Renal Function Improvement With Glucagon-Like Peptide-1 Receptor Agonist in a Patient With Type 2 Diabetes
Bibliographic record
Abstract
Diabetic kidney disease (DKD) includes hypertensive nephrosclerosis, aging, obesity, and atherosclerosis-related renal diseases, in addition to classical diabetic nephropathy. Sodium-glucose co-transporter 2 inhibitors (SGLT2is) have been approved for diabetic and non-diabetic patients at risk of chronic kidney disease progression. As the main mechanism for SGLT2i-mediated improvement of renal function, the normalization of tubulo-glomerular feedback (TGF) has been proposed. Enhanced TGF and resulting glomerular hypertension are observed in diabetic patients, and SGLT2is normalize TGF, reducing the intraglomerular pressure, which may reduce albuminuria and improve renal function. A type 2 diabetic patient with DKD complicated with hypertensive nephrosclerosis, whose renal function was deteriorated by SGLT2i and improved by glucagon-like peptide-1 receptor agonists (GLP-1RAs), was presented. In patients with hypertensive nephrosclerosis such as this case, the normalization of TGF by SGLT2i may further reduce afferent arteriolar blood flow which may worsen glomerular ischemia, resulting in deterioration of renal function. GLP-1RAs have no effect on TGF and have multiple effects to improve vascular endothelial function, which may be associated with an improvement in renal function in this patient.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".