Urinary Proteomic Changes Associated with a Healthier Kidney Phenotype After Dapagliflozin Therapy in Adolescent Patients with Type 1 Diabetes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".