Glucagon-Like Peptide-1 Receptor Agonism Improves Nephrotoxic Serum Nephritis by Inhibiting T-Cell Proliferation
Bibliographic record
Abstract
Glucagon-like peptide (GLP)-1 analogs such as liraglutide improved albuminuria in patients with type 2 diabetes in large randomized controlled trials. One of the suspected mechanisms is the anti-inflammatory potential of GLP-1 receptor (Glp1r) agonism. Thus, the anti-inflammatory action of Glp1r agonism was tested in a nondiabetic, T-cell–mediated murine model of nephrotoxic serum nephritis (NTS). The role of Glp1r in NTS was evaluated by using Glp1r −/− mice or C57BL/6 mice treated with liraglutide. In vitro , murine T cells were stimulated in the presence of liraglutide or vehicle. Glp1r −/− mice displayed increased renal infiltration of neutrophils and T cells after induction of NTS. Splenocyte proliferation and TH1 cytokine transcription were increased in spleen and lymph nodes of Glp1r −/− mice. Liraglutide treatment significantly improved the renal outcome of NTS in C57BL/6 mice by decreasing renal infiltration and proliferation of T cells, which resulted in decreased macrophage infiltration. In vitro , T cells stimulated in the presence of liraglutide showed decreased proliferation of TH1 and TH17 cells. Liraglutide blocked glycolysis in T cells and decreased their Glut1 mRNA expression. Together, Glp1r agonism protects mice from a T-cell–dependent glomerulonephritis model by inhibition of T-cell proliferation, possibly by interacting with their metabolic program. This mechanism may explain in part the renoprotective effects of Glp1r agonism in diabetic nephropathy.
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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) | 0.002 | 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".