Clinical Antipsychotic Trials Of Intervention Effectiveness Research Articles

Sex Differences Between Female and Male Individuals in Antipsychotic Efficacy and Adverse Effects in the Treatment of Schizophrenia.

Antipsychotics are core treatments for people living with psychotic disorders. Understanding individualised factors that influence both efficacy and adverse responses will improve outcomes. The objective of this study was to examine sex differences in antipsychotic-related efficacy and tolerability. This was a secondary analysis of data from phase 1 and 1a of Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE); participants with schizophrenia were randomly assigned to double-blinded treatment with oral olanzapine, quetiapine, risperidone, ziprasidone or perphenazine. Measures included Positive and Negative Syndrome Scale (PANSS), Clinical Global Impressions (CGI) scale and Calgary Depression Rating Scale, as well as self-reported side effects, medication compliance, dosage, weight measurements and various blood parameters. There were 1460 participants including 380 female and 1080 male individuals. Very few differences existed between male and female participants in response, adverse reactions, compliance or antipsychotic dosage. However, significantly more female participants than male participants reported constipation (28% vs 16%), dry mouth (50% vs 38%), gynecomastia/galactorrhea (11% vs 3%), incontinence/nocturia (16% vs 8%) and self reportedweight gain (37% vs 24%) [all p < 0.001]. Within the risperidone treatment group, there was a significantly greater increase in prolactin levels (p < 0.001) among female participants (n = 61) than male participants (n = 159). No overall differences in clinician-rated measures, weight gain or otherlaboratory indicators were found. While overall sex differences were limited across efficacy and tolerability for antipsychotic treatment, there were some specific findings with risperidone. Further examination of sex differences within antipsychotic trials will be important to improve efficacy and reduce adverse responses across as well as individualising care for people with schizophrenia.

Calibrated meta-analysis to estimate the efficacy of mental health treatments in target populations: an application to paliperidone trials for treatment of schizophrenia

BackgroundsMeta-analyses can be a powerful tool but need to calibrate potential unrepresentativeness of the included trials to a target population. Estimating target population average treatment effects (TATE) in meta-analyses is important to understand how treatments perform in well-defined target populations. This study estimated TATE of paliperidone palmitate in patients with schizophrenia using meta-analysis with individual patient trial data and target population data.MethodsWe conducted a meta-analysis with data from four randomized clinical trials and target population data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Efficacy was measured using the Positive and Negative Syndrome Scale (PANSS). Weights to equate the trial participants and target population were calculated by comparing baseline characteristics between the trials and CATIE. A calibrated weighted meta-analysis with random effects was performed to estimate the TATE of paliperidone compared to placebo.ResultsA total of 1,738 patients were included in the meta-analysis along with 1,458 patients in CATIE. After weighting, the covariate distributions of the trial participants and target population were similar. Compared to placebo, paliperidone palmitate was associated with a significant reduction of the PANSS total score under both unweighted (mean difference 9.07 [4.43, 13.71]) and calibrated weighted (mean difference 6.15 [2.22, 10.08]) meta-analysis.ConclusionsThe effect of paliperidone palmitate compared with placebo is slightly smaller in the target population than that estimated directly from the unweighted meta-analysis. Representativeness of samples of trials included in a meta-analysis to a target population should be assessed and incorporated properly to obtain the most reliable evidence of treatment effects in target populations.

Impaired insight in schizophrenia: impact on patient-reported and physician-reported outcome measures in a randomized controlled trial

