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June 7, 2026Diabetes

1717-P: Assessing Type 2 Diabetes and GLP-1 Agonist Response Trajectories with a Proteogenomic Atlas of Disease Progression

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Population

Median 47,963 UK Biobank participants across normoglycemia, prediabetes, and Type 2 Diabetes

Design

Cohort

Authors

CPChirag J. PatelSTSivateja TangiralaBTBraden Tierney

Discussion

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Overview

A proteogenomic atlas identified 'therapeutically intransigent' proteins associated with incident complications like CVD, suggesting targets for combined therapies to mitigate residual risk in T2D.

Key Points

  • The study aims to enhance precision medicine for Type 2 Diabetes by identifying proteomic signatures linked to disease progression and GLP-1 agonist responses.
  • Developed the Metabolic Atlas of Progression to Diabetes (MAP-D) utilizing proteomic data from 47,963 UK Biobank participants.
  • Analyzed associations between 2,923 proteins and seven metabolic hallmarks across normoglycemia, prediabetes, and T2D.
  • Integrated MAP-D findings with GLP1RA intervention trial data to identify significant protein-trait associations.
  • Identified 23,290 significant protein-trait associations, with notable signals for TRIG/HDL ratio and BMI.
  • Achieved improvements in predicting metabolic traits (combined R² up to 0.8; ΔR² up to 0.7).
  • Found strong links between intransigent proteins and future complications such as cardiovascular disease and chronic kidney disease.

Structured PICO

P
Population
Median 47,963 UK Biobank participants across normoglycemia, prediabetes, and Type 2 Diabetes
I
Intervention
Proteomic profiling (2,923 proteins) and integration with GLP-1 receptor agonist intervention trial data (STEP 1/2)
O
Outcome
Protein-trait associations with seven metabolic hallmarks (BMI, lipids, blood pressure, HbA1c) and identification of therapeutically intransigent proteinssurrogate

A proteogenomic atlas identified 'therapeutically intransigent' proteins associated with incident complications like CVD, suggesting targets for combined therapies to mitigate residual risk in T2D.

Cite This Study

Patel et al. (2026) studied this question.

synapsesocial.com/papers/6a250c027def13d035e1bfc2https://doi.org/10.2337/db26-1717-p
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Also Consider

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

  1. 1Integrative Metabolomics of Targeted and Non-Targeted Analyses in T2D Progression2025 · 4 citations
  2. 2Exploring Biomarkers in Type 2 Diabetes Mellitus versus Normoglycemia Identified through High-Throughput Proteomics: A Systematic Review and Meta-Analysis2025
  3. 32283-P: A Multiomics Atlas of Multisystem Complications in Type 2 Diabetes Reveals Molecular Signatures and Improves Risk Prediction2026
  4. 4Proteomic analysis for prediction of type 2 diabetes identifies cardiovascular disease-related proteins2025
  5. 52401-P: Genetic Regulation of Molecular Traits Enhances Precision Risk Stratification in Youth-Onset Type 2 Diabetes2026