Towards indicated prevention of psychosis: using probabilistic assessments of transition risk in psychosis prodrome

Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort

BACKGROUND

Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging.

METHODS

In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231).

RESULTS

The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%).

CONCLUSIONS

In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.

Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training

BACKGROUND AND HYPOTHESIS

In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions.

STUDY DESIGN

We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task-the fish task-with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual's behavior, could predict treatment response to Metacognitive Training using machine learning.

STUDY RESULTS

We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level.

CONCLUSIONS

Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders.

Anomalous self-experiences (ASE) in relation to clinical high risk for psychosis (CHRP), childhood trauma and general psychopathology among adolescent and young adult help seekers

BACKGROUND

Anomalous self-experiences (ASE) are suggested as a phenotypic core feature of schizophrenia spectrum disorders and present in at risk samples as well. In our study, we investigated the relation between ASE and clinical high risk state for psychosis (CHRP) against the background of further influencing factors like childhood trauma and general psychopathology.

METHODS

126 help-seeking adolescents were included. CHR-P patients were identified using the Structured Interview for Psychosis-Risk Syndromes (SIPS). ASE were assessed with the Inventory of Psychotic-like Anomalous Self-Experiences (IPASE). Childhood trauma, depression and anxiety were assessed with well-established questionnaires (CTQ; PHQ-9; GAD-7).

RESULTS

CHR-P subgroup (n = 50, 39.7%) show significantly higher scores in IPASE total (t (81.07) = -5.150, p = .000) and CTQ total (t (85.95) = -2.75, p = .007) in comparison with the non CHR-P subgroup. Logistic regression analysis confirmed that IPASE total could predict CHR-P status (OR 1.03, 95% CI 1.01-1.04, p = .000). Furthermore, CTQ total and IPASE total show moderate to strong positive correlation (r = 0.44, p < .001) as well as CTQ total with both IPASE subdomains Cognition (r = 0.404, p < .001) and Self- Awareness (r = 0.443, p < .001).

CONCLUSION

The CHR-P subgroup shows significantly more ASE than the non CHR-P subgroup. Further, ASE predicted CHR-P status. Our results indicated that ASE could play a considerable role in the identification of high risk for developing schizophrenia spectrum disorder and could complement CHR-P testing. Importantly, it seems that ASE may be related to exposure to childhood trauma.

Prediction of clinical outcomes beyond psychosis in the ultra-high risk for psychosis population

AIM

Several prediction models have been introduced to identify young people at greatest risk of transitioning to psychosis. To date, none has examined the possibility of developing a clinical prediction model of outcomes other than transition. The aims of this study were to examine the association between baseline clinical predictors and outcomes including, but not limited to, transition to psychosis in young people at risk for psychosis, and to develop a prediction model for these outcomes.

METHODS

Several evidence-based variables previously associated with transition to psychosis and some important clinical comorbidities experienced by ultra-high risk (UHR) individuals were identified in 202 UHR individuals. Secondary analysis of the Neurapro clinical trial were conducted to investigate the associations between these variables and favourable (remission and recovery) or unfavourable (transition to psychosis, no remission, any recurrence and relapse) clinical outcomes. Logistic regression, best subset selection, Akaike Information Criterion and receiver operating characteristic curves were used to seek the best prediction model for clinical outcomes from all combinations of possible predictors.

RESULTS

When considered individually, only higher general psychopathology levels (P = .023) was associated with the unfavourable outcomes. Prediction models suggest that general psychopathology and functioning are predictive of unfavourable outcomes.

CONCLUSION

The predictive performance of the resulting models was modest and further research is needed. Nonetheless, when designing early intervention centres aiming to support individuals in the early phases of a mental disorder, the proper assessment of general psychopathology and functioning should be considered in order to inform interventions and length of care provided.

Multimodal prognosis of negative symptom severity in individuals at increased risk of developing psychosis

Negative symptoms occur frequently in individuals at clinical high risk (CHR) for psychosis and contribute to functional impairments. The aim of this study was to predict negative symptom severity in CHR after 9 months. Predictive models either included baseline negative symptoms measured with the Structured Interview for Psychosis-Risk Syndromes (SIPS-N), whole-brain gyrification, or both to forecast negative symptoms of at least moderate severity in 94 CHR. We also conducted sequential risk stratification to stratify CHR into different risk groups based on the SIPS-N and gyrification model. Additionally, we assessed the models' ability to predict functional outcomes in CHR and their transdiagnostic generalizability to predict negative symptoms in 96 patients with recent-onset psychosis (ROP) and 97 patients with recent-onset depression (ROD). Baseline SIPS-N and gyrification predicted moderate/severe negative symptoms with significant balanced accuracies of 68 and 62%, while the combined model achieved 73% accuracy. Sequential risk stratification stratified CHR into a high (83%), medium (40-64%), and low (19%) risk group regarding their risk of having moderate/severe negative symptoms at 9 months follow-up. The baseline SIPS-N model was also able to predict social (61%), but not role functioning (59%) at above-chance accuracies, whereas the gyrification model achieved significant accuracies in predicting both social (76%) and role (74%) functioning in CHR. Finally, only the baseline SIPS-N model showed transdiagnostic generalization to ROP (63%). This study delivers a multimodal prognostic model to identify those CHR with a clinically relevant negative symptom severity and functional impairments, potentially requiring further therapeutic consideration.

