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Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

BackgroundThe clinical high-risk for psychosis (CHR-P) paradigm was introduced to detect individuals at risk of developing psychosis and to establish preventive strategies. While current prediction of outcomes in the CHR-P state is based mostly on the clinical assessment of presenting features, several emerging biomarkers have been investigated in an attempt to stratify CHR-P individuals according to their individual trajectories and refine the diagnostic process. However, heterogeneity across subgroups is a key challenge that has limited the impact of the CHR-P prediction strategies, as the clinical validity of the current research is limited by a lack of external validation across sites and modalities. Despite these challenges, electroencephalography (EEG) biomarkers have been studied in this field and evidence suggests that EEG used in combination with clinical assessments may be a key measure for improving diagnostic and prognostic accuracy in the CHR-P state. The PSYSCAN EEG study is an international, multi-site, multimodal longitudinal project that aims to advance knowledge in this field.MethodsParticipants at 6 international sites take part in an EEG protocol including EEG recording, cognitive and clinical assessments. CHR-P participants will be followed up after 2 years and subcategorised depending on their illness progression regarding transition to psychosis. Differences will be sought between CHR-P individuals and healthy controls and between CHR-P individuals who transition and those who do not transition to psychosis using data driven computational analyses.DiscussionThis protocol addresses the challenges faced by previous studies of this kind to enable valid identification of predictive EEG biomarkers which will be combined with other biomarkers across sites to develop a prognostic tool in CHR-P. The PSYSCAN EEG study aims to pave the way for incorporating EEG biomarkers in the assessment of CHR-P individuals, to refine the diagnostic process and help to stratify CHR-P subjects according to risk of transition. This may improve our understanding of the CHR-P state and therefore aid the development of more personalized treatment strategies.

Introduction Predicting transition from clinical high risk (CHR) to first episode psychosis has proven difficult. Assessment of oxidative stress biomarkers and the niacin skin flush response (NSFR) may improve prediction accuracy. Objectives To predict transition to psychosis based on combined clinical and blood biomarker. Aims To analyse data from patients in placebo group of a 12-week trial of omega-3 fatty acid supplementation in CHR. Transition likelihood ratios (LRs) for baseline historical risks, clinical assessments (PANSS subscales and total, GAF), NSFR and blood markers (nervonic acid, superoxide dismutase, glutathione) were calculated. Variables with the highest positive and lowest negative LRs were included in an odds ratio form of Bayes’ rule transition prediction models. Model accuracy was calculated by area under the receiver operating curves (AUROC) of each model. Results 1-year transition to psychosis was 28% (n=40). Historical data showed no predictability (sensitivity 30%, specificity 100% (AUROC)=0.688, p=0.085). Clinical assessments alone produced a sensitivity of 30% at a specificity of 95% (AUROC=0.83, p<0.0001). The biomarker panel alone predicted transition with 40% sensitivity and 100% specificity (AUROC=0.73, p=0.03). Combining history and clinical assessment provided no improvement above clinical data alone (sensitivity = 30%, specificity = 100%, AUROC=0.85, p< 0.0001). The combination of history, clinical assessment and biomarkers identified transition with a sensitivity of 60% and specificity of 100% (AUROC=0.87, p< 0.0001). Conclusions Probabilistic models combining biomarkers and clinical data are able to target high-risk subgroups within CHR and may help to personalise treatment. [ABSTRACT FROM AUTHOR]