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Lee R, Griffiths SL, Gkoutos GV, Wood SJ, Bravo-Merodio L, Lalousis PA, Everard L, Jones PB, Fowler D, Hodegkins J, Amos T, Freemantle N, Singh SP, Birchwood M, Upthegrove R. Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model. Schizophr Res 2024; 274:66-77. [PMID: 39260340 DOI: 10.1016/j.schres.2024.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 08/07/2024] [Accepted: 09/06/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP). METHODS Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures. RESULTS The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56). CONCLUSIONS Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.
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Affiliation(s)
- Rebecca Lee
- Institute for Mental Health, University of Birmingham, UK; Centre for Youth Mental Health, University of Melbourne, Australia.
| | | | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, UK; Health Data Research UK, Midlands Site, Birmingham, UK
| | - Stephen J Wood
- Centre for Youth Mental Health, University of Melbourne, Australia; Orygen, Melbourne, Australia; School of Psychology, University of Birmingham, UK
| | - Laura Bravo-Merodio
- Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, UK
| | - Paris A Lalousis
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Linda Everard
- Birmingham and Solihull Mental Health Foundation Trust, Birmingham, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge and CAMEO, Cambridge and Peterborough NHS Foundation Trust, Fulbourn, UK
| | - David Fowler
- Department of Psychology, University of Sussex, Brighton, UK
| | | | - Tim Amos
- Academic Unit of Psychiatry, University of Bristol, Bristol, UK
| | - Nick Freemantle
- Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Swaran P Singh
- Coventry and Warwickshire Partnership NHS Trust, UK; Mental Health and Wellbeing Warwick Medical School, University of Warwick, Coventry, UK
| | - Max Birchwood
- Mental Health and Wellbeing Warwick Medical School, University of Warwick, Coventry, UK
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, UK; Birmingham Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, UK
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2
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Wold KF, Kreis IV, Åsbø G, Flaaten CB, Widing L, Engen MJ, Lyngstad SH, Johnsen E, Ueland T, Simonsen C, Melle I. Long-term clinical recovery and treatment resistance in first-episode psychosis: a 10-year follow-up study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:69. [PMID: 39174576 PMCID: PMC11341913 DOI: 10.1038/s41537-024-00489-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 07/31/2024] [Indexed: 08/24/2024]
Abstract
Illness trajectories in people with first-episode psychosis (FEP) vary significantly over time. Identifying early-course parameters predicting outcomes is essential, but long-term data still needs to be provided. We conducted a 10-year follow-up study of a comprehensive first-episode psychosis (FEP) cohort investigating the prevalence of clinical recovery (CR) and treatment resistance (TR) after ten years, as well as clinical, demographic, and pre-illness predictors of long-term outcomes. 102 participants with FEP DSM-IV Schizophrenia spectrum disorders were recruited within their first year of treatment. The Treatment Response and Resistance in Psychosis Working Group (TRRIP) and the Remission in Schizophrenia Working Group (RSWG) criteria were used to define TR and CR, respectively. At 10-year follow-up, 29 (29%) of the participants were classified as in CR, while 32 (31%) were classified as TR. We also identified a larger middle group (n = 41, 40%) consisting of participants in partial recovery. 7% of all participants had tried Clozapine at the 10-year follow-up. Logistic regression analyses identified insidious onset (OR = 4.16) and baseline disorganized symptoms (OR = 2.96) as significantly associated with an increased risk of developing TR. Good premorbid academic adjustment (OR = 1.60) and acute onset (OR = 3.40) were associated with an increased chance of CR. We identified three long-term outcome groups by using recent consensus definitions. We also identified the potential importance of assessing baseline disorganized symptoms and monitoring patients with insidious onset more closely. Further, the findings suggest that clinicians should pay close attention to early-course parameters and provide adequate treatment to improve long-term outcomes of FEP.
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Grants
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #223273/F50 Norges Forskningsråd (Research Council of Norway)
- #287714 Norges Forskningsråd (Research Council of Norway)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
- #2006233, #2006258, #2011085, #2014102, #2015088 Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
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Affiliation(s)
- Kristin Fjelnseth Wold
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Division of Mental Health and Addiction, Department of Research and Innovation, Section for Clinical Psychosis Research, Oslo University Hospital, Oslo, Norway.
