Evidence map›Paper›PMID 39975907›Full record

ArticleResearch square2025

A type 1 diabetes prediction model has utility across multiple screening settings with recalibration.

Erin L Templeman, Lauric A Ferrat, Hemang M Parikh, Lu You, Taylor M Triolo, Andrea K Steck, William A Hagopian, Kendra Vehik, Suna Onengut-Gumuscu, Peter A Gottlieb and 5 more

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Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Erin L TemplemanDepartment of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
Lauric A FerratDepartment of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
Hemang M ParikhHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Lu YouHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Taylor M TrioloBarbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Andrea K SteckBarbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
William A HagopianPacific Northwest Research Institute, Seattle, WA, USA.
Kendra VehikHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Suna Onengut-GumuscuDepartment of Genome Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.
Peter A GottliebPacific Northwest Research Institute, Seattle, WA, USA.
Stephen S RichDepartment of Genome Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.
Jeffery P KrischerHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Maria J RedondoBaylor College of Medicine, Texas Children's Hospital, Houston, TX, USA.
Richard A OramDepartment of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
Type 1 diabetes TrialNet Study Group

Funding

Revision to the Coordinating Center for Type 1 Diabetes TrialNetU01DK106993 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI JEFFREY P KRISCHER, SAMUEL S WU · 2019 to 2026
$181.3M
Limited Competition: Continued Follow-up of Subjects and Initiation of a Second Case-control Cohort in The Environmental Determinants of Diabetes in The Young Study (TEDDY)U01DK128847 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI JEFFREY P KRISCHER · 2021 to 2026
$79.2M
Data Coordinating Center (DCC)for the Consortium for Identification of EnvironmenUC4DK095300 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2011 to 2011
$56.0M
NIH Prior Approval Process ProfessionalUL1TR002535 · NCATS · UNIVERSITY OF COLORADO DENVER · PI SOKOL, RONALD J. · 2018 to 2022
$51.1M
Data Coordinating CenterU01DK063790 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2002 to 2006
$50.1M
Data Coordinating Center for Type 1 Diabetes TrialNet (UC4)USF: TrialNet UC4-3UC4DK117009 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2017 to 2019
$49.6M
NIDDK Type 1 Diabetes TrialNet Data Coordinating CenterUC4DK106993 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2015 to 2015
$36.6M
Follow-up on Subjects, Integrative Data Analysis and Measurement of Viral Antibodies in The Environmental Determinants of Diabetes in The Young Study (TEDDY)U01DK124166 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2020 to 2022
$31.1M
NIDDK Follow-up on Subjects and Immunological Assessments in The Environmental Determinants of Diabetes In The Young Study (TEDDY) (UC4)UC4DK117483 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2017 to 2020
$30.4M
Establish a Data Coordinating Center (DCC) for the Consortium for Identification UC4DK100238 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2014 to 2014
$28.9M
The Study of Epigenetics and Infections in The Environmental Determinants of Diabetes in the Young (TEDDY)UC4DK112243 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2016 to 2016
$27.0M
Data Coordinating Center (DCC) for the Consortium for Identification of Environmental Determinants of Diabetes in the Young (TEDDY) StudyUC4DK106955 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2015 to 2015
$20.0M
NCATS NIH HHS UL1 TR000064NCATS NIH HHS UL1 TR002535NIDDK NIH HHS HHSN267200700014CNIDDK NIH HHS K23 DK136931NIDDK NIH HHS R01 DK121843NIDDK NIH HHS R01 DK124395NIDDK NIH HHS U01 DK061010NIDDK NIH HHS U01 DK061034NIDDK NIH HHS U01 DK061042NIDDK NIH HHS U01 DK061058NIDDK NIH HHS U01 DK063790NIDDK NIH HHS U01 DK063821NIDDK NIH HHS U01 DK063829NIDDK NIH HHS U01 DK063836NIDDK NIH HHS U01 DK063861NIDDK NIH HHS U01 DK063863NIDDK NIH HHS U01 DK063865NIDDK NIH HHS U01 DK085461NIDDK NIH HHS U01 DK085465NIDDK NIH HHS U01 DK085466NIDDK NIH HHS U01 DK085476NIDDK NIH HHS U01 DK085499NIDDK NIH HHS U01 DK085509NIDDK NIH HHS U01 DK103153NIDDK NIH HHS U01 DK103180NIDDK NIH HHS U01 DK103266NIDDK NIH HHS U01 DK103282NIDDK NIH HHS U01 DK106984NIDDK NIH HHS U01 DK106993NIDDK NIH HHS U01 DK106994NIDDK NIH HHS U01 DK107013NIDDK NIH HHS U01 DK107014NIDDK NIH HHS U01 DK124166NIDDK NIH HHS U01 DK128847NIDDK NIH HHS UC4 DK063821NIDDK NIH HHS UC4 DK063829NIDDK NIH HHS UC4 DK063836NIDDK NIH HHS UC4 DK063861NIDDK NIH HHS UC4 DK063863NIDDK NIH HHS UC4 DK063865NIDDK NIH HHS UC4 DK095300NIDDK NIH HHS UC4 DK100238NIDDK NIH HHS UC4 DK106955NIDDK NIH HHS UC4 DK106993NIDDK NIH HHS UC4 DK112243NIDDK NIH HHS UC4 DK117009NIDDK NIH HHS UC4 DK117483
6 · The paper itself

Abstract

Background: Accurate type 1 diabetes prediction is important to facilitate screening for pre-clinical type 1 diabetes to enable potential early disease-modifying interventions and to reduce the risk of severe presentation with diabetic ketoacidosis. We aimed to assess the generalisability of a prediction model developed in children followed from birth. Additionally, we sought to create an application for easy calculation and visualization of individualized risk prediction. Methods: We developed and refined a stratified prediction model combining a genetic risk score, age, islet autoantibodies, and family history using data from children followed since birth by The Environmental Determinants of Diabetes in the Young (TEDDY) study. We tested the validity of the model through external validation in the Type 1 Diabetes TrialNet Pathway to Prevention study, which conducts cross-sectional screening in relatives of people with type 1 diabetes. We recalibrated the model by adjusting for baseline risk and selection criteria in TrialNet using logistic recalibration to improve calibration across all ages. Results: The study included 7,798 TEDDY and 4,068 TrialNet participants, with 305 (4%) and 1,373 (34%) developing type 1 diabetes, respectively. The combined model showed similar discriminative ability in autoantibody-positive individuals across TEDDY and TrialNet (p=0.14), but inferior calibration in TrialNet (Brier score 0.40 [0.38,0.43]). Adjustment for baseline risk and selection criteria in TrialNet using logistic recalibration improved calibration across all ages (Brier score 0.16 [0.14,0.17]; p<0.001). A web calculator was developed to visualise individual risk estimates (https://t1dpredictor.diabetesgenes.org). Conclusions: A stratified model of type 1 diabetes genetic risk score, family history, age, and autoantibody status accurately predicts type 1 diabetes risk, but may need recalibration according to screening stategy.

Indexed as

adjustmentautoantibodyFDRgeneticspredictionrecalibrationtype 1 diabetes

Identifiers

PMID39975907
PMCPMC11838734

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