Causal evidence linking chronic pain genetics to late-onset asthma via the nervous system
Bibliographic record
Abstract
Abstract Background Chronic pain and asthma are associated, but the direction and basis of their genetic and biological relationship remain unclear. Methods We conducted genome-wide association (GWA), cross-trait meta-analysis, polygenic risk score (PRS) prediction, bivariate causal modeling, and Mendelian randomization (MR) across nine chronic pain traits and three asthma age-of-onset strata (<18, 18–40, and >40 years for childhood-, adult-, and late-onset asthma) in 456,958 UK Biobank (UKB) and 25,275 Canadian Longitudinal Study on Aging (CLSA) participants of European descent. We analyzed shared and distinct genetic architecture using gene-, pathway-, tissue-, and cell-type-based enrichment analyses. Results Multisite chronic pain (MCP) showed the strongest and most consistent genetic overlap with asthma, with genetic correlation increasing from childhood (rg = 0.01) to late-onset asthma (rg = 0.40). Estimated causal variants for late-onset asthma (∼1.8 K) were nested within a broader MCP profile (∼9.4 K), with fewer for childhood asthma (∼0.2 K). Using PRS, MR, and longitudinal analyses, we found that MCP contributes causally to late-onset asthma. Top causal variants from MR mapped to GMPPB–RNF123 , DCC , and FOXP2 . Conditioning by MCP amplified late-onset asthma variant effect sizes and uncovered genes enriched for immune and central nervous system pathways, tissues, and cell types. In contrast, childhood asthma showed immune-specific enrichment alone. Conclusion These findings reveal neurological function linking chronic pain to late-onset asthma, distinct from childhood asthma, and highlight a central nervous system contribution to asthma emerging later in life.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".