Mixed Race Studies

If doctors and clinical educators rigorously analyze algorithms that include race correction, they can judge, with fresh eyes, whether the use of race or ethnicity is appropriate. In many cases, this appraisal will require further research into the complex interactions among ancestry, race, racism, socioeconomic status, and environment. Much of the burden of this work falls on the researchers who propose race adjustment and on the institutions (e.g., professional societies, clinical laboratories) that endorse and implement clinical algorithms. But clinicians can be thoughtful and deliberate users. They can discern whether the correction is likely to relieve or exacerbate inequities. If the latter, then clinicians should examine whether the correction is warranted. Some tools, including eGFR and the VBAC calculator, have already been challenged; clinicians have advocated successfully for their institutions to remove the adjustment for race.^43,44 Other algorithms may succumb to similar scrutiny.⁴⁵ A full reckoning will require medical specialties to critically appraise their tools and revise them when indicated.

Darshali A. Vyas, Leo G. Eisenstein, and David S. Jones, “Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms,” The New England Journal of Medicine, Volume 2020, Number 383, 882. https://dx.doi.org/10.1056/NEJMms2004740.

Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms

The New England Journal of Medicine
Volume 2020, Number 383
pages 874-882
2020-08-27 (published on 2020-06-17, at NEJM.org.)
DOI: 10.1056/NEJMms2004740

Darshali A. Vyas, M.D., Resident Physician
Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts
Harvard University, Cambridge, Massachusetts

Leo G. Eisenstein, M.D., Resident Physician
New York University Langone Medical Center, New York, New York

David S. Jones, M.D., Ph.D., A. Bernard Ackerman Professor of the Culture of Medicine
Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts

Physicians still lack consensus on the meaning of race. When the Journal took up the topic in 2003 with a debate about the role of race in medicine, one side argued that racial and ethnic categories reflected underlying population genetics and could be clinically useful.¹ Others held that any small benefit was outweighed by potential harms that arose from the long, rotten history of racism in medicine.² Weighing the two sides, the accompanying Perspective article concluded that though the concept of race was “fraught with sensitivities and fueled by past abuses and the potential for future abuses,” race-based medicine still had potential: “it seems unwise to abandon the practice of recording race when we have barely begun to understand the architecture of the human genome.”³

The next year, a randomized trial showed that a combination of hydralazine and isosorbide dinitrate reduced mortality due to heart failure among patients who identified themselves as black. The Food and Drug Administration granted a race-specific indication for that product, BiDil, in 2005.⁴ Even though BiDil’s ultimate commercial failure cast doubt on race-based medicine, it did not lay the approach to rest. Prominent geneticists have repeatedly called on physicians to take race seriously,^5,6 while distinguished social scientists vehemently contest these calls.^7,8

Our understanding of race and human genetics has advanced considerably since 2003, yet these insights have not led to clear guidelines on the use of race in medicine. The result is ongoing conflict between the latest insights from population genetics and the clinical implementation of race. For example, despite mounting evidence that race is not a reliable proxy for genetic difference, the belief that it is has become embedded, sometimes insidiously, within medical practice. One subtle insertion of race into medicine involves diagnostic algorithms and practice guidelines that adjust or “correct” their outputs on the basis of a patient’s race or ethnicity. Physicians use these algorithms to individualize risk assessment and guide clinical decisions. By embedding race into the basic data and decisions of health care, these algorithms propagate race-based medicine. Many of these race-adjusted algorithms guide decisions in ways that may direct more attention or resources to white patients than to members of racial and ethnic minorities…

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The Illusion of Inclusion — The “All of Us” Research Program and Indigenous Peoples’ DNA

The New England Journal of Medicine
Issue 383 (2020-07-30)
pages 411-413
DOI: 10.1056/NEJMp1915987

Keolu Fox, Ph.D.
University of California, San Diego

Raw data, including digital sequence information derived from human genomes, have in recent years emerged as a top global commodity. This shift is so new that experts are still evaluating what such information is worth in a global market. In 2018, the direct-to-consumer genetic-testing company 23andMe sold access to its database containing digital sequence information from approximately 5 million people to GlaxoSmithKline for $300 million. Earlier this year, 23andMe partnered with Almirall, a Spanish drug company that is using the information to develop a new antiinflammatory drug for autoimmune disorders. This move marks the first time that 23andMe has signed a deal to license a drug for development.

