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June 7, 2026Diabetes

Among 6,759,145 individuals with prediabetes or T2D, 2.2% were reclassified as having T1D, and approximately 75% of these individuals could not be identified using AABBCC criteria.

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Population

6,759,145 individuals in the US with diagnosis codes for prediabetes or T2D identified using TriNetX…

Design

Cohort

Follow-up

From first prediabetes or T2D diagnosis until T1D…

Authors

JSJay ShubrookCSCYNTIA B. MANZANO SALGADOMBMireille Bonnemaire

Discussion

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Overview

The AABBCC criteria proposed by the ADA are insufficient to accurately identify adult patients with type 1 diabetes among those initially diagnosed with prediabetes or type 2 diabetes in real-world settings.

Key Points

  • This study aims to assess the effectiveness of the AABBCC criteria in reclassifying diabetes types among patients.
  • Identified individuals in the US with prediabetes or type 2 diabetes diagnoses using TriNetX electronic health records.
  • Analyzed data availability for age, BMI, comorbidities, and HbA1c to evaluate AABBCC criteria applicability.
  • Followed up individuals from diagnosis of prediabetes or T2D until diagnosis of T1D, end of study, death, or loss to follow-up.
  • 2.2% of individuals with prediabetes or T2D were reclassified as T1D.
  • Among individuals <35 years old, 5.1% were reclassified, constituting 23.6% of all reclassified cases.
  • Approximately 75% of individuals could not be identified using AABBCC criteria, revealing significant limitations.

Structured PICO

P
Population
6,759,145 individuals in the US with diagnosis codes for prediabetes (ICD-10-CM R73.09; R73.03; HbA1c 5.7–6.4%) or T2D (ICD-10-CM E11*) identified using TriNetX electronic health records.
I
Intervention
Application of the AABBCC criteria (Age <35 years, Autoimmunity, BMI <25 kg/m2, Background, Control, and Comorbidities) for diabetes classification
O
Outcome
Reclassification rates from prediabetes or type 2 diabetes to type 1 diabetes

The AABBCC criteria proposed by the ADA are insufficient to accurately identify adult patients with type 1 diabetes among those initially diagnosed with prediabetes or type 2 diabetes in real-world settings.

Limitations

  • Limited data availability for AABBCC criteria components in electronic health records (e.g., BMI 35.8%, comorbidities 9.7%, HbA1c 6.1%, autoimmune comorbidities 5.1%)

Cite This Study

Shubrook et al. (2026) studied this question.

synapsesocial.com/papers/6a250cbc7def13d035e1cdd6https://doi.org/10.2337/db26-2231-p
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Also Consider

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

  1. 12223-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 Approach2026
  2. 22214-P: Biomarker Validation Reveals Substantial Misclassification of Adult-Onset Type 1 Diabetes by EHR-Based Algorithms: Implications for Research Accuracy and Clinical Care2026
  3. 32222-P: Prospective Validation of AI for Detecting Misclassified Adult Type 1 Diabetes: Insights on Precision, Clinical Workflows, and Adoption2026
  4. 42401-P: Genetic Regulation of Molecular Traits Enhances Precision Risk Stratification in Youth-Onset Type 2 Diabetes2026
  5. 52351-P: Assessment of Phenotypic Clustering of Incident and Prevalent Type 2 Diabetes: A MESA Pilot Study in the Definition, Etiology, Function: Integration to Enhance Type 2 Diabetes Treatment (DEFINE-Type 2 Diabetes) Consortium2026