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Circulation. Author manuscript; available in PMC 2017 June 21. Published in final edited form as: Circulation. 2016 June 21; 133(25): 2626–2633. doi:10.1161/CIRCULATIONAHA.116.023528.
The Future of Cardiovascular Epidemiology Ramachandran S. Vasan, MD1,2,3,4 and Emelia J. Benjamin, MD, ScM1,2,3,4 1National
Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA
2Evans
Department of Medicine, Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, MA
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3Preventive
Medicine and Cardiology Sections, Department of Medicine, Boston University School of Medicine, Boston, MA
4Department
of Epidemiology, Boston University School of Public Health, Boston, MA Cardiovascular disease (CVD) is a major cause of morbidity and mortality world-wide, with the lifetime risk exceeding 60%.1 Over 2200 Americans die of CVD daily, one death every 40 seconds. A third of CVD deaths occur before age 75 years, which is lower than the average life expectancy of 78.8 years.1 Thus, prevention of CVD is a public health priority. Major advances in cardiovascular epidemiology over the last four decades have improved our understanding of the pathogenesis of CVD, with the identification and treatment of several major risk factors.2
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Yet, several factors are worth highlighting to place these past achievements in appropriate perspective.
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1.
There remains a substantial societal burden of risk factors. Data indicate that one in three Americans reports no leisure time physical activity, and is likely to have high blood pressure, pre-diabetes, or high blood levels of LDL cholesterol.1 A majority of adults in the United States are overweight or obese, about a fifth have low levels of HDL cholesterol, a sixth has the smoking habit, and about a tenth has diabetes mellitus.1
2.
Projections indicate that the prevalence of CVD in the United States may escalate by 10% between 2010 and 2030.3 The estimated increase stems in part from the aging of the population and is also fueled by the recent trends for increasing obesity rates, and the concomitant rising rates of hypertension (8% increase over next decade) and diabetes mellitus (100% increase over next three decades).1 These projections indicate that past achievements potentially may be challenged by some of the recent adverse trends in some risk factors.
Address for Correspondence: Ramachandran S. Vasan, MD, Framingham Heart Study, 73, Mt. Wayte Avenue, Suite #2, Framingham, MA 01702, [email protected].
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There is a glaring 14-year difference in life expectancy between select population groups in the United States, as identified in the Eight Americas Study,4 with CVD emerging as the greatest source of difference in life expectancy. Indeed, it is estimated that blacks higher burden of usual CVD risk factors, are two to three fold more likely to die of heart disease compared with whites, and have higher rates of premature death resulting from CVD.1 Thus, major declines in CVD mortality accrued over decades of advances in population health have not effaced the racial and ethnic gaps in CVD morbidity and mortality.5
4.
There is increasing emphasis that key research, including cardiovascular epidemiological research, must translate directly to improvements in public health, focus on diverse populations, and engage communities actively. Indeed, the utility of cardiovascular epidemiological research has been periodically questioned in the past and also more recently.6 Dr. Michael Lauer (NHLBI) noted in a recent JAMA editorial the answer to
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3.
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the question “what has epidemiology done lately suggests two answers: much and not enough.”6
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Thus, cardiovascular epidemiology is confronted by a dualism: unprecedented opportunities for new research directions, amidst a growing uncertainty about its potential value and a pressure to ‘do more’ and be more accountable. Additionally, the notion that a lot of epidemiological research results in false positive findings has gained momentum,7 thereby raising the bar for both study quality and the necessity for replication as a sine quo non for all future epidemiological investigations. Overall, thought leaders have underscored the importance of consequential8 and translational epidemiology9 in the decades ahead for the science to remain relevant in the face of greater expectations that epidemiological research must impact public health more directly.
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Given this background, we identify key transformative directions the field of cardiovascular epidemiology will undergo over the next decade. The specific areas we highlight in our review include: the changing framework of population health and disease, including a greater integration of individual and community approaches, and the combination of health promotion strategies with traditional disease prevention/treatment goals (cHealth); recognition of the social determinants of health and their incorporation into the biomedical model of cardiovascular disease (sHealth); better characterization of the exposome including the built environment and via the ‘quantified’ self, fueled aptly by the digital data age revolution (mHealth); the integration of electronic medical records (eHealth) resources into the monitoring and maintenance of the health of individuals and populations; and harnessing of the power of the genomic revolution towards better precision cardiovascular medicine delivery (gHealth);. We also address related challenges that require reconfiguration of traditional cardiovascular epidemiology training and the development of the public health workforce of this century.
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Recognition of the essential role of Community-based approaches to cardiovascular health over the life course: cHealth
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It has been well recognized for decades that CVD is a life-course and lifestyle disease. Traditional approaches to preventing CVD have focused on the assessment and treatment of key risk factors at the individual level. Sir Geoffrey Rose elegantly articulated the debate between individual and population-based approaches to prevention of disease.10 He underscored both the complementarity of the two approaches as well as the fundamental importance of not ignoring population level interventions.10 Yet, these concepts languished as most cardiovascular guidelines and position papers have emphasized targeting of individuals for screening and management of CVD risk factors. More recently, there is increased recognition that it is critical to target both individual-level behaviors in addition to treating the associated levels of measured biochemical risk factors for CVD for preventing CVD successfully.
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In parallel, studies within the United States have demonstrated that sustained, communitywide programs targeting CVD risk factors and behavior changes to improve population health at a county level (e.g., Franklin county, ME) were associated with concomitant reductions in hospitalization and mortality rates over a 40-year period (relative to the rest of the counties where similar programs did not exist).11 This was reminiscent of the experience from North Karelia in Finland.12 Of note these community programs were integrated with primary care, which may explain the difference from prior experiences in the United States with three other programs (the Stanford Five-City Project13 and the Minnesota14 and Pawtucket Heart Health15 Programs); the latter three programs had shown relative improvements in CVD risk factors, yet no concomitant mortality impact possibly because these programs were not integrated well with primary care.16, 17 A major underlying and emerging theme is that community programs with primary care involvement alter the default health-related behavioral decisions of individuals, thereby proving to be more effective and less costly in shifting the entire distribution of CVD risk factors, consistent with the ‘health impact pyramid’.18
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Supporting the vital importance of community health promotion, the AHA has developed a Community Guide19 that integrates optimal health behaviors, community settings, and public health interventions to improve CV health. A wide range of public health strategies help promote optimal levels of CVD health factors over the entire life course, including dietary salt restriction, limits on food portion size, tobacco laws (including taxes) to promote smoking cessation and mitigate second hand tobacco exposure, and changes to the built environment to enhance physical activity.19 Additional steps such as taxation of sugarsweetened beverages and subsidies on fruits and vegetables may potentially influence behavior favorably, a premise warranting more research. In parallel with community efforts, awareness has increased within the academic medical school communities to move towards the ‘third curve’ representing entire populations (as compared to the first two curves representing patients seeking care and the managed care populations).20 Over the next decade, we foresee a greater emphasis on CV health promotion and CVD prevention within and across communities, with greater engagement of Circulation. Author manuscript; available in PMC 2017 June 21.
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the entire community, maintenance of surveillance, and partnerships in the actual physical settings where people live, work, and study. Future studies of CVD risk factors and their burden will move from the traditional focus on individuals and cohorts to a greater attention towards the central role of communities that individuals live and work in.
Recognition of the social determinants of cardiovascular health and disease: sHealth
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As noted above, the benefits of major advances in cardiovascular epidemiology and the pathogenesis and treatment of CVD have not trickled down uniformly across all strata of the United States population. Specifically, select socioeconomic, racial, and ethnic groups in the United States have a much greater prevalence of CVD risk factors and a higher CVD incidence and mortality.18 Linguistic differences and cultural beliefs and practices have been demonstrated to influence the behavior of individuals, including their health-seeking behaviors, their access to care and their compliance with preventive treatments. However, a major contributor to disparities are socioeconomic deprivation, implicit bias, and racial stereotypes, which affect both cardiovascular health promotion and disease prevention, and exacerbate disparities in the access to and delivery of cardiovascular care.18
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On a parallel note, the traditional biomedical model of CVD has embraced the notion that individual-level risk factors, both behavioral and biological, mediate CVD risk. Such a model ignores the environmental contexts that actually shape and determine the evolution of the CVD risk factors themselves.18 Recent work has also thrown the spotlight on social networks and their impact on the clustering and the development of behaviors (such as smoking,21 alcohol,22 and physical activity23), and their association with spread of obesity through the community.24 There is also a greater acknowledgment that diminished social support contributes to CVD risk.18 Recent research has highlighted the fundamental role of the neighborhood built/physical environment in influencing CVD risk. Thus, features of one’s residential area such as urban design and public spaces, land use patterns, street connectivity, access to parks and exercise/ recreational facilities, air and noise pollution levels, access to public transportation systems all seem to contribute to both health behaviors and the levels of the CVD risk factors.18 Additional built environment indicators may capture features of social norms and connectivity, and psychosocial stressors (such as safety, violence, and social cohesion). Thus, investigators from the Multi-Ethnic Study of Atherosclerosis (MESA) have elucidated how CVD risk factors are linked to the built physical environments, including neighborhood healthy food access and resources for physical activity.25, 26
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The coming decades will see greater efforts to capture contributions of socioeconomic position (as reflected by income, education and occupation) to CVD risk and to identify the mechanisms by which social networks and forces affect cardiovascular health outcomes. Additional research will characterize the contributions of perceived racism27, 28 and other psychosocial stressors in contributing to the burden of high blood pressure and vascular reactivity. It is likely that future studies will use a comprehensive framework for capturing the exposome, including delineating the built environment, to fully characterize propensity
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for developing CVD risk factors and overt disease and to promote an understanding of how best to influences factors at the neighborhood and at the individual level. Future cardiovascular epidemiological studies will evaluate the key role of culturally appropriate educational and other interventions aimed at improving cardiovascular health care.
Role of digital technology and mHealth
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Major advances in the miniaturization of computer chips and blue tooth wireless technology have enabled the development of biosensors that convert physiological sensing inputs into a measurable and interpretable signal.29 Wearable devices are ‘on or in the body’ digital elements that transduce physiological signals. The combination of biosensors and wearables can provide a substantial window into the exposome and has revolutionized mHealth, the use of portable digital devices to obtain clinical data that can then be used to identify, monitor, and guide the management of various health outcomes. Examples of mHealth technology include smartphone health ‘apps’, devices that can connect to a smartphone, wearables and biosensors, and handheld-imaging platforms.30 Quantitative biosensor and wearable data are already being used in epidemiological studies for some time (accelerometry is a good example), and newer waves of data collection including physiological measures (like heart rate and rhythm, blood pressure, weight, physical activity, etc.) are accruing via projects such as the Health eHeart Study at the University of California at San Francisco, a PCORnet Patient Powered Research Network. Apple’s open-source ResearchKit is already facilitating smartphone-based medical research, and collaboration between Duke University, Stanford University and Verily Inc. (formerly Google Lifesciences) will gather extensive “physiome” data through wearable sensors for the NIH’s Precision Medicine Initiative cohort of 1 million US participants. A pilot program at the Vanderbilt University medical Center will launch the Direct Volunteers Pilot Studies under the aegis of the federal Precision Medicine Initiative Cohort Program, in partnership with Verily, with the additional dimension of developing a biobank capable of storing and managing blood, urine, and saliva samples for analysis of the Precision Medicine Initiative Cohort Program.
