Evidence map›Paper›PMID 40791684›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Enabling reproducible type 1 diabetes polygenic risk scoring for clinical and translational applications.

Maizy S Brasher, Matthew J Fisher, Carolina Sanchez Wild, Jonathan A Shortt, Krista Miller, Randi K Johnson, Nicholas M Rafaels, Elizabeth L Kudron, Ian M Brooks, Kristy R Crooks and 9 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

19 authors.

Maizy S BrasherDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Matthew J FisherColorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Carolina Sanchez WildColorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Jonathan A ShorttDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Krista MillerHealth Data Compass, University of Colorado Anschutz Medical Campus, Aurora, CO.
Randi K JohnsonDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.ORCID 0000-0001-9345-4439
Nicholas M RafaelsColorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Elizabeth L KudronDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Ian M BrooksDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Kristy R CrooksColorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Sean M OserDepartment of Epidemiology, Colorado School of Public Health.
Tamara K OserDepartment of Epidemiology, Colorado School of Public Health.
Joanne B ColeDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Colorado Center for Personalized Medicine
Laura K WileyDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Sridharan RaghavanDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.ORCID 0000-0003-0643-4873
Neda RasouliUS Department of Veterans Affairs Eastern Colorado Health Care System.
Meng LinDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.
Christopher R GignouxDepartment of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO.ORCID 0000-0001-9728-6567

Funding

Polygenic Risk Scores for Diverse Populations - Bridging Research and Clinical CareR01HL151152 · NHLBI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Christy Leigh Avery, Jennifer Below · 2020 to 2026
$12.3M
Genomic Approaches to Population Health in Multi-Ethnic Hospital SystemsR01HG011345 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI ARBOLEDA, VALERIE A, GIGNOUX, CHRISTOPHER R · 2020 to 2023
$3.1M
NHGRI NIH HHS R01 HG011345NHLBI NIH HHS R01 HL151152
6 · The paper itself

Abstract

Objective: Type 1 diabetes polygenic risk scores (PRS) offer a promising tool for identifying diabetes subtypes in adults with new-onset disease. We aimed to develop a pipeline for the clinical translation of type 1 diabetes PRS to support clinical decision-making within a large health system and to provide publicly available code for applying these methods to future PRS models. Research Design and Methods: We adapted two established type 1 diabetes PRS models: a 67-SNP (GRS2) and a 7-SNP (AA7) score for a clinical genotyping platform and applied them to 73,346 participants in the biobank at the Colorado Center for Personalized Medicine (CCPM). We evaluated the scores' performance differentiating between type 1 and type 2 diabetes in adults using a clinician-curated diabetes phenotyping algorithm and examined associations with diabetes-related clinical data extracted from patients' health records. The impact of technical genotyping missingness on score accuracy and ancestry calibration were assessed independently. Results: Both scores effectively distinguished type 1 from type 2 diabetes across genetically defined ancestry groups (all AUC > 0.80) and demonstrated consistent performance in the UK Biobank (all AUC > 0.75). Individuals in the top quintile of each PRS were enriched for diabetic ketoacidosis (DKA) cases, accounting for nearly half of all DKA cases in the cohort. Additionally, the top quintile showed nearly threefold increased odds of GAD autoantibody positivity (OR = 2.94 [95% CI 2.08-4.17]). Conclusions: Our evaluations demonstrated the potential utility of PRS for diabetes subtyping in a clinical setting. We present a framework of critical steps toward a standardized system for future translation of diabetes PRS to equitable clinical use, along with software to make it possible for others.

Identifiers

PMID40791684
PMCPMC12338922

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.