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June 7, 2026Diabetes

2223-P: Harnessing Machine Learning to Forecast Two-Year Progression to Stage 3 Type 1 Diabetes (T1D) in Individuals with Multiple Type 1 Diabetes Autoantibodies: A Noise-Resistant, Multidimensional Clustering Approach

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Why the study?

Can a machine learning model accurately predict 2-year progression to Stage 3 Type 1 Diabetes in individuals with multiple T1D autoantibodies?

Population

603 TrialNet Pathway to Prevention participants with ≥2 T1D-associated autoantibodies, median age 10 years…

Design

Cohort

Follow-up

2 years

Authors

ETERIN TALLONMSMelanie ShapiroEPEmily Paprocki

Discussion

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Overview

A machine learning model using multidimensional data demonstrated modest discriminative performance for predicting 2-year progression to Stage 3 Type 1 diabetes in high-risk individuals.

Key Points

  • This research aims to develop a machine learning model to predict the progression to Stage 3 Type 1 Diabetes in individuals with multiple autoantibodies.
  • Participants with ≥2 T1D-associated autoantibodies were analyzed, utilizing demographic, immunologic, metabolic, and genetic data.
  • Data was split into five subsets for feature selection and model training using cross-validation.
  • Synthetic samples were generated to address class imbalance, and a noise-resistant clustering algorithm was applied.
  • Among 603 participants, 205 (34.0%) progressed to Stage 3 T1D within 2 years, with key predictors being metabolic measures and risk scores.
  • The ML model achieved a mean specificity of 0.89 (±0.05), PPV of 0.71 (±0.07), and sensitivity of 0.50 (±0.13).
  • F1 score indicating balance between PPV and sensitivity was 0.58 (±0.09).

Structured PICO

Can a machine learning model accurately predict 2-year progression to Stage 3 Type 1 Diabetes in individuals with multiple T1D autoantibodies?

P
Population
603 TrialNet Pathway to Prevention (PTP) participants with ≥2 T1D-associated autoantibodies (AAb), median age 10 years, 45.8% female, 77.3% non-Hispanic White.
I
Intervention
Machine learning model using demographic, immunologic, metabolic, and genetic data to output 'Proximity Scores' (range: 0-200)
O
Outcome
Progression to Stage 3 T1D within 2 years

A machine learning model using multidimensional data demonstrated modest discriminative performance for predicting 2-year progression to Stage 3 Type 1 diabetes in high-risk individuals.

Cite This Study

TALLON et al. (2026) studied this question.

synapsesocial.com/papers/6a250ac07def13d035e1ad40https://doi.org/10.2337/db26-2223-p
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 12231-P: RECLASS-T1D: Real-World Evaluation of AABBCC Criteria for Diabetes Classification: Experience from 6.7 Million U.S. Patients2026
  2. 22222-P: Prospective Validation of AI for Detecting Misclassified Adult Type 1 Diabetes: Insights on Precision, Clinical Workflows, and Adoption2026
  3. 32401-P: Genetic Regulation of Molecular Traits Enhances Precision Risk Stratification in Youth-Onset Type 2 Diabetes2026
  4. 42214-P: Biomarker Validation Reveals Substantial Misclassification of Adult-Onset Type 1 Diabetes by EHR-Based Algorithms: Implications for Research Accuracy and Clinical Care2026
  5. 52321-P: Machine-Learning Modeling for T2DM Prediction in over 3 Million Adults2026