Genomics and Precision Medicine: How Can Emerging Technologies Address Population Health Disparities? Join the Conversation.
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Advances in genome sequencing, other “omic” technologies, and big data promise a new era of personalized medicine. However, there is an ongoing discussion how these new technologies can be used to understand and address existing population health disparities. On October 11, 2017, the Precision Medicine and Population Health Interest Group in the Division of Cancer Control and Population Sciences at the National Cancer Institute, the National Institutes for Health Genomics and Health Disparities Interest Group, and the CDC Office of Public Health Genomics co-sponsored a special one-hour online webinar that explored the intersection of genomics, precision medicine, and health disparities. Over 300 people virtually attended the webinar and engaged in a lively questions and answers session. (Watch the one-hour webinar online.
Essentially, new genomic and other precision medicine technologies offer insights into some population variation in disease prevalence, but do not explain the systematic differences in health outcomes seen among different populations. Health disparities are due largely to differences in social and environmental factors (e.g. equitable access to healthy foods, education, employment, health care, and safe environments), resulting in poorer health outcomes, across many disease conditions throughout the lifespan. Genetic variation among populations can, however, account for some differences in disease prevalence. These differences will sometimes align with health disparity outcomes; but, sometimes not. For example, APOL1 gene variants contribute to increased kidney disease risk among African Americans, adding to social factors (such as, institutional racism, poverty, and barriers to high quality health care) that contribute to this health disparity. Conversely, African Americans have a lower prevalence of other diseases, such as melanoma and acute lymphoblastic leukemia, due to protective genetic factors. Yet, those who develop these diseases experience poorer survival due to social factors and differential access to healthcare.
In addition to offering some insights into disease prevalence, population genetic differences must be considered in developing genomic medicine. Pharmacogenomics provides an example of how prevalence of gene variants affecting drug response can differ among different populations. For instance, patients infected with hepatitis C virus are usually treated with peginterferon and ribavirin to prevent progressive hepatic fibrosis, cirrhosis, and hepatocellular carcinoma. Response to treatment varies considerably with about half of patients showing sustained virological response after the standard course of treatment. For unclear reasons, African Americans have been known to be less responsive to treatment, independent of other host and viral factors. Two recent genomic studies (here and here) provided insight into observed ethnic disparities in treatment response by identifying a genetic variant in the IL28B gene that is an important predictor of treatment response and spontaneous clearance in patients infected with hepatitis C virus. Notably, allele frequencies differ between ethnic groups, explaining the observed differences in response rates between European Americans (68%), African Americans (36%), and Asians (95%). Indeed, the IL28B genetic variant may explain at least half of the difference in response rates observed between European Americans and African Americans who received the same treatment. Effective use of pharmacogenetics to increase the safety and efficacy of drug treatment will require research in diverse populations to ensure accurate identification of all relevant variants. Yet, most genomic data currently available derive from European populations. Efforts to increase the diversity of populations participating in genomic research will help to prevent healthcare disparities in genomic medicine in the future.
It is important to realize that the tools of precision medicine and public health present complementary approaches to disease prevention and treatment in populations experiencing health-related disparities. As public health sciences begin to use more complex sources of data, better surveillance, and targeted implementation strategies (i.e., precision public health), such studies may yield findings that could potentially improve our understanding of disease, and address subsets of the population that need available interventions the most. Ultimately, we will need both population approaches and individual precision interventions to improve overall population health and help reduce health disparities.
If you are interested in joining the conversation, please contribute your thoughts and feedback to the discussion on the role of genomics and precision medicine in health disparities. Submit your comments here.
Additional recent publications that can serve as resource to our readers.
- Genomics, Health Disparities, and Missed Opportunities for the Nation’s Research Agenda. West KM, Blacksher E, Burke W. JAMA. 2017 May 9;317(18):1831-1832.
- The African diaspora: history, adaptation and health. Rotimi CN, Tekola-Ayele F, Baker JL, Shriner D. Curr Opin Genet Dev. 2016 Dec;41:77-84.
- Will Precision Medicine Improve Population Health? Khoury MJ, Galea S. JAMA. 2016 Oct 4;316(13):1357-1358.
- Racial/Ethnic Disparities in Genomic Sequencing. Spratt DE, Chan T, Waldron L, Speers C, Feng FY, Ogunwobi OO, Osborne JR. JAMA Oncol. 2016 Aug 1;2(8):1070-4.
- The contribution of genomic research to explaining racial disparities in cardiovascular disease: a systematic review. Kaufman JS, Dolman L, Rushani D, Cooper RS. Am J Epidemiol. 2015 Apr 1;181(7):464-72.
- Genomics is failing on diversity. Popejoy AB, Fullerton SM. Nature. 2016 Oct 13;538(7624):161-164.
