRNAs are remarkably stable in circulation and share many of the essential characteristics of a good biomarker, such as noninvasive measurability, a long half-life in the sample, a high degree of sensitivity and specificity, and cost-effective laboratory testing. A number of recent studies have investigated the use of circulating miRNAs as biomarkers for the diagnosis and prognosis of cardiovascular diseases (see Condorelli et al.60 for review),

r­ eagent, digested to peptides, and sequenced to detect captured proteins by using unbiased MS. Captured protein targets can then be quantified using isotopically labeled synthetic peptides by targeted MS. 

ADVANCES IN MICRORNA RESEARCH FOR CARDIOVASCULAR RISK PREDICTION MicroRNAs (miRNAs) are short (∼19–25 nucleotides in length) noncoding RNA molecules that modulate the stability and/or the translational efficiency of target messenger RNAs (mRNA) by base pairing with complimentary sites within these target mRNAs. Generally miRNAs act as negative regulators of gene expression because miRNA:mRNA duplex formation leads to degradation or translational inhibition of the target mRNA, although examples of a few exceptions to this have been reported.46,47 More than 2500 miRNAs have been identified, and it is estimated that more than 50% of the human genome is under miRNA control.48,49 Evidence from a number of recent studies highlight the important role of miRNAs in regulating key biological pathways related to lipid metabolism,50 oxidative stress, and systemic inflammation,51 all of which are known to be involved in the etiology of cardiometabolic disease. A number of miRNAs have been shown to control the expression of genes affecting lipoproteins and the reverse cholesterol transfer pathway

Intestine microbiome

ABCG1

Liver

Macrophage

miR-10b

ABCA1

ABCG1

miR-144

ABCA1 Lipid-poor apoA-I

miR-758 miR-33/33* miR-185 miR-96 miR-223 miR-125a

ATP8B1

Lipid-poor apoA-I

ABCA1 miR-33/33* miR-758 miR-26

HDL

ABCB11 Biliary Excretion

LCAT

SMC

SR-BI Mature HDL miRNA delivery

7

miR-92a miR-126 miR-223

miRNA exchange?

miR-21 miR-124 miR-143/ miR-221/222 145 EC miR-126

MØ

miRNA exchange?

FIG. 1.3  Role of miRNAs in the regulation of HDL metabolism. (Modified from Rayner KJ, Moore KJ. Mi-

croRNA control of high-density lipoprotein metabolism and function. Circ Res. 2014;114:183–192.)

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Biomarkers in Cardiovascular Disease

including acute myocardial infarction,61,62 heart failure,63 cardiomyopathy,64 and atherosclerosis.65 Despite these promising findings, the use of miRNAs as biomarkers of cardiovascular disease will have to be validated in large population-based studies. Furthermore, the potential for miRNAs as therapeutic targets for the treatment of cardiovascular disease remains an active topic of research. 

IMPACT OF NEW BIOMARKERS ON CURRENT GUIDELINES With the rapidly emerging biomarker discoveries, clinicians need advice on the clinical validity and utility of new tests and whether they improve clinical, patient-centered, or economic outcomes. Biomarker tests initiate a cascade of decisions which subsequently determine the course and costs of patient management. Recognizing the importance of testing in medical decisions and given the limited healthcare resources, it is now generally viewed that clinical utilization and reimbursement of diagnostic tests should move from a cost-based toward a value- and evidence-based approach. Evidence-based clinical practice guidelines (CPGs) may be best suited in conveying this message to practicing clinicians and their patients. Careful evaluation of new biomarker tests should be carried out in a step-wise fashion before any recommendations can be made regarding their clinical utility or incorporation into the patient management pathway (Fig. 1.4 adapted from Horvath et al.66). High-quality CPGs are systematically developed statements that provide recommendations about the management of specific diseases and should be outcome oriented, reliable (i.e., developed in a transparent and reproducible manner free from commercial influence or bias), multidisciplinary, clinically applicable, clearly written, regularly reviewed and updated, appropriately disseminated and implemented, cost-effective, and amenable to measurement of their impact in clinical practice.67,68 Furthermore, in good CPGs the overall quality or strength of the

evidence and the strength of the recommendations should be graded separately. Unfortunately, there are currently a large number of CPGs for the management of cardiovascular diseases available on the internet (WWW), sometimes with seemingly contradicting recommendations for the same condition. Obviously this complicates matters for practicing physicians trying to understand which guideline to choose in everyday practice. Therefore it is of critical importance that a more transparent process for CPG development is adopted to aid physicians in harmonizing the approaches and standards of care. 

CONCLUSIONS Biomarkers play important diagnostic and prognostic roles in cardiovascular disease risk assessment and can serve as surrogate endpoints to evaluate safety and efficacy of therapeutic interventions. With the emergence of high-sensitivity assays, it is imperative that clinicians, researchers, and laboratorians learn about the unique characteristics of these assays which will enable their implementation into clinical practice. In particular, high-sensitivity troponin assays have shown added sensitivity for cardiac myocyte necrosis, but this greater sensitivity requires careful interpretation of these tests by the practicing clinician. Continued advancements in proteomics methodologies, such as MS and novel multiplexing methods using nucleotide-labeled immunoassays and aptamer reagents, allow for a more systematic investigation of the plasma proteome which may lead to unbiased discovery of novel biomarkers to improve diagnostic and predictive accuracy and identify new therapeutic targets. Research studies into the use of miRNAs as biomarkers of cardiovascular disease risk show promise but will need further validation in large population-based studies. Despite the significant progress in biomarker research, assay standardization remains a major hurdle in the assessment of the clinical validity and utility of new biomarker tests and their implementation into evidence-based CPGs.

PHASE I

PHASE II

PHASE III

PHASE IV

PHASE V

PHASE VI

Association of disease with new biomarker

Potential use of new biomarker in practice

Analytic validity

Clinical validity (efficacy)

Clinical Utility (effectiveness)

Clinical impact (efficiency)

FIG. 1.4  Phases of biomarker evaluation. (Adapted from Horvath AR, Kis E, Dobos E. Guidelines for the

use of biomarkers: principles, processes and practical considerations. Scand J Clin Lab Invest Suppl. 2010;242:109–116.)

CHAPTER 1  Lab Standards: A Practical Guide for Clinicians

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