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Use of Genomic Profiling to Assess Risk for Cardiovascular Disease and Identify Individualized Prevention Strategies—A Targeted Evidence-Based Review Glenn E

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Use of Genomic Profiling to Assess Risk for Cardiovascular Disease and Identify Individualized Prevention Strategies—A Targeted Evidence-Based Review Glenn E REVIEW Use of genomic profiling to assess risk for cardiovascular disease and identify individualized prevention strategies—A targeted evidence-based review Glenn E. Palomaki, BS1, Stephanie Melillo, MPH2,3, Louis Neveux, BA1, Michael P. Douglas, MS2,3, W. David Dotson, PhD2, A. Cecile J. W. Janssens, PhD4, Elizabeth A. Balkite, MS, CGC5, and Linda A. Bradley, PhD1,2 Purpose: To address the key question of whether using available “car- INTRODUCTION diogenomic profiles” leads to improved health outcomes (e.g., reduction in CVD is a major contributor to morbidity and mortality in the rates of myocardial infarction and stroke) and whether these profiles help in United States. An estimated 80 million adults have one or more making medical or personal decisions. Methods: A targeted evidence- types of CVD (48% occurring in individuals aged 60 years or based review based on published Evaluation of Genomic Applications in older), and preliminary 2006 mortality data indicate that CVD Practice and Prevention methodologies. Results: No study addressed the accounts for one in every 2.9 deaths.1 The 2005 overall death overarching question directly. Evidence for the analytic validity of genomic rate from CVD was 279 per 100,000, with death rates higher in profiles was inadequate for most genes (scale: convincing, adequate, and men than women, and in blacks than whites. Consequently, the inadequate), but based on gray data, the analytic sensitivity and specificity might be adequate. For the 29 candidate genes (58 separate associations burden of CVD is high, and the cost (direct and indirect) in 2008 2 reviewed), the credibility of evidence for clinical validity was weak (34 is estimated at 448.5 billion dollars. Prevention and manage- associations) to moderate (23 associations), based on limited evidence, ment of CVD, particularly ischemic heart disease and stroke, 3 potential biases, and/or variability between included studies. The associa- present a difficult challenge for health care and public health. tion of 9p21 variants with heart disease had strong credibility with odds Major nonmodifiable risk factors include increasing age, male ratios of 0.80 (95% confidence interval: 0.77–0.82) and 1.25 (95% confi- gender, and heredity, whereas modifiable risk factors include dence interval: 1.21–1.30), respectively, for individuals with no, or two, smoking, hypertension, dyslipidemia, obesity, physical inactiv- 4–6 at-risk alleles versus those with one at-risk allele. Using a multiplicative ity, and diabetes. African Americans have more severe hy- model, we combined information from 24 markers predicting heart disease pertension and increased risk of heart disease compared with 7,8 and from 13 markers for stroke. The areas under the curves (64.7% and whites. In men, the average annual rate of initial cardiovas- 55.2%, respectively), and overall screening performance (detection rates of cular events increases from three per 1000 at 35–44 years to 74 24% and 14% at a 10% false-positive rate, respectively) do not warrant use per 1000 at 85–94 years. Similar increases occur in women but as stand-alone tests. Conclusion: Even if genomic markers were indepen- approximately a decade later in life.2 dent of traditional risk factors, reports indicate that cardiovascular disease The term “CVD” encompasses a broad range of disorders of risk reclassification would be small. Improvement in health could occur the heart and circulatory system, and classification of CVDs is with earlier initiation or higher adherence to medical or behavioral inter- not uniformly applied in the literature. We defined two main ventions, but no prospective studies documented such improvements (clin- groups of outcomes based on the International Statistical Clas- ical utility). Genet Med 2010:12(12):772–784. sification of Diseases and Related Health Problems (I00-I99).9 The grouping of CHD includes coronary artery disease, isch- Key Words: evidence review, genomic profiles, cardiovascular dis- emic heart disease, and MI. The second grouping of stroke ease, analytic validity, clinical validity, clinical utility includes intracerebral and subarachnoid hemorrhage, ischemic stroke, and other diseases (e.g., cerebral infarction and occlu- sion/stenosis of cerebral arteries). In addition to the conven- From the 1Women & Infants Hospital/Alpert Medical School of Brown tional risk factors, biomarkers have also been identified for use 2 University, Providence, Rhode Island; Office of Public Health Genomics, in potentially improving the prediction of CVD risk. In