- Diversity and inclusion in genomic research: why the uneven progress? Bentley AR, Callier S, Rotimi CN. J Community Genet. 2017 Jul 18. [Epub ahead of print]
- Rotimi C, Shriner D, Adeyemo A. Genome science and health disparities: a growing success story? Genome Med. 2013 Jul 29;5(7):61
- Rotimi CN, Jorde LB. Ancestry and disease in the age of genomic medicine. N Engl J Med. 2010 Oct 14;363(16):1551-8. PMID: 20942671
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One of the fundamental problems with translation of the promise of precision medicine into real world practice, at both an individual and population level, has been the failure of its advocates to recognize that genomics represents an important, but relatively small piece of the constellation of -omics perspectives that uniquely define individual disease states. Notwithstanding the potentially powerful role of the human genome in both setting in motion and predisposing to specific disease states, the interplay between nature and nurture has long been recognized as central to understanding the emergence of individual disease processes. As such, one might expect that truly predictive models of personalized medicine would need to incorporate the intersecting roles of the epigenome, metabolome, proteome, inflammasome, microbiome, as well as the complex overlapping elements of the exposome, in making sense of the emergence of disease states in genetically predisposed individuals. Wylie Burke, in the blog posting above, “Genomics and Precision Medicine: How Can Emerging Technologies Address Population Health Disparities?”, notes “essentially, new genomic and other precision medicine technologies offer insights into some population variation in disease prevalence, but do not explain the systematic differences in health outcomes seen among different populations. Health disparities are due largely to differences in social and environmental factors (equitable access to healthy foods, education, employment, health care, and safe environments), resulting in poorer health outcomes, across many disease conditions throughout the lifespan.” In point of fact, precision medicine properly applied extends well beyond narrow identification of individual genomic risk factors, to life influences impacting on the epigenome (maternal starvation or in utero exposure to elevated cortisol levels), nutrient deficiencies and heavy metal exposures during critical developmental periods impacting the metabolome, antibiotic and other xenobiotic exposures influencing the microbiome, micronutrient deficiencies and environmental stressors impacting the inflammasome, ethnic dietary practices resulting in critical nutrient deficiencies, deprivation and exposure to violence at a familial and community level leading to undernutrition, poor access to health care, and toxic sociocultural influences, as well as myriad geographical influences, ranging from exposure to toxic disposal sites, contaminated water sources, or airborne particulate or VOC contaminants in either outdoor or indoor environments. Full integration of the spectrum of -omics influences into the understanding, prediction, and management of disease processes at an individual and public health level has the potential to exponentially magnify the utility of precision medicine beyond what is currently possible through application of genomics alone.
Ironically, most of the most critical data emerging from all of the various omics technologies is dependent on the analysis of large population data-sets to identify individual variations linking specific markers with specific disease states. Although the immediate utility of such linkages is the potential to identify specific individual treatments to reverse the pathologic impact of such genetic anomalies, clearly one can work backward from the dataset to discover patterns of ethnicity, geographical localization, linked genetic traits, and shared epigenetic exposures that would permit identification of subpopulations with a high predictive probability of sharing these markers placing them at risk for various disease states. This where big data has a potential to converge with public health to identify subpopulations likely to benefit from such precision medicine interventions, using risk stratification methodology widely used in contemporary epidemiologic analysis, potentially vastly broadening the scope of potential health benefits of such findings at both an individual and public health level.
An essential underpinning of precision medicine and the application of omics technologies is finally abandoning the conventional dualistic thinking that has dominated medicine for centuries, namely the notion of binary cause and effect: one etiology leading to a specific illness. Although seasoned clinicians have for some time recognized the multifactorial nature of complex illness processes such as autoimmune disorders, diabetes, and inflammatory bowel disease, comprehensive analysis of underlying risk factors has been slow in evolving, and frequently lags far behind developments in basic science research. Further as theories of pathophysiology become more diverse and complex, it naturally taxes the capacity of overextended primary care physicians to stay abreast of the most contemporary literature and incorporate those findings into their diagnostic and management protocols. The Public Health arena, in contrast, has the luxury of being able to examine the big picture at a global level, integrate new findings and conceptualizations coming from each level of the omics hierarchy, utilize Big Data to identify hidden associations not accessible to the individual clinician, and potentially disseminate that information both globally and to the medical community in a manner that could immeasurably enhance quality of care at every level. For this to occur, there clearly needs to be a systematic reconsideration of what are clearly artificial barriers between Public Health and traditional clinical Medicine, given that they are in fact inextricably intertwined. More today than ever, the two fields need to operate synergistically, with the practice of individual medicine breaking out of a self-imposed cocoon that imagines that every patient is an isolated organism, impervious the influences of family, early life experience, ancestry, neighborhood, religion, ethnicity, geography, and one’s immediate physical environment. Public Health clearly has the tools to fill in these missing gaps, with a uniquely comprehensive epidemiologic data set permitting the discovery of intermediary linkages between omics findings and endpoint disease processes, and access to Big Data tools for identifying these hidden linkages that would be inaccessible to the average practicing clinician. As an example, it is well-known that specific ethnic sub-populations have a higher incidence of single-nucleotide polymorphisms that either dramatically enhance or reduce their rate of hepatic metabolism of widely used pharmaceuticals, however most clinicians have a very limited knowledge of any such variations, unless they involve locally familiar ethnic groups, such as Caucasians, Blacks, or Latinos. Creation of an integrated clinically oriented database through collaboration between institutions of Public Health and Medicine would allow clinicians to readily access such widely available information, such as the fact that there is an extremely high-incidence of individuals with hepatic cytochrome 2D6 ultra-metabolizer status among individuals from Northwest India, Sudan, Ethiopia, and Iran that would cause them to be unresponsive to conventional doses of commonly used antidepressant agents, remedied by a simple upward dosage adjustment. Were similar data widely available around the prevalence of chromium deficiency in southern China, risks of heavy metal intoxication in Mongolia, arsenic poisoning in Western Nevada, omega-3 acid deficiencies in interior regions of Scandinavia, or widespread vitamin D deficiency in the higher latitudes of the northern hemisphere, a wide range of deleterious human health consequences might be readily avoided. The implications of applying the tools of Big Data to integrate these realms and incorporate the growing database coming out of omics technologies are profound. Hopefully their integration will be coming in the not too far off future.