Renal haemodynamic and protective effects of renoactive drugs in type 2 diabetes: Interaction with SGLT2 inhibitors
Bibliographic record
Abstract
Diabetic kidney disease remains the leading cause of end-stage kidney disease and a major risk factor for cardiovascular disease. Large cardiovascular outcome trials and dedicated kidney trials have shown that sodium-glucose cotransporter (SGLT)2 inhibitors reduce cardiovascular morbidity and mortality and attenuate hard renal outcomes in patients with type 2 diabetes (T2D). Underlying mechanisms explaining these renal benefits may be mediated by decreased glomerular hypertension, possibly by vasodilation of the post-glomerular arteriole. People with T2D often receive several different drugs, some of which could also impact the renal vasculature, and could therefore modify both renal efficacy and safety of SGLT2 inhibition. The most commonly prescribed drugs that could interact with SGLT2 inhibitors on renal haemodynamic function include renin-angiotensin system inhibitors, calcium channel blockers and diuretics. Herein, we review the effects of these drugs on renal haemodynamic function in people with T2D and focus on studies that measured glomerular filtration rate (GFR) and effective renal plasma flow (ERPF) with gold-standard techniques. In addition, we posit, based on these observations, potential interactions with SGLT2 inhibitors with an emphasis on efficacy and safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".