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Over the next decade, we anticipate major headway in our assessment of the plethora of mhealth devices and apps, to develop quality standards for such data and comparability of devices, to define processes for their integration into the clinical decision support systems, and to establish regulatory procedures for devices used for this purpose. It has been estimated that by the year 2020, 90% of individuals over age 6 years in the world will possess a smartphone!31 The ability to gather health data via smartphones on virtually everyone world-wide poses a mindboggling opportunity and a challenge, and undoubtedly will influence the field of cardiovascular epidemiology over the next decade. Issues of data harmonization, data security and data integration, and individual privacy concerns will require substantial investment of efforts, followed by the incorporation of streams of real-life continuous data on select traits into the research portfolios of scientists. Traditionally, epidemiology has focused on limited phenotyping obtained in a state of rest and repeated periodically over a period of years. The ability to phenotype individuals on a wide array of physiological measures in real time and for prolonged periods of times is an unprecedented development rendered feasible by digital technology. Also, such ‘deep phenotyping’ will capture human responses to perturbations, thereby increasing the granularity of the human Circulation. Author manuscript; available in PMC 2017 June 21.
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phenome. However, using information on the ‘quantified self’ for research purposes is a relatively new occurrence and will likely require implementation of machine learning tools (such as support vector machines, random forests, etc.) that have been traditionally used in industry for commercial purposes.
Role of Electronic Medical Records (EMR) and eHealth
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Hospitals in the United States received a mandate to transition their patient clinical information to an electronic format under the Patient Protection and Affordable Care Act of 2010 enacted by President Obama. The expectation was that medical information about patients’ past histories and current illnesses would be captured digitally, thereby facilitating better health care across various institutions and health care systems, and also simultaneously open up new avenues for clinical research. The widespread use of electronic medical records (EMR) introduces challenges related to the interoperability of different platforms and a relative lack of standardization of various data elements across systems. There was also a federal mandate to invest in large data infrastructure projects such as the National Institutes of Health (NIH) Collaboratory and the National Patient-Centered Clinical Research Network (PCORnet), which facilitates the linkage of multiple EMR data, thus harnessing medical information on millions of patients for clinical investigators across numerous institutions and health care systems. EMR data also enable large-scale postmarket surveillance studies and recruitment of patients into pragmatic clinical trials. However, the EMR captures the exposome imperfectly as a substantial proportion of exposures (e.g., behavioral, socioeconomic, environmental factors) are not captured within the current EMR systems. Nonetheless, initial attempts at linking EMR to genome-wide data have born fruition, with the confirmation of some known ‘hits’ demonstrated in large-scale genome-wide association analyses and with the additional yield of new associations as well. We expect over the next decade a consolidation of efforts to standardize and harmonize data elements within EMR and greater use of these data for research purposes.32 As noted above the Precision Medicine Initiative Cohort program involves recruitment of a cohort of nearly a million individuals and integration of research with their EMR is one of the central features of this projects. We anticipate greater integration of EMR with genomic data as well (EWAS studies noted above),33 paralleled with evolution of the quality of EMR data, including enhanced interoperability of the various platforms. EMR data will also likely see greater use for recruitment of patients into pragmatic trials and for development of community-based research networks. It is also likely that healthcare data will become accessible via standardized open software interfaces (apps) to researchers (and patients) with a focus on ‘efficacy, accuracy, utility, safety, privacy and security’ features.34
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Exposome-Phenome-Diseasome Associations: Genomic and biomarker revolution and impact on Cardiovascular epidemiology (gHealth, bHealth) The molecular revolution, marked by the HAPMAP,35 1000 genomes36 and ENCODE37 projects and the OMICs tool box,38, 39 is reshaping current concepts of CVD. New technologies developed through the Human Genome Project and related initiatives have radically altered both the types of data that quantitative scientists now work with and the Circulation. Author manuscript; available in PMC 2017 June 21.
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types of questions epidemiologists ask using the available multi-dimensional data. The OMICs tool box has expanded our ability to measure numerous biomarkers and characterize genetic, epigenetic, transcriptomic, microRNA and noncoding RNA, proteomic, metabolomic, and microbiome profiles. Understanding genomics is now a critical element in cardiovascular epidemiology because of our enhanced ability to identify common and rare genetic variants, use genomic markers in adaptive clinical trial designs, and to evaluate circulating molecules originating in the gut microflora that influence cardiometabolic and vascular risk. The parallel developments in better characterization of the exposome (as noted above), the phenome (dynamic characterization, including in response to perturbations like exercise, glucose challenge, etc.) and the diseasomes itself (better delineation of disease networks, subsets, and endophenotypes)40, 41 will transform our understanding of the molecular epidemiology of CVD.
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The genomic revolution will juxtapose concepts of personalized medicine and comparative effectiveness research (CER; see also section below) due to the increased realization that interindividual variation in both the genome and the genomic and epigenomic responses to exposures and pharmaceutical agents are varied, thereby identifying more homogeneous molecular subsets to collate and in which to perform CER studies. There will be renewed and sustained interest in synthesis of the evidentiary basis for exposome-phenomediseasome associations through efforts such as the Human Genome Epidemiology Network (HuGENet)42 and Evaluation of Genomic Applications in Practice and Prevention, known as EGAPP,43 initiatives.
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The focus of risk prediction will veer more and more towards prevention over the lifecourse, with a consideration of both short-term (the traditional 10-year time window) and also a much longer time horizon (20 to 30-year time window).44, 45 As such, the biomarker revolution (b-health) will herald the measurement of different panels of biomarkers at various ages that capture the complexity and evolution of risk factors themselves, reflect more accurately the antecedents of these risk factors (such as better nutritional and adiposity biomarkers), and indicators of subclinical disease measures over the entire life course. Targeted interventions will be based on both an individual’s genotype as well as the exposome as reflected by biomarker profiles and will likely be highly individualized. Elucidation of the molecular epidemiology of diseasomes will mean that treatment may be highly personalized based on an individual’s trajectory of changes along the spectrum of disease states, and the choice of treatment may be facilitated both by a person’s genomic profile and the response of his/her induced pluripotent stem cells in vitro. The coming decade will also witness advances in our understanding of pharmacogenetics testing and reporting of genetic variants identified and their plausible molecular pathogenicity (or lack thereof). Another specific advance that will gain firm ground is the integration of cardiovascular epidemiology with chronic disease epidemiology, with the emerging refrain of a shared commonality of risk factors across several forms of non-communicable diseases, including cancer and pulmonary disease. Thus the prevention of CVD will be integrated within the broader framework of prevention of non-communicable diseases in the upcoming decades.
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Transformative Vision for Epidemiology in this century: extension of Big Data into cardiovascular Epidemiology
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Khoury et al46 proposed a transformative vision for epidemiology in this century with eight overarching thematic recommendations: “(1) extend research beyond traditional domains of discovery and etiologic research to include multilevel analysis, interventions, implementation, and outcomes research; (2) greater access to and sharing of protocols, metadata, and biosamples, fostering collaboration, ensuring replication, and accelerating translation; (3) expand cohort studies to collect diverse exposures across the life course to examine multiple health-related endpoints; (4) develop and validate reliable methods and technologies to quantify exposures/outcomes on a massive scale; (5) integrate "big data" science; (6) use knowledge to drive research, policy, and practice; (7) transform training of 21st century epidemiologists to address interdisciplinary and translational research; and (8) optimize resources and infrastructure for epidemiologic studies.” each of these recommendations is very relevant to the field of cardiovascular epidemiology.
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In 2013 the NIH unveiled the "Big Data to Knowledge" (BD2K) Initiative with $200 million committed to research in this domain.47 The development of systematic approaches to robustly manage, integrate, analyze, and interpret large complex data sets is crucial. Overcoming the challenges of developing the architectural framework for data storage and management may benefit from the lessons learned and the knowledge gained from other disciplines. Adaptation of technological advancements like cloud-computing platforms, already in use by private industries (e.g., Amazon Cloud Drive and Apple iCloud), can further facilitate the virtual infrastructure and transform biomedical research and health care. With high-powered computers and advances in computing software, complex methods for the analysis of massive data sets are becoming more feasible. The advent of ‘big data’ has resulted in questioning the importance of meticulous sampling designs, greater acceptance of misclassification in the trade-off for more data, and a movement away from traditional causal inferences and hypothesis testing to more agnostic approaches of enquiry and generation of new knowledge.
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Big data trends and scientific advances emphasize both the challenges and opportunities for restructuring cardiovascular epidemiology over the next few decades, which may increasing may be informed by comparative effectiveness research (CER).48 The last five years has witnessed an unprecedented emphasis on CER with a view to identifying the most effective health interventions for diseases and to achieve best health outcomes, including CVD, propelled in part by the American Recovery and Reinvestment Act (ARRA) of 2009 that appropriated $1.1 billion for CER ($400 million allocated to the NIH and the remainder to AHRQ and the Office of the Secretary of the Department of Health and Human Services). New CER studies over the next decade will likely use “different trial designs, very large sample sizes, high-throughput technologies, routine utilization of electronic medical records, and adaptations that reflect the infrastructures of integrated health care systems”. Simultaneously, new CER studies must be complemented by randomized trials that can verify results of observational data and also suitable inroads in implementation science to ensure that the best interventions can be adequately implemented in the clinical domain.
