Peripheral Glutamate Levels in Schizophrenia: Evidence from a Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Recent research attempting to develop novel medications has turned to glutamatergic signaling pathways to find effective treatments for symptom clusters of schizophrenia. This meta-analysis was undertaken to clarify whether a difference in peripheral glutamate levels exists between patients with schizophrenia and healthy controls. METHODS: The electronic databases Ovid MEDLINE, PubMed, and PsycINFO were systematically searched up to April 2013. The search was limited to case-control studies of blood glutamate levels in schizophrenia written in English. The differences in glutamate levels were evaluated by calculating standardized mean differences (SMD). RESULTS: We found ten studies that met the inclusion criteria for a total of 320 schizophrenia patients and 294 controls. The meta-analysis showed that peripheral glutamate levels in schizophrenia patients were significantly higher overall than in controls (SMD = 0.635, p = 0.004). However, a significant effect of the method used to measure glutamate concentrations was found (F = 7.36, p = 0.01) where fluorometric assay was associated with effect sizes in the opposite direction. CONCLUSION: A higher blood glutamate concentration was found in patients with schizophrenia. However, given the small sample size and methodological differences among studies, this result is not conclusive. More comprehensive research is needed to understand the relationship between glutamate levels in schizophrenia in the blood and the brain.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".