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

IMPORTANCE

Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear.

OBJECTIVES

To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system.

DESIGN, SETTING, AND PARTICIPANTS

This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020.

MAIN OUTCOMES AND MEASURES

Accuracy and generalizability of prognostic systems.

RESULTS

A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results.

CONCLUSIONS AND RELEVANCE

These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

The Prodrome of Psychotic Disorders: Identification, Prediction, and Preventive Treatment

Twenty-five years ago "at risk" for psychosis criteria were introduced to the field. Prediction studies have identified a range of risk factors involved in transition from "at risk" status to first episode psychotic illness, with recent interest in dynamic and multimodal prediction models. Treatment studies have indicated that risk of transition to psychotic disorder can at least be delayed in this clinical population. Although the strongest evidence to date is for cognitive behavioral therapy, the optimal type and sequence of treatment remains an active area of research.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Clinical trajectories in the ultra-high risk for psychosis population

BACKGROUND

Traditionally, research in the ultra-high risk (UHR) for psychosis population has focused on the treatment of existing symptomatology and prevention of transition to psychosis. Recently, there has been an increase in focus on outcomes in individuals who do not transition to psychosis. However, there is a lack of standardised definitions of remission, recovery, recurrence and relapse in UHR, resulting in the inability to generalise and replicate outcomes.

METHOD

The aims of the current study were to develop definitions for remission, recovery, recurrence and relapse, and apply them to a UHR cohort allowing the identification of longitudinal clinical trajectories. Further stratification in broader categories of favourable and unfavourable outcomes was applied. The predictive value of various baseline factors on specific clinical trajectories was also assessed.

RESULTS

17 different trajectories were identified in a cohort of 202 individuals within a 12-month period and classified according to the suggested definitions for recovery (35.7%), remission (7.5%), any recurrence (20%), no remission (17.3%), relapse (4.0%) and transition to psychosis (15.8%). Favourable and unfavourable trajectories represented 43.2% and 57.1% respectively. Long duration of untreated symptoms and high depression scores were associated with unfavourable outcomes.

DISCUSSION

It is possible to apply clear definitions of remission, recovery, recurrence, relapse and transition to psychosis to a UHR cohort to evaluate longitudinal clinical trajectories. Acceptance and use of these definitions will help to facilitate comparisons between trials and to improve clinical clarity across the range of available therapeutic options in UHR individuals.

Predictors of longer-term outcome in the Vienna omega-3 high-risk study

Longer-term data on ω-3 polyunsaturated fatty acids (PUFA) for prevention of psychosis in (ultra high risk) UHR individuals have initially shown promising results. This analysis aimed to assess clinical predictors of longer-term outcome in UHR individuals treated with ω-3 PUFAs versus placebo. Data derived from an RCT in 81 UHR individuals treated with ω-3 PUFAs versus placebo for 12weeks and follow-up assessment after a median of 6.7years. Baseline GAF, baseline PANSS global score, pre-to-post-intervention change in EPA (Eicosapentaenoic acid) level were significant predictors of transition to psychosis, PANSS negative score and baseline MADRS reached trend-levels. In the final multivariate Cox regression analysis change in EPA levels remained the only significant predictor. Taking into account all other significant predictors, changes in EPA levels were found to be the single most significant predictor for transition to psychosis in a longer term observation of UHR individuals.

