ArticleDiabetes care2023
Polygenic Scores Help Reduce Racial Disparities in Predictive Accuracy of Automated Type 1 Diabetes Classification Algorithms.
Article in Diabetes care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Tailoring interventions to close gaps in diabetes mellitus care.Nature reviews. Endocrinology · 2026Review
- Article
- HLA-focused type 1 diabetes genetic risk prediction in populations of diverse ancestry.Diabetologia · 2026Article
- Ethnic diversity in precision medicine: a reality or an aspiration?Diabetologia · 2025Review
- Phenotypic and Genetic Diversity in Diabetes Across Populations.The Journal of clinical endocrinology and metabolism · 2025Review
- Various classification methods for diabetes mellitus in the management of blood glucose control.World journal of diabetes · 2025Article
- Article
- Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveAutomated algorithms to identify individuals with type 1 diabetes using electronic health records are increasingly used in biomedical research. It is not known whether the accuracy of these algorithms differs by self-reported race. We investigated whether polygenic scores improve identification of individuals with type 1 diabetes. RESEARCH DESIGN AND
methodsWe investigated two large hospital-based biobanks (Mass General Brigham [MGB] and BioMe) and identified individuals with type 1 diabetes using an established automated algorithm. We performed medical record reviews to validate the diagnosis of type 1 diabetes. We implemented two published polygenic scores for type 1 diabetes (developed in individuals of European or African ancestry). We assessed the classification algorithm before and after incorporating polygenic scores.
resultsThe automated algorithm was more likely to incorrectly assign a diagnosis of type 1 diabetes in self-reported non-White individuals than in self-reported White individuals (odds ratio 3.45; 95% CI 1.54-7.69; P = 0.0026). After incorporating polygenic scores into the MGB Biobank, the positive predictive value of the type 1 diabetes algorithm increased from 70 to 97% for self-reported White individuals (meaning that 97% of those predicted to have type 1 diabetes indeed had type 1 diabetes) and from 53 to 100% for self-reported non-White individuals. Similar results were found in BioMe.
conclusionsAutomated phenotyping algorithms may exacerbate health disparities because of an increased risk of misclassification of individuals from underrepresented populations. Polygenic scores may be used to improve the performance of phenotyping algorithms and potentially reduce this disparity.
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