Metabolomic in severe traumatic brain injury: exploring primary, secondary injuries, diagnosis, and severity
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a major public health concern worldwide, contributing to high rates of injury-related death and disability. Severe traumatic brain injury (sTBI), although it accounts for only 10% of all TBI cases, results in a mortality rate of 30-40% and a significant burden of disability in those that survive. This study explored the potential of metabolomics in the diagnosis of sTBI and explored the potential of metabolomics to examine probable primary and secondary brain injury in sTBI. METHODS: H-NMR) and direct infusion/liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS), to identify and quantify metabolites on days 1 and 4 post-injury. In addition, we used advanced analytical methods to discover metabo-patterns associated with sTBI diagnosis and those related to probable primary and secondary brain injury. RESULTS: Our results showed different serum metabolic profiles between sTBI and orthopedic injury (OI) controls, with significant changes in measured metabolites on day 1 and day 4 post-brain injury. The number of altered metabolites and the extent of their change were more pronounced on day 4 as compared to day 1 post-injury, suggesting an evolution of mechanisms from primary to secondary brain injury. Data showed high sensitivity and specificity in separating sTBI from OI controls for diagnosis. Energy-related metabolites such as glucose, pyruvate, lactate, mannose, and polyamine metabolism metabolites (spermine and putrescine), as well as increased acylcarnitines and sphingomyelins, occurred mainly on day 1 post-injury. Metabolites of neurotransmission, catecholamine, and excitotoxicity mechanisms such as glutamate, phenylalanine, tyrosine, and branched-chain amino acids (BCAAs) increased to a greater degree on day 4. Further, there was an association of multiple metabolites, including acylcarnitines (ACs), lysophosphatidylcholines (LysoPCs), glutamate, and phenylalanine, with injury severity at day 4, while lactate, glucose, and pyruvate correlated with injury severity on day 1. CONCLUSION: The results demonstrate that serum metabolomics has diagnostic potential for sTBI and may reflect molecular mechanisms of primary and secondary brain injuries when comparing metabolite profiles between day 1 and day 4 post-injury. These early changes in serum metabolites may provide insight into molecular pathways or mechanisms of primary injury and ongoing secondary injuries, revealing potential therapeutic targets for sTBI. This work also highlights the need for further research and validation of sTBI metabolite biomarkers in a larger cohort.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".