Eighty-eight percent of people included in large-scale studies of human genetic variation are of European ancestry, as are the majority of participants in clinical trials.¹ Corporations such as Geisinger Health System, Regeneron Pharmaceuticals, AncestryDNA, and 23andMe have already mined genomic databases for the strongest genotype–phenotype associations. For the field to advance, a new approach is needed. There are many potential ways to improve existing databases, including “deep phenotyping,” which involves collecting precise measurements from blood panels, questionnaires, cognitive surveys, and other tests administered to research participants. But this approach is costly and physiologically and mentally burdensome for participants. Another approach is to expand existing biobanks by adding genetic information from populations whose genomes have not yet been sequenced — information that may offer opportunities for discovering globally rare but locally common population-specific variants, which could be useful for identifying new potential drug targets.

Many Indigenous populations have been geographically isolated for tens of thousands of years. Over time, these populations have developed adaptations to their environments that have left specific variant signatures in their genomes. As a result, the genomes of Indigenous peoples are a treasure trove of unexplored variation. Some of this variation will inevitably be identified by programs like the National Institutes of Health (NIH) “All of Us” research program. NIH leaders have committed to the idea that at least 50% of this program’s participants should be members of underrepresented minority populations, including U.S. Indigenous communities (Native Americans, Alaskan Natives, and Native Hawaiians), a decision that explicitly connects diversity with the program’s goal of promoting equal enjoyment of the future benefits of precision medicine.

But there are reasons to believe that this promise may be an illusion. Previous government-funded, large-scale human genome sequencing efforts, such as the Human Genome Diversity Project, the International HapMap Project, and the 1000 Genomes Project, provide examples of the ways in which open-source data have been commodified in the past. These initiatives, which promised unrestricted, open access to data on population-specific biomarkers, ultimately enabled the generation of nearly a billion dollars’ worth of profits by pharmaceutical and ancestry-testing companies. If the All of Us program uses the same unrestricted data-access and sharing protocols, there will be no built-in mechanisms to protect against the commodification of Indigenous peoples’ DNA…

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Will Precision Medicine Move Us beyond Race?

The New England Journal of Medicine
2016-05-26 (Volume 374, Number 21)
DOI: 10.1056/NEJMp1511294

Vence L. Bonham, J.D., Senior Advisor to the NHGRI Director on Genomics and Health Disparities
National Human Genome Research Institute, Bethesda, Maryland

Shawneequa L. Callier, J.D., Professorial Lecturer in Law
Georgetown University, Washington, D.C.

Charmaine D. Royal, Ph.D., Associate Professor of African and African American Studies and Genome Sciences
Duke University, Durham, North Carolina

Although self-identified race may correlate with geographical ancestry, it does not predict an individual patient’s genotype or drug response. Precision medicine may eventually replace the use of race in treatment decisions, but several hurdles will have to be overcome.

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Self-Reported Race and Genetic Admixture

The New England Journal of Medicine
Number 354, Number 4 (2006-01-26)
pages 431-422
DOI: 10.1056/NEJMc052515

Moumita Sinha, M.Stat.
Case Western Reserve University, Cleveland, Ohio

Emma K. Larkin, M.H.S.
Case Western Reserve University, Cleveland, Ohio

Robert C. Elston, Ph.D.
Case Western Reserve University, Cleveland, Ohio

Susan Redline, M.D., M.P.H.
Case Western Reserve University, Cleveland, Ohio

To the Editor:

The use of data on self-reported race in health research has been highly debated. For example, Burchard et al. recently argued that important information on disease susceptibility may be derived from the use of data on self-reported race, whereas Cooper et al. cited Wilson et al., who argued that ethnic labels “are inaccurate representations of the inferred genetic clusters.” Cooper et al., however, ignored later work that identified limitations in the analyses of Wilson et al. — specifically, inappropriate classification of groups, the use of a suboptimal model for cluster identification, and reliance on only 39 microsatellite markers for cluster analyses. With larger numbers of markers, it was shown that genetically distinct groups can be almost completely inferred from self-reported race…

