Epigenetic signature of chronic low back pain in human T cells
Bibliographic record
Abstract
Abstract Objective: Determine if chronic low back pain (LBP) is associated with DNA methylation signatures in human T cells that will reveal novel mechanisms and potential therapeutic targets and explore the feasibility of epigenetic diagnostic markers for pain-related pathophysiology. Methods: Genome-wide DNA methylation analysis of 850,000 CpG sites in women and men with chronic LBP and pain-free controls was performed. T cells were isolated ( discovery cohort, n = 32) and used to identify differentially methylated CpG sites, and gene ontologies and molecular pathways were identified. A polygenic DNA methylation score for LBP was generated in both women and men. Validation was performed in an independent cohort ( validation cohort, n = 63) of chronic LBP and healthy controls. Results: Analysis with the discovery cohort revealed a total of 2,496 and 419 differentially methylated CpGs in women and men, respectively. In women, most of these sites were hypomethylated and enriched in genes with functions in the extracellular matrix, in the immune system (ie, cytokines), or in epigenetic processes. In men, a unique chronic LBP DNA methylation signature was identified characterized by significant enrichment for genes from the major histocompatibility complex. Sex-specific polygenic DNA methylation scores were generated to estimate the pain status of each individual and confirmed in the validation cohort using pyrosequencing. Conclusion: This study reveals sex-specific DNA methylation signatures in human T cells that discriminates chronic LBP participants from healthy controls.
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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) | 0.001 | 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".