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There is an increasing emphasis on interventionist approaches in epidemiology, along with the notion of consequential epidemiology emphasized by Galea,49 in an attempt to keep the science more directly relevant to public health.8 Newer methods such as critical time windows, directed acyclic graphs, marginal structural models, g-health, and time-varying social networks are likely to see increased use in epidemiology.50 In parallel with methodological advances, there will be a greater globalization of cardiovascular epidemiology with emphasis on steps to quantify global burden of disease, characterize regional burden, and elucidate steps to implement primordial, primary and secondary prevention over the lifecourse across the various regions.51, 52 Cohort Studies in Cardiovascular Epidemiology at Crossroads
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As noted above, the field of cardiovascular epidemiology is undergoing a revolution due to the burgeoning amounts of genetic and genomic data, a plethora of biomarkers, and the emergence of systems biology and collaborative science. Paralleling these major scientific advances is the spawning of the notion that epidemiological studies, especially traditional cohort studies, have limited utility in today’s era because of high costs, modestly incremental knowledge, an inherent inability to innovate at reasonable cost, and a failure to identify and answer contemporary research questions. Additionally, the concept of decentralized large cohort studies (like the UK Biobank and the Precision Medicine Initiative Cohort Program, as currently envisaged), the notion of large data and biorepositories with ease of access, the possibility of free and opens haring of federated datasets53 has become firmly ensconced in science today. The imperative to perform innovative research addressing the most meritorious question, nimbly and in a cost-effective manner has never been greater.54 As such, the future of cohort studies has been debated actively in several scientific forums.55, 56 The concepts of embedding clinical trials within pre-enrolled cohorts has drawn traction in recent times and novel approaches such as genotype-based phenotyping of cohort subsets have been envisaged. We anticipate that the notion of multi-cohort studies across the human lifespan will witness a major rise over the next decade as it becomes clearer that elucidating risk factor-disease associations within different race/ethnic and age- and sex-based groups will assume critical importance. These cohort studies will see a rise in use of m-health and EMR-based phenotyping and key innovations in the creative analyses of such cohort data will likely bear fruition. The recent emergence of the cross-cohort consortium (CCC)57 of NHLBI-supported studies is an example of the kind of the transconsortial collaborations that are likely to assume prominence in the decade ahead.
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The Multidisciplinary workforce in cardiovascular epidemiology in this millennium The cardiovascular epidemiologist of the future will undoubtedly be working as part of a multidisciplinary team of scientists including data scientists, bioinformaticians, physiologists, geneticists, molecular biologists, and mathematicians, and the list of disciplines is by means complete. The concept of team science will be critical to the success
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of all scientific endeavors48, 58–61 and open access to data will be key along with need for built-in replication strategies for all scientific studies.
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A recent Institute of Medicine (IOM) report noted that “critical to the implementation of big data science is the need for high-quality biomedical informatics, bioinformatics, and mathematics and biostatistical expertise.”62 Indeed, successful analysis and integration of massive and heterogeneous datasets require new quantitative skills, supported by a deep understanding of the biological and experimental processes underlying the data, and an ability to appropriately use the information available from public databases. New quantitative skills include the ability to: (1) model complex systems using statistical machine learning methods for the intelligent search of large databases; (2) develop the different network formalisms ranging from regulatory networks to gene expression mechanisms; (3) use graphical models and Bayesian networks; (4) describe complex multivariate associations; and (5) use scale free networks to relate genotypes to phenotypes. Additionally, the IOM report62 emphasized the importance of: (a) transdisciplinary training, with the development of quantitative skills, as noted above; (b) translational research; (c) competency-based approaches to training and evaluation; (d) evolving notions of team science, research networking and their critical role in academic careers; (e) individualized development plans (IDP) in career planning and mentoring; (f) alternate pathways in careers, including collaborations with industry, and policy; (g) diversity barriers in biomedical research and required shifts in recruitment strategies.
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On a parallel note, a group of leading epidemiologists have recently reviewed twelve macro level trends that will shape the future of epidemiology, including the training of its workforce (some of these trends are reviewed in the present article).63 The required new skill sets and the impending big data changes suggest that training programs for the cardiovascular epidemiology work force for this millennium will need to be redesigned dramatically emphasizing skill sets that are multidimensional and iteratively acquired over the course of one’s career (Table).46, 62, 64–66
Conclusions
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The current vision of cardiovascular epidemiology emphasizes an ecological approach that incorporates the full range of biological, environmental, and social determinants of cardiovascular health across the life-course. This approach leverages emerging sciences (genetics and genomics), information and digital technology, and communication sciences, and underscores the importance of the social determinants of health. Indeed, determinants of CVD “act within complex, often scale-free networks made up of a small number of “hubs” that are extensively linked to many more relatively isolated determinants and that are similar in structure and function at the genetic, molecular, cellular, clinical, environmental, and societal levels.”6 Likewise, a wide spectrum of environmental and physiological measurements can be made using portable devices and mobile technology applying increasingly sophisticated sensor technologies to people, and biosamples. A plethora of biomarkers will become available to better characterize the evolution of CVD over the adult lifespan. The coming decades will see the integration of these cHealth, sHealth, mHealth, eHealth, gHealth and bHealth into cardiovascular epidemiology will see the generation of a
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multiplicity of datasets available in publicly accessible biorepositories for scientists to evaluate and test novel hypotheses. The aforementioned developments will require the retooling of the existing work force in cardiovascular epidemiology and a redesigning of training programs to meet these newer needs. Hopefully, these advances in cardiovascular epidemiology will translate into better cardiovascular health of the global community and promote the primordial, primary and secondary prevention of CVD world-wide.
Acknowledgments This work was supported by the NHLBI, Framingham Heart Study, (NHLBI/NIH Contract #N01-HC-25195 and HHSN268201500001I), 1P50HL120163, and the Boston University School of Medicine Evan’s Scholar Award (RSV).
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1. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de FS, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jimenez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER III, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 2016; 133:e38–e360. [PubMed: 26673558] 2. Lauer MS. Cardiovascular science in the service of national strength. JAMA. 2011; 306:2145–2146. [PubMed: 22089722] 3. Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, Finkelstein EA, Hong Y, Johnston SC, Khera A, Lloyd-Jones DM, Nelson SA, Nichol G, Orenstein D, Wilson PW, Woo YJ. American Heart Association Advocacy Coordinating Committee; Stroke Council; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Council on Arteriosclerosis; Thrombosis and Vascular Biology; Council on Cardiopulmonary; Critical Care; Perioperative and Resuscitation; Council on Cardiovascular Nursing; Council on the Kidney in Cardiovascular Disease; Council on Cardiovascular Surgery and Anesthesia, and Interdisciplinary Council on Quality of Care and Outcomes Research. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011; 123:933–944. [PubMed: 21262990] 4. Murray CJ, Kulkarni SC, Michaud C, Tomijima N, Bulzacchelli MT, Iandiorio TJ, Ezzati M. Eight Americas: investigating mortality disparities across races, counties, and race-counties in the United States. PLoS Med. 2006; 3:e260. [PubMed: 16968116] 5. Mensah GA, Mokdad AH, Ford ES, Greenlund KJ, Croft JB. State of Disparities in Cardiovascular Health in the United States. Circulation. 2005; 111:1233–1241. [PubMed: 15769763] 6. Lauer MS. Time for a creative transformation of epidemiology in the United States. JAMA. 2012; 308:1804–1805. [PubMed: 23117782] 7. Ioannidis JP. Why most published research findings are false. PLoS Med. 2005; 2:e124. [PubMed: 16060722] 8. Keyes K, Galea S. What matters most: quantifying an epidemiology of consequence. Ann Epidemiol. 2015; 25:305–311. [PubMed: 25749559] 9. Khoury MJ, Gwinn M, Ioannidis JPA. The Emergence of Translational Epidemiology: From Scientific Discovery to Population Health Impact. American Journal of Epidemiology. 2010; 172:517–524. [PubMed: 20688899] 10. Rose G. Sick individuals and sick populations. Int J Epidemiol. 1985; 14:32–38. [PubMed: 3872850] 11. Record NB, Onion DK, Prior RE, Dixon DC, Record SS, Fowler FL, Cayer GR, Amos CI, Pearson TA. Community-wide cardiovascular disease prevention programs and health outcomes in a rural county, 1970–2010. JAMA. 2015; 313:147–155. [PubMed: 25585326] Circulation. Author manuscript; available in PMC 2017 June 21.
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12. Puska, P.; Tuomilehto, J.; Nissinen, A.; Vartiainen, A., editors. The North Karelia Project. 20 Year Results and Experiences. Helsinki, Finland: National Public Health Institute, KTL; 1995. 13. Fortmann SP, Varady AN. Effects of a community-wide health education program on cardiovascular disease morbidity and mortality: the Stanford Five-City Project. Am J Epidemiol. 2000; 152:316–323. [PubMed: 10968376] 14. Murray DM. Design and analysis of community trials: lessons from the Minnesota Heart Health Program. Am J Epidemiol. 1995; 142:569–575. [PubMed: 7653464] 15. Carleton RA, Lasater TM, Assaf AR, Feldman HA, McKinlay S. The Pawtucket Heart Health Program: community changes in cardiovascular risk factors and projected disease risk. Am J Public Health. 1995; 85:777–785. [PubMed: 7762709] 16. Ades PA, Kottke TE, Miller NH, McGrath JC, Record NB, Record SS. Task force #3--getting results: who, where, and how? 33rd Bethesda Conference. J Am Coll Cardiol. 2002; 40:615–630. [PubMed: 12204491] 17. Labarthe DR, Stamler J. Improving cardiovascular health in a rural population: can other communities do the same? JAMA. 2015; 313:139–140. [PubMed: 25585322] 18. Havranek EP, Mujahid MS, Barr DA, Blair IV, Cohen MS, Cruz-Flores S, Davey-Smith G, Dennison-Himmelfarb CR, Lauer MS, Lockwood DW, Rosal M, Yancy CW. Social Determinants of Risk and Outcomes for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2015; 132:873–898. [PubMed: 26240271] 19. Pearson TA, Palaniappan LP, Artinian NT, Carnethon MR, Criqui MH, Daniels SR, Fonarow GC, Fortmann SP, Franklin BA, Galloway JM, Goff DC, Heath GW, Frank ATH, Kris-Etherton PM, Labarthe DR, Murabito JM, Sacco RL, Sasson C, Turner MB. American Heart Association Guide for Improving Cardiovascular Health at the Community Level, 2013 Update: A Scientific Statement for Public Health Practitioners, Healthcare Providers, and Health Policy Makers. Circulation. 2013; 127:1730–1753. [PubMed: 23519758] 20. Washington AE, Coye MJ, Boulware LE. Academic Health Systems' Third Curve: Population Health Improvement. JAMA. 2016; 315:459–460. [PubMed: 26836726] 21. Christakis NA, Fowler JH. The collective dynamics of smoking in a large social network. N Engl J Med. 2008; 358:2249–2258. [PubMed: 18499567] 22. Rosenquist JN, Murabito J, Fowler JH, Christakis NA. The spread of alcohol consumption behavior in a large social network. Ann Intern Med. 2010; 152:426–433. [PubMed: 20368648] 23. McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006; 63:1011–1022. [PubMed: 16650513] 24. Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007; 357:370–379. [PubMed: 17652652] 25. Auchincloss AH, Diez Roux AV, Mujahid MS, Shen M, Bertoni AG, Carnethon MR. Neighborhood resources for physical activity and healthy foods and incidence of type 2 diabetes mellitus: the Multi-Ethnic study of Atherosclerosis. Arch Intern Med. 2009; 169:1698–1704. [PubMed: 19822827] 26. Auchincloss AH, Mujahid MS, Shen M, Michos ED, Whitt-Glover MC, Diez Roux AV. Neighborhood health-promoting resources and obesity risk (the multi-ethnic study of atherosclerosis). Obesity (Silver Spring). 2013; 21:621–628. [PubMed: 23592671] 27. Brondolo E, Libby DJ, Denton EG, Thompson S, Beatty DL, Schwartz J, Sweeney M, Tobin JN, Cassells A, Pickering TG, Gerin W. Racism and ambulatory blood pressure in a community sample. Psychosom Med. 2008; 70:49–56. [PubMed: 18158368] 28. Brondolo E, Love EE, Pencille M, Schoenthaler A, Ogedegbe G. Racism and hypertension: a review of the empirical evidence and implications for clinical practice. Am J Hypertens. 2011; 24:518–529. [PubMed: 21331054] 29. Topol EJ, Steinhubl SR, Torkamani A. Digital medical tools and sensors. JAMA. 2015; 313:353– 354. [PubMed: 25626031] 30. Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J. 2016 E-published Feb 11. pii: ehv770.
