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Zhang T, Wei Y, Tang X, Xu L, Hu Y, Liu H, Wang Z, Chen T, Li C, Wang J. Timeframe for Conversion to Psychosis From Individuals at Clinical High-Risk: A Quantile Regression. Schizophr Bull 2024:sbae129. [PMID: 39054751 DOI: 10.1093/schbul/sbae129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND HYPOTHESIS The time taken for an individual who is at the clinical high-risk (CHR) stage to transition to full-blown psychosis may vary from months to years. This temporal aspect, known as the timeframe for conversion to psychosis (TCP), is a crucial but relatively underexplored dimension of psychosis development. STUDY DESIGN The sample consisted of 145 individuals with CHR who completed a 5-year follow-up with a confirmed transition to psychosis within this period. Clinical variables along with functional variables such as the Global Assessment of Function (GAF) score at baseline (GAF baseline) and GAF-drop from the highest score in the past year. The TCP was defined as the duration from CHR identification to psychosis conversion. Participants were categorized into 3 groups based on TCP: "short" (≤6 months, ≤33.3%), "median" (7-17 months, 33.3%-66.6%), and "long" (≥18 months, ≥66.6%). The quantile regression analysis was applied. STUDY RESULTS The overall sample had a median TCP of 11 months. Significant differences among the three TCP groups were observed, particularly in GAF-drop (χ2 = 8.806, P = .012), disorganized symptoms (χ2 = 7.071, P = .029), and general symptoms (χ2 = 6.586, P = .037). Greater disorganized symptoms (odds ratio [OR] = 0.824, P = .009) and GAF-drop (OR = 0.867, P = .011) were significantly associated with a shorter TCP, whereas greater general symptoms (OR = 1.198, P = .012) predicted a longer TCP. Quantile regression analysis demonstrated a positive association between TCP and GAF baseline above the 0.7 quantile and a negative association between TCP rank and GAF drop below the 0.5 quantile. CONCLUSIONS This study underscores the pivotal role of functional characteristics in shaping TCP among individuals with CHR, emphasizing the necessity for a comprehensive consideration of temporal aspects in early prevention efforts.
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Affiliation(s)
- TianHong Zhang
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - YanYan Wei
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - XiaoChen Tang
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - LiHua Xu
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - YeGang Hu
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - ZiXuan Wang
- Department of Psychology, Shanghai Xinlianxin Psychological Counseling Center, Shanghai, PR China
| | - Tao Chen
- Department of Big Data Research Lab, University of Waterloo, Ontario, Canada
- Department of Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - JiJun Wang
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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Zhang T, Wei Y, Xu L, Tang X, Hu Y, Liu H, Wang Z, Chen T, Li C, Wang J. Association between serum cytokines and timeframe for conversion from clinical high-risk to psychosis. Psychiatry Clin Neurosci 2024; 78:385-392. [PMID: 38591426 DOI: 10.1111/pcn.13670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/22/2024] [Accepted: 03/11/2024] [Indexed: 04/10/2024]
Abstract
AIM Although many studies have explored the link between inflammatory markers and psychosis, there is a paucity of research investigating the temporal progression in individuals at clinical high-risk (CHR) who eventually develop full psychosis. To address this gap, we investigated the correlation between serum cytokine levels and Timeframe for Conversion to Psychosis (TCP) in individuals with CHR. METHODS We enrolled 53 individuals with CHR who completed a 5-year follow-up with a confirmed conversion to psychosis. Granulocyte macrophage-colony stimulating factor (GM-CSF), interleukin (IL)-1β, 2, 6, 8, 10, tumor necrosis factor-α (TNF-α), and vascular endothelial growth factor (VEGF) levels were measured at baseline and 1-year. Correlation and quantile regression analyses were performed. RESULTS The median TCP duration was 14 months. A significantly shorter TCP was associated with higher levels of TNF-α (P = 0.022) and VEGF (P = 0.016). A negative correlation was observed between TCP and TNF-α level (P = 0.006) and VEGF level (P = 0.04). Quantile regression indicated negative associations between TCP and GM-CSF levels below the 0.5 quantile, IL-10 levels below the 0.3 quantile, IL-2 levels below the 0.25 quantile, IL-6 levels between the 0.65 and 0.75 quantiles, TNF-α levels below the 0.8 quantile, and VEGF levels below the 0.7 quantile. A mixed linear effects model identified significant time effects for IL-10 and IL-2, and significant group effects for changes in IL-2 and TNF-α. CONCLUSIONS Our findings underscore that a more pronounced baseline inflammatory state is associated with faster progression of psychosis in individuals with CHR. This highlights the importance of considering individual inflammatory profiles during early intervention and of tailoring preventive measures for risk profiles.
