Diroximel fumarate acts through Nrf2 to attenuate methylglyoxal-induced nociception in mice and decrease ISR activation in DRG neurons
Bibliographic record
Abstract
Diabetic neuropathic pain is associated with elevated plasma levels of methylglyoxal (MGO). MGO is a metabolite of glycolysis that causes pain hypersensitivity in mice by stimulating the phosphorylation of eukaryotic initiation factor 2α (p-eIF2α) and subsequently activating the integrated stress response (ISR). We first established that Zucker diabetic fatty rats have enhanced MGO signaling, engage ISR, and develop pain hypersensitivity. Since nuclear factor erythroid 2-related factor 2 (Nrf2) regulates the expression of antioxidant proteins that neutralize MGO, we hypothesized that fumarates, like diroximel fumarate (DRF), will stimulate Nrf2 signaling, and prevent MGO-induced ISR and pain hypersensitivity. DRF (100 mg/kg) treated animals were protected from developing MGO (20 ng) induced mechanical and cold hypersensitivity. Mechanistically, DRF treatment protected against MGO-induced increase in p-eIF2α levels in the sciatic nerve and reduced loss of intraepidermal nerve fiber density. Using Nrf2 knockout mice, we demonstrate that Nrf2 is necessary for the antinociceptive effects of DRF. Cotreatment of MGO (1 µmol/L) with monomethyl fumarate (10, 20, and 50 µmol/L), the active metabolite of DRF, prevented ISR in both mouse and human dorsal root ganglia neurons. Our data show that targeting Nrf2 with DRF is a strategy to potentially alleviate pain associated with elevated MGO levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".