…With support from a U.S. Public Health Service grant, we applied an admixture analysis to a sample population in Cleveland. Participants were clearly separated into unique groups with the use of this genetic approach. Whereas 93 percent of self-reported whites were classified as having predominantly European ancestry, less than 2 percent of blacks were so classified. Only 4 percent who reported their race as black had predominantly African ancestry; yet, the admixture proportions of this group made it possible to separate the population into two groups, in which 94 percent of self-reported blacks and 7 percent of self-reported whites were classified as being of mixed race (Figure 1: Frequency Histogram Showing the Percentage of African Ancestry in a Population Living in Cleveland). The sharp peak at the left in Figure 1 indicates that there are many persons who have no African ancestry (i.e., the values correspond to those of self-reported whites), and the broad peak at the right indicates that most blacks are of mixed race and do not originate from any single population. Thus, self-reported race and genetic ethnic ancestry appear to be highly correlated as a dichotomy, with those who self-report as being black comprising, as expected from historical and cultural practices in the United States, a broad range of African ancestry…

Read the entire letter here.

Editorial: “Race Correction” in Pulmonary-Function Testing

New England Journal of Medicine
2010-07-07
DOI: 10.1056/NEJMe1005902

Paul D. Scanlon, M.D.
Division of Pulmonary and Critical Care Medicine
Mayo Clinic, Rochester, Minnesota

Mark D. Shriver, Ph.D.
Department of Anthropology
Pennsylvania State University, University Park (M.D.S.)

Tests of pulmonary function and radiographic imaging of the chest are the two key methods used in diagnostic evaluation of patients with pulmonary disease. Unlike blood pressure, acceptable normal values vary from person to person and from one demographic group to another. The first studies, in 1846, of spirometric assessment of forced vital capacity (FVC), the most basic pulmonary-function test, showed that normal values for vital capacity vary as a function of height and age. A few years later, it was shown that vital capacity was 6 to 12% lower in healthy black soldiers than in white or Native American soldiers. It has since become standard practice to calculate, for any individual patient, normal reference values for pulmonary-function tests on the basis of population-specific reference-value equations. In North America and Europe, where majority populations have primarily European ancestry, it is common practice to adjust reference values for persons of African or African-American ancestry, Hispanic ethnicity, or Asian ancestry—an adjustment termed “race correction” or “ethnic adjustment.”…

…There are practical problems with “race correction.” Self-identified race is the accepted standard for defining race, and no allowance is made for admixture (i.e., mixed parentage). The Asian-American adjustment factor is based on two studies with small numbers of participants representing a limited range of ages, ethnic groups, and socioeconomic status. A larger, recently published study showed that for Asian Americans, a correction factor of 0.88 is more accurate than 0.94.5 And little consideration has been given to the genetic diversity within Africa and within Asia.

Moreover, there is debate regarding the appropriateness of “race correction,” and a more general debate about the concepts of “race,” “ethnicity,” and “genetic ancestry” in medical research and treatment. Does race truly exist? If so, should it be taken into account, not only in pulmonary-function testing, but also in the broader practice of medicine and biomedical research?…

Read the entire editorial here.