Circulation. Author manuscript; available in PMC 2017 June 21.
Vasan and Benjamin
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31. Woods B. By 2020, 90% of world's population aged over 6 will have a mobile phone. 3-21-2016. The Next Web. [accessed 3-27-2016] http://thenextweb.com/insider/2014/11/18/2020-90-worldspopulation-aged-6-will-mobile-phone-report/. 32. Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc. 2016 epublished Feb 17, 2016. pii: ocv189. 33. Hall JL, Ryan JJ, Bray BE, Brown C, Lanfear D, Newby LK, Relling MV, Risch NJ, Roden DM, Shaw SY, Tcheng JE, Tenenbaum J, Wang TN, Weintraub WS. Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association. Circ Cardiovasc Genet. 2016 e-published March 2016. 34. Mandl KD, Mandel JC, Kohane IS. Driving Innovation in Health Systems through an Apps-Based Information Economy. Cell Syst. 2015; 1:8–13. [PubMed: 26339683] 35. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P. A haplotype map of the human genome. Nature. 2005; 437:1299–1320. [PubMed: 16255080] 36. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012; 491:56–65. [PubMed: 23128226] 37. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012; 489:57–74. [PubMed: 22955616] 38. Haring R, Wallaschofski H. Diving through the "-omics": the case for deep phenotyping and systems epidemiology. OMICS. 2012; 16:231–234. [PubMed: 22320900] 39. Kuster DWD, Merkus D, van der Velden J, Verhoeven AJM, Duncker DJ. Integrative Physiology 2.0': integration of systems biology into physiology and its application to cardiovascular homeostasis. The Journal of Physiology. 2011; 589:1037–1045. [PubMed: 21224228] 40. Barabasi AL. Network Medicine: From Obesity to the Diseasome. N Engl J Med. 2007; 357:404– 407. [PubMed: 17652657] 41. Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011; 12:56–68. [PubMed: 21164525] 42. Centers for Disease Control and Prevention. Atlanta G. Centers for Disease Control and Prevention. [accessed 3-22-2016] The Human Genome Epidemiology Network (HuGENet). 2010. http://www.cdc.gov/genomics/hugenet/default.htm 43. Teutsch SM, Bradley LA, Palomaki GE, Haddow JE, Piper M, Calonge N, Dotson WD, Douglas MP, Berg AO. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med. 2009; 11:3–14. [PubMed: 18813139] 44. Karmali KN, Lloyd-Jones DM. Adding a life-course perspective to cardiovascular-risk communication. Nat Rev Cardiol. 2013; 10:111–115. [PubMed: 23296067] 45. Pencina MJ, D'Agostino RB Sr, Larson MG, Massaro JM, Vasan RS. Predicting the 30-year risk of cardiovascular disease: the Framingham Heart Study. Circulation. 2009; 119:3078–3084. [PubMed: 19506114] 46. Khoury MJ, Lam TK, Ioannidis JP, Hartge P, Spitz MR, Buring JE, Chanock SJ, Croyle RT, Goddard KA, Ginsburg GS, Herceg Z, Hiatt RA, Hoover RN, Hunter DJ, Kramer BS, Lauer MS, Meyerhardt JA, Olopade OI, Palmer JR, Sellers TA, Seminara D, Ransohoff DF, Rebbeck TR, Tourassi G, Winn DM, Zauber A, Schully SD. Transforming epidemiology for 21st century medicine and public health. Cancer Epidemiol Biomarkers Prev. 2013; 22:508–516. [PubMed: 23462917] 47. [accessed 03-27-2016] Report of Workshop on Enhancing Training for Biomedical Big Data Big Data to Knowledge (BD2K) Initiative. 2013 Jul 29–30. https://datascience.nih.gov/sites/default/ files/bd2k/docs/july%2029%20bd2k_training_workshop_report.pdf 48. Kroenke K, Kapoor W, Helfand M, Meltzer DO, McDonald MA, Selker H. Training and career development for comparative effectiveness research workforce development: CTSA Consortium Strategic Goal Committee on comparative effectiveness research workgroup on workforce development. Clin Transl Sci. 2010; 3:258–262. [PubMed: 20973924] 49. Galea S. An Argument for a Consequentialist Epidemiology. American Journal of Epidemiology. 2013; 178:1185–1191. [PubMed: 24022890]
Circulation. Author manuscript; available in PMC 2017 June 21.
Vasan and Benjamin
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50. Buck Louis GM, Bloom MS, Gatto NM, Hogue CR, Westreich DJ, Zhang C. Epidemiology's Continuing Contribution to Public Health: The Power of Then and Now. American Journal of Epidemiology. 2015; 181:e1–e8. [PubMed: 25810458] 51. Castellano JM, Penalvo JL, Bansilal S, Fuster V. Promotion of cardiovascular health at three stages of life: never too soon, never too late. Rev Esp Cardiol (Engl Ed). 2014; 67:731–737. [PubMed: 25172069] 52. Castellano JM, Narula J, Castillo J, Fuster V. Promoting cardiovascular health worldwide: strategies, challenges, and opportunities. Rev Esp Cardiol (Engl Ed). 2014; 67:724–730. [PubMed: 25172068] 53. Mandl KD, Kohane IS. Federalist principles for healthcare data networks. Nat Biotechnol. 2015; 33:360–363. [PubMed: 25850061] 54. Sorlie PD, Bild DE, Lauer MS. Cardiovascular epidemiology in a changing world--challenges to investigators and the National Heart, Lung, and Blood Institute. Am J Epidemiol. 2012; 175:597– 601. [PubMed: 22415032] 55. Roger VL, Boerwinkle E, Crapo JD, Douglas PS, Epstein JA, Granger CB, Greenland P, Kohane I, Psaty BM. Strategic Transformation of Population Studies: Recommendations of the Working Group on Epidemiology and Population Sciences From the National Heart, Lung, and Blood Advisory Council and Board of External Experts. American Journal of Epidemiology. 2015; 181:363–368. [PubMed: 25743324] 56. Vasan RS, Folsom AR. Invited Commentary: Future of Population Studies: Defining Research Priorities and Processes. American Journal of Epidemiology. 2015; 181:369–371. [PubMed: 25743322] 57. The Cross-cohort Collaboration Consortium. [accessed 3-27-2016] https://chs-nhlbi.org/node/ 6539. 58. Borner K, Contractor N, Falk-Krzesinski HJ, Fiore SM, Hall KL, Keyton J, Spring B, Stokols D, Trochim W, Uzzi B. A multi-level systems perspective for the science of team science. Sci Transl Med. 2010; 2:49cm24. 59. Falk-Krzesinski HJ, Borner K, Contractor N, Fiore SM, Hall KL, Keyton J, Spring B, Stokols D, Trochim W, Uzzi B. Advancing the science of team science. Clin Transl Sci. 2010; 3:263–266. [PubMed: 20973925] 60. Guimera R, Uzzi B, Spiro J, Amaral LA. Team assembly mechanisms determine collaboration network structure and team performance. Science. 2005; 308:697–702. [PubMed: 15860629] 61. Stamatakis KA, Norton WE, Stirman SW, Melvin C, Brownson RC. Developing the next generation of dissemination and implementation researchers: insights from initial trainees. Implement Sci. 2013; 8:29. [PubMed: 23497462] 62. National Research Council (US) and Institute of Medicine (US) Committee on the Organizational Structure of the National Institutes of Health. Enhancing the Vitality of the National Institutes of Health: Organizational Change to Meet New Challenges. Washington DC: National Academies Press; 2003. New Opportunities, New Challenges: The Changing Nature of Biomedical Science. 63. Brownson RC, Samet JM, Chavez GF, Davies MM, Galea S, Hiatt RA, Hornung CA, Khoury MJ, Koo D, Mays VM, Remington P, Yarber L. Charting a future for epidemiologic training. Ann Epidemiol. 2015; 25:458–465. [PubMed: 25976024] 64. Council on Linkages Between Academia and Public Health Practice. Core competencies for public health professionals. [accessed 3-27-2016] http://www.phf.org/resourcestools/Pages/ Core_Public_Health_Competencies.aspx. 65. Koo D, Birkhead GS, Reingold AL. Competency-based epidemiologic training in public health practice. Public Health Rep. 2008; 123(Suppl 1):1–3. [PubMed: 18497008] 66. U. S. Department of Health and Human Services. A report of the Public Health Functions Project. Washington (DC): U. S. Government Printing Office; 1997. The Public Health Workforce: An Agenda for the 21st Century.
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Table A
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Training needs for epidemiology workforce in this millennium: a synthesis of major reports Khoury et al46* 1
2
3
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4
Multilevel analysis, interventions, implementation, & outcomes research Greater access to and sharing of protocols, metadata, biosamples, fostering collaboration, ensuring replication, & accelerating translation Collect diverse exposures across the life course to examine multiple health-related endpoints Develop & validate reliable methods & technologies to quantify exposures/outcomes on a massive scale
5
Integrate "big data" science
6
Use knowledge to drive research, policy, practice
7
Transform training to address interdisciplinary and translational research
8
Optimize resources and infrastructure
IOM report62**
COL64**†
Eight new core competencies:
Develop skills:
1
Informatics
2
Genomics
3
Communication
4
Cultural competence
Analytic/ Assessment
2
Policy Development / program planning
3
Communication
4
Cultural competency
5
Community dimensions of practice
5
Community-based participatory research
6
Global health
7
Policy and law
6
8
Public health ethics
Basic public health sciences
7
Financial planning & management
8
Leadership & Systems Thinking
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*
Focus on transforming epidemiology in 21st century
**
1
Focus on core competencies
†
Council On Linkages between Academia and Public health practice.
Author Manuscript Circulation. Author manuscript; available in PMC 2017 June 21.
