Blood Biomarkers Predict Future Cognitive Decline after Military-RelatedTraumatic Brain Injury
Bibliographic record
Abstract
Background:: Traumatic brain injury (TBI) has been associated with increased likelihood of late-life dementia; however, the mechanisms driving this relationship are elusive. Blood-based biomarkers may provide insight into these mechanisms and serve as useful prognostic indicators of cognitive recovery or decline following a TBI. Objective: The aim of this study was to examine blood biomarkers within one year of TBI and explore their relationship with cognitive decline. Methods: Service members and veterans (n=224) without injury (n=77), or with history of bodily injury (n=37), uncomplicated mild TBI (n=55), or more severe TBI (n=55), underwent a blood draw and neuropsychological assessment within one year of their injury as part of a case-control study. A subsample (n=87) completed follow-up cognitive assessment. Results: In the more severe TBI group, baseline glial fibrillary acidic protein (p=.008) and ubiquitin C-terminal hydrolase-L1 (p=.026) were associated with processing speed at baseline, and baseline ubiquitin C-terminal hydrolase-L1 predicted change in immediate (R2Δ=.244, p=.005) and delayed memory (R2Δ=.390, p=.003) over time. In the mild TBI group, higher baseline tau predicted greater negative change in perceptual reasoning (R2Δ=.188, p=.033) and executive functioning (R2Δ=.298, p=.007); higher baseline neurofilament light predicted greater negative change in perceptual reasoning (R2Δ=.211, p=.012). Conclusion: Baseline ubiquitin C-terminal hydrolase-L1 strongly predicted memory decline in the more severe TBI group, while tau and neurofilament light strongly predicted decline in the mild TBI group. A panel including these biomarkers could be particularly helpful in identifying those at risk for future cognitive decline following TBI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; both teacher heads agree on what is shown here.
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".