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Affiliation(s)
- TianHong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - YeGang Hu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - ZiXuan Wang
- Shanghai Xinlianxin Psychological Counseling Center, Shanghai, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- Labor and Worklife Program, Harvard University, Cambridge, Massachusetts, USA
| | - ChunBo Li
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
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3
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Schultze-Lutter F, Banaschewski T, Barth GM, Bechdolf A, Bender S, Flechtner HH, Hackler S, Heuer F, Hohmann S, Holzner L, Huss M, Koutsouleris N, Lipp M, Mandl S, Meisenzahl E, Munz M, Osman N, Peschl J, Reissner V, Renner T, Riedel A, Romanos M, Romer G, Schomerus G, Thiemann U, Uhlhaas PJ, Woopen C, Correll CU, Care-Konsortium D. [Ethical Considerations of Including Minors in Clinical Trials Using the Example of the Indicated Prevention of Psychotic Disorders]. ZEITSCHRIFT FUR KINDER- UND JUGENDPSYCHIATRIE UND PSYCHOTHERAPIE 2024. [PMID: 38809160 DOI: 10.1024/1422-4917/a000981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Ethical Considerations of Including Minors in Clinical Trials Using the Example of the Indicated Prevention of Psychotic Disorders Abstract: As a vulnerable group, minors require special protection in studies. For this reason, researchers are often reluctant to initiate studies, and ethics committees are reluctant to authorize such studies. This often excludes minors from participating in clinical studies. This exclusion can lead to researchers and clinicians receiving only incomplete data or having to rely on adult-based findings in the treatment of minors. Using the example of the study "Computer-Assisted Risk Evaluation in the Early Detection of Psychotic Disorders" (CARE), which was conducted as an 'other clinical investigation' according to the Medical Device Regulation, we present a line of argumentation for the inclusion of minors which weighs the ethical principles of nonmaleficence (especially regarding possible stigmatization), beneficence, autonomy, and fairness. We show the necessity of including minors based on the development-specific differences in diagnostics and early intervention. Further, we present specific protective measures. This argumentation can also be transferred to other disorders with the onset in childhood and adolescence and thus help to avoid excluding minors from appropriate evidence-based care because of insufficient studies.