Genetic Ancestry in Lung-Function Predictions

New England Journal of Medicine
2010-07-07
DOI: 10.1056/NEJMoa0907897

Rajesh Kumar, M.D.
Max A. Seibold, Ph.D.
Melinda C. Aldrich, Ph.D., M.P.H.
L. Keoki Williams, M.D., M.P.H.
Alex P. Reiner, M.D.
Laura Colangelo, M.S.
Joshua Galanter, M.D.
Christopher Gignoux, M.S.
Donglei Hu, Ph.D.
Saunak Sen, Ph.D.
Shweta Choudhry, Ph.D.
Edward L. Peterson, Ph.D.
Jose Rodriguez-Santana, M.D.
William Rodriguez-Cintron, M.D.
Michael A. Nalls, Ph.D.
Tennille S. Leak, Ph.D.
Ellen O’Meara, Ph.D.
Bernd Meibohm, Ph.D.
Stephen B. Kritchevsky, Ph.D.
Rongling Li, M.D., Ph.D., M.P.H.
Tamara B. Harris, M.D.
Deborah A. Nickerson, Ph.D.
Myriam Fornage, Ph.D.
Paul Enright, M.D.
Elad Ziv, M.D.
Lewis J. Smith, M.D.
Kiang Liu, Ph.D.
Esteban González Burchard, M.D., M.P.H.

ABSTRACT

Background Self-identified race or ethnic group is used to determine normal reference standards in the prediction of pulmonary function. We conducted a study to determine whether the genetically determined percentage of African ancestry is associated with lung function and whether its use could improve predictions of lung function among persons who identified themselves as African American.

Methods We assessed the ancestry of 777 participants self-identified as African American in the Coronary Artery Risk Development in Young Adults (CARDIA) study and evaluated the relation between pulmonary function and ancestry by means of linear regression. We performed similar analyses of data for two independent cohorts of subjects identifying themselves as African American: 813 participants in the Health, Aging, and Body Composition (HABC) study and 579 participants in the Cardiovascular Health Study (CHS). We compared the fit of two types of models to lung-function measurements: models based on the covariates used in standard prediction equations and models incorporating ancestry. We also evaluated the effect of the ancestry-based models on the classification of disease severity in two asthma-study populations.

Results African ancestry was inversely related to forced expiratory volume in 1 second (FEV₁) and forced vital capacity in the CARDIA cohort. These relations were also seen in the HABC and CHS cohorts. In predicting lung function, the ancestry-based model fit the data better than standard models. Ancestry-based models resulted in the reclassification of asthma severity (based on the percentage of the predicted FEV₁) in 4 to 5% of participants.

Conclusions Current predictive equations, which rely on self-identified race alone, may misestimate lung function among subjects who identify themselves as African American. Incorporating ancestry into normative equations may improve lung-function estimates and more accurately categorize disease severity. (Funded by the National Institutes of Health and others.)

…There are some important limitations of our study. First, our analysis does not address population groups other than self-identified African Americans, such as Latinos, who have more complex patterns of ancestral admixture. Second, the association between lung function and ancestry found in our study may be the result of factors other than genetic variation, such as premature birth, prenatal nutrition, socioeconomic status, and other environmental factors. Third, we did not study a replication population with the same age range as that of the CARDIA cohort. Thus, we may have overestimated the association between ancestry and lung function in the CARDIA participants, who were young adults. Finally, some researcher groups used different statistical approaches to estimate ancestry in their respective study populations. We have found previously, however, that different approaches (e.g., Markov models and maximum-likelihood estimation) produce highly correlated results from the same set of markers. The consistency of our findings across three cohorts, despite the different methods for estimating ancestry, underscores the robustness of the association with ancestry…

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The Importance of Race and Ethnic Background in Biomedical Research and Clinical Practice

New England Journal of Medicine
2003-03-20
Volume 348, Number 12
pages 1170-1175

Esteban González Burchard, M.D.
Elad Ziv, M.D.
Natasha Coyle, Ph.D.
Scarlett Lin Gomez, Ph.D.
Hua Tang, Ph.D.
Andrew J. Karter, Ph.D.
Joanna L. Mountain, Ph.D.
Eliseo J. Pérez-Stable, M.D.
Dean Sheppard, M.D.
Neil Risch, Ph.D.

