Hippocampal volume, FKBP5 genetic risk alleles, and childhood trauma interact to increase vulnerability to chronic multisite musculoskeletal pain
Bibliographic record
Abstract
Abstract Chronic multisite musculoskeletal pain (CMP) is common and highly morbid. However, vulnerability factors for CMP are poorly understood. Previous studies have independently shown that both small hippocampal brain volume and genetic risk alleles in a key stress system gene, FKBP5 , increase vulnerability for chronic pain. However, little is known regarding the relationship between these factors and CMP. Here we tested the hypothesis that both small hippocampal brain volume and FKBP5 genetic risk, assessed using the tagging risk variant, FKBP5 rs3800373, increase vulnerability for CMP. We used participant data from 36,822 individuals with available genetic, neuroimaging, and chronic pain data in the UK Biobank study. Although no main effects were observed, the interaction between FKBP5 genetic risk and right hippocampal volume was associated with CMP severity (β = −0.020, p raw = 0.002, p adj = 0.01). In secondary analyses, severity of childhood trauma further moderated the relationship between FKBP5 genetic risk, right hippocampal brain volume, and CMP (β = −0.081, p = 0.016). This study provides novel evidence that both FKBP5 genetic risk and childhood trauma moderate the relationship between right hippocampal brain volume and CMP. The data increases our understanding of vulnerability factors for CMP and builds a foundation for further work assessing causal relationships that might drive CMP development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".