Genome-wide analysis identifies significant contribution of brain-expressed genes in chronic, but not acute, back pain
Bibliographic record
Abstract
ABSTRACT Back pain is the leading cause of disability worldwide. Although most cases of back pain are acute, 20% of people with acute back pain go on to experience chronic back pain symptoms. It is unclear if acute and chronic pain states have similar or distinct underlying genetic mechanisms. Here we performed a genome-wide analysis for acute and chronic back pain in 375,158 individuals and found a significant genetic contribution to chronic, but not to acute, back pain. Using the UK Biobank cohort for discovery and the HUNT cohort for replication, we identified 7 loci for chronic back pain, of which 3 are novel. Pathway analyses, tissue-specific heritability enrichment analyses, epigenetic characterization, and tissue-specific transcriptome mapping in mouse pain models suggest a substantial genetic contribution to chronic, but not acute, back pain from the loci predominantly expressed in the central nervous system. Our findings show that chronic back pain is more heritable than acute back pain and is driven mostly by genes expressed in the central nervous system.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".