BackgroundImpaired insight poses a challenge in the treatment of patients with schizophrenia because of its potential to jeopardize therapeutic engagement and medication adherence. This study explored how insight impairment, graded from none to extreme, is related to patient-reported mental health status, depression, and neurocognition in schizophrenia.MethodsIn a post hoc analysis of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study (NCT00014001), insight was measured using the Positive and Negative Syndrome Scale (PANSS) Item G12 (lack of insight). Additional assessments for this analysis included the 12-Item Short-Form Health Survey (SF-12) Mental Component Summary (MCS), physician- and patient-reported Clinical Global Impression–Severity (CGI-S), MATRICS Consensus Cognitive Battery, and Calgary Depression Scale for Schizophrenia. Relationships between patient-reported outcomes and PANSS total and Item G12 ratings were evaluated.ResultsAmong 1431 CATIE study participants in this analysis, increasingly impaired insight at baseline was significantly associated with better patient-reported quality of life (QoL), lower baseline depression, and greater divergence between physician- and patient-reported illness severity. Patients with more severely impaired insight reported milder illness compared with physician reports, particularly those with moderate-severe to extreme impairment (PANSS Item G12 rating ≥ 5), approximately 10% (138/1431) of CATIE participants. For the 90% of patients with PANSS Item G12 ratings < 5, patient-reported QoL decreased with increasing symptoms. SF-12 MCS scores were linearly related to baseline PANSS total score only in patients with PANSS total score < 90 (moderately ill or better), and better symptom scores were associated with higher QoL. No significant relationship between insight and neurocognition was observed.ConclusionsIn the small subgroup (10%) of CATIE study patients with schizophrenia and PANSS Item G12 ratings ≥5, moderate-severe–severe/extreme insight impairment was associated with significantly more positive perception of QoL and illness severity by the patient versus the treating physician. This was not observed in the remaining 90% of patients with normal to moderately impaired insight, suggesting that poor insight as a threat to the validity of self-report is uncommon.

Prediction of quality of life in schizophrenia using machine learning models on data from Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial

While research focus remains mainly on psychotic symptoms, it is questionable whether we are placing enough emphasis on improving the quality of life (QoL) of schizophrenia patients. To date, the predictive power of QoL remained limited. Therefore, this study aimed to accurately predict the QoL within schizophrenia using supervised learning methods. The authors report findings from participants of a large randomized, double-blind clinical trial for schizophrenia treatment. Potential predictors of QoL included all available and non-redundant variables from the dataset. By optimizing parameters, three linear LASSO regressions were calculated (N = 697, 692, and 786), including 44, 47, and 41 variables, with adjusted R-squares ranging from 0.31 to 0.36. Best predictors included social and emotion-related symptoms, neurocognition (processing speed), education, female gender, treatment attitudes, and mental, emotional, and physical health. These results demonstrate that machine learning is an excellent predictive tool to process clinical data. It appears that the patient’s perception of their treatment has an important impact on patients’ QoL and that interventions should consider this aspect.Trial registration: ClinicalTrials.gov Identifier: NCT00014001.

Insomnia and inflammation in phase 1 of the clinical antipsychotic trials of intervention effectiveness study

Insomnia and inflammation are both common in schizophrenia. In the general population, insomnia is associated with inflammation. In n=519 subjects from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial, terminal insomnia was investigated as an indicator of inflammation using non-parametric ANCOVA. After controlling for potential confounders, insomnia was significantly associated with higher blood IL-6 (F=4.12, p=0.007) and leptin (F=9.67, p<0.001) with large effect sizes (d=1.03 and d=0.79, respectively). Findings suggest that the assessment of insomnia is relevant to studies of inflammation in schizophrenia, and germane to trials of adjunctive hypnotics and anti-inflammatory agents in these patients.

Large-scale evaluation of the Positive and Negative Syndrome Scale (PANSS) symptom architecture in schizophrenia.

