T7. UPDATED INDIVIDUAL PARTICIPANT DATA META-ANALYSIS CONFIRMS LOWER LEVELS OF THE GLIAL MARKER TSPO IN PSYCHOSIS PATIENTS
Notice bibliographique
Résumé
Abstract Background Treatment targeting the immune system is a promising new approach in schizophrenia. In search for tools for stratification and treatment monitoring, much effort has been invested in the use of positron emission tomography (PET) and radioligands binding to a glial marker, the 18 kDa translocator protein (TSPO). We previously demonstrated lower TSPO in psychosis patients in an individual participant data (IPD) meta-analysis of studies using second generation TSPO radioligands (Plavén-Sigray et al., 2018). Subsequently, a summary-statistics meta-analysis, including one newly published study, showed no difference (Marques et al., 2019). Here, the aim was to repeat the IPD analysis including this new sample, and an additional unpublished dataset in first episode psychosis patients. The primary objective was to re-evaluate the hypotheses of 1) higher or 2) lower or 3) no difference in radioligand binding between patients and healthy control subjects. Secondary objectives were to assess the effects of antipsychotic medication on TSPO binding, as well as relationships between TSPO binding and disease duration and symptom measures. Methods Individual participant data were obtained from PET studies that 1) used a second generation TSPO radioligand, 2) reported distribution volume (VT) values in brain in patients with psychosis as compared to healthy controls, and 3) reported TSPO affinity type of all participants. The outcome measure was VT in frontal cortex (FC), temporal cortex (TC) and hippocampus (HIP). Bayes factors (BF) were applied to examine the relative support for higher, lower, or no-change of TSPO levels in patients compared to healthy controls. Results Individual participant data from seven studies were included, amounting to 99 patients with first-episode psychosis or schizophrenia and 109 healthy control subjects. In all regions investigated, BF showed moderate to strong support (BF > 5) for lower VT in patients as compared to no difference, and strong support (BF > 10) for lower VT compared to higher VT in patients. Mean patient-control differences in standardized VT values were -0.41 for FC (95%CI -0.67 to -0.15, p = 0.0022), -0.38 for TC (95%CI -0.64 to -0.12, p = 0.0048) and -0.53 for HIP (95% CI -0.79 to -0.27, p = 0.0001). The mean change in standardized VT due to medication was 0.10 for FC (CI95% -0.10 to 0.30, p = 0.615), -0.08 for TC (CI95% -0.32 to 0.48, p = 0.666) and 0.08 for HIP (CI95% -0.46 to 0.30, p = 0.682). No association was observed between VT and disease duration or symptom levels (all p > 0.526). Discussion In this updated IPD meta-analysis including two new datasets, we found moderate to strong support for lower TSPO in psychosis patients compared to control subjects. In vitro data has shown a lack of correspondence between TSPO and pro-inflammatory activation, also recently confirmed in a post-mortem study in schizophrenia. Hence, based on the present results no firm conclusions can be made regarding the pro- versus anti-inflammatory status of glial cells in psychosis patients. Additional work is needed to understand the biological relevance of the observed lower TSPO in patients.
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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 | 0,016 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,039 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».