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      Real-world digital implementation of the Psychosis Polyrisk Score (PPS): A pilot feasibility study

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          Abstract

          Background

          The Psychosis Polyrisk Score (PPS) is a potential biomarker integrating non-purely genetic risk/protective factors for psychosis that may improve identification of individuals at risk and prediction of their outcomes at the individual subject level. Biomarkers that are easy to administer are direly needed in early psychosis to facilitate clinical implementation. This study digitally implements the PPS and pilots its feasibility of use in the real world.

          Methods

          The PPS was implemented digitally and prospectively piloted across individuals referred for a CHR-P assessment (n = 16) and healthy controls (n = 66). Distribution of PPS scores was further simulated in the general population.

          Results

          98.8% of individuals referred for a CHR-P assessment and healthy controls completed the PPS assessment with only one drop-out. 96.3% of participants completed the assessment in under 15 min. Individuals referred for a CHR-P assessment had high PPS scores (mean = 6.2, SD = 7.23) than healthy controls (mean = −1.79, SD = 6.78, p < 0.001). In simulated general population data, scores were normally distributed ranging from −15 (lowest risk, RR = 0.03) to 39.5 (highest risk, RR = 8912.51).

          Discussion

          The PPS is a promising biomarker which has been implemented digitally. The PPS can be easily administered to both healthy controls and individuals at potential risk for psychosis on a range of devices. It is feasible to use the PPS in real world settings to assess individuals with emerging mental disorders. The next phase of research should be to include the PPS in large-scale international cohort studies to evaluate its ability to refine the prognostication of outcomes.

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          Most cited references55

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          Biological Insights From 108 Schizophrenia-Associated Genetic Loci

          Summary Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here, we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain providing biological plausibility for the findings. Many findings have the potential to provide entirely novel insights into aetiology, but associations at DRD2 and multiple genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that play important roles in immunity, providing support for the hypothesized link between the immune system and schizophrenia.
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            Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study.

            Whether schizophrenia and bipolar disorder are the clinical outcomes of discrete or shared causative processes is much debated in psychiatry. We aimed to assess genetic and environmental contributions to liability for schizophrenia, bipolar disorder, and their comorbidity. We linked the multi-generation register, which contains information about all children and their parents in Sweden, and the hospital discharge register, which includes all public psychiatric inpatient admissions in Sweden. We identified 9 009 202 unique individuals in more than 2 million nuclear families between 1973 and 2004. Risks for schizophrenia, bipolar disorder, and their comorbidity were assessed for biological and adoptive parents, offspring, full-siblings and half-siblings of probands with one of the diseases. We used a multivariate generalised linear mixed model for analysis of genetic and environmental contributions to liability for schizophrenia, bipolar disorder, and the comorbidity. First-degree relatives of probands with either schizophrenia (n=35 985) or bipolar disorder (n=40 487) were at increased risk of these disorders. Half-siblings had a significantly increased risk (schizophrenia: relative risk [RR] 3.6, 95% CI 2.3-5.5 for maternal half-siblings, and 2.7, 1.9-3.8 for paternal half-siblings; bipolar disorder: 4.5, 2.7-7.4 for maternal half-siblings, and 2.4, 1.4-4.1 for paternal half-siblings), but substantially lower than that of the full-siblings (schizophrenia: 9.0, 8.5-11.6; bipolar disorder: 7.9, 7.1-8.8). When relatives of probands with bipolar disorder were analysed, increased risks for schizophrenia existed for all relationships, including adopted children to biological parents with bipolar disorder. Heritability for schizophrenia and bipolar disorder was 64% and 59%, respectively. Shared environmental effects were small but substantial (schizophrenia: 4.5%, 4.4%-7.4%; bipolar disorder: 3.4%, 2.3%-6.2%) for both disorders. The comorbidity between disorders was mainly (63%) due to additive genetic effects common to both disorders. Similar to molecular genetic studies, we showed evidence that schizophrenia and bipolar disorder partly share a common genetic cause. These results challenge the current nosological dichotomy between schizophrenia and bipolar disorder, and are consistent with a reappraisal of these disorders as distinct diagnostic entities.
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              Prevention of Psychosis: Advances in Detection, Prognosis, and Intervention

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                Author and article information

                Contributors
                Journal
                Schizophr Res
                Schizophr Res
                Schizophrenia Research
                Elsevier Science Publisher B. V
                0920-9964
                1573-2509
                1 December 2020
                December 2020
                : 226
                : 176-183
                Affiliations
                [a ]Early Psychosis: Interventions and Clinical Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
                [b ]OASIS Service, South London and the Maudsley NHS Foundation Trust, London, United Kingdom
                [c ]Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
                [d ]Imaging of Mood- and Anxiety-Related Disorders (IMARD), Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain
                [e ]Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
                [f ]Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
                [g ]Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
                [h ]Frieman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
                [i ]Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
                [j ]Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
                Author notes
                [* ]Corresponding author at: Early Psychosis: Interventions and Clinical Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom. paolo.fusar-poli@ 123456kcl.ac.uk
                Article
                S0920-9964(20)30222-X
                10.1016/j.schres.2020.04.015
                7774585
                32340785
                0ce89506-7518-4fb4-9479-df1c3e886f08
                © 2020 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 February 2020
                : 8 April 2020
                : 12 April 2020
                Categories
                Article

                Neurology
                clinical high risk,risk,prediction,environment,polygenic risk,implementation
                Neurology
                clinical high risk, risk, prediction, environment, polygenic risk, implementation

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