Circulation. Author manuscript; available in PMC 2017 June 21. Published in final edited form as: Circulation. 2016 June 21; 133(25): 2626–2633. doi:10.1161/CIRCULATIONAHA.116.023528.
The Future of Cardiovascular Epidemiology Ramachandran S. Vasan, MD1,2,3,4 and Emelia J. Benjamin, MD, ScM1,2,3,4 1National
Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA
2Evans
Department of Medicine, Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, MA
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3Preventive
Medicine and Cardiology Sections, Department of Medicine, Boston University School of Medicine, Boston, MA
4Department
of Epidemiology, Boston University School of Public Health, Boston, MA Cardiovascular disease (CVD) is a major cause of morbidity and mortality world-wide, with the lifetime risk exceeding 60%.1 Over 2200 Americans die of CVD daily, one death every 40 seconds. A third of CVD deaths occur before age 75 years, which is lower than the average life expectancy of 78.8 years.1 Thus, prevention of CVD is a public health priority. Major advances in cardiovascular epidemiology over the last four decades have improved our understanding of the pathogenesis of CVD, with the identification and treatment of several major risk factors.2
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Yet, several factors are worth highlighting to place these past achievements in appropriate perspective.
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1.
There remains a substantial societal burden of risk factors. Data indicate that one in three Americans reports no leisure time physical activity, and is likely to have high blood pressure, pre-diabetes, or high blood levels of LDL cholesterol.1 A majority of adults in the United States are overweight or obese, about a fifth have low levels of HDL cholesterol, a sixth has the smoking habit, and about a tenth has diabetes mellitus.1
2.
Projections indicate that the prevalence of CVD in the United States may escalate by 10% between 2010 and 2030.3 The estimated increase stems in part from the aging of the population and is also fueled by the recent trends for increasing obesity rates, and the concomitant rising rates of hypertension (8% increase over next decade) and diabetes mellitus (100% increase over next three decades).1 These projections indicate that past achievements potentially may be challenged by some of the recent adverse trends in some risk factors.
Address for Correspondence: Ramachandran S. Vasan, MD, Framingham Heart Study, 73, Mt. Wayte Avenue, Suite #2, Framingham, MA 01702, [email protected].
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There is a glaring 14-year difference in life expectancy between select population groups in the United States, as identified in the Eight Americas Study,4 with CVD emerging as the greatest source of difference in life expectancy. Indeed, it is estimated that blacks higher burden of usual CVD risk factors, are two to three fold more likely to die of heart disease compared with whites, and have higher rates of premature death resulting from CVD.1 Thus, major declines in CVD mortality accrued over decades of advances in population health have not effaced the racial and ethnic gaps in CVD morbidity and mortality.5
4.
There is increasing emphasis that key research, including cardiovascular epidemiological research, must translate directly to improvements in public health, focus on diverse populations, and engage communities actively. Indeed, the utility of cardiovascular epidemiological research has been periodically questioned in the past and also more recently.6 Dr. Michael Lauer (NHLBI) noted in a recent JAMA editorial the answer to
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3.
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the question “what has epidemiology done lately suggests two answers: much and not enough.”6
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Thus, cardiovascular epidemiology is confronted by a dualism: unprecedented opportunities for new research directions, amidst a growing uncertainty about its potential value and a pressure to ‘do more’ and be more accountable. Additionally, the notion that a lot of epidemiological research results in false positive findings has gained momentum,7 thereby raising the bar for both study quality and the necessity for replication as a sine quo non for all future epidemiological investigations. Overall, thought leaders have underscored the importance of consequential8 and translational epidemiology9 in the decades ahead for the science to remain relevant in the face of greater expectations that epidemiological research must impact public health more directly.
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Given this background, we identify key transformative directions the field of cardiovascular epidemiology will undergo over the next decade. The specific areas we highlight in our review include: the changing framework of population health and disease, including a greater integration of individual and community approaches, and the combination of health promotion strategies with traditional disease prevention/treatment goals (cHealth); recognition of the social determinants of health and their incorporation into the biomedical model of cardiovascular disease (sHealth); better characterization of the exposome including the built environment and via the ‘quantified’ self, fueled aptly by the digital data age revolution (mHealth); the integration of electronic medical records (eHealth) resources into the monitoring and maintenance of the health of individuals and populations; and harnessing of the power of the genomic revolution towards better precision cardiovascular medicine delivery (gHealth);. We also address related challenges that require reconfiguration of traditional cardiovascular epidemiology training and the development of the public health workforce of this century.
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Recognition of the essential role of Community-based approaches to cardiovascular health over the life course: cHealth
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It has been well recognized for decades that CVD is a life-course and lifestyle disease. Traditional approaches to preventing CVD have focused on the assessment and treatment of key risk factors at the individual level. Sir Geoffrey Rose elegantly articulated the debate between individual and population-based approaches to prevention of disease.10 He underscored both the complementarity of the two approaches as well as the fundamental importance of not ignoring population level interventions.10 Yet, these concepts languished as most cardiovascular guidelines and position papers have emphasized targeting of individuals for screening and management of CVD risk factors. More recently, there is increased recognition that it is critical to target both individual-level behaviors in addition to treating the associated levels of measured biochemical risk factors for CVD for preventing CVD successfully.
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In parallel, studies within the United States have demonstrated that sustained, communitywide programs targeting CVD risk factors and behavior changes to improve population health at a county level (e.g., Franklin county, ME) were associated with concomitant reductions in hospitalization and mortality rates over a 40-year period (relative to the rest of the counties where similar programs did not exist).11 This was reminiscent of the experience from North Karelia in Finland.12 Of note these community programs were integrated with primary care, which may explain the difference from prior experiences in the United States with three other programs (the Stanford Five-City Project13 and the Minnesota14 and Pawtucket Heart Health15 Programs); the latter three programs had shown relative improvements in CVD risk factors, yet no concomitant mortality impact possibly because these programs were not integrated well with primary care.16, 17 A major underlying and emerging theme is that community programs with primary care involvement alter the default health-related behavioral decisions of individuals, thereby proving to be more effective and less costly in shifting the entire distribution of CVD risk factors, consistent with the ‘health impact pyramid’.18
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Supporting the vital importance of community health promotion, the AHA has developed a Community Guide19 that integrates optimal health behaviors, community settings, and public health interventions to improve CV health. A wide range of public health strategies help promote optimal levels of CVD health factors over the entire life course, including dietary salt restriction, limits on food portion size, tobacco laws (including taxes) to promote smoking cessation and mitigate second hand tobacco exposure, and changes to the built environment to enhance physical activity.19 Additional steps such as taxation of sugarsweetened beverages and subsidies on fruits and vegetables may potentially influence behavior favorably, a premise warranting more research. In parallel with community efforts, awareness has increased within the academic medical school communities to move towards the ‘third curve’ representing entire populations (as compared to the first two curves representing patients seeking care and the managed care populations).20 Over the next decade, we foresee a greater emphasis on CV health promotion and CVD prevention within and across communities, with greater engagement of Circulation. Author manuscript; available in PMC 2017 June 21.
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the entire community, maintenance of surveillance, and partnerships in the actual physical settings where people live, work, and study. Future studies of CVD risk factors and their burden will move from the traditional focus on individuals and cohorts to a greater attention towards the central role of communities that individuals live and work in.
Recognition of the social determinants of cardiovascular health and disease: sHealth
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As noted above, the benefits of major advances in cardiovascular epidemiology and the pathogenesis and treatment of CVD have not trickled down uniformly across all strata of the United States population. Specifically, select socioeconomic, racial, and ethnic groups in the United States have a much greater prevalence of CVD risk factors and a higher CVD incidence and mortality.18 Linguistic differences and cultural beliefs and practices have been demonstrated to influence the behavior of individuals, including their health-seeking behaviors, their access to care and their compliance with preventive treatments. However, a major contributor to disparities are socioeconomic deprivation, implicit bias, and racial stereotypes, which affect both cardiovascular health promotion and disease prevention, and exacerbate disparities in the access to and delivery of cardiovascular care.18
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On a parallel note, the traditional biomedical model of CVD has embraced the notion that individual-level risk factors, both behavioral and biological, mediate CVD risk. Such a model ignores the environmental contexts that actually shape and determine the evolution of the CVD risk factors themselves.18 Recent work has also thrown the spotlight on social networks and their impact on the clustering and the development of behaviors (such as smoking,21 alcohol,22 and physical activity23), and their association with spread of obesity through the community.24 There is also a greater acknowledgment that diminished social support contributes to CVD risk.18 Recent research has highlighted the fundamental role of the neighborhood built/physical environment in influencing CVD risk. Thus, features of one’s residential area such as urban design and public spaces, land use patterns, street connectivity, access to parks and exercise/ recreational facilities, air and noise pollution levels, access to public transportation systems all seem to contribute to both health behaviors and the levels of the CVD risk factors.18 Additional built environment indicators may capture features of social norms and connectivity, and psychosocial stressors (such as safety, violence, and social cohesion). Thus, investigators from the Multi-Ethnic Study of Atherosclerosis (MESA) have elucidated how CVD risk factors are linked to the built physical environments, including neighborhood healthy food access and resources for physical activity.25, 26
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The coming decades will see greater efforts to capture contributions of socioeconomic position (as reflected by income, education and occupation) to CVD risk and to identify the mechanisms by which social networks and forces affect cardiovascular health outcomes. Additional research will characterize the contributions of perceived racism27, 28 and other psychosocial stressors in contributing to the burden of high blood pressure and vascular reactivity. It is likely that future studies will use a comprehensive framework for capturing the exposome, including delineating the built environment, to fully characterize propensity
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for developing CVD risk factors and overt disease and to promote an understanding of how best to influences factors at the neighborhood and at the individual level. Future cardiovascular epidemiological studies will evaluate the key role of culturally appropriate educational and other interventions aimed at improving cardiovascular health care.
Role of digital technology and mHealth
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Major advances in the miniaturization of computer chips and blue tooth wireless technology have enabled the development of biosensors that convert physiological sensing inputs into a measurable and interpretable signal.29 Wearable devices are ‘on or in the body’ digital elements that transduce physiological signals. The combination of biosensors and wearables can provide a substantial window into the exposome and has revolutionized mHealth, the use of portable digital devices to obtain clinical data that can then be used to identify, monitor, and guide the management of various health outcomes. Examples of mHealth technology include smartphone health ‘apps’, devices that can connect to a smartphone, wearables and biosensors, and handheld-imaging platforms.30 Quantitative biosensor and wearable data are already being used in epidemiological studies for some time (accelerometry is a good example), and newer waves of data collection including physiological measures (like heart rate and rhythm, blood pressure, weight, physical activity, etc.) are accruing via projects such as the Health eHeart Study at the University of California at San Francisco, a PCORnet Patient Powered Research Network. Apple’s open-source ResearchKit is already facilitating smartphone-based medical research, and collaboration between Duke University, Stanford University and Verily Inc. (formerly Google Lifesciences) will gather extensive “physiome” data through wearable sensors for the NIH’s Precision Medicine Initiative cohort of 1 million US participants. A pilot program at the Vanderbilt University medical Center will launch the Direct Volunteers Pilot Studies under the aegis of the federal Precision Medicine Initiative Cohort Program, in partnership with Verily, with the additional dimension of developing a biobank capable of storing and managing blood, urine, and saliva samples for analysis of the Precision Medicine Initiative Cohort Program.
