A genetic polymorphism that is associated with mitochondrial energy metabolism increases risk of fibromyalgia
Bibliographic record
Abstract
Alterations in cellular energy metabolism have been implicated in chronic pain, suggesting a role for mitochondrial DNA. Previous studies reported associations of a limited number of mitochondrial DNA polymorphisms with specific pain conditions. In this study, we examined the full mitochondrial genomes of people with a variety of chronic pain conditions. A discovery cohort consisting of 609 participants either with or without a complex persistent pain conditions (CPPCs) was examined. Mitochondrial DNA was subjected to deep sequencing for identification of rare mutations, common variants, haplogroups, and heteroplasmy associated with 5 CPPCs: episodic migraine, irritable bowel syndrome, fibromyalgia, vulvar vestibulitis, or temporomandibular disorders. The strongest association found was the presence of the C allele at the single nucleotide polymorphism m.2352T>C (rs28358579) that significantly increased the risk for fibromyalgia (odds ratio [OR] = 4.6, P = 4.3 × 10). This relationship was even stronger in women (OR = 5.1, P = 2.8 × 10), and m.2352T>C was associated with all other CPPCs in a consistent risk-increasing fashion. This finding was replicated in another cohort (OR = 4.3, P = 2.6 × 10) of the Orofacial Pain: Prospective Evaluation and Risk Assessment study consisting of 1754 female participants. To gain insight into the cellular consequences of the associated genetic variability, we conducted an assay testing metabolic reprogramming in human cell lines with defined genotypes. The minor allele C was associated with decreased mitochondrial membrane potential under conditions where oxidative phosphorylation is required, indicating a role of oxidative phosphorylation in pathophysiology of chronic pain. Our results suggest that cellular energy metabolism, modulated by m.2352T>C, contributes to fibromyalgia and possibly other chronic pain conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".