Brain‐derived neurotropic factor polymorphisms, traumatic stress, mild traumatic brain injury, and combat exposure contribute to postdeployment traumatic stress
Bibliographic record
Abstract
BACKGROUND: In addition to experiencing traumatic events while deployed in a combat environment, there are other factors that contribute to the development of posttraumatic stress disorder (PTSD) in military service members. This study explored the contribution of genetics, childhood environment, prior trauma, psychological, cognitive, and deployment factors to the development of traumatic stress following deployment. METHODS: Both pre- and postdeployment data on 231 of 458 soldiers were analyzed. Postdeployment assessments occurred within 30 days from returning stateside and included a battery of psychological health, medical history, and demographic questionnaires; neurocognitive tests; and blood serum for the D2 dopamine receptor (DRD2), apolipoprotein E (APOE), and brain-derived neurotropic factor (BDNF) genes. RESULTS: Soldiers who screened positive for traumatic stress at postdeployment had significantly higher scores in depression (d = 1.91), anxiety (d = 1.61), poor sleep quality (d = 0.92), postconcussion symptoms (d = 2.21), alcohol use (d = 0.63), traumatic life events (d = 0.42), and combat exposure (d = 0.91). BDNF Val66 Met genotype was significantly associated with risk for sustaining a mild traumatic brain injury (mTBI) and screening positive for traumatic stress. Predeployment traumatic stress, greater combat exposure and sustaining an mTBI while deployed, and the BDNF Met/Met genotype accounted for 22% of the variance of postdeployment PTSD scores (R (2) = 0.22, P < 0.001). However, predeployment traumatic stress, alone, accounted for 17% of the postdeployment PTSD scores. CONCLUSION: These findings suggest predeployment traumatic stress, genetic, and environmental factors have unique contributions to the development of combat-related traumatic stress in military service members.
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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 | 0.001 |
| 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) | 0.003 | 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".