S118. TRANSDIAGNOSTIC SYMPTOM DIMENSIONS OF PSYCHOSIS AND THE PREDICTIVE ROLE OF PREMORBID ADJUSTMENT AND COGNITIVE CHARACTERISTICS IN THE MULTINATIONAL EU-GEI STUDY

Abstract Background A symptom dimension approach may best examine the heterogeneous expression of psychosis. However, whether and how premorbid predisposition and cognitive factors explain phenotypes variation is still debated. This study aimed to test the predictive value of combined cognition and premorbid adjustment on transdiagnostic symptom dimensions in a large sample of people suffering from the first episode of psychosis (FEP). Methods FEP patients were part of the EUGEI study. Psychopathology was rated using the OPerational CRITeria system. Multidimensional item response modelling estimate a bifactor model of psychosis by Mplus, composed of a general factor and five specific symptom dimensions. WAIS-brief version measured IQ, and Premorbid Adjustment Scale estimated premorbid social (SF) and academic adjustment (AF). We set a multivariate analysis of the covariance having symptom-dimensions as the output and SF, AF, and IQ as the main predictors, adjusted by age, gender, country, and ethnicity. Since lifetime frequency of cannabis use related to both premorbid and cognitive characteristics, and symptom dimensions, it was added as an independent predictor, collected by the Cannabis Experience Questionnaire. Sensitivity analyses were run with IQ subtests. Results The sample included 785 FEP patients [61.2% (N=481) males; Mean age=33.8 (12.3)]. IQ [Pillai=0.019, F(6, 765)=2.52; p=0.020] and SF [Pillai=0.032, F(6, 765)=4.26; p<0.001] had a discriminant effect on the bifactor model as a whole, whereas AF did not [Pillai=0.007, F(6, 765)=0.93; p=0.468]. Cannabis use had a marginally effect [Pillai=0.027, F(12, 1532)=1.76; p=0.049]. Positive symptoms were slightly predicted by a lower IQ (B=-0.005, 95% C.I. -0.01, 0.0, p=0.038) and sensitivity analysis revealed the role of a worse processing speed (B=-0.04, 95% C.I. -0.07, -0.01, p=0.003) and working memory (B=-0.02, 95% C.I. -0.05, -0.04, p=0.013) abilities in this relationship. Negative symptoms were higher in subjects with a worse SF (B=-0.12, 95% C.I. -0.18, -0.06, p<0.001) and a lower IQ (B=-0.005, 95% C.I. -0.01, -0.001, p=0.014), due to a lower perceptual reasoning (B=-0.02, 95% C.I. 0.04, 0.003, p=0.023). On the opposite, manic symptoms were more present if patients had better SF (B=0.07, 95% C.I. 0.01, 0.14, p=0.023) and IQ (B=0.005, 95% C.I. 0.0, 0.009, p=0.030), particularly, a higher perceptual reasoning (B=0.02, 95% C.I. 0.04, 0.008, p=0.014). Lower SF predicted depressive symptoms (B=-0.09, 95% C.I. -0.15, -0.03, p=0.032). The model was not predictive of general and disorganization symptoms. Cannabis use had an effect on positive symptoms (F(2, 770)=4.9, p=0.011); that means, patients who smoked cannabis everyday had higher positive symptoms than occasional (M_difference=0.27, 95% C.I. 0.01, 0.52, p=0.034) and never-users (M_difference=0.25, 95% C.I. 0.02, 0.48, p=0.021). Discussion The opposed relationship between premorbid sociability and an IQ, particularly its “hold” perceptual-reasoning sub-test – linked to an early coherent self-construction – with manic and negative symptoms, suggests divergent neurodevelopmental trajectories among patients endorsing these different phenotypes. On the other hand, the specific predictive value for positive symptoms of lower processing speed and working memory – well-recognised cognitive markers in psychosis – implies an encoding problem. Worse premorbid sociability but not low general cognition predicted depressive symptoms, thus suggesting a difference between mood downregulation and flat affect, often unrecognised. The cannabis-effect on positive symptoms was expected; this analysis suggests its possible interaction with cognitive characteristics, which deserves further attention.