Although the Positive and Negative Syndrome Scale (PANSS) is widely utilized in schizophrenia research, variability in specific item loading exist, hindering reproducibility and generalizability of findings across schizophrenia samples. We aim to establish a common PANSS factor structure from a large multi-ethnic sample and validate it against a meta-analysis of existing PANSS models. Schizophrenia participants (N = 3511) included in the current study were part of the Singapore Translational and Clinical Research Program (STCRP) and the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE). Exploratory Factor Analysis (EFA) was conducted to identify the factor structure of PANSS and validated with a meta-analysis (N = 16,171) of existing PANSS models. Temporal stability of the PANSS model and generalizability to individuals at ultra-high risk (UHR) of psychosis were evaluated. A five-factor solution best fit the PANSS data. These were the i) Positive, ii) Negative, iii) Cognitive/disorganization, iv) Depression/anxiety and v) Hostility factors. Convergence of PANSS symptom architecture between EFA model and meta-analysis was observed. Modest longitudinal reliability was observed. The schizophrenia derived PANSS factor model fit the UHR population, but not vice versa. We found that two other domains, Social Amotivation (SA) and Diminished Expression (DE), were nested within the negative symptoms factor. Here, we report one of the largest transethnic factorial structures of PANSS symptom domains (N = 19,682). Evidence reported here serves as crucial consolidation of a common PANSS structure that could aid in furthering our understanding of schizophrenia.

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof‐of‐concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either “improvement,” defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or “no improvement,” defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree‐based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof‐of‐concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.

Prediction of quality of life in schizophrenia using machine learning models on data from clinical antipsychotic trials of intervention effectiveness (CATIE) schizophrenia trial

IntroductionSchizophrenia is a chronic and severe mental disorder. While research focus remains mainly on negative outcomes, it is questionable whether we are placing enough emphasis on improving their sense of well-being and functioning. This could be accessed through the study of the quality of life (QoL). To date, QoL prediction models mainly focused on neurocognition and psychotic symptoms, but their predictive power remained limited.ObjectivesThe aim is to accurately predict the QoL within schizophrenia using unsupervised learning methods.MethodsWe computed variables from 952 patients from the CATIE study, a randomized, double-blind clinical trial for schizophrenia treatment. QoL was measured using the Heinrichs-Carpenter Quality of Life Scale and potential predictors included almost all available variables: symptoms, neurocognition, medication adherence, insight, adverse effects, etc. By optimizing parameters to reach optimal models, three linear regressions were calculated: (1) baseline predictors of 12-month QoL, (2) 6-month predictors of 12-month QoL, and (3) baseline predictors of 6-month QoL. Adjustments were made to ensure that included variables were not collinear nor redundant with QoL.ResultsCalculated models had adjusted R-squared of 0.918, 0.922 and 0.913, respectively. Best predictors were medication side effects, sociodemographic and neurocognitive variables. Low psychotic and depressive symptoms were also included, as well as lab values suggesting the absence of problems with chloremia and calcemia.ConclusionsCalculated predictive models explain almost all subsequent QoL. It appears that physical health variables, generally omitted from mental health-related studies, have an important impact on patients’ QoL. Therefore, interventions should also consider these aspects.DisclosureNo significant relationships.

Bifactor model of cognition in schizophrenia: Evidence for general and specific abilities

BackgroundDespite extensive study of cognition in schizophrenia, it remains unclear as to whether cognitive deficits and their latent structure are best characterized as reflecting a generalized deficit, specific deficits, or some combination of general and specific constructs. MethodTo clarify latent structure of cognitive abilities, confirmatory factor analysis was used to examine the latent structure of cognitive data collected for the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for Schizophrenia study. Baseline assessment data (n = 813) were randomly divided into calibration (n = 413) and cross-validation samples (n = 400). To examine whether generalized or specific deficit models provided better explanation of the data, we estimated first-order, hierarchical, and bifactor models. ResultsA bifactor model with seven specific factors and one general factor provided the best fit to the data for both the calibration and cross-validation samples. ConclusionsThese findings lend support for a replicable bifactor model of cognition in schizophrenia, characterized by both a general cognitive factor and specific domains. This suggests that cognitive deficits in schizophrenia might be best understood by separate general and specific contributions.