| | | | - Gina Åsbø
- Division of Mental Health and Addiction, Department of Research and Innovation, Section for Clinical Psychosis Research, Oslo University Hospital, Oslo, Norway
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Camilla Bärthel Flaaten
- Division of Mental Health and Addiction, Department of Research and Innovation, Section for Clinical Psychosis Research, Oslo University Hospital, Oslo, Norway
| | - Line Widing
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Substance Use, Department of Child and Adolescent Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Magnus Johan Engen
- Division of Mental Health and Addiction, Nydalen District Psychiatric Centre, Oslo University Hospital, Oslo, Norway
| | - Siv Hege Lyngstad
- Division of Mental Health and Addiction, Nydalen District Psychiatric Centre, Oslo University Hospital, Oslo, Norway
| | - Erik Johnsen
- Department of Clinical Medicine, University of Bergen, Haukeland University Hospital, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Torill Ueland
- Division of Mental Health and Addiction, Department of Research and Innovation, Section for Clinical Psychosis Research, Oslo University Hospital, Oslo, Norway
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Carmen Simonsen
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Department of Research and Innovation, Early Intervention in Psychosis Advisory Unit for Southeast Norway, Oslo Universy Hospital, Oslo, Norway
| | - Ingrid Melle
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Department of Research and Innovation, Section for Clinical Psychosis Research, Oslo University Hospital, Oslo, Norway
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Barruel D, Hilbey J, Charlet J, Chaumette B, Krebs MO, Dauriac-Le Masson V. Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features. Schizophr Res 2024; 270:1-10. [PMID: 38823319 DOI: 10.1016/j.schres.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 06/03/2024]
Abstract
Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, with machine learning methods, the impact of demographic and clinical patient characteristics on TRS prediction, for already established risk factors and unexplored ones. This was a retrospective study of 500 patients admitted during 2020 to the University Hospital Group for Paris Psychiatry. We hypothesized potential TRS risk factors. The selected features were coded into structured variables in a new dataset, by processing patients discharge summaries and medical narratives with natural-language processing methods. We compared three machine learning models (XGBoost, logistic elastic net regression, logistic regression without regularization) for predicting TRS outcome. We analysed feature impact on the models, suggesting the following factors as markers of a high-risk TRS profile: early age at first contact with psychiatry, antipsychotic treatment interruptions due to non-adherence, absence of positive symptoms at baseline, educational problems and adolescence mental disorders in the personal psychiatric history. Specifically, we found a significant association with TRS outcome for age at first contact with psychiatry and medication non-adherence. Our findings on TRS risk factors are consistent with the review of the literature and suggest potential in using early pathophysiologic features for TRS prediction. Results were encouraging with the use of natural-langage processing techniques to leverage raw data provided by discharge summaries, combined with machine leaning models. These findings are a promising step for helping clinicians adapt their guidelines to early detection of TRS.
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Affiliation(s)
- David Barruel
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, 1, rue Cabanis, 75014 Paris, France.
| | - Jacques Hilbey
- Sorbonne Université, Paris, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Paris, France
| | - Jean Charlet
- Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Paris, France; Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Boris Chaumette
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM, U1266 Paris, France; Department of Psychiatry, McGill University, Montréal, QC, Canada
| | - Marie-Odile Krebs
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM, U1266 Paris, France
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4
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Peng Z, Li Q, Liu X, Zhang H, Luosang-Zhuoma, Ran M, Liu M, Tan X, Stein MJ. A new schizophrenia screening instrument based on evaluating the patient's writing. Schizophr Res 2024; 266:127-135. [PMID: 38401411 DOI: 10.1016/j.schres.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 01/18/2024] [Accepted: 02/10/2024] [Indexed: 02/26/2024]
Abstract
Formal Thought Disorder (FTD) is a defining feature of schizophrenia, which is often assessed through patients' speech. Meanwhile, the written language is less studied. The aim of the present study is to establish and validate a comprehensive clinical screening scale, capturing the full variety of empirical characteristics of writing in patients with schizophrenia. The 16-item Screening Instrument for Schizophrenic Features in Writing (SISFiW) is derived from detailed literature review and a "brainstorming" discussion on 30 samples written by patients with schizophrenia. One hundred and fifty-seven participants (114 patients with an ICD-10 diagnoses of schizophrenia; 43 healthy control subjects) were interviewed and symptoms assessed with the Positive and Negative Syndrome Scale (PANSS) and the Scale for the Assessment of Thought, Language, and Communication (TLC). Article samples written by each participant were rated with the SISFiW. Results demonstrated significant difference of the SISFiW-total between the patient group and healthy controls [(3.61 ± 1.72) vs. (0.49 ± 0.63), t = 16.64, p<0.001]. The inter-rater reliability (weighted kappa = 0.72) and the internal consistency (Cronbach's alpha coefficient = 0.613) were acceptable, but correlations with the criterion (PANSS and TLC) were unremarkable. The ROC analysis indicated a cutoff point at 2 with the maximal sensitivity (93.0 %)/specificity (93.0 %). Discriminant analysis of the SISFiW items yielded 8 classifiers that discriminated between the diagnostic groups at a perfect overall performance (with 90.4 % of original and 88.5 % cross-validated grouped cases classified correctly). This instrument appears to be practicable and reliable, with relatively robust discriminatory power, and may serve as a complementary tool to existing FTD rating scales.
