S10. ASTROGLIAL PATHOLOGY IN SCHIZOPHRENIA: A META-ANALYSIS OF MRS STUDIES OF ANTERIOR CINGULATE MYOINOSITOL
Notice bibliographique
Résumé
Several lines of evidence support a role for astroglial pathology in schizophrenia. 1H-MRS does not specifically differentiate between brain cell types; nevertheless, given that myo-inositol (mIns) is particularly abundant in astroglia rather than neuron and microglial cells, it can be considered an astroglial marker. mIns levels in the brain can decrease after brain injury with an efflux of mIns from astrocytes occurring as an osmoregulatory response. Many small sized studies have reported on mIns concentration in schizophrenia, but to date these have not been pooled to estimate a collective effect size. Examining the state of mIns deficit is a critical step to delineate the role of astroglial cells in schizophrenia. We conducted a meta-analysis to investigate the aberrations in myo-inositol levels in the ACC of patients with schizophrenia and measured using magnetic resonance spectroscopy (MRS). Medline, Google Scholar, Ovid Online and EMBASE databases were searched for studies published until September 2017. Search terms included full forms and variations of magnetic resonance spectroscopy, MRS, schizophrenia, psychosis, myo-inositol, inositol, Ins, mI, mIns. We included all 1H-MRS studies reporting mIns values for patients satisfying DSM or ICD based criteria for a primary psychotic disorder (SCZ) in comparison to a healthy controls (HC) group. We screened all identified abstracts, filtered studies that did not satisfy inclusion criteria, hand-searched references and contacted experts to locate further studies. 9 studies were identified that included 223 patients in SCZ group and 231 HCs. We excluded studies that reported only on comorbid illnesses, did not compare patients and HCs, or failed to report data required to construct effect size metrics. A random-effects and fixed-effects, inverse-weighted variance model was used to calculate the pooled effect size. Mean values were extracted and verified independently and effect sizes were computed based on Excel Macro produced by Major Depressive Disorder Neuroimaging Database (MaND) investigators. Contrary to our expectations, in SCZ, there were no significant differences in ACC mIns in patients compared to HC (RFX=0.359, p= 0.057; 95% CI, -0.728 to 0.011; heterogeneity p = 0.0004). In the SCZ group, the mean effect size (Cohen’s d) was d= 0.39, indicating a medium sized difference. There were several methodological issues in the reported studies. Notably, most studies reported on mIns spectrum only when seeking differences in other metabolites; voxel placements were not standardized across the published studies; majority of patients were medicated, in various stages of illness. There was no statistical evidence for a publication bias (p=0.8). There is a medium effect-size, albeit statistically insignificant, reduction in the concentration of mIns in the anterior cingulate cortex in patients with schizophrenia. Given that mIns is the most readily accessible cortical marker in vivo for astroglial activity, it may be feasible to use MRS to stratify patients with astroglial abnormality from those without such an abnormality in schizophrenia.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,010 | 0,031 |
| Bibliométrie | 0,005 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».