Multimodal Kidney Mechanisms of SGLT2 Inhibition in Patients with Type 1 Diabetes
Bibliographic record
Abstract
Background: SGLT2 inhibitors slow chronic kidney disease progression, but intrarenal mechanisms in type 1 diabetes (T1D) remain unclear. Methods: Single-cell RNA-seq (scRNA-seq) from paired kidney biopsies (subcohort, Fig.) and multiparametric kidney MRI (BOLD R2*) were obtained at baseline and after 16 weeks of treatment with dapagliflozin (dapa) or placebo in youth with T1D and preserved kidney function in a pre-specified ancillary study of the ATTEMPT trial (N=98). Transcriptomic effects were tested with negative binomial mixed models (NBMM) with fixed effects of treatment, visit, their interaction, and within-subject correlation, with FDR<0.05. Transcript changes were regressed against changes in iohexol mGFR, HbA1c and time-in-range (TIR), adjusting for treatment effects within each cell type. An independent external cohort of youth with T1D and healthy controls was used to test whether dapa-responsive transcripts shifted toward healthy-control levels. Results: Dapa reduced GFR, lowered HbA1c, and increased TIR, as previously published. MRI showed increased whole-kidney R2* (p=0.03), consistent with reversal of diabetic medullary hyperoxia (p<0.001). scRNA-seq from 27 biopsies (~214k cells) revealed placebo-controlled shifts across nephron segments, especially PT, TAL, EC, IC, and POD (q<0.001; Fig.). Pseudotime trajectories indicated a shift from stress-prone PT toward healthier PT states. In the external T1D cohort, ~55% of shared significant dapa-responsive transcripts moved toward healthy-control levels. Conclusion: SGLT2 inhibition engages convergent kidney mechanisms in T1D including metabolic reprogramming, vascular quiescence, improved oxygen handling, and acid–base adaptation, providing causal, multimodal evidence for mechanisms underlying kidney protection. Funding: Private Foundation Support
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".