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Affiliation(s)
- Zulai Peng
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Qingjun Li
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Xinglan Liu
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Huangzhiheng Zhang
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Luosang-Zhuoma
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Manli Ran
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Maohang Liu
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
| | - Xiaolin Tan
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China.
| | - Mark J Stein
- Chongqing Mental Health Center, Chongqing, China; Affiliated Hospital of Southwest University, Chongqing, China
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5
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Lin X, Huo Y, Wang Q, Liu G, Shi J, Fan Y, Lu L, Jing R, Li P. Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia. Cereb Cortex 2024; 34:bhae003. [PMID: 38252996 DOI: 10.1093/cercor/bhae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/28/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, United States
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
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6
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Sagud M, Tudor L, Nedic Erjavec G, Nikolac Perkovic M, Uzun S, Mimica N, Madzarac Z, Zivkovic M, Kozumplik O, Konjevod M, Svob Strac D, Pivac N. Genotypic and Haplotypic Association of Catechol- O-Methyltransferase rs4680 and rs4818 Gene Polymorphisms with Particular Clinical Symptoms in Schizophrenia. Genes (Basel) 2023; 14:1358. [PMID: 37510262 PMCID: PMC10379812 DOI: 10.3390/genes14071358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Catechol-O-methyl transferase (COMT) gene variants are involved in different neuropsychiatric disorders and cognitive impairments, associated with altered dopamine function. This study investigated the genotypic and haplotypic association of COMT rs4680 and rs4618 polymorphisms with the severity of cognitive and other clinical symptoms in 544 male and 385 female subjects with schizophrenia. COMT rs4818 G carriers were more frequent in male patients with mild abstract thinking difficulties, compared to CC homozygotes or C allele carriers. Male carriers of COMT rs4680 A allele had worse abstract thinking (N5) scores than GG carriers, whereas AA homozygotes were more frequent in male subjects with lower scores on the intensity of the somatic concern (G1) item, compared to G carriers. Male carriers of COMT rs4818-rs4680 GA haplotype had the highest scores on the G1 item (somatic concern), whereas GG haplotype carriers had the lowest scores on G2 (anxiety) and G6 (depression) items. COMT GG haplotype was less frequent in female patients with severe disturbance of volition (G13 item) compared to the group with mild symptoms, while CG haplotype was more frequent in female patients with severe then mild symptoms. These findings suggest the sex-specific genotypic and haplotypic association of COMT variants with a severity of cognitive and other clinical symptoms of schizophrenia.
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Affiliation(s)
- Marina Sagud
- Department for Psychiatry and Psychological Medicine, University Hospital Centre Zagreb, 10000 Zagreb, Croatia; (M.S.); (Z.M.); (M.Z.)
- School of Medicine, University of Zagreb, 10000 Zagreb, Croatia; (S.U.); (N.M.)
| | - Lucija Tudor
- Laboratory for Molecular Neuropsychiatry, Division of Molecular Medicine, Ruder Boskovic Institute, 10000 Zagreb, Croatia; (L.T.); (G.N.E.); (M.N.P.); (M.K.); (D.S.S.)
| | - Gordana Nedic Erjavec
- Laboratory for Molecular Neuropsychiatry, Division of Molecular Medicine, Ruder Boskovic Institute, 10000 Zagreb, Croatia; (L.T.); (G.N.E.); (M.N.P.); (M.K.); (D.S.S.)
| | - Matea Nikolac Perkovic
- Laboratory for Molecular Neuropsychiatry, Division of Molecular Medicine, Ruder Boskovic Institute, 10000 Zagreb, Croatia; (L.T.); (G.N.E.); (M.N.P.); (M.K.); (D.S.S.)
| | - Suzana Uzun
- School of Medicine, University of Zagreb, 10000 Zagreb, Croatia; (S.U.); (N.M.)