Longitudinal study of inflammatory markers and psychopathology in schizophrenia

ObjectiveSchizophrenia is associated with abnormal levels of blood inflammatory markers, which may be correlated with levels of psychopathology. Few previous studies have explored whether baseline inflammatory marker levels predict longitudinal changes in psychopathology. In the present study, we explored this association in a cohort of patients with schizophrenia. MethodWe investigated inflammatory markers and psychopathology after 3, 6, and 12 months of antipsychotic treatment for subjects with baseline and follow-up data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial. Linear regression models, controlling for multiple potential confounding factors, were used to investigate these associations. ResultsThere was a significant decrease in monocyte, ICAM, and adiponectin levels between baseline and 12 months. Higher baseline blood interleukin-6 (IL-6) predicted greater reduction in PANSS total and general subscale scores at 3 and 6 months, and PANSS negative subscale scores at 3 months (β = −0.10 to −0.16, p < 0.05 for each). Higher baseline blood leptin levels predicted greater reduction in PANSS total, negative and general subscale scores at 6 months (β = −0.09 to −0.11, p < 0.05 for each). In post-hoc analyses, associations between baseline IL-6 levels and symptom reduction were strongest in patients treated with either ziprasidone or quetiapine. Changes in blood inflammatory markers were generally not associated with changes in psychopathology. ConclusionsOur findings provide additional support that measuring blood inflammatory markers may be relevant to the clinical care of patients with schizophrenia. Specifically, these markers may help guide selection of antipsychotic treatment towards more personalized medicine approaches for patients with schizophrenia.

T221. CAUSAL RELATIONSHIPS BETWEEN EMPATHY, MOTIVATION AND FUNCTIONAL OUTCOMES IN EARLY SCHIZOPHRENIA IDENTIFIED WITH CAUSAL DISCOVERY MODELING

BackgroundSchizophrenia is characterized by devastating impairments in occupational and social functioning. These impairments result in poor quality of life, have significant societal burden, and are resistant to current treatments. Although a number of predictors of poor functional outcomes have been identified, the causal relationships between malleable illness variables and functional status must be discovered to establish the most promising candidate treatment targets. The aim of this research is to employ a data-driven causal network approach in the Recovery After an Initial Schizophrenia Episode (RAISE) trial to identify variables that have causal effects on occupational and social functioning. Our second aim was to validate our results in an independent sample drawn from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study.MethodsMeasures of neurocognition, motivation, empathy, psychiatric symptoms, duration of untreated psychosis, and social and occupational functioning were obtained for 279 participants with early schizophrenia enrolled in RAISE. Data were analyzed using a machine learning causal discovery algorithm, Greedy Fast Causal Inference, which infers causal relationships among the variables while identifying potential influences of latent variables. Causal effect sizes (ES) were estimated by fitting a linear structural equation model to the causal graph. Graph stability was evaluated by running GFCI on 1000 jackknifed and 1000 bootstrapped datasets. Results were validated using a similar set of variables in 187 participants in the CATIE study who were within five years of initiating antipsychotic treatment.ResultsBaseline empathy caused baseline motivation (ES = 0.77), which in turn caused both baseline social (ES = 1.5) and occupational (ES = 0.96) functioning. Baseline social and occupational functioning then caused six-month social and occupational functioning, respectively. Additionally, at the six-month time point, social functioning caused motivation (ES = 0.21), which in turn caused occupational functioning (ES = .92). Causal relationships between motivation and social functioning were cyclical through time. The causal structure in the CATIE dataset had some less determinate edge orientations, but otherwise confirmed the findings from RAISE. The jackknife and bootstrap causal graphs showed high concordance with all graph features in the social and occupational functioning subgraphs.DiscussionMotivation and empathy at baseline have causal effects on both occupational and social functioning six months after entering treatment in early schizophrenia. In addition, an improvement in social functioning over six-months may drive improvements in occupational functioning. This indicates that future research should test the efficacy of interventions targeting motivation and empathy for promoting functional recovery in early schizophrenia as soon as individuals enter treatment.

Robust kernel association testing (RobKAT).

Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other robust procedures such as the quantile regression kernel machine (QRKM) restrict the choice of the loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with a flexible choice of the loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through a simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power with SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust testing method to data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial to detect associations between selected genes including the major histocompatibility complex (MHC) region on chromosome six and neurotropic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

P.382 Circadian rhythm of hallucinatory experiences in patients with schizophrenia

The metabolic syndrome is highly prevalent in patients with schizophrenia. We previously found that blood C-reactive protein (CRP), interleukin-6 (IL-6), and leptin levels were predictors of current metabolic syndrome in schizophrenia. In the present study, we investigated whether baseline levels of total and differential white blood cell (WBC) counts, inflammatory markers, and adipokines predicted incident metabolic syndrome in schizophrenia.For subjects from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial who did not have metabolic syndrome at baseline (n = 726), WBC counts, inflammatory markers, and adipokines were investigated as predictors of incident metabolic syndrome over 12 months of antipsychotic treatment. Cox proportional hazards regression models, controlling for multiple potential confounding factors, were used to investigate these associations.39% of subjects (n = 280) had incident metabolic syndrome over 12 months. After controlling for potential confounders, baseline blood IL-6 (HR = 1.12, 95% CI 1.01–1.24, p = 0.031) and leptin (HR = 1.12, 95% CI 1.01–1.24, p = 0.038) were significant predictors of incident metabolic syndrome, and there was a trend-level association with CRP (HR = 1.09, 95% CI 1.00–1.19, p = 0.059).Our findings provide additional evidence that measurement of inflammatory markers and adipokines are germane to the clinical care of patients with schizophrenia. Specifically, these markers may identify—prior to treatment—patients with schizophrenia at heightened risk for incident adverse cardiometabolic effects of antipsychotics. Given the tremendous burden of cardiovascular disease morbidity and mortality in schizophrenia, vigilant screening for and treatment of metabolic risk factors in this patient population are warranted.

Total and differential white blood cell counts, inflammatory markers, adipokines, and incident metabolic syndrome in phase 1 of the clinical antipsychotic trials of intervention effectiveness study

ObjectiveThe metabolic syndrome is highly prevalent in patients with schizophrenia. We previously found that blood C-reactive protein (CRP), interleukin-6 (IL-6), and leptin levels were predictors of current metabolic syndrome in schizophrenia. In the present study, we investigated whether baseline levels of total and differential white blood cell (WBC) counts, inflammatory markers, and adipokines predicted incident metabolic syndrome in schizophrenia. MethodFor subjects from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial who did not have metabolic syndrome at baseline (n = 726), WBC counts, inflammatory markers, and adipokines were investigated as predictors of incident metabolic syndrome over 12 months of antipsychotic treatment. Cox proportional hazards regression models, controlling for multiple potential confounding factors, were used to investigate these associations. Results39% of subjects (n = 280) had incident metabolic syndrome over 12 months. After controlling for potential confounders, baseline blood IL-6 (HR = 1.12, 95% CI 1.01–1.24, p = 0.031) and leptin (HR = 1.12, 95% CI 1.01–1.24, p = 0.038) were significant predictors of incident metabolic syndrome, and there was a trend-level association with CRP (HR = 1.09, 95% CI 1.00–1.19, p = 0.059). ConclusionsOur findings provide additional evidence that measurement of inflammatory markers and adipokines are germane to the clinical care of patients with schizophrenia. Specifically, these markers may identify—prior to treatment—patients with schizophrenia at heightened risk for incident adverse cardiometabolic effects of antipsychotics. Given the tremendous burden of cardiovascular disease morbidity and mortality in schizophrenia, vigilant screening for and treatment of metabolic risk factors in this patient population are warranted.

Insight and medication adherence in schizophrenia: An analysis of the CATIE trial