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Affiliation(s)
- Frauke Schultze-Lutter
- Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
- Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesien
- Universitätsklinik für Kinder- und Jugendpsychiatrie und Psychotherapie, Universität Bern, Schweiz
| | - Tobias Banaschewski
- Klinik für Psychiatrie und Psychotherapie des Kindes- und Jugendalters, Zentralinstitut für Seelische Gesundheit, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Deutschland
| | - Gottfried M Barth
- Abteilung Psychiatrie, Psychosomatik und Psychotherapie im Kindes- und Jugendalter, Klinik für Psychiatrie und Psychotherapie, Universitätsklinikum Tübingen, Deutschland
| | - Andreas Bechdolf
- Vivantes Klinikum Am Urban und Vivantes Klinikum im Friedrichshain, Klinik für Psychiatrie, Psychotherapie und Psychosomatik, Berlin, Deutschland
- Klinik für Psychiatrie und Psychotherapie, CCM, Charité - Universitätsmedizin Berlin, Deutschland
- Deutsches Zentrum für Psychische Gesundheit, Standort Berlin, Deutschland
| | - Stephan Bender
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, Medizinische Fakultät und Uniklinik Köln, Universität zu Köln, Köln, Deutschland
| | - Hans-Henning Flechtner
- Universitätsklinik für Psychiatrie, Psychotherapie und psychosomatische Medizin des Kindes- und Jugendalters, Otto-von-Guericke Universität Magdeburg, Magdeburg, Deutschland
| | - Sandra Hackler
- Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie, LVR-Klinik Bonn, Bonn, Deutschland
| | - Fabiola Heuer
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, Charité - Universitätsmedizin Berlin, Deutschland
| | - Sarah Hohmann
- Klinik für Kinder- und Jugendpsychiatrie, -psychotherapie und -psychosomatik, Universitätsklinikums Hamburg-Eppendorf, Hamburg, Deutschland
| | - Laura Holzner
- Vivantes Klinikum Am Urban und Vivantes Klinikum im Friedrichshain, Klinik für Psychiatrie, Psychotherapie und Psychosomatik, Berlin, Deutschland
| | - Michael Huss
- Klinik und Poliklinik für Kinder- und Jugendpsychiatrie und -psychotherapie, Universitätsmedizin der Johannes-Gutenberg-Universität Mainz; Mainz, Deutschland
| | - Nikolaos Koutsouleris
- Klinik für Psychiatrie und Psychotherapie, Klinikum der Universität München, München, Deutschland
- Max Planck Institute of Psychiatry, Max Planck Fellow Group Precision Psychiatry, München, Deutschland
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Michael Lipp
- Klinik für Psychiatrie und Psychotherapie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Selina Mandl
- Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie der Universität München, Klinikum der Universität München, Deutschland
| | - Eva Meisenzahl
- Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Manuel Munz
- Klinik für Psychiatrie, Psychotherapie und Psychosomatik des Kindes- und Jugendalters des Zentrums für Integrative Psychiatrie, Universitätsklinikum Schleswig-Holstein (UKSH), Campus Kiel, Deutschland
| | - Naweed Osman
- Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Jens Peschl
- Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Volker Reissner
- Abteilung für Kinder- und Jugendpsychiatrie, LVR-Klinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Deutschland
| | - Tobias Renner
- Abteilung Psychiatrie, Psychosomatik und Psychotherapie im Kindes- und Jugendalter, Klinik für Psychiatrie und Psychotherapie, Universitätsklinikum Tübingen, Deutschland
| | - Anett Riedel
- Universitätsklinik für Psychiatrie, Psychotherapie und psychosomatische Medizin des Kindes- und Jugendalters, Otto-von-Guericke Universität Magdeburg, Magdeburg, Deutschland
| | - Marcel Romanos
- Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie, Universitätsklinikum Würzburg, Deutschland
| | - Georg Romer
- Klinik für Kinder- und Jugendpsychiatrie, -psychosomatik und -psychotherapie, Universitätsklinikum Münster, Deutschland
| | - Georg Schomerus
- Klinik und Poliklinik für Psychiatrie und Psychotherapie, Universität Leipzig, Leipzig, Deutschland
| | - Ulf Thiemann
- Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie, LVR-Klinik Bonn, Bonn, Deutschland
| | - Peter J Uhlhaas
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, Charité - Universitätsmedizin Berlin, Deutschland
- Institute of Neuroscience and Psychology, University of Glasgow, UK
| | | | - Christoph U Correll
- Deutsches Zentrum für Psychische Gesundheit, Standort Berlin, Deutschland
- Klinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, Charité - Universitätsmedizin Berlin, Deutschland
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry and Molecular Medicine, Hempstead, NY, USA
| | - das Care-Konsortium
- Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
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4