- Department for Biological Psychiatry and Psychogeriatrics, University Psychiatric Hospital Vrapce, 10090 Zagreb, Croatia;
| | - Ninoslav Mimica
- School of Medicine, University of Zagreb, 10000 Zagreb, Croatia; (S.U.); (N.M.)
- Department for Biological Psychiatry and Psychogeriatrics, University Psychiatric Hospital Vrapce, 10090 Zagreb, Croatia;
| | - Zoran Madzarac
- Department for Psychiatry and Psychological Medicine, University Hospital Centre Zagreb, 10000 Zagreb, Croatia; (M.S.); (Z.M.); (M.Z.)
| | - Maja Zivkovic
- Department for Psychiatry and Psychological Medicine, University Hospital Centre Zagreb, 10000 Zagreb, Croatia; (M.S.); (Z.M.); (M.Z.)
| | - Oliver Kozumplik
- Department for Biological Psychiatry and Psychogeriatrics, University Psychiatric Hospital Vrapce, 10090 Zagreb, Croatia;
| | - Marcela Konjevod
- Laboratory for Molecular Neuropsychiatry, Division of Molecular Medicine, Ruder Boskovic Institute, 10000 Zagreb, Croatia; (L.T.); (G.N.E.); (M.N.P.); (M.K.); (D.S.S.)
| | - Dubravka Svob Strac
- Laboratory for Molecular Neuropsychiatry, Division of Molecular Medicine, Ruder Boskovic Institute, 10000 Zagreb, Croatia; (L.T.); (G.N.E.); (M.N.P.); (M.K.); (D.S.S.)
| | - Nela Pivac
- Laboratory for Molecular Neuropsychiatry, Division of Molecular Medicine, Ruder Boskovic Institute, 10000 Zagreb, Croatia; (L.T.); (G.N.E.); (M.N.P.); (M.K.); (D.S.S.)
- University of Applied Sciences Hrvatsko Zagorje Krapina, 49000 Krapina, Croatia
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7
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Psychotic disorders as a framework for precision psychiatry. Nat Rev Neurol 2023; 19:221-234. [PMID: 36879033 DOI: 10.1038/s41582-023-00779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2023] [Indexed: 03/08/2023]
Abstract
People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.
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Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Transl Psychiatry 2023; 13:75. [PMID: 36864017 PMCID: PMC9981732 DOI: 10.1038/s41398-023-02371-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/01/2023] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.
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Tang Y, Wu Y, Li X, Hao Q, Deng W, Yue W, Yan H, Zhang Y, Tan L, Chen Q, Yang G, Lu T, Wang L, Yang F, Zhang F, Yang J, Li K, Lv L, Tan Q, Zhang H, Ma X, Li L, Wang C, Ma X, Zhang D, Yu H, Zhao L, Ren H, Wang Y, Zhang G, Li C, Du X, Hu X, Li T, Wang Q. Early Efficacy of Antipsychotic Medications at Week 2 Predicts Subsequent Responses at Week 6 in a Large-scale Randomized Controlled Trial. Curr Neuropharmacol 2023; 21:424-436. [PMID: 36411567 PMCID: PMC10190139 DOI: 10.2174/1570159x21666221118164612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/25/2022] [Accepted: 10/18/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Since the early clinical efficacy of antipsychotics has not yet been well perceived, this study sought to decide whether the efficacy of antipsychotics at week 2 can predict subsequent responses at week 6 and identify how such predictive capacities vary among different antipsychotics and psychotic symptoms. METHODS A total of 3010 patients with schizophrenia enrolled in a randomized controlled trial (RCT) and received a 6-week treatment with one antipsychotic drug randomly chosen from five atypical antipsychotics (risperidone 2-6 mg/d, olanzapine 5-20 mg/d, quetiapine 400-750 mg/d, aripiprazole 10-30 mg/d, and ziprasidone 80-160 mg/d) and two typical antipsychotics (perphenazine 20-60 mg/d and haloperidol 6-20 mg/d). Early efficacy was defined as the reduction rate using the Positive and Negative Syndrome Scale (PANSS) total score at week 2. With cut-offs at 50% reduction, logistic regression, receiver operating characteristic (ROC) and random forests were adopted. RESULTS The reduction rate of PANSS total score and improvement of psychotic symptoms at week 2 enabled subsequent responses to 7 antipsychotics to be predicted, in which improvements in delusions, lack of judgment and insight, unusual thought content, and suspiciousness/ persecution were endowed with the greatest weight. CONCLUSION It is robust enough to clinically predict treatment responses to antipsychotics at week 6 using the reduction rate of PANSS total score and symptom relief at week 2. Psychiatric clinicians had better determine whether to switch the treatment plan by the first 2 weeks.
