Data Science: Transformation of Research and Scholarship

Abstract

The emergence of data science as a practice and discipline is revolutionizing research potential in all disciplines, but healthcare science has the potential to affect the health of individual lives. The use of existing data provides fertile ground for healthcare professionals to conduct research that will maximize quality outcomes, develop algorithms of care to increase efficiency and safety, and create predictive models that have the ability to prevent illness events and reduce healthcare expenditures. Data science can change practice through using existing and growing amounts of data to conduct research and build scholarship. Clinical trials, in some cases, may no longer be required to examine interventions. The pragmatic and efficient use of existing large cohort datasets has the ability to generate sample and control groups to determine efficacy. The collection of data from electronic medical records can provide substantial data to determine trends, construct algorithms, and consider disease and health behaviors modeling that alter patient care. Digital research incorporating vast amounts of data and new analytics has the ability to influence global healthcare.

Case Study 10.1: Complexity of Common Disease and Big Data

• Sandra Daack-Hirsch Ph.D., R.N. &
• Lisa Shah M.S.N., R.N. 

1. The University of Iowa, Iowa city, IA, USA

   Sandra Daack-Hirsch Ph.D., R.N. & Lisa Shah M.S.N., R.N.

Abstract

Human development, health, and disease processes are the culmination of complex interactions among DNA sequences, gene regulation—epigenetics, and the environment. To truly create individualized interventions to address prevention and treatment, complex data systems that are integrated are needed. As an exemplar, this case study will explore the complexity of information and the vast sources of big data and related analytics needed to better understand causes of Type 2 Diabetes (T2D), which in turn will drive individualized interventions to reduce risk and better treat individuals. Personalized healthcare (also called precision medicine) is becoming a reality. President Obama announced the Precision Medicine Initiative in February 2015. Conceptually, precision medicine has been defined as prevention and treatment strategies that take individual differences into account to generate knowledge applicable to the continuum of health and disease. To that end, this case study describes current initiatives to assemble and analyze the vast and complex phenotypic, genetic, epigenetic, and exposure information generated or that will be generated by researchers and clinicians on individuals, within a specific clinical example of T2D.

Keywords

Type 2 diabetes • Genetics/genomics • Exposome • Epigenetics • Omics • Big data • Personalized health care

1.1 10.1.1 Type 2 Diabetes (T2D) as a Significant Health Problem

Diabetes is a significant public health problem and its prevalence is increasing. As of 2012 an astounding 29.1 (9.3%) million Americans of all ages and racial/ethnic groups were affected by either Type 1 or Type 2 diabetes although, roughly 90–95% are Type 2. An estimated 8.1 million of those who have diabetes are undiagnosed (National Diabetes Information Clearinghouse [NDIC] 2014; Valdez et al. 2007) which potentiates the likelihood that they will also develop secondary health complications related to untreated diabetes (Klein Woolthuis et al. 2007). Diabetes is the leading cause of kidney failure, nontraumatic lower-limb amputation, heart disease, stroke, and new cases of blindness in the United States. In 2010 it was the seventh leading cause of death. As of 2012 medical expenses for people with diabetes (any form) were more than twice those for people without diabetes, costing Americans an estimated $245 billion in direct and indirect costs (NDIC 2014).

Type 2 diabetes (T2D) is a complex metabolic disease characterized by persistently elevated blood glucose caused by insulin resistance coupled with insulin deficiency. The substantial genetic component is thought to interact with environmental risk factors (plentiful diets and limited physical activity) to produce T2D. Several genetic variants have been associated with an increased risk to develop T2D (Wolfs et al. 2009), but the clinical validity of genetic variants alone to estimate diabetes risk remains limited, and genetic variants explain only a small portion of total risk variation (Burgio et al. 2015). While the staggering increase in prevalence of T2D is well documented, until recently the sharp increase in prevalence was largely thought to be driven by environmental factors experienced during adulthood (mainly an imbalance between energy intake and energy expenditure). However, there is also a sharp increase in T2D and obesity among persons under the age of twenty that challenges our understanding of risk factors (NDIC 2014). Just how genetic and environmental factors work in concert to produce the T2D phenotype remains unclear. Moreover, the environmental contribution to T2D is far more complex than an imbalance between energy intake and energy expenditure.

