Genome-wide association studies with experimental validation identify a protective role for B lymphocytes against chronic post-surgical pain
Bibliographic record
Abstract
BACKGROUND: Chronic post-surgical pain (CPSP) significantly impacts patients' recovery and quality of life. Although environmental risk factors are well-established, genetic risk remains less understood. METHODS: A meta-analysis of genome-wide association studies followed by partitioned heritability was performed on 1350 individuals across five surgery types: hysterectomy, mastectomy, abdominal, hernia, and knee. In subsequent animal studies, withdrawal thresholds to evoked mechanical stimulation were measured in Rag1 null mutant and wild-type mice after plantar incision and laparotomy. Cell sorting by flow cytometry tracked recruitment of immune cell types. RESULTS: We discovered 77 genome-wide significant single-nucleotide polymorphism (SNP) hits, distributed among 24 loci and 244 genes. Meta-analysis of all cohorts estimated a SNP-based narrow-sense heritability for CPSP at ∼39%, indicating a substantial genetic contribution. Partitioned heritability analysis across a wide variety of tissues revealed enrichment of heritability in immune system-related genes, particularly those associated with B and T cells. Rag1 null mutant mice lacking both T and B cells exhibited exacerbated and prolonged allodynia up to 42 days after surgery, which was rescued by B-cell transfer. Recruitment patterns of B cells but not T cells differed significantly during the first 7 days after injury in the footpad, lymph nodes, and dorsal root ganglia. CONCLUSIONS: These findings suggest a key protective role for the adaptive immune system in the development of 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".