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Affiliation(s)
- Yiguo Tang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Yulu Wu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Xiaojing Li
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - QinJian Hao
- The Center of Gerontology and Geriatrics, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Weihua Yue
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Hao Yan
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Yamin Zhang
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Liwen Tan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qi Chen
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Guigang Yang
- Beijing Anding Hospital, Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Tianlan Lu
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Lifang Wang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Fude Yang
- Beijing HuiLongGuan Hospital, Beijing, China
| | - Fuquan Zhang
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Jianli Yang
- Institute of Mental Health, Tianjin Anding Hospital, Tianjin, China
- Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Keqing Li
- Hebei Mental Health Center, Baoding, Hebei, China
| | - Luxian Lv
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Qingrong Tan
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shanxi, China
| | - Hongyan Zhang
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Xin Ma
- Beijing Anding Hospital, Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chuanyue Wang
- Beijing Anding Hospital, Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaohong Ma
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Dai Zhang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, Jining, China
| | - Liansheng Zhao
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Hongyan Ren
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Yingcheng Wang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Guangya Zhang
- Department of Psychiatry, Suzhou Psychiatric Hospital, Suzhou, China
- The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Chuanwei Li
- Department of Psychiatry, Suzhou Psychiatric Hospital, Suzhou, China
- The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xiangdong Du
- Department of Psychiatry, Suzhou Psychiatric Hospital, Suzhou, China
- The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xun Hu
- The Clinical Research Center and the Department of Pathology, Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tao Li
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Qiang Wang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
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10
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Disorganization domain as a putative predictor of Treatment Resistant Schizophrenia (TRS) diagnosis: A machine learning approach. J Psychiatr Res 2022; 155:572-578. [PMID: 36206601 DOI: 10.1016/j.jpsychires.2022.09.044] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Treatment Resistant Schizophrenia (TRS) is the persistence of significant symptoms despite adequate antipsychotic treatment. Although consensus guidelines are available, this condition remains often unrecognized and an average delay of 4-9 years in the initiation of clozapine, the gold standard for the pharmacological treatment of TRS, has been reported. We aimed to determine through a machine learning approach which domain of the Positive and Negative Syndrome Scale (PANSS) 5-factor model was most associated with TRS. METHODS In a cross-sectional design, 128 schizophrenia patients were classified as TRS (n = 58) or non-TRS (n = 60) after a structured retrospective-prospective analysis of treatment response. The random forest algorithm (RF) was trained to analyze the relationship between the presence/absence of TRS and PANSS-based psychopathological factor scores (positive, negative, disorganization, excitement, and emotional distress). As a complementary strategy to identify the variables most associated with the diagnosis of TRS, we included the variables selected by the RF algorithm in a multivariate logistic regression model. RESULTS according to the RF model, patients with higher disorganization, positive, and excitement symptom scores were more likely to be classified as TRS. The model showed an accuracy of 67.19%, a sensitivity of 62.07%, and a specificity of 71.43%, with an area under the curve (AUC) of 76.56%. The multivariate model including disorganization, positive, and excitement factors showed that disorganization was the only factor significantly associated with TRS. Therefore, the disorganization factor was the variable most consistently associated with the diagnosis of TRS in our sample.
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11
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Correll CU, Agid O, Crespo-Facorro B, de Bartolomeis A, Fagiolini A, Seppälä N, Howes OD. A Guideline and Checklist for Initiating and Managing Clozapine Treatment in Patients with Treatment-Resistant Schizophrenia. CNS Drugs 2022; 36:659-679. [PMID: 35759211 PMCID: PMC9243911 DOI: 10.1007/s40263-022-00932-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/26/2022] [Indexed: 12/14/2022]
Abstract
Treatment-resistant schizophrenia (TRS) will affect about one in three patients with schizophrenia. Clozapine is the only treatment approved for TRS, and patients should be treated as soon as possible to improve their chances of achieving remission. Despite its effectiveness, concern over side effects, monitoring requirements, and inexperience with prescribing often result in long delays that can expose patients to unnecessary risks and compromise their chances of achieving favorable long-term outcomes. We critically reviewed the literature on clozapine use in TRS, focusing on guidelines, systematic reviews, and algorithms to identify strategies for improving clozapine safety and tolerability. Based on this, we have provided an overview of strategies to support early initiation of clozapine in patients with TRS based on the latest evidence and our clinical experience, and have summarized the key elements in a practical, evidence-based checklist for identifying and managing patients with TRS, with the aim of increasing confidence in prescribing and monitoring clozapine therapy.