1.2 10.1.2 Factors Contributing to T2D

1.2.1 10.1.2.1 Genetics/Genomics

The genetic contribution to T2D has been established through research involving family history, twins studies, and genetic analysis. The lifetime risk for developing T2D in the Western world is reported to be between 7 and 10% (Burgio et al. 2015; Wolfs et al. 2009). Narayan et al. (2003) estimated that for individuals born in the United States in 2000, the lifetime risk for T2D is 1 in 3 for males and 2 in 5 for females. Family studies reveal that unaffected first-degree relatives of individuals with T2D have a two- to almost six-fold increase in risk to develop T2D over the course of a lifetime compared to people without a family history of T2D (Harrison et al. 2003; Valdez et al. 2007). The concordance rate for T2D among identical twins is high and is consistently reported to be greater than 50% in many populations (Medici et al. 1999; Newman et al. 1987; Poulsen et al. 2009). The fact that there are monogenetic (single gene) forms of diabetes (e.g., Maturity Onset Diabetes of the Young [MODY] and Permanent Neonatal Diabetes Mellitus [PNDM]) provides further evidence for a genetic role in the diabetes phenotype. Nevertheless, the majority of cases of diabetes are not one of the monogenetic forms; rather, T2D is a genetically complex disorder in which any number of genetic variants predispose an individual to develop the disease. Advances in genotyping technology have led to large-scale, population-based genetic studies to identify genetic variants (single nucleotide polymorphisms [SNPs]) associated with T2D. For example, the most recent studies have verified up to 65 SNPs to be associated with T2D (Morris et al. 2012; Talmud et al. 2015).

Discounting the rare monogenetic forms of diabetes and given the genetic and phenotypic heterogeneity of T2D¹ drawing firm conclusions about genotype—phenotype correlation using standard statistical analyses is difficult. To address statistical limitations, several efforts are underway to develop new algorithms to generate genetic risk scores (GRS) that predict T2D (Keating 2015; Talmud et al. 2015). As knowledge of the underlying genetic contribution grows, the prediction models improve. Mounting evidence shows that combining GRS and clinical risk factors (e.g., BMI, age, and sex) further improves the ability to detect incident cases (Keating 2015). However, the clinical utility of GRS remains problematic (Lyssenko and Laakso 2013). Developing risk prediction algorithms that combine GRS and phenotypic data for T2D is challenging in part due to the high heterogeneity in both the genetic factors and phenotypic elements of the disease. Moreover, genes and genetic variants at different locations in the genome (polygenic loci) that are associated with T2D are involved in multiple physiologic processes such as gluconeogenesis, glucose transport, and insulin homeostasis and many are also implicated in obesity (Burgio et al. 2015; Keating 2015; Slomko et al. 2012; Wolfs et al. 2009). To date, GRS use common genetic variants that have the strongest main effects. Other sources of genetic variance include rarer higher-penetrant variants, epigenetics, gene-gene and gene-environment interactions, and sex-specific genetic signals (Keating 2015; Lyssenko and Laakso 2013). The phenotype is also highly complex with patients presenting in various combinations of body type, age, family history, gestational diabetes, drug treatments, and comorbidities including obesity and metabolic syndrome. Detecting genetic differences is difficult when they are rare. Combining complex phenotype, interaction (gene x gene and or gene x environment), and gene variant information requires new data science approaches in order to leverage the complexity and create information that is clinically useful.

1.2.2 10.1.2.2 The Environment

In 2005 Wild coined the term “exposome” to describe the complementary environmental component of the gene-environment interaction indicative of complex traits and diseases (Wild 2005). As with the genetic component of T2D, the environmental component is also complex and plays a major role in the diabetes phenotype. Most of our knowledge of the T2D exposome is limited to the behavioral or modern living environment (Slomko et al. 2012). The modern living environment is characterized by increased access to low-cost, calorie-dense foods and increased sedentary lifestyle. The modern living environment is most amenable to intervention, and in fact interventions targeting diet and exercise are known to be effective in preventing or delaying the onset of T2D (Diabetes Prevention Program [DPP] Research Group 2002; Lindström et al. 2003; Venditti 2007). In the context of the modern living environment there is an emerging awareness of “unavoidable exposures” and their connection to T2D (Slomko et al. 2012). These are exposures to man-made chemicals through ambient particles, water, food, and use of consumer or personal care products—some are found in plastics and resins. These chemicals are ubiquitous in the everyday environment at levels below standards set by the Environmental Protection Agency and other regulatory agencies. While a single exposure is not likely to cause harm, little is known about chronic low-level exposure and risk for disease. Burgio et al. (2015) summarized the growing evidence that suggests endocrine-disrupting chemicals such as brominated flame retardants and organochlorine pesticides, heavy metals, and pharmaceuticals (e.g., corticosteroids, antipsychotics, beta-blockers, statins, thiazide diuretics) may interfere with β-cell function and induce insulin resistance (Burgio et al. 2015, p.809; Diabetes.co.uk 2015).