Cognitive and Functional Assessment of Psychosis Stratification Study (CoFAPSS): Rationale, Design, and Characteristics

Prediction of treatment response and illness trajectory in psychotic disorders including schizophrenia, bipolar affective disorder, schizoaffective disorder, and psychotic depression is difficult due to heterogeneity in presentation and outcome. Consequently, patients may receive prolonged ineffective treatments leading to functional decline, illness chronicity, and iatrogenic physical illness. One approach to addressing these problems is to stratify patients based on historical, clinical, and biological signatures. Such an approach has the potential to improve categorization resulting in better understanding of underlying mechanisms and earlier evidence-based treatment with reduced side effect burden. To investigate these multimodal signatures we developed the Cognitive and Functional Assessment of Psychosis Stratification Study (CoFAPSS) employing a prospective study design and a healthy control group comparison. The main aim of this study is to investigate cognitive, and biological "genomics" markers of psychotic illnesses that can be integrated with clinical data to improve prediction of risk and define functional trajectories. We also aim to identify biological "genomic" signatures underpinning variation in treatment response and adverse medical outcomes. The study commenced in June 2016, including patients with primary diagnosis of psychotic disorders including schizophrenia, bipolar affective disorder, schizoaffective disorder, and psychotic depression according to DSM-5 criteria. The assessment covers a wide range of participant history (life stressors, trauma, and family history), cognitive dimensions (social perception, memory and learning, attention, executive function, and general cognition), measures to assess psychosocial function and quality of life, psychotic symptom severity, clinical course of illness, and parameters for adverse medical outcome. Blood is collected for comprehensive genomic discovery analyses of biological (genomic, transcriptomic, proteomic, and cell-biologic) markers. The CoFAPSS is a novel approach that integrates clinical, cognitive and biological "genomic" markers to clarify clinico-pathological basis of risk, functional trajectories, disease stratification, treatment response, and adverse medical outcome. The CoFAPSS team welcomes collaborations with both national and international investigators.

Depressive symptom trajectories in late adolescence and early adulthood: A systematic review

OBJECTIVE

In adolescents and young adults, depressive symptoms are highly prevalent and dynamic. For clinicians, it is difficult to determine whether a young person reporting depressive symptoms is at risk of developing ongoing mood difficulties or whether symptoms form part of a transient maturational process. Trajectory analyses of longitudinally assessed symptoms in large cohorts have the potential to untangle clinical heterogeneity by determining subgroups or classes of symptom course and their risk factors, by interrogating the impact of known or suspected risk factors on trajectory slope and intercept and by tracing the interrelation between depressive symptoms and other clinical outcomes over time.

METHOD

We conducted a systematic review of trajectory studies conducted in cohorts including people aged between 15 and 25 years.

RESULTS

We retrieved 47 relevant articles. These studies suggest that young people fall into common mood trajectory classes and that class membership and symptom course are mediated by biological and environmental risk factors. Furthermore, studies provide evidence that high and persistent depressive symptoms are associated with a range of concurrent health and behavioral outcomes.

CONCLUSION

Findings could assist in the formulation of novel concepts of depressive disorders in young people and inform preventive strategies and predictive models for clinical practice.

Structural neuroimaging across early-stage psychosis: Aberrations in neurobiological trajectories and implications for the staging model

OBJECTIVE

This review critically examines the structural neuroimaging evidence in psychotic illness, with a focus on longitudinal imaging across the first-episode psychosis and ultra-high-risk of psychosis illness stages.

METHODS

A thorough search of the literature involving specifically longitudinal neuroimaging in early illness stages of psychosis was conducted. The evidence supporting abnormalities in brain morphology and altered neurodevelopmental trajectories is discussed in the context of a clinical staging model.

RESULTS

In general, grey matter (and, to a lesser extent, white matter) declines across multiple frontal, temporal (especially superior regions), insular and parietal regions during the first episode of psychosis, which has a steeper trajectory than that of age-matched healthy counterparts. Although the ultra-high-risk of psychosis literature is considerably mixed, evidence indicates that certain volumetric structural aberrations predate psychotic illness onset (e.g. prefrontal cortex thinning), while other abnormalities present in ultra-high-risk of psychosis populations are potentially non-psychosis-specific (e.g. hippocampal volume reductions).

CONCLUSION

We highlight the advantages of longitudinal designs, discuss the implications such studies have on clinical staging and provide directions for future research.

Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation

Discriminating subjects at clinical high risk (CHR) for psychosis who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalized prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modeling studies using clinical, biological, neurocognitive, environmental, and combinations of predictors. In a second step, we performed statistical simulations to test different probabilistic sequential 3-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive, and combined models) yielded PPVs of above 82%. Using only data from validated models, 3-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + electroencephalography), then structural magnetic resonance imaging and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT = 2) for an individual with 3 positive sequential tests, 71%-82% (NNT = 3) with 2 positive tests, 12%-21% (NNT = 11-18) with 1 positive test, and 1% (NNT = 219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multistage sequential testing may allow individual risk stratification of CHR individuals and optimize the prediction of psychosis.

