Effects of Dapagliflozin on Sodium Excretion and Blood Pressure in Patients with Type 2 Diabetes and CKD during Standardized Sodium Intake
Bibliographic record
Abstract
Background: We previously demonstrated that dapagliflozin reduced blood pressure (BP) without altering natriuresis in patients with type 2 diabetes (T2D) and preserved kidney function. Chronic kidney disease (CKD) is often associated with salt-sensitive hypertension, which is resistant to many anti-hypertensive drugs. In this prospective study, we assessed the effects of dapagliflozin on sodium excretion and BP in patients with T2D and CKD during standardized sodium intake. Methods: We conducted a prospective open-label study to evaluate the effects of dapagliflozin on 24-hour sodium excretion, 24-hour BP, and extracellular volume (EV) in patients with T2D with CKD (estimated glomerular filtration rate [eGFR] of ≥25 and ≤50 mL/min per 1.73m2) at the start of treatment (ST) (days 2-4), end of treatment (ET) (days 12-14), and washout (days 15-18) during controlled sodium intake (150 mmol/day). Results: Thirteen patients (mean age 70 ± 9.6 years, eGFR 41 ± 12.6 mL/min per 1.73m2, 24-hour systolic BP 130 ± 23.1 mmHg) were included. Mean 24-hour sodium excretion did not change during the study (Table 1). Nominal decreases in 24-hour systolic and diastolic BP were observed, without changes in EV. Mean 24-hour urinary glucose excretion increased at ST and ET and reversed during wash-out. Conclusion: In patients with T2D and CKD, dapagliflozin reduced BP during standardized sodium intake, without increasing natriuresis. These findings are consistent with those in patients with T2D and preserved kidney function, and suggest that BP lowering with dapagliflozin may be attributed to different mechanisms than natriuresis in patients with and without CKD. Funding: Commercial Support - AstraZeneca Table 1: Change from baseline (95% CI) - Outcome ST ET Washout Urinary sodium excretion, mmol/24-hour -0.5 (-15.4, 14.5) P = 0.95 -11.7(-30.4, 7.1) P = 0.20 -19.1 (-36.5, -1.7) P = 0.03 Urinary glucose excretion, mmol/24-hour 125.0(55.6, 194.3) P = 0.006 85.5 (15.1, 156.0) P = 0.03 64.5(-9.1, 138.1) P = 0.07 24-hour systolic blood pressure, mmHg -6.1(-19.8, 7.7) P = 0.31 -3.7(-13.7, 6.2) P = 0.38 -7.9(-12.3, -3.5) P = 0.006 24-hour diastolic blood pressure, mmHg -3.0(-9.6, 3.7) P = 0.30 -1.5(-6.4, 3.3) P = 0.45 -4.0(-6.8, -1.1) P = 0.02 Extracellular volume, L -0.4(-0.8, 0.1) P = 0.08 -0.4(-0.9, 0.2) P = 0.18 0.1(-0.6, 0.8) P = 0.79
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".