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Zhang T, Xu L, Wei Y, Cui H, Tang X, Hu Y, Tang Y, Wang Z, Liu H, Chen T, Li C, Wang J. Advancements and Future Directions in Prevention Based on Evaluation for Individuals With Clinical High Risk of Psychosis: Insights From the SHARP Study. Schizophr Bull 2024:sbae066. [PMID: 38741342 DOI: 10.1093/schbul/sbae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
BACKGROUND AND HYPOTHESIS This review examines the evolution and future prospects of prevention based on evaluation (PBE) for individuals at clinical high risk (CHR) of psychosis, drawing insights from the SHARP (Shanghai At Risk for Psychosis) study. It aims to assess the effectiveness of non-pharmacological interventions in preventing psychosis onset among CHR individuals. STUDY DESIGN The review provides an overview of the developmental history of the SHARP study and its contributions to understanding the needs of CHR individuals. It explores the limitations of traditional antipsychotic approaches and introduces PBE as a promising framework for intervention. STUDY RESULTS Three key interventions implemented by the SHARP team are discussed: nutritional supplementation based on niacin skin response blunting, precision transcranial magnetic stimulation targeting cognitive and brain functional abnormalities, and cognitive behavioral therapy for psychotic symptoms addressing symptomatology and impaired insight characteristics. Each intervention is evaluated within the context of PBE, emphasizing the potential for tailored approaches to CHR individuals. CONCLUSIONS The review highlights the strengths and clinical applications of the discussed interventions, underscoring their potential to revolutionize preventive care for CHR individuals. It also provides insights into future directions for PBE in CHR populations, including efforts to expand evaluation techniques and enhance precision in interventions.
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Affiliation(s)
- TianHong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - HuiRu Cui
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YeGang Hu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YingYing Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - ZiXuan Wang
- Department of Psychology, Shanghai Xinlianxin Psychological Counseling Center, Shanghai, China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Ontario, Canada
- Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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5
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Zhang T, Wei Y, Tang X, Cui H, Xu L, Hu Y, Tang Y, Hu Q, Liu H, Wang Z, Chen T, Li C, Wang J. Cognitive functions following initiation of antipsychotic medication in adolescents and adults at clinical high risk for psychosis: a naturalistic sub group analysis using the MATRICS consensus cognitive battery. Child Adolesc Psychiatry Ment Health 2024; 18:53. [PMID: 38704567 PMCID: PMC11070077 DOI: 10.1186/s13034-024-00743-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND The effects of antipsychotic (AP) medications on cognitive functions in individuals at clinical high-risk (CHR) of psychosis are poorly understood. This study compared the effects of AP treatment on cognitive improvement in CHR adolescents and adults. METHODS A total of 327 CHR participants, with an age range of 13 to 45 years, who underwent baseline neuropsychological assessments and a 1-year clinical follow-up were included. Participants with CHR were categorized into four groups based on their age: adolescents (aged < 18) and adults (aged ≥ 18), as well as their antipsychotic medication status (AP+ or AP-). Therefore, the four groups were defined as Adolescent-AP-, Adolescent-AP+, Adult-AP-, and Adult-AP+. RESULTS During the follow-up, 231 CHR patients received AP treatment, 94 converted to psychosis, and 161 completed the 1-year follow-up. The Adolescent-AP+ group had more positive symptoms, lower general functions, and cognitive impairments than the Adolescent-AP- group at baseline, but no significant differences were observed among adults. The Adolescent-AP+ group showed a significant increase in the risk of conversion to psychosis (p < 0.001) compared to the Adolescent-AP- group. The Adult-AP+ group showed a decreasing trend in the risk of conversion (p = 0.088) compared to the Adult-AP- group. The Adolescent-AP- group had greater improvement in general functions (p < 0.001), neuropsychological assessment battery mazes (p = 0.025), and brief visuospatial memory test-revised (p = 0.020), as well as a greater decrease in positive symptoms (p < 0.001) at follow-up compared to the Adolescent-AP+ group. No significant differences were observed among adults. CONCLUSIONS Early use of AP was not associated with a positive effect on cognitive function in CHR adolescents. Instead, the absence of AP treatment was associated with better cognitive recovery, suggesting that AP exposure might not be the preferred choice for cognitive recovery in CHR adolescents, but may be more reasonable for use in adults.
