A type 1 diabetes prediction model has utility across multiple screening settings with recalibration Article Swipe
Erin L. Templeman
,
Lauric Ferrat
,
Hemang Parikh
,
Lu You
,
Taylor M. Triolo
,
Andrea K. Steck
,
William Hagopian
,
Kendra Vehik
,
Suna Önengüt-Gümüşcü
,
Peter A. Gottlieb
,
Stephen S. Rich
,
Jeffrey P. Krischer
,
María J. Redondo
,
Richard A. Oram
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5773430/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5773430/v1
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-5773430/v1
- OA Status
- gold
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407200083
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407200083Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-5773430/v1Digital Object Identifier
- Title
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A type 1 diabetes prediction model has utility across multiple screening settings with recalibrationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
- Publication date
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2025-02-06Full publication date if available
- Authors
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Erin L. Templeman, Lauric Ferrat, Hemang Parikh, Lu You, Taylor M. Triolo, Andrea K. Steck, William Hagopian, Kendra Vehik, Suna Önengüt-Gümüşcü, Peter A. Gottlieb, Stephen S. Rich, Jeffrey P. Krischer, María J. Redondo, Richard A. OramList of authors in order
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https://doi.org/10.21203/rs.3.rs-5773430/v1Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.21203/rs.3.rs-5773430/v1Direct OA link when available
- Concepts
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Medicine, Logistic regression, Brier score, Type 2 diabetes, Type 1 diabetes, Diabetes mellitus, Machine learning, Computer science, Internal medicine, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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