one Centers for Disease Control and Prevention, Atlanta, Georgia; 3McKing Consulting Corporation, Atlanta, Georgia; 4Department of Public Health, study, 10 biomarkers (e.g., C-reactive protein, fibrinogen and Erasmus MC University Medical Center, Rotterdam, The Netherlands; and homocysteine) provided only modest additive contribution to 5ABQ Associates, Inc. Durham, North Carolina. risk prediction compared with the use of conventional risk 10 Glenn E. Palomaki, BS, Women & Infants Hospital, 70 Elm Street, 2nd factors alone. Floor, Providence, RI 02903. E-mail: [email protected]. Over the last decade, candidate genes for CVD susceptibility Disclaimer: The findings and conclusions in this report are those of the have been the focus of many relatively small studies that in- authors and do not necessarily represent the official position of the Centers clude one or a few genes at a time.11 These candidate gene- for Disease Control and Prevention. disease association studies often have lacked power, and the Disclosure: The authors declare no conflict of interest. reproducibility in subsequent confirmatory studies has generally been poor. More recently, genome-wide association (GWA) Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of studies have tested tens of thousands of single-nucleotide poly- this article on the journal’s Web site (www.geneticsinmedicine.org). morphisms (SNPs) in an effort to identify associations with 11–13 14 Submitted for publication April 27, 2010. CVD. Despite notable gaps in knowledge, genomic pro- filing tests for CVD (sometimes referred to as “heart health”) Accepted for publication August 10, 2010. continue to be offered in the health care market and can be Published online ahead of print November 11, 2010. ordered online without the involvement of a physician. The goal DOI: 10.1097/GIM.0b013e3181f8728d of this evidence review is to assess what is known about the 772 Genetics IN Medicine • Volume 12, Number 12, December 2010 Genetics IN Medicine • Volume 12, Number 12, December 2010 Genomic profiling for risk of CVD analytic validity, clinical validity, and clinical utility of these cardiogenomic profiling tests. Table 1 Key questions relating to the analytic framework A recent HuGE Navigator search (Phenopedia, using the 1. Does the use of “cardiogenomic profiling” lead to improved disease term “cardiovascular diseases”) identified 6493 publi- outcomes for the patient/consumer, or is it useful in medical or cations, 3331 genes, 169 meta-analyses, and 60 GWA study personal decision making? (Overarching question) publications. This targeted review addresses only those genes included on genomic profiles aimed at CVD or “heart health” 2. What is known about the analytic validity of tests that identify variations in genes associated with “heart” or cardiovascular that were available when the study was undertaken in mid 2008. health, including the analytic sensitivity and specificity, assay robustness (e.g., failure rates and resistance to changes in variables such as sample quality), and other factors? Clinical scenario 3. What is the clinical validity of cardiogenomic profiles, including The clinical scenario focuses on individuals in the general clinical sensitivity and specificity and positive and negative population being offered “cardiogenomic profiling” or “heart predictive value? health” tests. In general, these individuals will not have an existing diagnosis of a specific CVD event (e.g., stroke and MI) a. What is the strength of association of cardiovascular health but may have intermediate findings (e.g., hyperlipidemia). The outcomes with the presence of specific gene variants (e.g., odds proposed clinical utility for testing is that the knowledge of a ratios)? personal gene variant(s) associated with an increase in risk for b. How well does this testing alone predict specific cardiovascular CVD might improve health outcomes (e.g., morbidity and mor- outcomes (e.g., MI and stroke)? tality) by (1) providing motivation beyond routine health mes- c. How well does this testing in combination with other CVD sages to change health behaviors; (2) bringing potential risk to testing (e.g., cholesterol) predict specific cardiovascular the attention of health care providers for clinical follow-up; or outcomes? (3) making or revising treatment decisions. The evidence review was commissioned and will be used by the Evaluation of d. Do other factors (e.g., race/ethnicity, family history, smoking, Genomic Applications in Practice and Prevention (EGAPP) diet, exercise level, and other conditions) affect the clinical Working Group (EWG) to inform the development of formal validity of the testing? recommendations for clinical practice. This review addresses 4. What are the issues relating to the use of cardiogenomic profiles the overarching question: “Does the use of ‘cardiogenomic in the designated populations and what is the impact on patient/
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