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Over the next decade, we anticipate major headway in our assessment of the plethora of mhealth devices and apps, to develop quality standards for such data and comparability of devices, to define processes for their integration into the clinical decision support systems, and to establish regulatory procedures for devices used for this purpose. It has been estimated that by the year 2020, 90% of individuals over age 6 years in the world will possess a smartphone!31 The ability to gather health data via smartphones on virtually everyone world-wide poses a mindboggling opportunity and a challenge, and undoubtedly will influence the field of cardiovascular epidemiology over the next decade. Issues of data harmonization, data security and data integration, and individual privacy concerns will require substantial investment of efforts, followed by the incorporation of streams of real-life continuous data on select traits into the research portfolios of scientists. Traditionally, epidemiology has focused on limited phenotyping obtained in a state of rest and repeated periodically over a period of years. The ability to phenotype individuals on a wide array of physiological measures in real time and for prolonged periods of times is an unprecedented development rendered feasible by digital technology. Also, such ‘deep phenotyping’ will capture human responses to perturbations, thereby increasing the granularity of the human Circulation. Author manuscript; available in PMC 2017 June 21.
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phenome. However, using information on the ‘quantified self’ for research purposes is a relatively new occurrence and will likely require implementation of machine learning tools (such as support vector machines, random forests, etc.) that have been traditionally used in industry for commercial purposes.
Role of Electronic Medical Records (EMR) and eHealth
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Hospitals in the United States received a mandate to transition their patient clinical information to an electronic format under the Patient Protection and Affordable Care Act of 2010 enacted by President Obama. The expectation was that medical information about patients’ past histories and current illnesses would be captured digitally, thereby facilitating better health care across various institutions and health care systems, and also simultaneously open up new avenues for clinical research. The widespread use of electronic medical records (EMR) introduces challenges related to the interoperability of different platforms and a relative lack of standardization of various data elements across systems. There was also a federal mandate to invest in large data infrastructure projects such as the National Institutes of Health (NIH) Collaboratory and the National Patient-Centered Clinical Research Network (PCORnet), which facilitates the linkage of multiple EMR data, thus harnessing medical information on millions of patients for clinical investigators across numerous institutions and health care systems. EMR data also enable large-scale postmarket surveillance studies and recruitment of patients into pragmatic clinical trials. However, the EMR captures the exposome imperfectly as a substantial proportion of exposures (e.g., behavioral, socioeconomic, environmental factors) are not captured within the current EMR systems. Nonetheless, initial attempts at linking EMR to genome-wide data have born fruition, with the confirmation of some known ‘hits’ demonstrated in large-scale genome-wide association analyses and with the additional yield of new associations as well. We expect over the next decade a consolidation of efforts to standardize and harmonize data elements within EMR and greater use of these data for research purposes.32 As noted above the Precision Medicine Initiative Cohort program involves recruitment of a cohort of nearly a million individuals and integration of research with their EMR is one of the central features of this projects. We anticipate greater integration of EMR with genomic data as well (EWAS studies noted above),33 paralleled with evolution of the quality of EMR data, including enhanced interoperability of the various platforms. EMR data will also likely see greater use for recruitment of patients into pragmatic trials and for development of community-based research networks. It is also likely that healthcare data will become accessible via standardized open software interfaces (apps) to researchers (and patients) with a focus on ‘efficacy, accuracy, utility, safety, privacy and security’ features.34
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Exposome-Phenome-Diseasome Associations: Genomic and biomarker revolution and impact on Cardiovascular epidemiology (gHealth, bHealth) The molecular revolution, marked by the HAPMAP,35 1000 genomes36 and ENCODE37 projects and the OMICs tool box,38, 39 is reshaping current concepts of CVD. New technologies developed through the Human Genome Project and related initiatives have radically altered both the types of data that quantitative scientists now work with and the Circulation. Author manuscript; available in PMC 2017 June 21.
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types of questions epidemiologists ask using the available multi-dimensional data. The OMICs tool box has expanded our ability to measure numerous biomarkers and characterize genetic, epigenetic, transcriptomic, microRNA and noncoding RNA, proteomic, metabolomic, and microbiome profiles. Understanding genomics is now a critical element in cardiovascular epidemiology because of our enhanced ability to identify common and rare genetic variants, use genomic markers in adaptive clinical trial designs, and to evaluate circulating molecules originating in the gut microflora that influence cardiometabolic and vascular risk. The parallel developments in better characterization of the exposome (as noted above), the phenome (dynamic characterization, including in response to perturbations like exercise, glucose challenge, etc.) and the diseasomes itself (better delineation of disease networks, subsets, and endophenotypes)40, 41 will transform our understanding of the molecular epidemiology of CVD.
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The genomic revolution will juxtapose concepts of personalized medicine and comparative effectiveness research (CER; see also section below) due to the increased realization that interindividual variation in both the genome and the genomic and epigenomic responses to exposures and pharmaceutical agents are varied, thereby identifying more homogeneous molecular subsets to collate and in which to perform CER studies. There will be renewed and sustained interest in synthesis of the evidentiary basis for exposome-phenomediseasome associations through efforts such as the Human Genome Epidemiology Network (HuGENet)42 and Evaluation of Genomic Applications in Practice and Prevention, known as EGAPP,43 initiatives.
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The focus of risk prediction will veer more and more towards prevention over the lifecourse, with a consideration of both short-term (the traditional 10-year time window) and also a much longer time horizon (20 to 30-year time window).44, 45 As such, the biomarker revolution (b-health) will herald the measurement of different panels of biomarkers at various ages that capture the complexity and evolution of risk factors themselves, reflect more accurately the antecedents of these risk factors (such as better nutritional and adiposity biomarkers), and indicators of subclinical disease measures over the entire life course. Targeted interventions will be based on both an individual’s genotype as well as the exposome as reflected by biomarker profiles and will likely be highly individualized. Elucidation of the molecular epidemiology of diseasomes will mean that treatment may be highly personalized based on an individual’s trajectory of changes along the spectrum of disease states, and the choice of treatment may be facilitated both by a person’s genomic profile and the response of his/her induced pluripotent stem cells in vitro. The coming decade will also witness advances in our understanding of pharmacogenetics testing and reporting of genetic variants identified and their plausible molecular pathogenicity (or lack thereof). Another specific advance that will gain firm ground is the integration of cardiovascular epidemiology with chronic disease epidemiology, with the emerging refrain of a shared commonality of risk factors across several forms of non-communicable diseases, including cancer and pulmonary disease. Thus the prevention of CVD will be integrated within the broader framework of prevention of non-communicable diseases in the upcoming decades.
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Transformative Vision for Epidemiology in this century: extension of Big Data into cardiovascular Epidemiology
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Khoury et al46 proposed a transformative vision for epidemiology in this century with eight overarching thematic recommendations: “(1) extend research beyond traditional domains of discovery and etiologic research to include multilevel analysis, interventions, implementation, and outcomes research; (2) greater access to and sharing of protocols, metadata, and biosamples, fostering collaboration, ensuring replication, and accelerating translation; (3) expand cohort studies to collect diverse exposures across the life course to examine multiple health-related endpoints; (4) develop and validate reliable methods and technologies to quantify exposures/outcomes on a massive scale; (5) integrate "big data" science; (6) use knowledge to drive research, policy, and practice; (7) transform training of 21st century epidemiologists to address interdisciplinary and translational research; and (8) optimize resources and infrastructure for epidemiologic studies.” each of these recommendations is very relevant to the field of cardiovascular epidemiology.
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In 2013 the NIH unveiled the "Big Data to Knowledge" (BD2K) Initiative with $200 million committed to research in this domain.47 The development of systematic approaches to robustly manage, integrate, analyze, and interpret large complex data sets is crucial. Overcoming the challenges of developing the architectural framework for data storage and management may benefit from the lessons learned and the knowledge gained from other disciplines. Adaptation of technological advancements like cloud-computing platforms, already in use by private industries (e.g., Amazon Cloud Drive and Apple iCloud), can further facilitate the virtual infrastructure and transform biomedical research and health care. With high-powered computers and advances in computing software, complex methods for the analysis of massive data sets are becoming more feasible. The advent of ‘big data’ has resulted in questioning the importance of meticulous sampling designs, greater acceptance of misclassification in the trade-off for more data, and a movement away from traditional causal inferences and hypothesis testing to more agnostic approaches of enquiry and generation of new knowledge.
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Big data trends and scientific advances emphasize both the challenges and opportunities for restructuring cardiovascular epidemiology over the next few decades, which may increasing may be informed by comparative effectiveness research (CER).48 The last five years has witnessed an unprecedented emphasis on CER with a view to identifying the most effective health interventions for diseases and to achieve best health outcomes, including CVD, propelled in part by the American Recovery and Reinvestment Act (ARRA) of 2009 that appropriated $1.1 billion for CER ($400 million allocated to the NIH and the remainder to AHRQ and the Office of the Secretary of the Department of Health and Human Services). New CER studies over the next decade will likely use “different trial designs, very large sample sizes, high-throughput technologies, routine utilization of electronic medical records, and adaptations that reflect the infrastructures of integrated health care systems”. Simultaneously, new CER studies must be complemented by randomized trials that can verify results of observational data and also suitable inroads in implementation science to ensure that the best interventions can be adequately implemented in the clinical domain.
