Advancing kidney protection in type 1 diabetes: insights from emerging therapies in type 2 diabetes and chronic kidney disease
Bibliographic record
Abstract
INTRODUCTION: For decades, the first-line treatment for kidney protection in people with type 1 diabetes (T1D) and chronic kidney disease (CKD) has been limited to intensive glycemic control, renin-angiotensin system blockers and managing modifiable risk factors. Accordingly, risk of CKD progression has remained unacceptably high, emphasizing the need for novel kidney protective therapies in T1D. AREAS COVERED: This review summarizes the recent evidence supporting the use of existing treatments used for kidney protection in people with type 2 diabetes (T2D) for potential repurposing for people with T1D. First, we highlight the putative structural and functional changes that actively contribute to CKD progression and highlight key hemodynamic, pro-inflammatory, and kidney injury risk markers in T1D. Next, we discuss emerging nephroprotective therapies targeting these pathophysiological factors, review mechanistic studies that have assessed kidney benefits of these agents in T1D, and highlight ongoing kidney-focused trials in T1D, including SUGARNSALT (NCT06217302), FINE-ONE (NCT05901831) and REMODEL-T1D (NCT05822609). EXPERT OPINION: Several medications have become available that could potentially transform CKD care in T1D. It is essential to devise strategies that could address the treatment landscape for kidney protection in T1D by assessing the risk-benefit calculus and expanding the nephrologist's toolkit for minimizing kidney risk in T1D.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".