Putative Astroglial Dysfunction in Schizophrenia: A Meta-Analysis of 1H-MRS Studies of Medial Prefrontal Myo-Inositol
Bibliographic record
Abstract
Background Several lines of evidence support a role for astroglial pathology in schizophrenia. Myo-inositol is particularly abundant in astroglia. Many small sized studies have reported on myo-inositol concentration in schizophrenia, but to date these have not been pooled to estimate a collective effect size. Methods We reviewed all proton magnetic resonance spectroscopy (1H-MRS) studies reporting myo-inositol values for patients satisfying DSM or ICD based criteria for schizophrenia in comparison to a healthy controls group in the medial prefrontal cortex published until February 2018. A random-effects model was used to calculate the pooled effect size using metafor package. A meta-regression analysis of moderator variables was also undertaken. Results The literature search identified 19 studies published with a total sample size of 585 controls, 561 patients with schizophrenia. Patients with schizophrenia had significantly reduced medial prefrontal myo-inositol compared to controls (RFX standardised mean difference=0.19, 95% CI [0.05-0.32], z=2.72, p=0.0067; heterogeneity p = 0.09). Studies with more female patients reported more notable schizophrenia-related reduction in myo-inositol (z=2.53, p=0.011). Discussion We report a small, but significant reduction in myo-inositol concentration in the medial prefrontal cortex in schizophrenia. The size of the reported effect indicates that the biological pathways affecting the astroglia are likely to operate only in a subset of patients with schizophrenia. MRS myo-inositol could be a useful tool to stratify and investigate such patients.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".