A debate has recently arisen over the use of racial classification in medicine and biomedical research. In particular, with the completion of a rough draft of the human genome, some have suggested that racial classification may not be useful for biomedical studies, since it reflects “a fairly small number of genes that describe appearance” and “there is no basis in the genetic code for race.” In part on the basis of these conclusions, some have argued for the exclusion of racial and ethnic classification from biomedical research. In the United States, race and ethnic background have been used as cause for discrimination, prejudice, marginalization, and even subjugation. Excessive focus on racial or ethnic differences runs the risk of undervaluing the great diversity that exists among persons within groups. However, this risk needs to be weighed against the fact that in epidemiologic and clinical research, racial and ethnic categories are useful for generating and exploring hypotheses about environmental and genetic risk factors, as well as interactions between risk factors, for important medical outcomes. Erecting barriers to the collection of information such as race and ethnic background may provide protection against the aforementioned risks; however, it will simultaneously retard progress in biomedical research and limit the effectiveness of clinical decision making.

Race and Ethnic Background as Geographic and Sociocultural Constructs with Biologic Ramifications

Definitions of race and ethnic background have often been applied inconsistently. The classification scheme used in the 2000 U.S. Census, which is often used in biomedical research, includes five major groups: black or African American, white, Asian, native Hawaiian or other Pacific Islander, and American Indian or Alaska native. In general, this classification scheme emphasizes the geographic region of origin of a person’s ancestry. Ethnic background is a broader construct that takes into consideration cultural tradition, common history, religion, and often a shared genetic heritage…

…Sociocultural Correlates of Race and Ethnic Background

The racial or ethnic groups described above do not differ from each other solely in terms of genetic makeup, especially in a multiracial and multicultural society such as the United States. Socioeconomic status is strongly correlated with race and ethnic background and is a robust predictor of access to and quality of health care and education, which, in turn, may be associated with differences in the incidence of diseases and the outcomes of those diseases. For example, black Americans with end-stage renal disease are referred for renal transplantation at lower rates than white Americans. Black Americans are also referred for cardiac catheterization less frequently than white Americans. In some cases, these differences may be due to bias on the part of physicians and discriminatory practices in medicine. Nonetheless, racial or ethnic differences in the outcomes of disease sometimes persist even when discrepancies in the use of interventions known to be beneficial are considered. For example, the rate of complications from type 2 diabetes mellitus varies according to racial or ethnic category among members of the same health maintenance organization, despite uniform utilization of outpatient services and after adjustment for levels of education and income, health behavior, and clinical characteristics. The evaluation of whether genetic (as well as nongenetic) differences underlie racial disparities is appropriate in cases in which important racial and ethnic differences persist after socioeconomic status and access to care are properly taken into account…

…Racially Admixed Populations

Although studies of population genetics have clustered persons into a small number of groups corresponding roughly to five major racial categories, such classification is not completely discontinuous, because there has been intermixing among groups both over the course of history and in recent times. In particular, genetic admixture, or the presence in a population of persons with multiple races or ethnic backgrounds, is well documented in the border regions of continents and may represent genetic gradations (clines) — for example, among East Africans (e.g., Ethiopians) and some central Asian groups. In the United States, mixture among different racial groups has occurred recently, although in the 2000 U.S. Census, the majority of respondents still identified themselves as members of a single racial group. Genetic studies of black Americans have documented a range of 7 to 20 percent white admixture, depending on the geographic location of the population studied. Despite the admixture, black Americans, as a group, are still genetically similar to Africans. Hispanics, the largest and fastest growing minority population in the United States, are an admixed group that includes white and Native American ancestry, as well as African ancestry. The proportions of admixture in this group also vary according to geographic region.

Although the categorization of admixed groups poses special challenges, groups containing persons with varying levels of admixture can also be particularly useful for genetic-epidemiologic studies. For example, Williams et al. studied the association between the degree of white admixture and the incidence of type 2 diabetes mellitus among Pima Indians. They found that the self-reported degree of white admixture (reported as a percentage) was strongly correlated with protection from diabetes in this population. Furthermore, as noted above, information on race or ethnic background can provide important clues to effects of culture, access to care, and bias on the part of caregivers, even in genetically admixed populations. It is also important to recognize that many groups (e.g., most Asian groups) are highly underrepresented both in the population of the United States and in typical surveys of population genetics, relative to their global numbers. Thus, primary categories that are relevant for the current U.S. population might not be optimal for a globally derived sample…

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