Adherence to antipsychotic medication is critical for the treatment of patients with schizophrenia. Impaired insight into illness is one of the principal drivers of medication nonadherence, which contributes to negative clinical outcomes. The aims of this study were to examine the relationships between impaired insight and (1) rates of antipsychotic medication nonadherence, and (2) time to medication nonadherence using data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Insight was assessed using the Positive and Negative Syndrome Scale (PANSS) item G12 (lack of judgment and insight). Patients were divided into 3 groups based on their degree of insight impairment, i.e. no impairment (PANSS G12 = 1), minimal impairment (PANSS G12 = 2–3), and moderate-to-severe insight impairment (PANSS G12 ≥ 4). Medication nonadherence was defined as taking less than 80% of monthly pill counts. Kaplan-Meier survival and Cox regression analyses were performed to examine differences in time to medication nonadherence between insight groups. There were significant differences between insight groups in the percentage of nonadherent patients at 6 months (χ2(2) = 8.80, p = 0.012) and 18 months (χ2(2) = 10.04, p = 0.007) after study initiation. Moderate-to-severe insight impairment was associated with earlier nonadherence compared to minimal (χ2 = 4.70, p = 0.030) or no impairment (χ2 = 11.92, p = 0.001). The association remained significant after adjustment for illness severity, substance use, attitudes toward medication, cognition, level of hostility, and depression. The results of this study indicate a strong link between impaired insight and antipsychotic medication nonadherence. Interventions to enhance insight early during treatment may help improve medication adherence, and in turn, long-term clinical and functional outcomes in patients with schizophrenia.This article is part of the issue entitled ‘Special Issue on Antipsychotics’.

Correlates of Future Violence in People Being Treated for Schizophrenia.

Violent behavior is infrequent among individuals with schizophrenia but is clinically important. The purpose of this study was to provide data on the correlates of violence, which may allow better risk assessment and care. A total of 1,435 individuals with schizophrenia who participated in the National Institute of Mental Health's Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study and were followed for 18 months were examined. The dependent variables were self-reported injurious and noninjurious violence during follow-up. The independent variables, assessed at study entry, comprised participants' recent injurious and noninjurious violence, demographic and background variables, childhood risk factors, clinical condition, current circumstances, and recent contact with hospitals and prisons. Proportional hazards models of time to first injurious violence were used to generate bivariable and multivariable hazard ratios for all participants and, separately, for participants with no injurious violence at study entry. Seventy-seven participants (5.4%) reported engaging in injurious violence during follow-up, and 119 (8.3%) reported engaging in exclusively noninjurious violence. In the multivariable analysis, baseline injurious violence (hazard ratio=4.02), recent violent victimization (hazard ratio=3.52), severity of drug use (hazard ratio=2.93), baseline noninjurious violence (hazard ratio=2.72), childhood sexual abuse (hazard ratio=1.85), and medication nonadherence (hazard ratio=1.39) were associated with future injurious violence. For participants with no history of injurious violence at study entry, baseline noninjurious violence was the strongest predictor (hazard ratio=3.02). Recent violent victimization was no longer a significant correlate. The remaining correlates and the strength of their association with future injurious violence were similar to those for all participants. This is the first longitudinal multivariable analysis of predictors of injurious violence in a large cohort of patients with schizophrenia followed over 18 months. The results revealed simultaneous strong effects of baseline injurious violence and recent violent victimization on future injurious violent behavior. Among clinical variables, poor medication adherence, but not baseline symptoms of psychosis or depression, significantly predicted injurious violence. Treatment strategies to reduce risk should emphasize medication adherence.

Correlation Between Violence and Antipsychotic Dosage in Schizophrenia: A Secondary Analysis of The Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) Dataset.

Violent behavior is more common in individuals with schizophrenia, compared to the general population. Studies suggest higher psychotic symptoms are predictive of greater violent behavior. On the other hand, violent behaviors are reduced with antipsychotic treatment. However, the relationship between antipsychotic dosage and violence has not been studied to date. Thus, we aimed to determine if there exists an association between antipsychotic dosage and violence scores and whether the maximum violence would be predictive of the final antipsychotic dosage. We hypothesized that the violence scores at the final assessment in the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) would be correlated with the corresponding drug dosage and the maximum violence severity score would be predictive of the final dosage. Antipsychotic dosage at the end of the trial was converted into defined daily dosage and chlorpromazine equivalents (CPZe). Final and maximum violence sum scores were analyzed from the final violence assessment interviews. Spearman's rank-order correlation and linear regression analyses were used to analyze the relationship between the violence scores and standardized antipsychotic dosages. The analysis was on 952 individuals with schizophrenia. There was a significant association between maximum violence severity score and the final CPZe dosage (p=0.049). Exploratory analysis of age and ethnicity revealed younger non-white individuals to be at a higher risk of engaging in violent activities. Violence in schizophrenia is associated with poor illness course. Further studies focusing on violence in younger non-white individuals are warranted.