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Affiliation(s)
- TianHong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China.
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China
| | - HuiRu Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China
| | - YeGang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China
| | - Qiang Hu
- Department of Psychiatry, ZhenJiang Mental Health Center, Zhenjiang, People's Republic of China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, 200240, Shanghai, China
| | - ZiXuan Wang
- Shanghai Xinlianxin Psychological Counseling Center, Shanghai, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada
- Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, 200030, Shanghai, China.
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, People's Republic of China.
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
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6
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Pelizza L, Leuci E, Quattrone E, Azzali S, Paulillo G, Pupo S, Poletti M, Raballo A, Pellegrini P, Menchetti M. Baseline antipsychotic prescription and short-term outcome indicators in individuals at clinical high-risk for psychosis: Findings from the Parma At-Risk Mental States (PARMS) program. Early Interv Psychiatry 2024; 18:71-81. [PMID: 37194411 DOI: 10.1111/eip.13434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/29/2023] [Accepted: 05/05/2023] [Indexed: 05/18/2023]
Abstract
AIM The prognostic prediction of outcomes in individuals at clinical high-risk for psychosis (CHR-P) is still a significant clinical challenge. Among multiple baseline variables of risk calculator models, the role of ongoing pharmacological medications has been partially neglected, despite meta-analytical evidence of higher risk of psychosis transition associated with baseline prescription exposure to antipsychotics (AP) in CHR-P individuals. The main aim of the current study was to test the hypothesis that ongoing AP need at baseline indexes a subgroup of CHR-P individuals with more severe psychopathology and worse prognostic trajectories along a 1-year follow-up period. METHODS This research was settled within the 'Parma At-Risk Mental States' program. Baseline and 1-year follow-up assessment included the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF). CHR-P individuals who were taking AP medications at entry were included in the CHR-P-AP+ subgroup. The remaining participants were grouped as CHR-P-AP-. RESULTS Hundred and seventy-eight CHR-P individuals (aged 12-25 years) were enrolled (91 CHR-P-AP+, 87 CHR-P-AP-). Compared to CHR-P AP-, CHR-P AP+ individuals had older age, greater baseline PANSS 'Positive Symptoms' and 'Negative Symptoms' factor subscores and a lower GAF score. At the end of our follow-up, CHR-P-AP+ subjects showed higher rates of psychosis transition, new hospitalizations and urgent/non-planned visits compared to CHRP- AP- individuals. CONCLUSIONS In agreement with increasing empirical evidence, also the results of the current study suggest that AP need is a significant prognostic variable in cohorts of CHR-P individuals and should be included in risk calculators.
