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Enregistrement W2937409374 · doi:10.1093/schbul/sbz019.291

T11. THE STRUCTURAL VARIANTS OF COMPLEMENT COMPONENT (C4) IN THE RISK AND CLINICAL CHARACTERISTICS OF SCHIZOPHRENIA

2019· article· en· W2937409374 sur OpenAlexaffabout
Cheng Cheng Chen, Julia Woo, Jennie G. Pouget, Clement C. Zai, James L. Kennedy

Notice bibliographique

RevueSchizophrenia Bulletin · 2019
Typearticle
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensUniversity of TorontoCentre for Addiction and Mental Health
Organismes subventionnairesnon disponible
Mots-clésSchizophrenia (object-oriented programming)Copy-number variationC4ASchizoaffective disorderSynaptic pruningPopulationLogistic regressionPsychologyMedicinePsychosisPsychiatryBiologyInternal medicineGeneticsGene

Résumé

récupéré en direct d'OpenAlex

Schizophrenia (SCZ) is a heritable psychiatric disorder which affects approximately 1% of the population. The disease is characterized by both positive and negative symptoms, as well as declines in cognitive functioning. SCZ often becomes clinically apparent from late adolescence to early adulthood and severely impacts the quality of life. Although treatments exist, the development of preventive or curative interventions is hindered by the lack of mechanistic understanding of the pathophysiology of SCZ. The complement component 4 (C4) gene has been identified as one of the largest effect size markers for SCZ risk (Sekar et al., 2016). Sekar et al. have shown the longer version of C4A (C4AL) is linked with higher neural C4A expression which is associated with higher SCZ risk. The C4 gene discovery opened a new direction in SCZ research. This study aims to further explore the relationship between C4 structural variations and clinical characteristics in our Toronto Schizophrenia sample. 599 adults (age 18+) with SCZ or schizoaffective disorder were recruited from our CAMH hospital. Clinical and demographic information was gathered through structured clinical interviews (SCID) and chart review. The copy numbers of the C4A, C4B, C4L, and C4S in each sample were determined using ABI TaqMan copy number variation (CNV) protocol. Also, C4 CNV data on healthy controls were obtained on a small preliminary sample (n=111) from the Sekar et al. paper. Fisher’s exact test was performed to compare C4 CNV distribution between patients and controls. Linear and binomial logistic regression were performed to assess the relationship between C4 CNV and clinical characteristics of SCZ. The additive genotypic model in the analyses and sex was adjusted in the model of the age of onset. All statistical analyses were conducted using IBM SPSS software. The CNV counts of C4 structural variants in our sample ranges from 0–6, 0–5, 0–6, and 0–4 for C4A, C4B, C4L, and C4S respectively. No significant difference was observed in C4 CNV distribution between patients and healthy controls (p = 0.866, 0.795, 0.570, 0.430 for C4A, C4B, C4L, and C4S respectively). Age of onset was the only clinical characteristic that showed nominal association with C4A CNV, and the effect became more robust with sex was as a covariate (p = 0.05, p = 0.008, adjusted for sex). There was no significant association observed between C4 CNV and other clinical characteristics tested, such as symptom severity; Global Assessment of Function; and presence of symptoms such as delusions, hallucinations, disorganized speech or behavior, catatonia, alogia, avolition, inappropriate affect, and affective flattening. In the brain, C4 plays a crucial role in synaptic pruning. The process of synaptic pruning reaches a peak in late adolescence which is the same time when SCZ becomes clinically apparent. Synaptic pruning also explains the deficit of synaptic connections, as well as the cognitive decline that is commonly seen in SCZ. Sekar et al. previously identified the combination of C4A and the retroviral insertion creating - long version (C4AL) as a risk haplotype for SCZ. In our sample, we found no difference in C4 CNV distribution between patients and healthy control. Age of onset was the only clinical characteristic showed nominal significance with CA4 CNV, but the finding is contrary to the current knowledge of C4 in SCZ. Our results suggested higher C4A copy number leads to a later age of onset. The lack of significant finds could be due to the discordance in ancestry between patients and healthy controls, and small sample size. The next step is to estimate neural C4A expression using C4 structural haplotype data and explore the roles of neural C4A expression in SCZ risk and phenotypes.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,084
Score d'incertitude au seuil0,168

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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.

Tête enseignante Opus0,026
Tête enseignante GPT0,339
Écart entre enseignants0,313 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission2
Résumé présentoui

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