The cognitive metabolomic signatures in schizophrenia spectrum disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND: Understanding the metabolome, the metabolite profile within biological samples, can provide insight into the mechanisms and processes underlying schizophrenia spectrum disorders (SSDs). Given that cognitive impairments are a core symptom domain of SSDs, investigating metabolomic alterations and their associations with cognitive impairments may help uncover pathways and mechanisms, with the potential to provide future treatments. Currently, there are no systematic reviews exploring the associations between cognition and metabolomic signatures. Therefore, the objective of this systematic review is to identify and examine these associations. METHOD: A systematic database search was conducted in Ovid MEDLINE, EMBASE and Scopus for studies related to the following three conceptual domains: "schizophrenia spectrum disorders", "metabolomics", and "cognition." Studies with a case-control component and/or association analyses were included in the review. Risk of bias assessments were conducted using the appropriate Johanna Briggs Institute Critical Appraisal Tool. RESULTS: Nine studies met inclusions for this review. Within these studies, 44 metabolites were identified as being dysregulated in cognitively impaired individuals with SSDs as compared to cognitively normal patients; 38 metabolites were downregulated while 6 were upregulated. The most frequently dysregulated classes of metabolites were carboxylic acids and derivatives, organooxygen compounds, and fatty acyls. For the association analyses, a mixture of positive and negative associations was found between cognition and metabolites, and the metabolite classes mainly dysregulated were glycerophospholipids, glycerolipids and fatty acyls. CONCLUSION: The identified metabolites may impact pathways related to inflammation, glutamate and glutamine metabolism, and lipid metabolism which may have an influence on cognition impairment in SSDs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".