What question did this study set out to answer?

This research aims to uncover proteomic signatures related to clinical clustering in pediatric diabetes to enhance classification and understanding of disease progression.

June 7, 2026Diabetes

2940-LB: Molecular Profiling of Pediatric Diabetes: Proteomic Determinants of Data-Driven Clusters

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Authors

MGMIGUEL ÁNGEL JUÁREZ GARZÓN JMJanne MolnesHaukeland University Hospital BJBENTE B. JOHANSSON

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Implication

Randomized trial investigates proteomic signatures in newly diagnosed pediatric diabetes, suggesting novel classification implications.

Key Points

This research aims to uncover proteomic signatures related to clinical clustering in pediatric diabetes to enhance classification and understanding of disease progression.
Conducted K-means clustering (k = 5) in 3,064 newly diagnosed pediatric patients from the Norwegian Childhood Diabetes Registry.
Stratified patients based on autoantibody status, age at diagnosis, fasting glucose, HbA1c, fasting C-peptide Z-score, and BMI Z-score.
Performed differential protein expression analysis on 800 serum samples with adjustments for age-related proteomic variation against healthy controls.
Identified five distinct clusters, including CSAID, ASAID, CSIDD, ASIDD, and ASIRD, each showing unique proteomic profiles.
Childhood-onset clusters exhibited heightened inflammatory signatures and increased immunoglobulin subunits compared to adolescence-onset clusters.
Notably, CSIDD and ASIDD displayed autoimmune-like proteomic profiles despite a lack of autoantibodies, hinting at developing autoimmunity.

Cite This Study

GARZÓN et al. (2026) studied this question.

synapsesocial.com/papers/6a250b4c7def13d035e1b421 https://doi.org/10.2337/db26-2940-lb

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2940-LB: Molecular Profiling of Pediatric Diabetes: Proteomic Determinants of Data-Driven Clusters

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Key Points

Cite This Study

2940-LB: Molecular Profiling of Pediatric Diabetes: Proteomic Determinants of Data-Driven Clusters

Discussion

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Implication

Key Points

Cite This Study

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