Renal Functional Reserve Predicts GFR Response to Empagliflozin but Not Linagliptin or Sulfonylureas in Patients with Type 2 Diabetes
Bibliographic record
Abstract
Background: Glomerular hyperfiltration is common in type 2 diabetes (T2D) and may be due to reduced nephron number and/or altered intrarenal hemodynamics. Renal functional reserve (RFR), the kidney’s ability to increase GFR upon stimulation (e.g., a meal), can help identify single-nephron hyperfiltration in patients with preserved baseline (BL) whole-kidney GFR. We evaluated whether postprandial RFR is associated with an acute hemodynamic GFR response to SGLT2i empagliflozin (EMPA), DPP-4i linagliptin (LINA), or a sulfonylurea (SU) in T2D. Methods: This analysis pools data from two 8-week randomized, double-blind, parallel-group trials, including 71 T2D patients with preserved whole-kidney GFR (mean±SD age 65±7 yrs, 83% male, BMI 30.4±3.9 kg/m2, HbA1c 7.8±1.0%, measured (m)GFR 86.5±17.6 mL/min/1.73m2). Patients received EMPA (10mg; N=20), LINA (5mg; N=27) or SU (glimepiride 1mg or gliclazide 30mg; N=24), added to stable metformin. mGFR and effective renal plasma flow (ERPF) were determined by inulin/iohexol and PAH-clearance, respectively, based on timed urine sampling in fasting and post-protein-rich meal conditions. Intrarenal hemodynamics were calculated using Gomez-equations, and fractional sodium excretion (FENa) and systemic hemodynamics were also evaluated. Results: The meal increased mGFR (+7.3±1.7 mL/min/1.73m2; p<0.001) and ERPF (+44.3±14.9 mL/min/1.73m2; p=0.005), with a decrease in renal vascular resistance (RVR; −0.02±0.01 mmHg/L/min; p<0.001), likely driven by reduced afferent arteriolar resistance (−1068±241 dyne/sec/cm-5; p<0.001) and lower FENa (−0.21±0.05; p<0.001). Postprandial mGFR-changes correlated with BL HbA1c (r:0.29; p=0.032) but not with BL mGFR, and postprandial RVR change (r −0.57; p<0.001). After 8 weeks, mGFR tended to decrease with SU (p=0.054) and decreased with EMPA (−9.1±3.2 mL/min/1.73m2; p=0.016), with no effect with LINA. BL postprandial mGFR changes correlated with 8-week treatment-induced mGFR changes across all patients; strongly in the EMPA group (r:0.88; p<0.001), but not with LINA or SU. Conclusion: Postprandial RFR links to the acute GFR dip with EMPA, but not GFR changes in response to LINA or SU. As initial GFR-dipping is associated with long-term kidney benefit, RFR may be a potential biomarker to personalize SGLT2i-therapy. Funding: Commercial Support - Boehringer Ingelheim
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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 | 0.004 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".