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Affiliation(s)
- Lorenzo Pelizza
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
- Department of Biomedical and Neuromotor Sciences, "Alma Mater Studiorum" - University of Bologna, Bologna, Italy
| | - Emanuela Leuci
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Emanuela Quattrone
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Silvia Azzali
- Department of Mental Health and Pathological Addictions, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Giuseppina Paulillo
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Simona Pupo
- Pain Therapy Service, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Michele Poletti
- Department of Mental Health and Pathological Addictions, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Andrea Raballo
- Department of Medicine, Section of Psychiatry, Clinical Psychology and Rehabilitation, University of Perugia, Perugia, Italy
- Center for Translational, Phenomenological and Developmental Psychopathology, Perugia, Italy
| | - Pietro Pellegrini
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Marco Menchetti
- Department of Biomedical and Neuromotor Sciences, "Alma Mater Studiorum" - University of Bologna, Bologna, Italy
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7
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Zhang T, Xu L, Tang X, Wei Y, Hu Y, Cui H, Tang Y, Li C, Wang J. Comprehensive review of multidimensional biomarkers in the ShangHai At Risk for Psychosis (SHARP) program for early psychosis identification. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2023; 2:e152. [PMID: 38868725 PMCID: PMC11114265 DOI: 10.1002/pcn5.152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/28/2023] [Accepted: 10/20/2023] [Indexed: 06/14/2024]
Abstract
Psychosis is recognized as one of the largest contributors to nonfatal health loss, and early identification can largely improve routine clinical activity by predicting the psychotic course and guiding treatment. Clinicians have used the clinical high-risk for psychosis (CHR) paradigm to better understand the risk factors that contribute to the onset of psychotic disorders. Clinical factors have been widely applied to calculate the individualized risks for conversion to psychosis 1-2 years later. However, there is still a dearth of valid biomarkers to predict psychosis. Biomarkers, in the context of this paper, refer to measurable biological indicators that can provide valuable information about the early identification of individuals at risk for psychosis. The aim of this paper is to critically review studies assessing CHR and suggest possible biomarkers for application of prediction. We summarized the studies on biomarkers derived from the findings of the ShangHai at Risk for Psychosis (SHARP) program, including those that are considered to have the most potential. This comprehensive review was conducted based on expert opinions within the SHARP research team, and the selection of studies and results presented in this paper reflects the collective expertise of the team in the field of early psychosis identification. The three dimensions with potential candidates include neuroimaging dimension of brain structure and function, electrophysiological dimension of event-related potentials (ERPs), and immune dimension of inflammatory cytokines and complement proteins, which proved to be useful in supporting the prediction of psychosis from the CHR state. We suggest that these three dimensions could be useful as risk biomarkers for treatment optimization. In the future, when available for the integration of multiple dimensions, clinicians may be able to obtain a comprehensive report with detailed information of psychosis risk and specific indications about preferred prevention.
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Affiliation(s)
- TianHong Zhang
- Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - YeGang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - HuiRu Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
- CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)Chinese Academy of SciencesShanghaiChina
- Institute of Psychology and Behavioral ScienceShanghai Jiaotong UniversityShanghaiChina
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8
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Zhang T, Xu L, Wei Y, Tang X, Hu Y, Cui H, Tang Y, Wang Z, Liu H, Chen T, Li C, Wang J. Duration of untreated prodromal psychosis among individuals with clinical high risk for psychosis. Psychiatry Res 2023; 329:115522. [PMID: 37812943 DOI: 10.1016/j.psychres.2023.115522] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/11/2023]
Abstract
The impact of the duration of untreated psychosis on the outcomes of schizophrenia has been extensively studied. However, there is a notable gap in the current understanding of the relationship between the duration of untreated prodromal symptoms (DUPrS) and the development of psychosis in individuals at clinical high risk (CHR). A sample of 704 individuals with CHR was identified through a structured interview, of who 145 (20.6 %) converted to psychosis (CHR-C) during the 3-year follow-up. The DUPrS was defined as the period between the onset of the first attenuated psychotic positive symptom and the commencement of professional assistance at mental health services. Quantile regression was applied for quantile levels between 0.1 and 0.9, and adjusted for age, sex, and education.The overall sample had a mean DUPrS of 7.1 months. No significant differences were observed in the DUPrS between the CHR-C and non-converter (CHR-NC) groups. Quantile regression analysis highlighted variations in the effects of the DUPrS on clinical variables across the different quantiles. We observed a positive association between DUPrS rank and positive symptoms below the 0.3 quantile, while a positive association between DUPrS rank and negative symptoms above the 0.3 quantile (except 0.7 and 0.9 quantile). A longer DUPrS (> 3 months) was associated with younger age (odds ratio [OR] = 0.948, p = 0.003), a higher proportion of women (OR = 1.474, p = 0.003), higher baseline global function (OR = 1.044, p = 0.003), lower previous global function (OR = 0.921, p < 0.001), and higher negative symptoms (OR = 1.061, p = 0.001). This study sheds light on the pivotal role of DUPrS as a potential intermediary factor in the complex pathway of psychosis.
