Biomarkers in Cardiovascular Disease

Vijay Nambi

• Publisher: Elsevier
• Year: 2018
• ISBN:
  9780323548366
• Topic: Medicine
• Subtopic: Cardiology

Get a quick, expert overview of the ways in which biomarkers can be used to assess and guide the management of cardiovascular disease in the clinical setting. This concise, clinically-focused resource by Dr. Vijay Nambi consolidates today's available information on this rapidly changing topic into one convenient resource, making it an ideal, easy-to-digest reference for cardiology practitioners, fellows, and residents.

• Covers lab standards and statistical interpretation of biomarkers with a clinical focus.
• Discusses relevant conditions such as hypertension and diabetes as key markers of injury and prognosis.
• Includes current information on biomarkers to assess and guide the management of heart failure, acute coronary syndrome, chest pain, shortness of breath, and more.
• Concludes the book with a timely chapter on how biomarkers may guide cardiologists in the future.

Chapter 1

Lab Standards

A Practical Guide for Clinicians

Ron Hoogeveen, PhD

Abstract

Cardiovascular diseases, including coronary heart disease, stroke, and heart failure, remain the leading causes of death and hospitalization worldwide. Biomarkers play a crucial role in clinical decision-making in cardiovascular medicine. Continued advancements in proteomics methodologies allow for a more systematic investigation of the plasma proteome that may lead to unbiased discovery of novel biomarkers to improve risk assessment for cardiovascular diseases and identification of new therapeutic targets. In this chapter we discuss some of the most promising emerging technologies used in proteomic biomarker development today. Furthermore, we highlight useful biomarker characteristics and hurdles in the assessment of the clinical validity and utility of new biomarker tests. Finally, we look at the impact of new biomarkers and their implementation into evidence-based clinical practice guidelines that can aid physicians in harmonizing clinical decision-making and standards of care.

Keywords

Aptamers; Biomarkers; Clinical practice guidelines; High-sensitivity troponins; Immunoaffinity assays; Mass spectrometry; MicroRNA; Multiplexing; Proteomics

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 single-nucleotide 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 concentrations of the same biomarkers but at much lower concentrations. With the development of high-sensitivity 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 = mean_blank + 1.645 (SD_blank). 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 (SD_{low 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-sensitivit...