Metabolomics in severe traumatic brain injury: a scoping review
Bibliographic record
Abstract
Abstract Background: Diagnosis and prognostication of severe traumatic brain injury (sTBI) continue to be problematic despite research efforts for years. There is currently no clinically reliable biomarkers, though advances in protein biomarkers are being made. Utilizing Omics technology, particularly metabolomics, may provide new diagnostic biomarkers for severe traumatic brain injury. Several published studies have attempted to determine specific metabolites and metabolic pathways involved; these studies will be reviewed. Aims: This scoping review aims to summarize current literature concerning metabolomics in severe traumatic brain injury, review the comprehensive data and identify commonalities, if any, to define metabolites with potential clinical use. In addition, we will examine related metabolic pathways through pathway analysis. Methods: Scoping review methodology was used to examine the current literature published in Embase, Scopus, PubMed and Medline. An initial 1090 publications were found and vetted with specific inclusion/exclusion criteria. 20 publications were selected for further examination and summary. Metabolic data was classified using the Human Metabolome Database (HMDB) and arranged to determine the recurrent metabolites and classes found in severe traumatic brain injury. To help understand potential mechanisms of injury, pathway analysis was performed using these metabolites and the Kyoto Encylcopedia of Genes and Genomes (KEGG) Pathway Database. Results: Several metabolites related to severe traumatic brain injury and their effects on biological pathways are identified in this review. Proline, citrulline, lactate, alanine, valine, leucine and serine were all decreased in adults post severe traumatic brain injury, whereas both octanoic and decanoic acid were increased post injury. Carboxylic acids tend to decrease following severe traumatic brain injury while hydroxy acids and organooxygen compounds tend to increase. Pathway analysis showed significantly affected glycine and serine metabolism, glycolysis, branched chain amino acid (BCAA) metabolism and other amino acid metabolisms. Surprisingly, no tricarboxylic acid cycle metabolites were affected. Conclusion: Aside from select few metabolites, classification of a metabolic profile proved difficult due to significant ambiguity between study design, type of sample, sample size, metabolomic detection techniques and other confounding variables. Given the trends found in some studies, further metabolomics investigation of severe traumatic brain injury may be useful to identify clinically relevant metabolites.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".