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Affiliation(s)
- TianHong Zhang
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - YeGang Hu
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - HuiRu Cui
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - YingYing Tang
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - ZiXuan Wang
- Shanghai Xinlianxin Psychological Counseling Co., Ltd, Shanghai, PR China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Ontario, Canada; Senior Research Fellow, Labor and Worklife Program, Harvard University, Cambridge, MA, United States
| | - ChunBo Li
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China; Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China.
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9
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Loch AA, Gondim JM, Argolo FC, Lopes-Rocha AC, Andrade JC, van de Bilt MT, de Jesus LP, Haddad NM, Cecchi GA, Mota NB, Gattaz WF, Corcoran CM, Ara A. Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features. Schizophr Res 2023; 258:45-52. [PMID: 37473667 PMCID: PMC10448183 DOI: 10.1016/j.schres.2023.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 04/26/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
AIMS Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings. METHODS 58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used. RESULTS Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item). CONCLUSION Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.
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Affiliation(s)
- Alexandre Andrade Loch
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil.
| | - João Medrado Gondim
- Instituto de Computação, Universidade Federal da Bahia, Salvador, BA, Brazil
| | - Felipe Coelho Argolo
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Ana Caroline Lopes-Rocha
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Julio Cesar Andrade
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Martinus Theodorus van de Bilt
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
| | - Leonardo Peroni de Jesus
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Natalia Mansur Haddad
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | | | - Natalia Bezerra Mota
- Instituto de Psiquiatria (IPUB), Departamento de Psiquiatria e Medicina Legal, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil; Research Department at Motrix Lab - Motrix, Rio de Janeiro, Brazil
| | - Wagner Farid Gattaz
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
| | - Cheryl Mary Corcoran
- Icahn School of Medicine at Mount Sinai, New York, NY, USA; James J. Peters VA Medical Center Bronx, NY, USA
| | - Anderson Ara
- Statistics Department, Federal University of Paraná, Curitiba, PR, Brazil
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10
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Tarbox-Berry SI, Walsh BC, Pogue-Geile MF, Woods SW. Symptoms of Attenuated Psychosis Syndrome in Relatives of Clinical High-Risk Youth: Preliminary Evidence. Schizophr Bull 2023; 49:1022-1031. [PMID: 36752824 PMCID: PMC10318861 DOI: 10.1093/schbul/sbad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
BACKGROUND AND HYPOTHESIS Attenuated Psychosis Syndrome (APS) impacts functioning and predicts increased risk of psychosis. Risk for developing APS itself has received minimal attention. Knowledge of familial and environmental contributions to APS symptoms would advance understanding of APS and risk for psychosis. As an initial step, this report presents the first data on APS symptoms in family members of APS patients. STUDY DESIGN This study utilized a discordant sibling-pair family study design. The Structured Interview for Psychosis-risk Syndromes (SIPS) was administered to 17 APS probands and 26 non-APS biological siblings. Probands and siblings were compared on positive, negative, disorganized, and general SIPS symptom scales and factors derived from those scales. STUDY RESULTS There was significantly greater symptom severity in probands compared to siblings on nine of 19 SIPS scales. Negative/anxiety, functioning, and positive symptom factors were identified. Probands showed significantly greater severity than siblings on the negative/anxiety and positive factors. Elevated pathology on the negative/anxiety factor best differentiated between probands and siblings, over and above the contribution of the positive factor. No difference was found for the functioning factor. CONCLUSIONS Results support the importance of non-familial effects on risk for APS and suggest differences in familial contribution to APS symptoms. Understanding the relative contribution of familial and environmental effects on APS symptoms may reveal important differences among APS patients, with implications for risk characterization, symptom course, and treatment selection.
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Affiliation(s)
- Sarah I Tarbox-Berry
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Barbara C Walsh
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | | | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
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