Tubuloglomerular Feedback in Renal Glucosuria: Mimicking Long-term SGLT-2 Inhibitor Therapy
Bibliographic record
Abstract
A patient with renal glucosuria due to a congenital knock-out of the sodium-glucose cotransporter 2 (SGLT-2) protein because of a compound heterozygous mutation in the SLC5A2 gene may provide a natural model mimicking the effects of long-term SGLT-2 inhibitor therapy, which has been shown to exert kidney-protective effects beyond its antidiabetic properties. One possible mechanism for the protective effects of SGLT-2 inhibitor therapy might be the activation of tubuloglomerular feedback by increased outflow of sodium, chloride, and glucose to distal parts of the nephron, including the macula densa. Subsequently, afferent arteriolar vasoconstriction is induced and blood flow, intraglomerular filtration pressure, and glomerular filtration rate (GFR) all decline. However, prolonged tubuloglomerular feedback activation could change the sensitivity of tubuloglomerular feedback and hence decrease the beneficial effects of SGLT-2 inhibition on kidney function. Tubuloglomerular feedback is mediated by the Na + /K + /2Cl − cotransporter. Hence furosemide, which blocks this cotransporter, is a medical option to test tubuloglomerular feedback because GFR should increase after administration of this loop diuretic. In our patient with long-term activated tubuloglomerular feedback due to SGLT-2 mutations, we show that the sensitivity of tubuloglomerular feedback is maintained, demonstrated by an increase in GFR measured using iohexol clearance following furosemide administration. This observation supports the idea that long-term SGLT-2 inhibitor therapy is kidney protective through a functional tubuloglomerular feedback.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".