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Development and recalibration of a multivariable type 1 diabetes prediction model for type 1 diabetes across multiple

Erin L Templeman1, Lauric A Ferrat1,2, Hemang M Parikh3

  • 1Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.

BMC Medicine
|July 21, 2025
PubMed

Insights

Accurate type 1 diabetes prediction is crucial for early intervention. A new model combining genetic risk, age, autoantibodies, and family history shows promise for predicting type 1 diabetes risk.

Area of Science:

  • Endocrinology and Metabolism
  • Genetics and Genomics
  • Epidemiology

Background:

  • Accurate prediction of type 1 diabetes (T1D) is vital for early detection and intervention.
  • Facilitating screening for pre-clinical T1D can prevent severe complications like diabetic ketoacidosis.
  • Assessing the generalizability of existing prediction models and developing user-friendly tools are key research objectives.

Purpose of the Study:

  • To evaluate the generalizability of a T1D prediction model developed in children followed from birth.
  • To create a practical application for calculating and visualizing individual T1D risk predictions.

Main Methods:

  • A stratified prediction model was developed and internally validated using data from the TEDDY study (The Environmental Determinants of Diabetes in the Young).
  • The model incorporates genetic risk score, age, islet autoantibodies, and family history.
  • External validation was performed in the Type 1 Diabetes TrialNet Pathway to Prevention study, followed by logistic recalibration to improve calibration.

Main Results:

  • The study included 7798 participants from TEDDY and 4068 from TrialNet.
  • The stratified model demonstrated similar discriminative ability in autoantibody-positive individuals across both cohorts.
  • Logistic recalibration significantly improved model calibration in the TrialNet cohort (Brier score 0.16), and a web calculator was developed.

Conclusions:

  • A stratified model integrating genetic risk score, family history, age, and autoantibody status accurately predicts T1D risk.
  • Model recalibration may be necessary depending on the specific screening strategy employed.
  • The developed web calculator provides accessible individual risk estimates.
Abstract

Keywords:
AdjustmentAutoantibodyFDRGeneticsPredictionRecalibrationType 1 diabetes

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