The Responsible Use of Racial and Ethnic Categories in Biomedical Research

Where Do We Go from Here?

Exploring the ethical and scientific considerations of using racial and ethnic categories to advance health equity without reinforcing harmful stereotypes

The Classification Conundrum: Why Getting Race Right Matters in Medicine

Imagine a world where your medical treatment is determined by the color of your skin rather than the specific biological or environmental factors most relevant to your health. This is the ethical and scientific tightrope that biomedical researchers walk every day when they incorporate racial and ethnic categories into their work.

For decades, medicine has grappled with a fundamental paradox: race is not a biological reality in the way once thought, yet racial health disparities are very real and demand scientific attention. The question facing today's researchers isn't simply whether to use racial categories, but how to do so responsibly in ways that advance health equity without reinforcing harmful stereotypes.

The stakes couldn't be higher. From heart disease to diabetes and COVID-19, significant health disparities exist among different population groups. Understanding these patterns is crucial for addressing them, yet the tools we use to categorize people can themselves become obstacles to good science and equitable care.

The Challenge

As geneticist Dr. Charmaine Royal once noted, "The challenge is to avoid throwing the baby out with the bathwater"—preserving the ability to study health disparities while abandoning outdated biological concepts of race 1 .

The Balance

This article explores the delicate balance between these competing priorities and charts a path toward more responsible research practices.

Unraveling the Race Puzzle: Key Concepts and Theories

Social Construction of Race

Modern science has reached a powerful consensus: race is primarily a social construct rather than a biological reality 6 .

The Human Genome Project revealed that all humans share 99.9% of their genetic code, with more genetic variation within any given racial group than between groups 2 .

Problem with Proxies

Researchers often use race as a proxy for unmeasured factors—a practice that can lead to imprecise science.

Racial categories may crudely represent:

  • Genetic ancestry
  • Environmental exposures
  • Socioeconomic factors
  • Experiences of discrimination
Appropriate Uses

Racial categories remain valuable when:

  • Studying health disparities
  • Examining effects of discrimination
  • Ensuring diverse representation
  • Monitoring health equity goals

As one analysis notes, "In health disparities research... the 'race' variable is appropriately treated as a combination of biological and social factors" 1 .

Understanding the Evolution of Race Concepts in Science

Historical Biological Concepts

Early scientific approaches treated race as a biological classification system with distinct categories.

Human Genome Project (2003)

Revealed that humans share 99.9% of genetic code, with more variation within than between racial groups 2 .

Modern Understanding

Race is recognized as a social construct with biological consequences, not a biological reality itself.

A Landmark Investigation: The CYP2D6 Drug Metabolism Study

The Experimental Framework

One compelling example of both the promises and pitfalls of using racial categories comes from pharmacogenetic research on the CYP2D6 gene, which affects the metabolism of approximately 25% of commonly prescribed drugs 1 .

Researchers conducted a multi-ethnic comparative study examining the distribution of CYP2D6 genetic variants across different populations.

Methodology Steps:
  1. Population Sampling: Recruiting participants from diverse geographic ancestries
  2. Genotype Analysis: Sequencing the CYP2D6 gene to identify specific variants
  3. Phenotype Categorization: Classifying individuals as ultra-rapid, extensive, intermediate, or poor metabolizers
  4. Frequency Calculation: Determining variant and phenotype prevalence across groups
  5. Clinical Correlation: Linking metabolic status to actual drug response outcomes

Results and Implications

The findings revealed complex patterns that challenged simplistic racial explanations:

Population Group Poor Metabolizers Ultra-Rapid Metabolizers Key Geographic Variations
European Descent 5-10% 1-2% in Sweden North-South gradient observed
Japanese Descent ~1% Not reported Less regional variation
Northern Spanish Not specified ~10% Higher ultra-rapid frequency
Ethiopian Descent 1-3% 10-30% Highest ultra-rapid frequency

The data revealed that while broad group differences exist, the variation within groups was often as significant as variation between them.

Most importantly, the study demonstrated that knowing an individual's specific genetic variants provided far more precise guidance for medication dosing than relying on broad racial categories. As the authors noted, "Pharmacogenetic testing will not eliminate the need for careful clinical monitoring of adverse drug reactions," but it represents a move toward more personalized medicine 1 .

