Population designations in biomedical research: Limitations and perspectives

Abstract

In biomedical research, population differences are of central interest. Variations in the frequency and severity of diseases and in treatment effects among human subpopulation groups are common in many medical conditions. Unfortunately, the practices in terms of subpopulation labeling do not exhibit the level of rigor one would expect in biomedical research, especially when studying multifactorial diseases such as cancer or atherosclerosis. The reporting of population differences in clinical research is characterized by large disparities in practices, and fraught with methodological issues and inconsistencies. The actual designations such as "Black" or "Asian" refer to broad and heterogeneous groups, with a great discrepancy among countries. Moreover, the use of obsolete concepts such as "Caucasian" is unfortunate and imprecise. The use of adequate labeling to reflect the scientific hypothesis needs to be promoted. Furthermore, the use of "race/ethnicity" as a unique cause of human heterogeneity may distract from investigating other factors related to a medical condition, particularly if this label is employed as a proxy for cultural habits, diet, or environmental exposure. In addition, the wide range of opinions among researchers does not facilitate the attempts made for resolving this heterogeneity in labeling. "Race," "ethnicity," "ancestry," "geographical origin," and other similar concepts are saturated with meanings. Even if the feasibility of a global consensus on labeling seems difficult, geneticists, sociologists, anthropologists, and ethicists should help develop policies and practices for the biomedical field.

Keywords: biomedical translational science; cultural diversity; ethnicity; genetic variation; racial groups.

FIGURE 1

A proposal for the analysis and reporting of data from subpopulations in biomedical journals. An example of legal purpose may be the inclusion of groups that have been historically marginalized in randomized trials.19 The systematic search for associations between “race/ethnicity” and outcome/biomarker should be avoided due to the high risk of false positive results and/or confounding biases (e.g., sociological differences, environmental exposures)