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There is an increasing emphasis on interventionist approaches in epidemiology, along with the notion of consequential epidemiology emphasized by Galea,49 in an attempt to keep the science more directly relevant to public health.8 Newer methods such as critical time windows, directed acyclic graphs, marginal structural models, g-health, and time-varying social networks are likely to see increased use in epidemiology.50 In parallel with methodological advances, there will be a greater globalization of cardiovascular epidemiology with emphasis on steps to quantify global burden of disease, characterize regional burden, and elucidate steps to implement primordial, primary and secondary prevention over the lifecourse across the various regions.51, 52 Cohort Studies in Cardiovascular Epidemiology at Crossroads
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As noted above, the field of cardiovascular epidemiology is undergoing a revolution due to the burgeoning amounts of genetic and genomic data, a plethora of biomarkers, and the emergence of systems biology and collaborative science. Paralleling these major scientific advances is the spawning of the notion that epidemiological studies, especially traditional cohort studies, have limited utility in today’s era because of high costs, modestly incremental knowledge, an inherent inability to innovate at reasonable cost, and a failure to identify and answer contemporary research questions. Additionally, the concept of decentralized large cohort studies (like the UK Biobank and the Precision Medicine Initiative Cohort Program, as currently envisaged), the notion of large data and biorepositories with ease of access, the possibility of free and opens haring of federated datasets53 has become firmly ensconced in science today. The imperative to perform innovative research addressing the most meritorious question, nimbly and in a cost-effective manner has never been greater.54 As such, the future of cohort studies has been debated actively in several scientific forums.55, 56 The concepts of embedding clinical trials within pre-enrolled cohorts has drawn traction in recent times and novel approaches such as genotype-based phenotyping of cohort subsets have been envisaged. We anticipate that the notion of multi-cohort studies across the human lifespan will witness a major rise over the next decade as it becomes clearer that elucidating risk factor-disease associations within different race/ethnic and age- and sex-based groups will assume critical importance. These cohort studies will see a rise in use of m-health and EMR-based phenotyping and key innovations in the creative analyses of such cohort data will likely bear fruition. The recent emergence of the cross-cohort consortium (CCC)57 of NHLBI-supported studies is an example of the kind of the transconsortial collaborations that are likely to assume prominence in the decade ahead.
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The Multidisciplinary workforce in cardiovascular epidemiology in this millennium The cardiovascular epidemiologist of the future will undoubtedly be working as part of a multidisciplinary team of scientists including data scientists, bioinformaticians, physiologists, geneticists, molecular biologists, and mathematicians, and the list of disciplines is by means complete. The concept of team science will be critical to the success
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of all scientific endeavors48, 58–61 and open access to data will be key along with need for built-in replication strategies for all scientific studies.
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A recent Institute of Medicine (IOM) report noted that “critical to the implementation of big data science is the need for high-quality biomedical informatics, bioinformatics, and mathematics and biostatistical expertise.”62 Indeed, successful analysis and integration of massive and heterogeneous datasets require new quantitative skills, supported by a deep understanding of the biological and experimental processes underlying the data, and an ability to appropriately use the information available from public databases. New quantitative skills include the ability to: (1) model complex systems using statistical machine learning methods for the intelligent search of large databases; (2) develop the different network formalisms ranging from regulatory networks to gene expression mechanisms; (3) use graphical models and Bayesian networks; (4) describe complex multivariate associations; and (5) use scale free networks to relate genotypes to phenotypes. Additionally, the IOM report62 emphasized the importance of: (a) transdisciplinary training, with the development of quantitative skills, as noted above; (b) translational research; (c) competency-based approaches to training and evaluation; (d) evolving notions of team science, research networking and their critical role in academic careers; (e) individualized development plans (IDP) in career planning and mentoring; (f) alternate pathways in careers, including collaborations with industry, and policy; (g) diversity barriers in biomedical research and required shifts in recruitment strategies.
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On a parallel note, a group of leading epidemiologists have recently reviewed twelve macro level trends that will shape the future of epidemiology, including the training of its workforce (some of these trends are reviewed in the present article).63 The required new skill sets and the impending big data changes suggest that training programs for the cardiovascular epidemiology work force for this millennium will need to be redesigned dramatically emphasizing skill sets that are multidimensional and iteratively acquired over the course of one’s career (Table).46, 62, 64–66
Conclusions
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The current vision of cardiovascular epidemiology emphasizes an ecological approach that incorporates the full range of biological, environmental, and social determinants of cardiovascular health across the life-course. This approach leverages emerging sciences (genetics and genomics), information and digital technology, and communication sciences, and underscores the importance of the social determinants of health. Indeed, determinants of CVD “act within complex, often scale-free networks made up of a small number of “hubs” that are extensively linked to many more relatively isolated determinants and that are similar in structure and function at the genetic, molecular, cellular, clinical, environmental, and societal levels.”6 Likewise, a wide spectrum of environmental and physiological measurements can be made using portable devices and mobile technology applying increasingly sophisticated sensor technologies to people, and biosamples. A plethora of biomarkers will become available to better characterize the evolution of CVD over the adult lifespan. The coming decades will see the integration of these cHealth, sHealth, mHealth, eHealth, gHealth and bHealth into cardiovascular epidemiology will see the generation of a
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multiplicity of datasets available in publicly accessible biorepositories for scientists to evaluate and test novel hypotheses. The aforementioned developments will require the retooling of the existing work force in cardiovascular epidemiology and a redesigning of training programs to meet these newer needs. Hopefully, these advances in cardiovascular epidemiology will translate into better cardiovascular health of the global community and promote the primordial, primary and secondary prevention of CVD world-wide.
Acknowledgments This work was supported by the NHLBI, Framingham Heart Study, (NHLBI/NIH Contract #N01-HC-25195 and HHSN268201500001I), 1P50HL120163, and the Boston University School of Medicine Evan’s Scholar Award (RSV).
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1. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de FS, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jimenez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER III, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 2016; 133:e38–e360. [PubMed: 26673558] 2. Lauer MS. Cardiovascular science in the service of national strength. JAMA. 2011; 306:2145–2146. [PubMed: 22089722] 3. Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, Finkelstein EA, Hong Y, Johnston SC, Khera A, Lloyd-Jones DM, Nelson SA, Nichol G, Orenstein D, Wilson PW, Woo YJ. American Heart Association Advocacy Coordinating Committee; Stroke Council; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Council on Arteriosclerosis; Thrombosis and Vascular Biology; Council on Cardiopulmonary; Critical Care; Perioperative and Resuscitation; Council on Cardiovascular Nursing; Council on the Kidney in Cardiovascular Disease; Council on Cardiovascular Surgery and Anesthesia, and Interdisciplinary Council on Quality of Care and Outcomes Research. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011; 123:933–944. [PubMed: 21262990] 4. Murray CJ, Kulkarni SC, Michaud C, Tomijima N, Bulzacchelli MT, Iandiorio TJ, Ezzati M. Eight Americas: investigating mortality disparities across races, counties, and race-counties in the United States. PLoS Med. 2006; 3:e260. [PubMed: 16968116] 5. Mensah GA, Mokdad AH, Ford ES, Greenlund KJ, Croft JB. State of Disparities in Cardiovascular Health in the United States. Circulation. 2005; 111:1233–1241. [PubMed: 15769763] 6. Lauer MS. Time for a creative transformation of epidemiology in the United States. JAMA. 2012; 308:1804–1805. [PubMed: 23117782] 7. Ioannidis JP. Why most published research findings are false. PLoS Med. 2005; 2:e124. [PubMed: 16060722] 8. Keyes K, Galea S. What matters most: quantifying an epidemiology of consequence. Ann Epidemiol. 2015; 25:305–311. [PubMed: 25749559] 9. Khoury MJ, Gwinn M, Ioannidis JPA. The Emergence of Translational Epidemiology: From Scientific Discovery to Population Health Impact. American Journal of Epidemiology. 2010; 172:517–524. [PubMed: 20688899] 10. Rose G. Sick individuals and sick populations. Int J Epidemiol. 1985; 14:32–38. [PubMed: 3872850] 11. Record NB, Onion DK, Prior RE, Dixon DC, Record SS, Fowler FL, Cayer GR, Amos CI, Pearson TA. Community-wide cardiovascular disease prevention programs and health outcomes in a rural county, 1970–2010. JAMA. 2015; 313:147–155. [PubMed: 25585326] Circulation. Author manuscript; available in PMC 2017 June 21.
Vasan and Benjamin
Page 12
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
12. Puska, P.; Tuomilehto, J.; Nissinen, A.; Vartiainen, A., editors. The North Karelia Project. 20 Year Results and Experiences. Helsinki, Finland: National Public Health Institute, KTL; 1995. 13. Fortmann SP, Varady AN. Effects of a community-wide health education program on cardiovascular disease morbidity and mortality: the Stanford Five-City Project. Am J Epidemiol. 2000; 152:316–323. [PubMed: 10968376] 14. Murray DM. Design and analysis of community trials: lessons from the Minnesota Heart Health Program. Am J Epidemiol. 1995; 142:569–575. [PubMed: 7653464] 15. Carleton RA, Lasater TM, Assaf AR, Feldman HA, McKinlay S. The Pawtucket Heart Health Program: community changes in cardiovascular risk factors and projected disease risk. Am J Public Health. 1995; 85:777–785. [PubMed: 7762709] 16. Ades PA, Kottke TE, Miller NH, McGrath JC, Record NB, Record SS. Task force #3--getting results: who, where, and how? 33rd Bethesda Conference. J Am Coll Cardiol. 2002; 40:615–630. [PubMed: 12204491] 17. Labarthe DR, Stamler J. Improving cardiovascular health in a rural population: can other communities do the same? JAMA. 2015; 313:139–140. [PubMed: 25585322] 18. Havranek EP, Mujahid MS, Barr DA, Blair IV, Cohen MS, Cruz-Flores S, Davey-Smith G, Dennison-Himmelfarb CR, Lauer MS, Lockwood DW, Rosal M, Yancy CW. Social Determinants of Risk and Outcomes for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2015; 132:873–898. [PubMed: 26240271] 19. Pearson TA, Palaniappan LP, Artinian NT, Carnethon MR, Criqui MH, Daniels SR, Fonarow GC, Fortmann SP, Franklin BA, Galloway JM, Goff DC, Heath GW, Frank ATH, Kris-Etherton PM, Labarthe DR, Murabito JM, Sacco RL, Sasson C, Turner MB. American Heart Association Guide for Improving Cardiovascular Health at the Community Level, 2013 Update: A Scientific Statement for Public Health Practitioners, Healthcare Providers, and Health Policy Makers. Circulation. 2013; 127:1730–1753. [PubMed: 23519758] 20. Washington AE, Coye MJ, Boulware LE. Academic Health Systems' Third Curve: Population Health Improvement. JAMA. 2016; 315:459–460. [PubMed: 26836726] 21. Christakis NA, Fowler JH. The collective dynamics of smoking in a large social network. N Engl J Med. 2008; 358:2249–2258. [PubMed: 18499567] 22. Rosenquist JN, Murabito J, Fowler JH, Christakis NA. The spread of alcohol consumption behavior in a large social network. Ann Intern Med. 2010; 152:426–433. [PubMed: 20368648] 23. McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006; 63:1011–1022. [PubMed: 16650513] 24. Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007; 357:370–379. [PubMed: 17652652] 25. Auchincloss AH, Diez Roux AV, Mujahid MS, Shen M, Bertoni AG, Carnethon MR. Neighborhood resources for physical activity and healthy foods and incidence of type 2 diabetes mellitus: the Multi-Ethnic study of Atherosclerosis. Arch Intern Med. 2009; 169:1698–1704. [PubMed: 19822827] 26. Auchincloss AH, Mujahid MS, Shen M, Michos ED, Whitt-Glover MC, Diez Roux AV. Neighborhood health-promoting resources and obesity risk (the multi-ethnic study of atherosclerosis). Obesity (Silver Spring). 2013; 21:621–628. [PubMed: 23592671] 27. Brondolo E, Libby DJ, Denton EG, Thompson S, Beatty DL, Schwartz J, Sweeney M, Tobin JN, Cassells A, Pickering TG, Gerin W. Racism and ambulatory blood pressure in a community sample. Psychosom Med. 2008; 70:49–56. [PubMed: 18158368] 28. Brondolo E, Love EE, Pencille M, Schoenthaler A, Ogedegbe G. Racism and hypertension: a review of the empirical evidence and implications for clinical practice. Am J Hypertens. 2011; 24:518–529. [PubMed: 21331054] 29. Topol EJ, Steinhubl SR, Torkamani A. Digital medical tools and sensors. JAMA. 2015; 313:353– 354. [PubMed: 25626031] 30. Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J. 2016 E-published Feb 11. pii: ehv770.
