Evidence map›Paper›PMID 36258305›Full record

ReviewHLA2023

Population designations in biomedical research: Limitations and perspectives.

Caroline Gombault, Guillaume Grenet, Laure Segurel, Laurent Duret, François Gueyffier, Pascal Cathébras, Dominique Pontier, Sabine Mainbourg, Alicia Sanchez-Mazas, Jean-Christophe Lega

Open access · hybridAbstract readReview
In one paragraph

Review in HLA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Ancestry and self-reported race in Brazilian breast cancer women.Revista da Associacao Medica Brasileira (1992) · 2023
    Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Caroline GombaultLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.
Guillaume GrenetLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.
Laure SegurelLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.
Laurent DuretLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.
François GueyffierLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.
Pascal CathébrasService de Médecine Interne, Hôpital Nord, CHU de Saint-Etienne, Saint-Etienne, France.
Dominique PontierLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.
Sabine MainbourgLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.
Alicia Sanchez-MazasLaboratory of Anthropology, Genetics and Peopling history, Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland.ORCID 0000-0002-7714-2432
Jean-Christophe LegaLaboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, UMR CNRS 5558, Lyon, France.ORCID 0000-0002-9398-2968
Université Claude Bernard Lyon 1 · FRHôpital Nord · FRUniversity of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biomedical ResearchPopulation GroupsGeographyHumansbiomedical translational sciencecultural diversityethnicitygenetic variationracial groups

Identifiers

PMID36258305
PMCPMC10099491
OpenAlexW4306755629

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.