Visualizing Global Genetic Diversity: What the Data Reveals

The CYP2D6 study reflects broader patterns in human genetic variation. Multiple large-scale genetic analyses have produced consistent findings about human diversity:

Genetic Pattern Scientific Finding Research Implications
Within-Group Diversity 85-95% of genetic variation occurs within geographically defined populations Assumptions of group genetic uniformity are scientifically inaccurate
Between-Group Differences Only 5-15% of genetic variation distinguishes continental groups Racial boundaries do not align with sharp genetic divides
African Diversity Highest genetic diversity exists within African populations Supports "Out of Africa" model of human migration
Gradient Patterns Genetic variation changes gradually along geographic clines Sharp racial categories don't capture continuous human diversity

These patterns explain why using race as a biological variable is problematic from a genetic perspective. As researchers from the National Academies noted, "The distribution of variants within and among human populations also differs from that of many other species" 2 .

The Researcher's Toolkit: Principles for Responsible Categorization

Moving toward more responsible research practices requires both conceptual shifts and practical methodological changes.

Mechanistic Hypotheses

Application: Pre-specify how racial categorization might connect to biological, environmental or social mechanisms

Benefit: Prevents fishing expeditions for spurious race-effects

Fine-Scale Ancestry

Application: Use genetic ancestry markers when biological ancestry is relevant

Benefit: Provides more precise biological data than race

Measures of Racism

Application: Include validated measures of discrimination and structural inequity

Benefit: Directly assesses social determinants of health

Intersectional Analysis

Application: Examine how race interacts with gender, class, immigration status

Benefit: Captures complex lived experiences

Institutional Guidance

Leading institutions are now advocating for this more nuanced approach. As a 2024 National Academies report emphasized, "The committee highlighted the importance of carefully considering the use of race and ethnicity across the research process" 4 .

The Path Forward: Implementing Responsible Research Practices

From Classification to Mechanism

The most significant shift in biomedical research involves moving from simply documenting differences by race to understanding the mechanisms that produce those differences.

Specify Hypotheses

Pre-specify how racial categorization might connect to biological, environmental, or social mechanisms 5

Collect Precise Data

Gather detailed information about environmental exposures, experiences of discrimination, and genetic ancestry when relevant

Contextualize Findings

Interpret results within broader social and historical contexts of structural racism

Regulatory and Institutional Changes

Policy frameworks are evolving to support these improved approaches:

Updated Regulatory Guidance

From agencies like the NIH and FDA that emphasize mechanism over mere correlation 6

Journal Standards

That require explicit justification for the use of racial categories in research

Training Programs

That educate researchers about both historical misuse and emerging best practices

Community Review

Processes that include diverse perspectives in research design and interpretation

As one analysis of current regulations notes, "The US is the only major country that explicitly enlists legal requirements regarding gathering ethno-racial data in biomedical research" 5 , highlighting the particular responsibility of American researchers to use these categories thoughtfully.

Conclusion: Toward a More Precise and Equitable Future

The journey toward responsibly using racial and ethnic categories in biomedical research is both a scientific and ethical imperative. By recognizing race as a social construct with biological consequences—rather than a biological reality itself—researchers can develop more precise models of health and disease.

The path forward requires abandoning race as a crude biological proxy while vigorously investigating how racism and social inequality become embodied as health disparities. It demands that we study both the specific genetic variants that influence drug metabolism and the social structures that determine exposure to stress, toxins, and unequal medical care.

As we stand at this crossroads, the choice is not between colorblindness and racial categorization, but between simplistic classification systems and nuanced approaches that do justice to human complexity. The future of biomedical research depends on getting this balance right—developing methods that acknowledge the reality of health disparities without reinforcing the biological myths that have historically divided us.

The goal is within reach: a research paradigm that uses racial categories not as explanations, but as signposts pointing toward the complex interplay of genes, environments, and social experiences that ultimately determine our health.

Key Takeaways
  • Race is a social construct, not a biological reality
  • Health disparities are real and require investigation
  • Move from classification to understanding mechanisms
  • Use precise measures instead of race as a proxy
  • Implement responsible categorization practices

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