Abstract: Background: Schizophrenia is a severe psychiatric disorder with excess morbidity and mortality mainly due to cardio-vascular disorders (Hjorthøj et al., 2017). The Metabolic Syndrome (MetS) was defined to predict cardio-vascular morbi-mortality and encompasses some of the most relevant cardiovascular risk factors (Alberti et al., 2009). MetS is more prevalent among patients with psychosis. It has been described for this syndrome a heritability between 10 and 30% (Henneman et al., 2008), and there is evidence of its polygenic architecture (Malan-Müller et al, 2016), being some of the genes reported, implicated in both schizophrenia and metabolic disorders. We aim to explore if there is an association between a polygenic risk score (PRS) and the occurrence of MetS in a sample of patients with psychosis. Methods: 184 subjects presenting a first episode of non-affective psychosis were recruited for the present study. All of them were drug-naïve and none of them present MetS at baseline. Anthropometric measurements and glycemic and lipid parameters were obtained at baseline and after 3 years of having initiated treatment. After the genotype quality control steps, these samples were imputed using the standard SHAPEIT2/IMPUTE2 pipeline. PLINK 1.90 was used for the calculation of PRS. These scores were calculated multiplying the imputation probability for each risk allele by the effect size for each genetic variant as reported in Ripke et al., 2014. The resulting values were summed up in an additive fashion obtaining an individual estimate of the genetic load in each subject. Three different P-value thresholds were used (5?×?10-8, 0.05, 1). Regression analysis were carried out in order to explore the possible role of PRS as predictor of MetS. Results: 26 patients (14%) developed a MetS in the first 3 years of treatment with antipsychotic medication. The model obtained from regression analyses (X2(9)=31.34, R2 Nalgelkerke=0.403, p=0.005) predicted the occurrence of MetS with 89.7% accuracy, and although only classified correctly 35% of subjects with MetS, it correctly classified 98.2% of healthy subjects. However, this prediction model was not significantly improved when adding the PRS.

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