Glucagon-like peptide-1 receptor signaling modifies the extent of diabetic kidney disease through dampening the receptor for advanced glycation end products–induced inflammation
Bibliographic record
Abstract
Glucagon like peptide-1 (GLP-1) is a hormone produced and released by cells of the gastrointestinal tract following meal ingestion. GLP-1 receptor agonists (GLP-1RA) exhibit kidney-protective actions through poorly understood mechanisms. Here we interrogated whether the receptor for advanced glycation end products (RAGE) plays a role in mediating the actions of GLP-1 on inflammation and diabetic kidney disease. Mice with deletion of the GLP-1 receptor displayed an abnormal kidney phenotype that was accelerated by diabetes and improved with co-deletion of RAGE in vivo. Activation of the GLP-1 receptor pathway with liraglutide, an anti-diabetic treatment, downregulated kidney RAGE, reduced the expansion of bone marrow myeloid progenitors, promoted M2-like macrophage polarization and lessened markers of kidney damage in diabetic mice. Single cell transcriptomics revealed that liraglutide induced distinct transcriptional changes in kidney endothelial, proximal tubular, podocyte and macrophage cells, which were dominated by pathways involved in nutrient transport and utilization, redox sensing and the resolution of inflammation. The kidney-protective action of liraglutide was corroborated in a non-diabetic model of chronic kidney disease, the subtotal nephrectomised rat. Thus, our findings identify a novel glucose-independent kidney-protective action of GLP-1-based therapies in diabetic kidney disease and provide a valuable resource for exploring the cell-specific kidney transcriptional response ensuing from pharmacological GLP-1R agonism.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".