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BackgroundCannabis use shows a robust dose-dependent relationship with psychosis risk among the general population. Despite this, it has been difficult to link cannabis use with risk for transitioning to a psychotic disorder among individuals at ultra-high risk (UHR) for psychosis. The present study examined UHR transition risk as a function of cannabis use characteristics which vary substantially between individuals including age of first use, cannabis abuse severity and a history of cannabis-induced attenuated psychotic symptoms (APS).MethodParticipants were 190 UHR individuals (76 males) recruited at entry to treatment between 2000 and 2006. They completed a comprehensive baseline assessment including a survey of cannabis use characteristics during the period of heaviest use. Outcome was transition to a psychotic disorder, with mean time to follow-up of 5.0 years (range 2.4–8.7 years).ResultsA history of cannabis abuse was reported in 58% of the sample. Of these, 26% reported a history of cannabis-induced APS. These individuals were 4.90 (95% confidence interval 1.93–12.44) times more likely to transition to a psychotic disorder (p = 0.001). Greater severity of cannabis abuse also predicted transition to psychosis (p = 0.036). However, this effect was mediated by higher abuse severity among individuals with a history of cannabis-induced APS.ConclusionsFindings suggest that cannabis use poses risk in a subpopulation of UHR individuals who manifest cannabis-induced APS. Whether this reflects underlying genetic vulnerability requires further study. Nevertheless, findings reveal an important early marker of risk with potentially significant prognostic utility for UHR individuals. [ABSTRACT FROM PUBLISHER]

BackgroundMany research groups have attempted to predict which individuals with an at-risk mental state (ARMS) for psychosis will later develop a psychotic disorder. However, it is difficult to predict the course and outcome based on individual symptoms scores.MethodData from 318 ARMS individuals from two specialized services for ARMS subjects were analysed using latent class cluster analysis (LCCA). The score on the Comprehensive Assessment of At-Risk Mental States (CAARMS) was used to explore the number, size and symptom profiles of latent classes.ResultsLCCA produced four high-risk classes, censored after 2 years of follow-up: class 1 (mild) had the lowest transition risk (4.9%). Subjects in this group had the lowest scores on all the CAARMS items, they were younger, more likely to be students and had the highest Global Assessment of Functioning (GAF) score. Subjects in class 2 (moderate) had a transition risk of 10.9%, scored moderately on all CAARMS items and were more likely to be in employment. Those in class 3 (moderate–severe) had a transition risk of 11.4% and scored moderately severe on the CAARMS. Subjects in class 4 (severe) had the highest transition risk (41.2%), they scored highest on the CAARMS, had the lowest GAF score and were more likely to be unemployed. Overall, class 4 was best distinguished from the other classes on the alogia, avolition/apathy, anhedonia, social isolation and impaired role functioning.ConclusionsThe different classes of symptoms were associated with significant differences in the risk of transition at 2 years of follow-up. Symptomatic clustering predicts prognosis better than individual symptoms. [ABSTRACT FROM PUBLISHER]