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June 7, 2026Diabetes

2087-P: Predictive Modeling of HbA1c Progression for Use in Synthetic Controls in Youth-Onset Type 2 Diabetes

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Why the study?

Can a predictive model of HbA1c trajectories accurately generate patient-matched synthetic controls for youth-onset type 2 diabetes?

Population

2,254 youth with type 2 diabetes

Design

Cohort

Follow-up

median 4.5 yr

Authors

EYEunsol YangSHSEJUNG HWANGXLXing Luu

Discussion

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Overview

A nonlinear mixed effects model accurately predicts HbA1c progression in youth-onset type 2 diabetes, enabling the creation of synthetic controls for future trials and individualized management.

Key Points

  • This research aims to create a predictive model for HbA1c trajectories in youth-onset type 2 diabetes to enable individualized management.
  • Analyzed 12,970 HbA1c measurements over 4.5 years from 699 youth in the TODAY study.
  • Simulated HbA1c trajectories for 1,555 youth using baseline data from the SEARCH study and UC Health Data Warehouse.
  • Utilized a nonlinear mixed effects approach to model individual-level HbA1c progression.
  • The HbA1c progression model demonstrated strong fit (R2=0.81) to the data.
  • Rapid HbA1c progression was predicted by elevated triglycerides, low AST/ALT ratio, longer T2D duration, and higher BMI Z-score.
  • Simulations showed that weight loss and low-carb intake reduced 1-yr HbA1c by 0.2% compared to weight gain and high intake.

Structured PICO

Can a predictive model of HbA1c trajectories accurately generate patient-matched synthetic controls for youth-onset type 2 diabetes?

P
Population
2,254 youth with type 2 diabetes (699 from the TODAY study, median age 14 yr; 1,555 from the SEARCH study and UC Health Data Warehouse)
I
Intervention
Predictive modeling of HbA1c trajectories using routine clinical features to generate patient-matched synthetic controls (digital twins)
O
Outcome
HbA1c progression/trajectoriessurrogate

A nonlinear mixed effects model accurately predicts HbA1c progression in youth-onset type 2 diabetes, enabling the creation of synthetic controls for future trials and individualized management.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/6a250c3b7def13d035e1c3dfhttps://doi.org/10.2337/db26-2087-p
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Also Consider

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

  1. 11136-OR: Longitudinal Time-to-Event Modeling and Identification of Risk Factors for the Occurrence of Microvascular Complications in Youth-Onset Type 2 Diabetes2026
  2. 22223-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
  3. 32983-LB: Trajectories of Risk Factors among Participants with Long-Duration Type 1 Diabetes2026
  4. 42321-P: Machine-Learning Modeling for T2DM Prediction in over 3 Million Adults2026
  5. 5Latent class growth mixture modeling of HbA1C trajectories identifies individuals at high risk of developing complications of type 2 diabetes mellitus in the UK Biobank2025 · 5 citations