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Record W4416880670 · doi:10.1681/asn.20252gfdnmkj

Urinary Proteomic Changes Associated with a Healthier Kidney Phenotype After Dapagliflozin Therapy in Adolescent Patients with Type 1 Diabetes

2025· article· en· W4416880670 on OpenAlexaff
Hailey E Hampson, Kalie L. Tommerdahl, Farid H. Mahmud, Anil Karihaloo, Antoine Clarke, Ye Ji Choi, David Z.I. Cherney, Hiddo Jan L. Heerspink, Phoom Narongkiatikhun, Cheril Clarson, Petter Bjornstad, Laura Pyle

Bibliographic record

VenueJournal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLondon Health Sciences CentreToronto General HospitalUniversity Health NetworkHospital for Sick Children
Fundersnot available
KeywordsType 1 diabetesUrinary systemDapagliflozinPhenotypeDiabetes mellitusKidneyKidney disease

Abstract

fetched live from OpenAlex

Background: In youth, sodium-glucose co-transporter 2 inhibitors (SGLT2i) demonstrate cardio-kidney-metabolic protection through attenuation of hyperglycemia and hyperfiltration. We investigated the effect of dapagliflozin on the plasma and urine proteome in youth with type 1 diabetes (T1D). Methods: Proteins were quantified using the SomaScan platform (plasma: 11k panel, urine: 7k panel) before and after 16 weeks of dapagliflozin 5 mg daily or placebo in youth with T1D from the randomized-controlled trial, Adolescent T1D Treatment with SGLT2i for hyperglycEMia & hyperfiltration Trial (ATTEMPT). SomaScan relative fluorescence units were log2 transformed post normalization. Linear mixed models were fit for each protein with treatment, visit, and their interaction as fixed effects, and a random intercept for participant. Multiple testing was controlled at false discovery rate (FDR) <0.05. Results: Participants (N=97) were 16.0±2.3 years, 47% male, with T1D duration 7.3±4.2 years, HbA1c 7.7±0.8%, and BMI 25.0±5.7 kg/m2. Mixed effects analyses of urinary proteins found 12 proteins involved in energy metabolism or epithelial repair that increased with dapagliflozin vs. placebo, while 74 proteins associated with tubular injury, fibrosis, and inflammation decreased. Urinary epidermal growth factor (EGF, a kidney protective factor) increased, while growth differentiation factor 15 (GDF15, a tubular stress marker) fell markedly. Pro-fibrotic and injury-associated markers, including tissue inhibitor of metalloproteinases-2 (TIMP2), matrix metalloproteinase-7 (MMP7), and insulin-like growth factor binding protein 2/5 (IGFBP2/5), were significantly reduced in urine with dapagliflozin. Conclusion: Dapagliflozin therapy in youth with T1D favorably shifted urinary protein signatures toward reduced tubular injury and fibrosis, while enhancing urinary markers of epithelial repair and kidney protection, underscoring the kidney-specific effects of SGLT2i. These findings provide molecular evidence supporting the kidney protective potential of SGLT2 inhibition in youth with T1D and highlight urinary proteomics as a sensitive tool for detecting early kidney-specific therapeutic effects. Funding: Private Foundation Support, Government Support - Non-U.S.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.248
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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