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June 7, 2026Diabetes

Proteogenomic profiling improves metabolic trait prediction with a combined R² up to 0.8.

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Why the study?

To advance precision medicine in T2D by identifying proteomic signatures across glycemic stages and evaluating proteins associated with T2D risk and GLP-1 receptor agonist response.

Population

Median of 47,963 UK Biobank participants across normoglycemia, prediabetes, and T2D

Comparison

Protein associations across glycemic stages integrated with GLP1RA intervention trial data (STEP 1/2)

Design

Observational cohort study integrating trial data

Key result

A proteogenomic atlas of 47,963 participants identified 23,290 significant protein-trait associations, improving prediction of metabolic traits with combined R² up to 0.8.

Authors

CPChirag J. PatelSTSivateja TangiralaBTBraden Tierney

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Overview

May aid T2D risk stratification; leaves open causal validation and therapeutic targeting of intransigent proteins.

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.

Study Design

Type

Observational (n=47,963)

Structured PICO

P
Population
47,963 UK Biobank participants with proteomic data analyzed across normoglycemia, prediabetes, and T2D stages.
E
Exposure
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

Main Result

Effect estimate: combined R² up to 0.8

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) conducted an observational in Type 2 Diabetes (n=47,963). Proteomic signatures was evaluated on Protein-trait associations and prediction of metabolic traits (combined R² up to 0.8). A proteogenomic atlas of 47,963 participants identified 23,290 significant protein-trait associations, improving prediction of metabolic traits with combined R² up to 0.8.

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. 1Assessing Type 2 Diabetes and GLP-1 agonist response trajectories with a proteogenomic atlas of disease progression2025
  2. 2Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities2024
  3. 3Integrative Metabolomics of Targeted and Non-Targeted Analyses in T2D Progression2025 · 4 citations
  4. 4Exploring Biomarkers in Type 2 Diabetes Mellitus versus Normoglycemia Identified through High-Throughput Proteomics: A Systematic Review and Meta-Analysis2025
  5. 52283-P: A Multiomics Atlas of Multisystem Complications in Type 2 Diabetes Reveals Molecular Signatures and Improves Risk Prediction2026