Dapagliflozin Treatment Induces Alternate RAAS Activation in Stable Kidney Transplant Recipients
Bibliographic record
Abstract
Background: Kidney transplant recipients (KTRs) remain at high risk for graft dysfunction and cardiovascular disease, yet the role of the renin–angiotensin–aldosterone system (RAAS) in these outcomes remains unclear. While sodium-glucose cotransporter 2 inhibitors (SGLT2i) offer kidney protection in chronic kidney disease (CKD), their effects on RAAS activity in KTRs are not well defined particularly in the context of renal denervation. We therefore evaluated the impact of dapagliflozin on RAAS profiles in stable KTRs. Methods: We conducted an exploratory, secondary analysis of the INFINITI double-blind, randomized controlled trial (NCT04965935), which evaluated the efficacy and safety of 12 weeks of the SGLT2i dapagliflozin vs placebo in 52 stable KTR. Serum samples collected pre-treatment and 12 weeks post initiation were used for mass spectrometry–based RAAS profiling, including quantitative assessment of circulating angiotensin peptides and related metabolites. Statistical analysis was performed by applying a 3-way linear mixed-effects model. This may be clearer, as post treatment may suggest 12 weeks after discontinuation of the drug. Results: We detected a significant interaction (time x group) on the downstream product of alternate RAAS activation Ang 1-5: (b=3.29, 95%CI [0.87; 5.80], p=0.008) as well as on the RAAS activation marker Ang II: (b=48.44, 95%CI [2.37; 96.24], p=0.041), showing an increase over time for both peptides (Figure 1). Conclusion: Dapagliflozin increased Ang 1–5 and Ang II 1-8 levels in stable KTRs . These findings suggest activation of the protective alternate RAAS axis and adaptation of the classical RAAS pathway, possibly due to increased natriuresis and effects on systemic volume. Our findings in the transplant setting are consistent with known effects of SGLT2i in native kidney physiology, offering additional mechanistic insights in this population. Funding: 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 | 0.003 | 0.002 |
| 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 | 0.000 | 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".