Electrophysiological, cognitive and clinical profiles of at-risk mental state: The longitudinal Minds in Transition (MinT) study

The onset of schizophrenia is typically preceded by a prodromal period lasting several years during which sub-threshold symptoms may be identified retrospectively. Clinical interviews are currently used to identify individuals who have an ultra-high risk (UHR) of developing a psychotic illness with a view to provision of interventions that prevent, delay or reduce severity of future mental health issues. The utility of bio-markers as an adjunct in the identification of UHR individuals is not yet established. Several event-related potential measures, especially mismatch-negativity (MMN), have been identified as potential biomarkers for schizophrenia. In this 12-month longitudinal study, demographic, clinical and neuropsychological data were acquired from 102 anti-psychotic naive UHR and 61 healthy controls, of whom 80 UHR and 58 controls provided valid EEG data during a passive auditory task at baseline. Despite widespread differences between UHR and controls on demographic, clinical and neuropsychological measures, MMN and P3a did not differ between these groups. Of 67 UHR at the 12-month follow-up, 7 (10%) had transitioned to a psychotic illness. The statistical power to detect differences between those who did or did not transition was limited by the lower than expected transition rate. ERPs did not predict transition, with trends in the opposite direction to that predicted. In exploratory analysis, the strongest predictors of transition were measures of verbal memory and subjective emotional disturbance.

Identifying Electrophysiological Prodromes of Post-traumatic Stress Disorder: Results from a Pilot Study

The objective of this research project is the identification of a physiological prodrome of post-traumatic stress disorder (PTSD) that has a reliability that could justify preemptive treatment in the sub-syndromal state. Because abnormalities in event-related potentials (ERPs) have been observed in fully expressed PTSD, the possible utility of abnormal ERPs in predicting delayed-onset PTSD was investigated. ERPs were recorded from military service members recently returned from Iraq or Afghanistan who did not meet PTSD diagnostic criteria at the time of ERP acquisition. Participants (n = 65) were followed for up to 1 year, and 7.7% of the cohorts (n = 5) were PTSD-positive at follow-up. The initial analysis of the receiver operating characteristic (ROC) curve constructed using ERP metrics was encouraging. The average amplitude to target stimuli gave an area under the ROC curve of greater than 0.8. Classification based on the Youden index, which is determined from the ROC, gave positive results. Using average target amplitude at electrode Cz yielded Sensitivity = 0.80 and Specificity = 0.87. A more systematic statistical analysis of the ERP data indicated that the ROC results may simply represent a fortuitous consequence of small sample size. Predicted error rates based on the distribution of target ERP amplitudes approached those of random classification. A leave-one-out cross validation using a Gaussian likelihood classifier with Bayesian priors gave lower values of sensitivity and specificity. In contrast with the ROC results, the leave-one-out classification at Cz gave Sensitivity = 0.65 and Specificity = 0.60. A bootstrap calculation, again using the Gaussian likelihood classifier at Cz, gave Sensitivity = 0.59 and Specificity = 0.68. Two provisional conclusions can be offered. First, the results can only be considered preliminary due to the small sample size, and a much larger study will be required to assess definitively the utility of ERP prodromes of PTSD. Second, it may be necessary to combine ERPs with other biomarkers in a multivariate metric to produce a prodrome that can justify preemptive treatment.

Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers

Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview.

Differential expression of the inflammation marker IL12p40 in the at-risk mental state for psychosis: a predictor of transition to psychotic disorder?

BACKGROUND

The identification of biomarkers of transition from the at-risk mental state (ARMS) to psychotic disorder is important because early treatment of psychosis is associated with improved outcome. Increasing evidence points to an inflammatory contribution to psychosis. We questioned whether raised levels of plasma inflammatory markers predict transition from ARMS to psychotic disorder and whether any such predictors could be reduced by omega-3 (ω-3) polyunsaturated fatty acids (PUFAs).

METHODS

We measured the levels of 40 neuroinflammation biomarkers using a commercially available immunoassay kit. Firstly, we compared inflammatory markers in subjects in the ARMS who transitioned to psychotic disorder (n = 11) compared to subjects who did not (n = 28). Then we compared inflammatory markers in all subjects before and after ω-3 PUFA treatment (n = 40).

RESULTS

Our data provides preliminary evidence that elevations in the baseline plasma levels of the inflammatory marker IL12/IL23p40 are associated with transition from ARMS to psychotic disorder. IL12/IL23p40 levels did not change following 12 weeks administration of ω-3 PUFAs. These findings provide evidence that elevated plasma IL12/IL23p40 is a potential biomarker of increased risk for transition to psychotic disorder.

CONCLUSION

Further studies are required to confirm and extend this finding. Our results do not provide support for the possibility that administration of ω-3 PUFAs act to reduced transition to psychotic disorder by reducing blood levels of IL12/IL23p40.