Circulation. Author manuscript; available in PMC 2017 June 21.
Vasan and Benjamin
Page 13
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
31. Woods B. By 2020, 90% of world's population aged over 6 will have a mobile phone. 3-21-2016. The Next Web. [accessed 3-27-2016] http://thenextweb.com/insider/2014/11/18/2020-90-worldspopulation-aged-6-will-mobile-phone-report/. 32. Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc. 2016 epublished Feb 17, 2016. pii: ocv189. 33. Hall JL, Ryan JJ, Bray BE, Brown C, Lanfear D, Newby LK, Relling MV, Risch NJ, Roden DM, Shaw SY, Tcheng JE, Tenenbaum J, Wang TN, Weintraub WS. Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association. Circ Cardiovasc Genet. 2016 e-published March 2016. 34. Mandl KD, Mandel JC, Kohane IS. Driving Innovation in Health Systems through an Apps-Based Information Economy. Cell Syst. 2015; 1:8–13. [PubMed: 26339683] 35. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P. A haplotype map of the human genome. Nature. 2005; 437:1299–1320. [PubMed: 16255080] 36. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012; 491:56–65. [PubMed: 23128226] 37. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012; 489:57–74. [PubMed: 22955616] 38. Haring R, Wallaschofski H. Diving through the "-omics": the case for deep phenotyping and systems epidemiology. OMICS. 2012; 16:231–234. [PubMed: 22320900] 39. Kuster DWD, Merkus D, van der Velden J, Verhoeven AJM, Duncker DJ. Integrative Physiology 2.0': integration of systems biology into physiology and its application to cardiovascular homeostasis. The Journal of Physiology. 2011; 589:1037–1045. [PubMed: 21224228] 40. Barabasi AL. Network Medicine: From Obesity to the Diseasome. N Engl J Med. 2007; 357:404– 407. [PubMed: 17652657] 41. Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011; 12:56–68. [PubMed: 21164525] 42. Centers for Disease Control and Prevention. Atlanta G. Centers for Disease Control and Prevention. [accessed 3-22-2016] The Human Genome Epidemiology Network (HuGENet). 2010. http://www.cdc.gov/genomics/hugenet/default.htm 43. Teutsch SM, Bradley LA, Palomaki GE, Haddow JE, Piper M, Calonge N, Dotson WD, Douglas MP, Berg AO. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med. 2009; 11:3–14. [PubMed: 18813139] 44. Karmali KN, Lloyd-Jones DM. Adding a life-course perspective to cardiovascular-risk communication. Nat Rev Cardiol. 2013; 10:111–115. [PubMed: 23296067] 45. Pencina MJ, D'Agostino RB Sr, Larson MG, Massaro JM, Vasan RS. Predicting the 30-year risk of cardiovascular disease: the Framingham Heart Study. Circulation. 2009; 119:3078–3084. [PubMed: 19506114] 46. Khoury MJ, Lam TK, Ioannidis JP, Hartge P, Spitz MR, Buring JE, Chanock SJ, Croyle RT, Goddard KA, Ginsburg GS, Herceg Z, Hiatt RA, Hoover RN, Hunter DJ, Kramer BS, Lauer MS, Meyerhardt JA, Olopade OI, Palmer JR, Sellers TA, Seminara D, Ransohoff DF, Rebbeck TR, Tourassi G, Winn DM, Zauber A, Schully SD. Transforming epidemiology for 21st century medicine and public health. Cancer Epidemiol Biomarkers Prev. 2013; 22:508–516. [PubMed: 23462917] 47. [accessed 03-27-2016] Report of Workshop on Enhancing Training for Biomedical Big Data Big Data to Knowledge (BD2K) Initiative. 2013 Jul 29–30. https://datascience.nih.gov/sites/default/ files/bd2k/docs/july%2029%20bd2k_training_workshop_report.pdf 48. Kroenke K, Kapoor W, Helfand M, Meltzer DO, McDonald MA, Selker H. Training and career development for comparative effectiveness research workforce development: CTSA Consortium Strategic Goal Committee on comparative effectiveness research workgroup on workforce development. Clin Transl Sci. 2010; 3:258–262. [PubMed: 20973924] 49. Galea S. An Argument for a Consequentialist Epidemiology. American Journal of Epidemiology. 2013; 178:1185–1191. [PubMed: 24022890]
Circulation. Author manuscript; available in PMC 2017 June 21.
Vasan and Benjamin
Page 14
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
50. Buck Louis GM, Bloom MS, Gatto NM, Hogue CR, Westreich DJ, Zhang C. Epidemiology's Continuing Contribution to Public Health: The Power of Then and Now. American Journal of Epidemiology. 2015; 181:e1–e8. [PubMed: 25810458] 51. Castellano JM, Penalvo JL, Bansilal S, Fuster V. Promotion of cardiovascular health at three stages of life: never too soon, never too late. Rev Esp Cardiol (Engl Ed). 2014; 67:731–737. [PubMed: 25172069] 52. Castellano JM, Narula J, Castillo J, Fuster V. Promoting cardiovascular health worldwide: strategies, challenges, and opportunities. Rev Esp Cardiol (Engl Ed). 2014; 67:724–730. [PubMed: 25172068] 53. Mandl KD, Kohane IS. Federalist principles for healthcare data networks. Nat Biotechnol. 2015; 33:360–363. [PubMed: 25850061] 54. Sorlie PD, Bild DE, Lauer MS. Cardiovascular epidemiology in a changing world--challenges to investigators and the National Heart, Lung, and Blood Institute. Am J Epidemiol. 2012; 175:597– 601. [PubMed: 22415032] 55. Roger VL, Boerwinkle E, Crapo JD, Douglas PS, Epstein JA, Granger CB, Greenland P, Kohane I, Psaty BM. Strategic Transformation of Population Studies: Recommendations of the Working Group on Epidemiology and Population Sciences From the National Heart, Lung, and Blood Advisory Council and Board of External Experts. American Journal of Epidemiology. 2015; 181:363–368. [PubMed: 25743324] 56. Vasan RS, Folsom AR. Invited Commentary: Future of Population Studies: Defining Research Priorities and Processes. American Journal of Epidemiology. 2015; 181:369–371. [PubMed: 25743322] 57. The Cross-cohort Collaboration Consortium. [accessed 3-27-2016] https://chs-nhlbi.org/node/ 6539. 58. Borner K, Contractor N, Falk-Krzesinski HJ, Fiore SM, Hall KL, Keyton J, Spring B, Stokols D, Trochim W, Uzzi B. A multi-level systems perspective for the science of team science. Sci Transl Med. 2010; 2:49cm24. 59. Falk-Krzesinski HJ, Borner K, Contractor N, Fiore SM, Hall KL, Keyton J, Spring B, Stokols D, Trochim W, Uzzi B. Advancing the science of team science. Clin Transl Sci. 2010; 3:263–266. [PubMed: 20973925] 60. Guimera R, Uzzi B, Spiro J, Amaral LA. Team assembly mechanisms determine collaboration network structure and team performance. Science. 2005; 308:697–702. [PubMed: 15860629] 61. Stamatakis KA, Norton WE, Stirman SW, Melvin C, Brownson RC. Developing the next generation of dissemination and implementation researchers: insights from initial trainees. Implement Sci. 2013; 8:29. [PubMed: 23497462] 62. National Research Council (US) and Institute of Medicine (US) Committee on the Organizational Structure of the National Institutes of Health. Enhancing the Vitality of the National Institutes of Health: Organizational Change to Meet New Challenges. Washington DC: National Academies Press; 2003. New Opportunities, New Challenges: The Changing Nature of Biomedical Science. 63. Brownson RC, Samet JM, Chavez GF, Davies MM, Galea S, Hiatt RA, Hornung CA, Khoury MJ, Koo D, Mays VM, Remington P, Yarber L. Charting a future for epidemiologic training. Ann Epidemiol. 2015; 25:458–465. [PubMed: 25976024] 64. Council on Linkages Between Academia and Public Health Practice. Core competencies for public health professionals. [accessed 3-27-2016] http://www.phf.org/resourcestools/Pages/ Core_Public_Health_Competencies.aspx. 65. Koo D, Birkhead GS, Reingold AL. Competency-based epidemiologic training in public health practice. Public Health Rep. 2008; 123(Suppl 1):1–3. [PubMed: 18497008] 66. U. S. Department of Health and Human Services. A report of the Public Health Functions Project. Washington (DC): U. S. Government Printing Office; 1997. The Public Health Workforce: An Agenda for the 21st Century.
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Table A
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Training needs for epidemiology workforce in this millennium: a synthesis of major reports Khoury et al46* 1
2
3
Author Manuscript
4
Multilevel analysis, interventions, implementation, & outcomes research Greater access to and sharing of protocols, metadata, biosamples, fostering collaboration, ensuring replication, & accelerating translation Collect diverse exposures across the life course to examine multiple health-related endpoints Develop & validate reliable methods & technologies to quantify exposures/outcomes on a massive scale
5
Integrate "big data" science
6
Use knowledge to drive research, policy, practice
7
Transform training to address interdisciplinary and translational research
8
Optimize resources and infrastructure
IOM report62**
COL64**†
Eight new core competencies:
Develop skills:
1
Informatics
2
Genomics
3
Communication
4
Cultural competence
Analytic/ Assessment
2
Policy Development / program planning
3
Communication
4
Cultural competency
5
Community dimensions of practice
5
Community-based participatory research
6
Global health
7
Policy and law
6
8
Public health ethics
Basic public health sciences
7
Financial planning & management
8
Leadership & Systems Thinking
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*
Focus on transforming epidemiology in 21st century
**
1
Focus on core competencies
†
Council On Linkages between Academia and Public health practice.
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