Associations between testosterone and future PTSD symptoms among middle age and older UK residents
Bibliographic record
Abstract
Abstract Testosterone has been theorized to influence the development of post-traumatic stress disorder (PTSD). However, the relationship between testosterone level and PTSD is still not well understood. We evaluated the potential association between testosterone and subsequent development of PTSD symptoms using a large sample size, in a civilian context, inclusive of both males and females. Out of around 500,000 total UK Biobank participants, our sample had 130,471 participants who: had testosterone measures, completed the mental health questionnaire, and passed outlier exclusion. After adjusting for relevant covariates, we used linear regression to assess the relationship between testosterone level and future development of symptoms, in males and females separately (N males = 61,758, N females = 67,053). In both males and females, small but significant nonlinear (and oftentimes U-shaped) relationships were observed between testosterone levels and PTSD symptoms. When grouping participants into deciles of testosterone for both sexes, the strongest associations between testosterone levels and PTSD symptoms were observed in the central deciles. For example, for total testosterone, compared to decile 1: individuals in decile 7 had the lowest PTSD symptom scores in both males ( beta = −0.16, p = 1.58 × 10 −3 ) and females ( beta = −0.23, p = 3.04 × 10 −5 ). We also found that body mass index (BMI) moderated the relationship between testosterone and PTSD symptoms, such that the relationship was considerably stronger among individuals with higher BMI. Results were similar for depression and anxiety measures. Analyses using calculated free testosterone (cFT) and the free androgen index (FAI) were generally consistent with total testosterone (TT) results. These findings suggest that mid-range testosterone levels are associated with the lowest risk of PTSD symptoms in both sexes, and future work should seek to examine if this relationship is causal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".