TRIAL REGISTRATION

ClinicalTrials.gov, a service of the U.S. National Institutes of Health, Identifier: NCT00396643 , last updated December 20, 2007. Retrospectively registered.

Impact of polygenic schizophrenia-related risk and hippocampal volumes on the onset of psychosis

Alterations in hippocampal volume are a known marker for first-episode psychosis (FEP) as well as for the clinical high-risk state. The Polygenic Schizophrenia-related Risk Score (PSRS), derived from a large case-control study, indicates the polygenic predisposition for schizophrenia in our clinical sample. A total of 65 at-risk mental state (ARMS) and FEP patients underwent structural magnetic resonance imaging. We used automatic segmentation of hippocampal volumes using the FSL-FIRST software and an odds-ratio-weighted PSRS based on the publicly available top single-nucleotide polymorphisms from the Psychiatric Genomics Consortium genome-wide association study (GWAS). We observed a negative association between the PSRS and hippocampal volumes (β=-0.42, P=0.01, 95% confidence interval (CI)=(-0.72 to -0.12)) across FEP and ARMS patients. Moreover, a higher PSRS was significantly associated with a higher probability of an individual being assigned to the FEP group relative to the ARMS group (β=0.64, P=0.03, 95% CI=(0.08-1.29)). These findings provide evidence that a subset of schizophrenia risk variants is negatively associated with hippocampal volumes, and higher values of this PSRS are significantly associated with FEP compared with the ARMS. This implies that FEP patients have a higher genetic risk for schizophrenia than the total cohort of ARMS patients. The identification of associations between genetic risk variants and structural brain alterations will increase our understanding of the neurobiology underlying the transition to psychosis.

A review of vulnerability and risks for schizophrenia: Beyond the two hit hypothesis

Schizophrenia risk has often been conceptualized using a model which requires two hits in order to generate the clinical phenotype-the first as an early priming in a genetically predisposed individual and the second a likely environmental insult. The aim of this paper was to review the literature and reformulate this binary risk-vulnerability model. We sourced the data for this narrative review from the electronic database PUBMED. Our search terms were not limited by language or date of publication. The development of schizophrenia may be driven by genetic vulnerability interacting with multiple vulnerability factors including lowered prenatal vitamin D exposure, viral infections, smoking intelligence quotient, social cognition cannabis use, social defeat, nutrition and childhood trauma. It is likely that these genetic risks, environmental risks and vulnerability factors are cumulative and interactive with each other and with critical periods of neurodevelopmental vulnerability. The development of schizophrenia is likely to be more complex and nuanced than the binary two hit model originally proposed nearly thirty years ago. Risk appears influenced by a more complex process involving genetic risk interfacing with multiple potentially interacting hits and vulnerability factors occurring at key periods of neurodevelopmental activity, which culminate in the expression of disease state. These risks are common across a number of neuropsychiatric and medical disorders, which might inform common preventive and intervention strategies across non-communicable disorders.

Progress and Future Directions in Research on the Psychosis Prodrome: A Review for Clinicians

LEARNING OBJECTIVES

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ABSTRACT

The psychosis prodrome, or period of clinical and functional decline leading up to acute psychosis, offers a unique opportunity for identifying mechanisms of psychosis onset and for testing early-intervention strategies. We summarize major findings and emerging directions in prodromal research and provide recommendations for clinicians working with individuals suspected to be at high risk for psychosis. The past two decades of research have led to three major advances. First, tools and criteria have been developed that can reliably identify imminent risk for a psychotic disorder. Second, longitudinal clinical and psychobiological data from large multisite studies are strengthening individual risk assessment and offering insights into potential mechanisms of illness onset. Third, psychosocial and pharmacological interventions are demonstrating promise for delaying or preventing the onset of psychosis in help-seeking, high-risk individuals. The dynamic psychobiological processes implicated in both risk and onset of psychosis, including altered gene expression, cognitive dysfunction, inflammation, gray and white matter brain changes, and vulnerability-stress interactions suggest a wide range of potential treatment targets and strategies. The expansion of resources devoted to early intervention and prodromal research worldwide raises hope for investigating them. Future directions include identifying psychosis-specific risk and resilience factors in children, adolescents, and non-help-seeking community samples, improving study designs to test hypothesized mechanisms of change, and intervening with strategies that, in order to improve functional outcomes, better engage youth, address their environmental contexts, and focus on evidence-based neurodevelopmental targets. Prospective research on putatively prodromal samples has the potential to substantially reshape our understanding of mental illness and our efforts to combat it.