Whole-genome methylation profiling reveals regions associated with painful temporomandibular disorders and active recovery processes
Bibliographic record
Abstract
ABSTRACT: Temporomandibular disorders (TMDs), collectively representing one of the most common chronic pain conditions, have a substantial genetic component, but genetic variation alone has not fully explained the heritability of TMD risk. Reasoning that the unexplained heritability may be because of DNA methylation, an epigenetic phenomenon, we measured genome-wide DNA methylation using the Illumina MethylationEPIC platform with blood samples from participants in the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study. Associations with chronic TMD used methylation data from 496 chronic painful TMD cases and 452 TMD-free controls. Changes in methylation between enrollment and a 6-month follow-up visit were determined for a separate sample of 62 people with recent-onset painful TMD. More than 750,000 individual CpG sites were examined for association with chronic painful TMD. Six differentially methylated regions were significantly ( P < 5 × 10 -8 ) associated with chronic painful TMD, including loci near genes involved in the regulation of inflammatory and neuronal response. A majority of loci were similarly differentially methylated in acute TMD consistent with observed transience or persistence of symptoms at follow-up. Functional characterization of the identified regions found relationships between methylation at these loci and nearby genetic variation contributing to chronic painful TMD and with gene expression of proximal genes. These findings reveal epigenetic contributions to chronic painful TMD through methylation of the genes FMOD , PM20D1 , ZNF718 , ZFP57 , and RNF39 , following the development of acute painful TMD. Epigenetic regulation of these genes likely contributes to the trajectory of transcriptional events in affected tissues leading to resolution or chronicity of pain.
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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 | 0.000 |
| 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".