There is also emerging evidence that the gut microbiota composition could affect risk for T2D. Gut microbiota are important for intestinal permeability, host metabolism, host energy homeostasis, and human toxicodyamics (how chemicals affect the body). Changes in microbiota composition that interfere with these functions can lead to increased activation of inflammatory pathways which in turn interferes with insulin signaling, increase in energy harvesting and fat storage in adipose tissue, and potentiate the effect of chemical exposure—all potential pathways to increase risk for metabolic syndrome, obesity and or T2D. For a more in depth review of gut microbiota and T2D refer to Burgio et al. (2015) and Slomko et al. (2012).

1.3 10.1.3 Epigenetics

1.3.1 10.1.3.1 Overview of Epigenetics

Epigenetics may explain how genetic and environmental factors work in concert to produce T2D. “Epigenetics is the study of heritable changes (either mitotically or meiotically) that alter gene expression and phenotypes, but are independent from the underlying DNA sequence …”(Loi et al. 2013, p. 143). The epigenome is a series of chemical modifications (often referred to as tags or marks) that are superimposed on to the genome. In humans epigenetic modifications can either affect the proteins that are involved in the packaging of DNA into chromatin (known as chromatin modification), or directly attach to the DNA (e.g., DNA methylation). Epigenetic modification regulates gene expression by either activating (turning on) or deactiving (turning off) genes or segments of the DNA at given times (Genetics Learning Center 2014). Chromatin modification and DNA methylation are functionally linked to transcription and likely provide the mechanisms by which cells are programmed from one generation to the next. In other words, the epigenome activates genome in what is manifested as the phenotype.

During the pre-genomic era it was thought that disease and specific human traits were the direct result of variants in the DNA sequence (e.g., direct mutation of a single gene). However, very few diseases/traits are associated with only gene variants. To varying degrees, other factors such as poverty, nutrition, stress, and environmental toxin exposures can also contribute to health or lack thereof; yet none fully explain susceptibility to disease or variations in human traits. Environmental and social signals such as diet and stress can trigger changes in gene expression without changing the sequence of the DNA (Heijmans et al. 2008; McGowan et al. 2009; Mathers et al. 2010; Radtke et al. 2011; Weaver et al. 2004). Some of these epigenetic tags are cell specific and differentiate phenotype at a cellular level with respect to cell type and function. In a differentiated cell, only 10 to 20% of the genes are active (Genetic Science Learning Center 2014). Some epigenetic tags are acquired and lost over the life course of an individual, and some tags are passed on from generation to generation and may take several generations to change.

1.3.2 10.1.3.2 Examples of Epigenetic Modification and T2D

While it is widely known and accepted that maternal nutrition is of paramount importance to the health and development of the offspring, the precise biologic mechanisms linking maternal nutrition to offsprings’ wellbeing are just beginning to be understood. Epigenetic mechanisms may provide one such link. Evidence for epigenetic modification in the form of fetal programing can be found among individuals who were prenatally exposed to famine during the Dutch Hunger Winter in 1944–45. These individuals had less DNA methylation (hypomethylation) of the insulin-like growth factor 2 (IGF2) gene compared to their unexposed, same-sex siblings. IGF2 is a key factor in human growth and development. This epigenetic modification acquired in utero persisted throughout the children’s lifetime (Heijmans et al. 2008) and has been associated with higher rates of T2D, obesity, altered lipid profiles, and cardiovascular disease (Schulz 2010) among these children (Burgio et al. 2015).

A number of recent studies report changes in methylation patterns of specific genes associated with T2D (Rönn et al. 2013; Zhang et al. 2013; Ling et al. 2008; Yang et al. 2011; Kulkarni et al. 2012; Yang et al. 2012; Hall et al. 2013; Ribel-Madsen et al. 2012). Studies also reveal differential methylation patterns in genes associated with T2D among those affected by T2D (Zhang et al. 2013; Ling et al. 2008; Yang et al. 2011, 2012; Kulkarni et al. 2012; Hall et al. 2013) and in tissue specific samples (pancreases and mitochondria). These types of studies provide evidence that genotypes (DNA sequences) and their regulation (epigenetic modifications) are important factors contributing to T2D and that the epigenome is modifiable providing targets for interventions.

