Associations of premorbid adjustment with type and timing of childhood trauma in first-episode schizophrenia spectrum disorders.

BACKGROUND: Childhood trauma may contribute to poorer premorbid social and academic adjustment which may be a risk factor for schizophrenia. AIM: We explored the relationship between premorbid adjustment and childhood trauma, timing of childhood trauma's moderating role as well as the association of clinical and treatment-related confounders with premorbid adjustment. SETTING: We conducted a secondary analysis in 111 patients with first-episode schizophrenia (FES) disorders that formed part of two parent studies, EONKCS study (n =73) and the Shared Roots study (n =38). METHODS: Type of childhood trauma was assessed with the Childhood Trauma Questionnaire, short-form and premorbid adjustment using the Premorbid Adjustment Scale. Timing of childhood trauma was assessed using the Life Events Checklist and life events timeline. Linear regression analyses were used to assess the moderating effect of timing of childhood trauma. Clinical and treatment-related confounders were entered into sequential hierarchical regression models to identify independent predictors of premorbid adjustment across key life stages. RESULTS: Childhood physical neglect was associated with poorer premorbid academic functioning during childhood and early adolescence, and poorer premorbid social functioning during early and late adolescence. By hierarchical regression modelling (r 2 = 0.13), higher physical neglect subscale scores (p = 0.011) independently predicted poorer premorbid social adjustment during early adolescence. Timing of childhood trauma did not moderate the relationship between childhood trauma and premorbid functioning. CONCLUSION: In patients with FES, childhood physical neglect may contribute to poorer premorbid social functioning during early adolescence. This may provide us with an opportunity to identify and treat at-risk individuals earlier.

Relationship between changes in metabolic syndrome constituent components over 12 months of treatment and cognitive performance in first-episode schizophrenia.

Few studies have investigated the longitudinal effects of treatment-emergent metabolic syndrome changes on cognitive performance in first-episode psychosis. The aim of the present study was to determine the associations between changes in metabolic syndrome constituent component over 12 months of treatment and end-point cognitive performance in schizophrenia spectrum disorders. This single site-cohort study included 72 minimally treated or antipsychotic-naïve first-episode patients. Cognitive performance was evaluated using the MATRICS Consensus Cognitive Battery (MCCB). Our primary objective of interest was the relationship between metabolic syndrome constituent component changes over 12 months of treatment and end-point cognitive performance. Secondary objectives included investigating whether this relationship was affected by age, sex, antipsychotic dose, treatment duration and substance use. Weight gain predicted better overall cognition (p = 0.02) at end-point, adjusting for age, sex, substance use, baseline cognitive score and BMI, modal antipsychotic dose and treatment duration. Weight loss (p = 0.04) and substance use (p = 0.01) were both associated with poorer working memory performance at end-point. Low baseline BMI showed differential effects on end-point working memory performance in substance users (unfavorable) compared to non-users (favorable) (p < 0.05). In conclusion, weight gain over the course of antipsychotic treatment is associated with better overall cognitive performance and the working memory domain in first-episode schizophrenia spectrum disorder patients. In contrast, low baseline BMI may represent an unfavorable marker in substance users, who demonstrated weight loss compared to non-users.

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.

BACKGROUND: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential of MRI in establishing a psychiatric diagnosis. Machine learning has previously been predominantly tested on gray-matter structural or functional MRI data. In this paper we used a machine learning classifier to differentiate patients with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls using diffusion tensor imaging. METHODS: We applied linear support-vector machine (SVM) and traditional tract based spatial statistics between group analyses to brain fractional anisotropy (FA) data from 77 FES and 77 age and sex matched healthy controls. We also evaluated the effects of medication and symptoms on the SVM classification. RESULTS: The SVM distinguished between patients and controls with significant accuracy of 62.34% (p = 0.005). Participants with FES showed widespread FA reductions relative to controls in a large cluster (N = 56,647 voxels, corrected p = 0.002). The white matter regions, which contributed to the correct identification of participants with FES, overlapped with the regions, which showed lower FA in patients relative to controls. There was no association between the classification performance and medication or symptoms. CONCLUSIONS: Our results provide a proof of concept that SVM might help differentiate FES patients early in the course of illness from healthy controls using white-matter fractional anisotropy. As there was no effect of medications or symptoms, the SVM classification seemed to be based on trait rather than state markers and appeared to capture the lower FA in FES participants relative to controls.

Gender role endorsement in first-episode schizophrenia spectrum disorders.

Sex (a biological distinction) and gender (a social construct) are inter-related, but semi-independent measures. The aim of our research was to compare gender role endorsement between first-episode schizophrenia spectrum disorder patients (n=77) and matched controls (n=64). The Bem Sex Role Inventory (BSRI) was used to assess masculinity and femininity scores as separate linear measures. This well-known research instrument also allowed us to examine gender as a categorical measure based on sex-specific cut-off scores calculated for controls as our normative reference sample using a median-split technique. First, we found that both masculinity and femininity scores differed between patients and controls. The distribution of gender as a categorical measure also differed between the two groups. Post-hoc testing with correction for multiple comparisons identified masculinity scores in particular as being lower in both male and female patients compared to controls of the corresponding sex. In conclusion, lower masculinity scores reported for chronic schizophrenia also affects first-episode patients with minimal prior treatment exposure irrespective of their biological sex. Future studies would do well to examine the associations of sex and gender with clinical and treatment outcomes from the perspective of the neurodevelopmental model of schizophrenia as a proposed "disorder of the self".