SU110 - ROLE OF 108 SCHIZOPHRENIA-ASSOCIATED LOCI IN MODULATING PSYCHOPATOLOGICAL DIMENSIONS IN SCHIZOPHRENIA AND BIPOLAR DISORDER

Background Schizophrenia is a highly heritable disorder and the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC) identified 108 loci with genome-wide association with broad schizophrenia diagnosis. Schizophrenia is a clinically complex entity and the role of these 108 loci in modulating specific clinical features of the disease is still unknown. This study investigated which symptom dimensions may be affected by these loci in schizophrenia and bipolar disorder. Methods Positive, negative and depressive symptoms, suicidal ideation, cognition, violent behaviors, quality of life and early onset were investigated in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) samples. Only white subjects were included to avoid possible confounding from ancestry stratification. Imputation of genotypes was carried out after standard quality control using the Haplotype Reference Consortium panel as reference. Individual loci were investigated using linear or logistic regression, then genes within 50 Kbp from polymorphisms with p Results 89 polymorphisms among the 108 of interest were available in both samples, 479 and 810 white subjects were included from CATIE and STEP-BD, respectively. rs75059851 (IGSF9B gene) was associated with negative symptoms in CATIE (p=0.00048); no other variants survived after multiple-testing correction. rs1023500 (CENPM gene) showed a trend of association with early onset with consistent direction in both samples (p=0.033 in STEP-BD and p=0.073 in CATIE). GeneMania analysis showed that negative symptoms and suicidal ideation had a relevant enrichment of Gene Ontology (GO) terms related to acetylcholine neurotransmission, monoamines and ion channel activity while pyridoxal phosphate binding and fucose metabolism showed enrichment for cognition. GO terms referred to processes involved in extracellular matrix structure were found enriched for early onset. PRS showed nominal associations with violent behaviors (p=0.034, Nagelkerke's R2=0.014) and depressive symptoms (p=0.04, R2=0.009). Discussion This study provided preliminary evidence that a schizophrenia-associated variant in the IGSF9B (immunoglobulin superfamily member 9B) gene may modulate negative symptoms. IGSF9B is involved in GABAergic signaling and it may mediate negative symptoms by affecting depression and cognition. A polymorphism in the CENPM (centromere protein M) gene showed an interesting trend of association with early onset. CENPM encodes for an inner protein of the kinetochore, the multi-protein complex that binds spindle microtubules to regulate chromosome segregation during cell division. Multi-locus models may provide interesting insights about the biological mechanisms that medicate specific symptom domains and the genetic overlap between schizophrenia and bipolar disorder.