The environmental exposures described above (“unavoidable exposures”) could also lead to changes in gut microbiota composition. In fact, changes in gut microbiota composition have been shown to interfere with epigenetic regulation of FFAR3 gene in patients with T2D (Remely et al. 2014). FFAR3 is normally expressed in the pancreatic β-cells and mediates an inhibition of insulin secretion by coupling with other proteins (National Center for Biotechnology Information [NCBI] 2015). Interfering with the epigenetic regulation of FFAR3 would in turn lead to an inability to regulate insulin secretion appropriately.

1.3.3 10.1.3.3 Summary of Factors Contributing to T2D

T2D is the combination of biological contributing factors (genetics), environmental contributing factors (exposome), and the synthesis of biology and environment (epigenetics). Evolving epigenetic evidence suggests that epigenetic modifications could be important biomarkers for predicting risk, monitoring effectiveness of interventions, and targeting for therapy development to both prevent and treat T2D. Epigenetic patterns may serve as biomarkers connecting the exposome and genome (Fig. 10.1.1), thereby providing more comprehensive risk information for T2D. Unfavorable epigenetic modification may be reversed by lifestyle interventions, such as by modifying diet, increasing physical activity, and enriching the in utero environment. The rapid advances in genetic, exposome, and epigenetic sciences offer exciting possibilities for future discovery that will deepen our understanding of the complex balance between the environment and the genome, and how that balance influences health. Clearly an in-depth understanding T2D is largely dependent on big data and related advanced analytics.

Fig. 10.1.1

Epigenetic modifications: the interaction of genetic and environmental risk factors over the life course

1.4 10.1.4 Current Initiatives to Leverage the Power of Big Data for Common Disease

1.4.1 10.1.4.1 Omics

Omics is the application of powerful high through-put molecular techniques to generate a comprehensive understanding of DNA, RNA, proteins, intermediary metabolites, micronutrients, and microbiota involved in biological pathways resulting in phenotypes. Scientists and informaticians are working on ways to integrate the layers of omic sciences and the exposome to better quantify an individual’s susceptibility to diseases such as T2D and to capitalize on his or her inherent protections against disease (Slomko et al. 2012). These techniques would allow for massive amounts of genomic, epigenomic, exposure, and phenotypic data to be analyzed in concert in order to build more powerful prediction models and provide targets for the development of prevention and treatment modalities.

1.4.2 10.1.4.2 Clinical Genomic Resources

Several initiatives are underway to assemble the vast and complex phenotypic, genetic, epigenetic and exposure information pertaining to wellness and disease states. These initiatives will leverage health information that is currently generated or will be generated by researchers and clinicians on individuals.

ClinGen. (http://clinicalgenome.org/) is a project to develop standard approaches for sharing genomic and phenotypic data provided by clinicians, researchers, and patients through centralized databases, such as ClinVar—a National Database of Clinically Relevant Genetic Variants (CRGV). ClinGen investigators are working to standardize the clinical annotation and interpretation of genomic variants. Goals of ClinGen include:

• Share genomic and phenotypic data through centralized databases for clinical and research use

• Standardize clinical annotation and interpretation of variants

• Improve understanding of variation in diverse populations

• Develop machine-learning algorithms to improve the throughput of variant interpretation

• Implement evidence-based expert consensus for curation of clinical validity

• Assess the ‘medical actionability’ of genes and variants to support their use in clinical care systems

• Disseminate the collective knowledge/resources and ensure EHR interoperability (http://www.genome.gov/27558993)

Currently ClinGen efforts are focused on cardiovascular disease, pharmacogenomics, hereditary (germline) cancer, somatic cancer, and inborn errors of metabolism. However, knowledge generated on structure and process will serve as a template for approaching other diseases.

eMERGE. The Electronic Medical Records and Genomics (eMERGE) Network is a National Institutes of Health (NIH)-organized and funded consortium of U.S. medical research institutions. The eMERGE Network brings together researchers from leading medical research institutions across the country to conduct research in genomics, including discovery, clinical implementation and public resources. eMERGE was announced in September 2007 and began its third and final phase in September 2015 (http://www.genome.gov/27558993). The Network is comprised of six workgroups (see Table 10.1.1).

Table 10.1.1 eMerge workgroup summary. eMERGE https://emerge.mc.vanderbilt.edu/

The primary goal of the eMERGE Network is to develop, disseminate, and apply approaches to research that combine biorepositories with electronic medical record (EMR) systems for genomic discovery and genomic medicine implementation research. In addition, the consortium includes a focus on social and ethical issues such as privacy, confidentiality, and interactions with the broader community (eMERGE https://emerge.mc.vanderbilt.edu/).

