Research Methodologies

In this study, we investigated changes in the expression of 12 genes in different stages of schizophrenia:

• Prior to disease onset (CHR)
• During the conversion to psychosis (FEP)
• During chronic schizophrenia (CSZ), and
• In healthy controls (HCs).

Investigation of 12 genes in 394 individuals including:

• 27 individuals with CHR
• 70 antipsychotic-naive individuals with FEP, 157 CSZ patients, and
• 140 healthy controls (HCs).

The study selected single-nucleotide polymorphisms (SNPs) that were considered to be eQTLs to verify whether they are associated with schizophrenia or correlated to expression of their genes, thereby generating a mediation model in which a putative cause (SNP) is related to a presumed effect (disorder) via an intermediate variable (gene expression).

In this way, we could verify whether the gene expression changes that we found are related to genomic variants or to other factors (e.g., environmental factors), and once such a relationship has been established, whether these genomic variants could be directly associated with schizophrenia or indirectly via gene expression. 

• From the Stockholm County In-Patient Register and community registers, we identified 524 cases of schizophrenia and 1043 controls, matched for age, gender, hospital and parish of birth. Data on obstetric complications were obtained from birth records.
• The study conducted a matched case-control study to examine the association between schizophrenia and obstetric complication. 
• They distinguidshed between hypoxia occurring during delivery and abnormalities with foetal development

Relevant papers were identified by a MEDLINE search, by examination of reference lists of published papers, and through personal contact with researchers in the field. Studies were grouped in chronological order according to common themes or methods. Meta-analytic techniques were used to summarize the findings of prospective population-based studies.

• Gene Set Enrichment Analysis (GSEA) of two independent SCZ-related GWAS to identify the pathways and genetic factors that contribute to SCZ.
• Findings in a two-step case-control study involving Shandong migrants from northeastern China.

• The Authors conducted a detailed search of peer-reviewed empirical studies to evaluate evidence for accelerated biological aging in schizophrenia based on systemic, age-related biomarkers.
• They included studies that investigated differences between persons with schizophrenia and healthy comparison subjects in levels of biomarkers known to be associated with aging and examined the relationship of these biomarkers to age in the 2 groups.

• The research paper uses a  longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total)
• Machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia (“schizophrenia score”), based on the gray matter density maps.
• Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan.
• Differences between brain age and chronological age (“brain age gap”) and between schizophrenia score and healthy reference score (“schizophrenia gap”) were calculated.
• Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample.

Thirty-eight participants were introduced to four resources of their choosing by peer workers over a fourweek period. The helpfulness of resources was evaluated using mixed methods, including a quasi-experimental analysis of change in hope, an anonymous survey and in-depth interviews