The Sweet Pee Model for Sglt2 Mutation
Bibliographic record
Abstract
Inhibiting renal glucose transport is a potential pharmacologic approach to treat diabetes. The renal tubular sodium-glucose transporter 2 (SGLT2) reabsorbs approximately 90% of the filtered glucose load. An animal model with sglt2 dysfunction could provide information regarding the potential long-term safety and efficacy of SGLT2 inhibitors, which are currently under clinical investigation. Here, we describe Sweet Pee, a mouse model that carries a nonsense mutation in the Slc5a2 gene, which results in the loss of sglt2 protein function. The phenotype of Sweet Pee mutants was remarkably similar to patients with mutations in the Scl5a2 gene. The Sweet Pee mutants had improved glucose tolerance, higher urinary excretion of calcium and magnesium, and growth retardation. Renal physiologic studies demonstrated a prominent distal osmotic diuresis without enhanced natriuresis. Sweet Pee mutants did not exhibit increased KIM-1 or NGAL, markers of acute tubular injury. After induction of diabetes, Sweet Pee mice had better overall glycemic control than wild-type control mice, but had a higher risk for infection and an increased mortality rate (70% in homozygous mutants versus 10% in controls at 20 weeks). In summary, the Sweet Pee model allows study of the long-term benefits and risks associated with inhibition of SGLT2 for the management of diabetes. Our model suggests that inhibiting SGLT2 may improve glucose control but may confer increased risks for infection, malnutrition, volume contraction, and mortality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".