Race, ethnicity and ancestry in global diabetes research: grappling with complexity to advance equity and scientific integrity – a narrative review and viewpoint
Bibliographic record
Abstract
The global burden of diabetes-across major forms such as type 2 diabetes, type 1 diabetes and gestational diabetes mellitus-disproportionately affects people of non-European ancestry, the majority of whom live in low- and middle-income countries. The heterogeneity of diabetes risks and phenotypes indicates that knowledge derived principally from European-origin populations may not be readily transferable to other groups. In this review our aim is to enhance the quality of diabetes research by championing the inclusion of diverse populations, ensuring clarity of population definition and encouraging exploration of population differences. We review the terminology used to define populations and make recommendations on the use of these terms. We argue that population membership by itself does not determine risks or response to intervention; rather, it is the confluence of genetic, environmental, sociocultural and policy factors that are causal and should be identified. We note that, while common diabetes forms are polygenic and populations are unlikely to harbour single genes that account for significant risk, environmental change that impacts lifestyle and biology demonstrably alters diabetes risk and provides opportunities for effective intervention. Similarly, while genetic variants are associated with adverse events, population group membership may sometimes not be a valid proxy for such variants, which has implications for healthcare equity. For most drugs used in diabetes there is little evidence that drug responsiveness materially differs by population grouping, although it is only recently that well-designed studies have been performed. In contrast, other population characteristics, such as sex, age and obesity, appear to alter glucose-lowering drug effectiveness and should be considered when prescribing. Inclusion of diverse populations in diabetes research, combined with a multidisciplinary approach, is essential if we are to combat the global burden of diabetes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".