Association between oxidative stress and chronic orofacial pain and potential druggable targets: Evidence from a Mendelian randomization study
Bibliographic record
Abstract
BACKGROUND: Oxidative stress indicators affect chronic orofacial pain (COFP), but how to reduce these effects is uncertain. OBJECTIVES: 11 oxidative stress biomarkers were collected as exposures, while four forms of COFP were chosen as outcomes for Mendelian randomization (MR) study. METHODS: The effect estimates between oxidative stress and COFP were calculated using inverse variance-weighted MR (IVW-MR). Then, functional mapping and annotation (FUMA) was utilized in order to carry out SNP-based functional enrichment analyses. In addition, the IVW-MR method was applied to combine effect estimates when using genetic variants associated with oxidative stress biomarkers as an instrument for exploring potential druggable targets. RESULTS: The results indicated that oxidative stress biomarkers (causal OR of uric acid (UA), 0.998 for myofascial pain, 95% CI 0.996-1.000, p < .05; and OR of glutathione transferase (GST), 1.002 for dentoalveolar pain, 95% CI 1.000-1.003, p < .05) were significantly linked with the probability of COFP. Functional analysis also demonstrated that UA and myofascial pain genes were prominent in nitrogen and uracil metabolism, while GST and dentoalveolar pain genes were enriched in glutathione metabolism. Also, the study provided evidence that solute carrier family 2 member 9 (SLC2A9) and glutathione S-transferase alpha 2 (GSTA2) cause discomfort in the myofascial pain (OR = 1.003, 95% CI 1.000-1.006; p < .05) and dentoalveolar region (OR = 1.001, 95% CI 1.000-1.002; p < .05), respectively. CONCLUSIONS: In conclusion, this MR study indicates that genetically predicted myofascial pain was significantly associated with decreased UA and dentoalveolar pain was significantly associated with increased GST level. SLC2A9 inhibitor and GSTA2 inhibitor were novel chronic orofacial pain therapies and biomarkers, but clinical trials are called to examine if these oxidative biomarkers have the protective effect against orofacial pain, and further research are needed to explore the underlying mechanisms.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".