eGFR Decline after SGLT2 Inhibitor Initiation: The Tortoise and the Hare Reimagined
Bibliographic record
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2is) have emerged as an effective class of medications to treat CKD and heart failure. SGLT2is improve cardiovascular outcomes in patients with type 2 diabetes mellitus (1⇓–3) and in those with heart failure with reduced ejection fraction (4⇓⇓–7). Moreover, they have been shown to attenuate kidney disease progression in patients with proteinuric CKD, irrespective of diabetes status (8,9). The rapid uptake of SGLT2is into practice necessitates a careful understanding of their risks. Furthermore, clinicians need to know what to expect when prescribing these agents, including an early decline in eGFR after initiation. We aim to put this observation into context, while providing clinicians with a practical approach to managing this scenario. The suggested mechanism of action of the SGLT2is has previously been described (10). These drugs inhibit sodium and glucose reabsorption in the proximal tubule, leading to increased sodium and chloride delivery to the macula densa. This results in afferent arteriolar vasoconstriction secondary to adenosine-mediated myogenic activation, leading to a reduction in the intraglomerular pressure and the GFR (10). Therefore, it is not surprising that the major SGLT2i outcome trials have reported an early decline in eGFR (around 3–6 ml/min per 1.73 m2) shortly after initiating these drugs compared with placebo controls (5,8,9,11,12). These early declines or dips were typically observed at 2–4 weeks after initiation of the SGLT2i, with subsequent partial recovery of the eGFR curve by week 12, and, ultimately, followed by an attenuation of the slope of eGFR decline compared with placebo controls after 52 weeks (Table 1). View this table: Table 1. Randomized controlled trials reporting an initial dip of eGFR How can we reconcile early dips in eGFR with long-term nephroprotection? Maladaptive glomerular hemodynamics play a central …
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".