PhenX. One of the limitations in being able to interpret findings from genome-wide association (GWA) studies is lack of uniform phenotypic descriptions and measures. For example, hundreds of associations between genetic variants and diabetes have been identified. However, most GWA studies have had relatively few phenotypic and exposure measures in common. Development and adoption of standard phenotypic and exposure measures could facilitate the creation of larger and more comprehensive datasets with a variety of phenotype and exposure data for cross-study analysis, thus increasing statistical power and the ability to detect associations of modest effect sizes and gene-gene and gene-environment interactions (http://www.genome.gov/27558993). PhenX was developed in recognition of the need for standard phenotypic and exposure measures, particularly as related to GWA studies. The National Human Genome Research Institute (NHGRI) initiated the PhenX Toolkit in 2006 with the goal of identifying and cataloguing 15 high-quality, well-established, and broadly applicable measures for each of 21 research domains (diabetes is one of these) for use in GWA studies and other large-scale genomic research (www.phenxtoolkit.org).

Roadmap Epigenetics Mapping Consortium. The National Institute of Health (NIH) Roadmap Epigenetics Mapping Consortium was created in an effort to understand epigenetic modifications and how these interact with underlying DNA sequences to contribute to health and disease. The project will provide publically available epigenetic maps on normal human tissues, support technology development, and provide funding in epigenetics research (National Institutes of Health 2015; Slomko et al. 2012).

Precision Medicine/Personalized Healthcare. Precision Medicine/personalized healthcare is a medical model that proposes to customize healthcare by incorporating medical decisions, practices, and products that are based on individual variability in genes, environment, and lifestyle. The potential ability of applying this concept more broadly has been dramatically improved by the recent development of large-scale biologic databases described above. The Precision Medicine Initiative Cohort Program proposes to:

• Identify genomic variants that affect drug response

• Assess clinical validity of genomic variants associated with disease

• Identify biomarkers that are early indicators of disease

• Understand chronic diseases and best management strategies

• Understand genes/pathways/factors that protect from disease

• Assess how well novel cellphone-based monitors of health work

• Evaluate the ability of EHRs to integrate research data

• Learn and apply new ways of engaging participants in research

• Develop methodology for data mining and statistical analysis (https://www.nih.gov/precision-medicine-initiative-cohort-program)

1.5 10.1.5 Scope and Practice of Genetics/Genomics Nursing

The American Nurses Association in collaboration with the International Society of Nurses in Genetics provides an excellent resource for nurses interested in clinical genetics and nursing, the Genetics/Genomics Nursing: Scope and Standards of Practice, 2nd Edition (2016). This resource summarizes the role of nurses in genetics/genomics, which focuses on the actual and potential impact of genetic/genomic influences on health. Genetics/genomics nurses educate clients and families on genetic/genomic influences that might impact their health and intervene with the goals of optimizing health, reducing health risks, treating disease, and promoting wellness. This practice depends upon research and evidence-based practice, interprofessional collegiality and collaboration with genetics/genomics professionals and other healthcare professionals to provide quality patient care.

1.6 10.1.6 Conclusion

In conclusion, T2D is an increasingly common and complex disorder with genome, exposome, and epigenome factors contributing to the widely variable phenotype. Initiatives in precision medicine propose to customize healthcare by integrating data and information pertaining to individual variability in genes, environment, and lifestyle and interpreting this information to inform medical decisions, practices, and products that prevent, delay, and more effectively treat individuals who are at risk or have T2D. While many of our current initiatives build the evidence base needed to guide clinical practice for the individual, society also needs to be mindful of the social inequalities of opportunity including education, environmental quality, and access, not only to health care but to nutritious food, recreation, and community supports that contrite health and disease. These social determinants are part of the individual’s exposome, and yet are often beyond the control of the individual. Finally, motivating individuals at higher risk to engage in lifestyle changes to reduce their risk for T2D remains challenging. Communicating risk information about T2D is further complicated by how a person personalizes and rationalizes his or her risk to develop it (Shah et al. in press; Walter and Emory 2005). Knowing about genetic risk is not enough to motivate people to change behaviors (Grant et al. 2013). An important knowledge gap to fill is our understanding of how people at increased risk for T2D come to understand and manage behaviors to reduce their risk for disease. Understanding a person’s beliefs may facilitate effective collaboration with healthcare providers, and improve risk reduction education using a truly comprehensive personalized approach.

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