Evidence map›Paper›PMID 40773074›Full record

ReviewDiabetologia2025

Ethnic diversity in precision medicine: a reality or an aspiration?

Shivani Misra

Abstract readReview
In one paragraph

Review in Diabetologia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
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

1 author.

Shivani MisraDivision of Metabolism, Digestion and Reproduction, Imperial College London, London, UK. s.misra@imperial.ac.uk.ORCID http://orcid.org/0000-0003-2886-0726

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precision medicine seeks to tailor prevention, diagnosis and treatment strategies to the biological and contextual characteristics of individuals. In diabetes, where there is substantial heterogeneity in risk, progression and treatment response, this approach holds particular promise. However, the majority of precision medicine research has been conducted in populations of White European ancestry, limiting its relevance and equity across ethnically diverse groups. This review examines the role of ethnicity in precision diabetes medicine, highlighting its potential value and limitations. It summarises evidence for ethnic variation in both type 1 and type 2 diabetes, including differences in susceptibility, phenotypes and complications, and demonstrates how under-representation in research has led to diagnostic and therapeutic blind spots. Three conceptual models for incorporating ethnicity into precision approaches are described: ancestry-anchored discovery, ethnicity-adapted translation and trait-based individualisation. Each model presents distinct opportunities for and challenges to equitable implementation. The review also outlines key barriers, such as limited diversity in discovery science, inconsistent data infrastructure, and resource constraints, and proposes practical strategies to overcome them. It concludes by identifying critical research gaps and emphasises the need for inclusive and context-sensitive models of precision medicine that account for both inter- and intra-ethnic diversity. Realising this vision will be essential to delivering effective and equitable diabetes care globally.

Indexed as

Diabetes Mellitus, Type 1Diabetes Mellitus, Type 2EthnicityPrecision MedicineHumansAncestryEquity, diversity and inclusionEthnicityHeterogeneityPersonalised medicinePrecision medicineReviewType 1 diabetesType 2 diabetes

Identifiers

PMID40773074
PMCPMC12534348

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.