SU86 - VALIDATION STUDY IN TWO GENOME-WIDE SIGNIFICANT RISK VARIANTS FOR ANTIPSYCHOTIC INDUCED WEIGHT GAIN

Background Antipsychotic drugs are the primary intervention in the treatment of psychotic disorders, however, the use of antipsychotics is often accompanied by weight gain and metabolic syndrome. A recent Genome-Wide Association Study (GWAS) investigating a Han Chinese sample suggested a possible role of rs1097714 and rs10977154 variants of the protein tyrosine phosphatase, receptor type D (PTPRD) gene in antipsychotic induced weight gain (AIWG). Additionally, two upstream variants (rs1886243 and rs6915627) of T-complex 11 (TCP11) were among the 20 most significant SNPs in the original study. The purpose of this study was to replicate the association of the top 20 variants from the aforementioned GWAS in two independent samples to evaluate whether these associations are also present in patients of European and African American ancestry. Methods The following two independent samples were used: 1) schizophrenia or schizoaffective disorder patients (based on DSM III and DSM IV criteria) primary undergoing first exposure to atypical antipsychotic drugs and 2) patients in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) sample. Samples 1 and 2 were genotyped using Illumina Omni 2.5 Chip, Affymetrix 500 K “A” chipset, and the Perlegen custom 164 K chip, respectively. In the main analyses in two replication samples. Analysis of Covariance (ANCOVA) was used to investigate the percentage of change in weight (Sample 1) or Body Mass Index (BMI) (Sample 2) from baseline to last observation carried forward (LOCF), where genotypes were coded as factors. Models in both samples were corrected for study duration, and additionally for age in Sample 1, and medications used in Sample 2. Results In Sample 1 (122 men (65.9%), mean±SD age; 36±11 years, baseline body weight; 79.72±15.9 kg % of weight change (LOCF); 5.08±7.11, weight gainer (>7%); n=57 (31.0%), medication: clozapine, olanzapine, and risperidone were mainly used), none of 14 genetic variants showed a significant association with % of weight change in the European population. In African Americans, the nominal association was observed (F2,50=4.814; p=0.034) between rs3853114 variant of dynein axonemal heavy chain 5 (DNAH5). In Sample 2 (118 men (78.1%), mean±SD age; 40±12 years, baseline BMI; 28.63±5.43, % of BMI change (LOCF); 2.95±7.79, weight gainer (>7%); n=31 (20.5%), medication: olanzapine, risperidone, and quetiapine were used), nominal associations with two TCP11 variants rs1886243 (F2,148=3.14; p=0.046) and AIWG were observed. None of the variants described above survived correction for multiple testing. Discussion To the best of our knowledge, this is the first genetic association study evaluating PTPRD and TCP11 in European and African American patients. However, the associations between PTPRD variants and AIWG were not replicated; whereas the effects of TCP11 remained nominally significant. More studies across different ancestries are needed to examine if PTPRD and TCP11 can be considered susceptibility genes for AIWG.

SU97 - GENETICALLY INFORMED SUBTYPING OF SCHIZOPHRENIA

Background Schizophrenia is a mental disorder with heterogeneous clinical presentation and genetic predisposition. In DSM IV, schizophrenia was classified into five subtypes based on clinical features. Due to its limited application in clinical practice and the lack of biological underpinning, these subtypes were removed in DSM V. In recent years, many large scale genome-wide association studies have been conducted and numerous genetic factors have been identified for various diseases and traits, including schizophrenia. This provides an opportunity to explore subtyping of complex diseases into genetically homogeneous subgroups so that more effective treatments can be developed. Methods We calculated polygenic scores for the cases of the Molecular Genetics of Schizophrenia (MGS) study (N=2,681) for a variety of diseases and traits, and evaluated their potential as classifiers to subtype schizophrenia using k-means clustering algorithm. We verified the cluster structure in the Sweden Study of Schizophrenia (SSS) study (N=2,895). We further validated the clusters with functional data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study (N=741). Results Using k-means algorithm, we found that the polygenic scores of neuroticism and blood creatinine level could reliably classify MGS patients into two clusters (subtypes) and the cluster structures had been verified in the patients of SSCC and CATIE studies. Furthermore, using the rich data of neurocognitive and lab tests in the CATIE study, we found that the cluster memberships were associated neurocognitive functions (Wide Range Achievement Test 3rd edition, Reading score: P=0.0015) and lab tests (creatine phosphokinase test: P=7.2×10-7; HDL: P=0.0003; triglycerides: P=0.0015; white blood cells: P=0.0022; Neutrophil cells: 0.0064). Furthermore, by the end of phase 1 trial, the minor subtype (84 out of 650 subjects) had improved positive symptoms (P=0.0479) comparing to the baseline at enrollment. The negative symptoms showed a trend (P=0.0843). Discussion The results indicate that genetically informed subtyping has functional and biological underpinning. More importantly, our subtyping may be linked to treatment outcomes when measured by the symptom presentation. While our results need to be verified, the implication to clinical practice is clear and significant.