Genome-wide association study suggests a critical contribution of the adaptive immune system to chronic post-surgical pain
Bibliographic record
Abstract
Chronic post-surgical pain affects a large proportion of people undergoing surgery, delaying recovery time and worsening quality of life. Although many environmental variables have been established as risk factors, less is known about genetic risk. To uncover genetic risk factors we performed genome-wide association studies in post-surgical cohorts of five surgery types- hysterectomy, mastectomy, abdominal, hernia, and knee- totaling 1350 individuals. Genetic associations between post-surgical chronic pain levels on a numeric rating scale (NRS) and additive genetic effects at common SNPs were evaluated. We observed genome-wide significant hits in almost all cohorts that displayed significance at the SNP, gene, and pathway levels. The cohorts were then combined via a GWAS meta-analysis framework for further analyses. Using partitioned heritability, we found that loci at genes specifically expressed in the immune system carried enriched heritability, especially genes related to B and T cells. The relevance of B cells in particular was then demonstrated in mouse postoperative pain assays. Taken altogether, our results suggest a role for the adaptive immune system in chronic post-surgical 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".