Metabolomics in patients with psychosis: A systematic review
Bibliographic record
Abstract
The purpose of this article is to provide a comprehensive review of metabolomics studies for psychosis, as a means of biomarker discovery. Manuscripts were selected for review if they involved discovery of metabolites using high-throughput analysis in human subjects and were published in the last decade. The metabolites identified were searched in Human Metabolome Data Base (HMDB) for a link to psychosis. Metabolites associated with psychosis based on evidence in HMBD were then searched using PubMed to explore the availability of further evidence. Almost all of the studies which underwent full review involved patients with schizophrenia. Ten biomarkers were identified. Six of them were reported in two or more independent metabolomics studies: N-acetyl aspartate, lactate, tryptophan, kynurenine, glutamate, and creatine. Four additional metabolites were encountered in a single metabolomics study but had significant evidence (two supporting articles or more) for a link to psychosis based on PubMed: linoleic acid, D-serine, glutathione, and 3-hydroxybutyrate. The pathways affected are discussed as they may be relevant to the pathophysiology of psychosis, and specifically of schizophrenia, as well as, constitute new drug targets for treatment of related conditions. Based on the biomarkers identified, early diagnosis of schizophrenia and/or monitoring may be possible.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".