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Affiliation(s)
- C U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany.,Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.,Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
| | - Ofer Agid
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | | | - Andrea de Bartolomeis
- Section on Clinical Psychiatry and Psychology, Laboratory of Molecular and Translational Psychiatry and Unit of Treatment Resistant Psychosis, University of Naples Federico II, Naples, Italy
| | - Andrea Fagiolini
- Department of Molecular Medicine, University of Siena, Siena, Italy
| | - Niko Seppälä
- Department of Psychiatry Satasairaala, Harjavalta, Finland
| | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
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12
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Oh HS, Lee BJ, Lee YS, Jang OJ, Nakagami Y, Inada T, Kato TA, Kanba S, Chong MY, Lin SK, Si T, Xiang YT, Avasthi A, Grover S, Kallivayalil RA, Pariwatcharakul P, Chee KY, Tanra AJ, Rabbani G, Javed A, Kathiarachchi S, Myint WA, Cuong TV, Wang Y, Sim K, Sartorius N, Tan CH, Shinfuku N, Park YC, Park SC. Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia. J Pers Med 2022; 12:969. [PMID: 35743753 PMCID: PMC9224640 DOI: 10.3390/jpm12060969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/10/2022] [Accepted: 06/12/2022] [Indexed: 12/17/2022] Open
Abstract
The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793−0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615−0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context.
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Affiliation(s)
- Hong Seok Oh
- Department of Psychiatry, Konyang University Hospital, Daejeon 35356, Korea;
| | - Bong Ju Lee
- Department of Psychiatry, Inje University Haeundae Paik Hospital, Busan 48108, Korea;
| | - Yu Sang Lee
- Department of Psychiatry, Yong-In Mental Hospital, Yongin 17089, Korea;
| | - Ok-Jin Jang
- Department of Psychiatry, Bugok National Hospital, Changyeong 50365, Korea;
| | - Yukako Nakagami
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan;
| | - Toshiya Inada
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan;
| | - Takahiro A. Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (T.A.K.); (S.K.)
| | - Shigenobu Kanba
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (T.A.K.); (S.K.)
| | - Mian-Yoon Chong
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung & Chang Gung University School of Medicine, Taoyuan 83301, Taiwan;
| | - Sih-Ku Lin
- Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan;
| | - Tianmei Si
- Peking Institute of Mental Health (PIMH), Peking University, Beijing 100083, China;
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China;
| | - Ajit Avasthi
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India; (A.A.); (S.G.)
| | - Sandeep Grover
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India; (A.A.); (S.G.)
| | | | - Pornjira Pariwatcharakul
- Department of Psychiatry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10400, Thailand;
| | - Kok Yoon Chee
- Tunku Abdul Rahman Institute of Neuroscience, Kuala Lumpur Hospital, Kuala Lumpur 502586, Malaysia;
| | - Andi J. Tanra
- Wahidin Sudirohusodo University, Makassar 90245, Sulawesi Selatan, Indonesia;
| | - Golam Rabbani
- National Institute of Mental Health, Dhaka 1207, Bangladesh;
| | - Afzal Javed
- Pakistan Psychiatric Research Centre, Fountain House, Lahore 39020, Pakistan;
| | - Samudra Kathiarachchi
- Department of Psychiatry, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka;
| | - Win Aung Myint
- Department of Mental Health, University of Medicine (1), Yangon 15032, Myanmar;
| | | | - Yuxi Wang
- West Region, Institute of Mental Health, Singapore 119228, Singapore; (Y.W.); (K.S.)
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore 119228, Singapore; (Y.W.); (K.S.)
- Research Division, Institute of Mental Health, Singapore 119228, Singapore
| | - Norman Sartorius
- Association of the Improvement of Mental Health Programs (AMH), 1209 Geneva, Switzerland;
| | - Chay-Hoon Tan
- Department of Pharmacology, National University Hospital, Singapore 119228, Singapore;
| | - Naotaka Shinfuku
- Department of Social Welfare, School of Human Sciences, Seinan Gakuin University, Fukuoka 814-8511, Japan;
| | - Yong Chon Park
- Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Korea;
| | - Seon-Cheol Park
- Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Korea;
- Department of Psychiatry, Hanyang University Guri Hospital, Guri 11923, Korea
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13
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Network Analysis-Based Disentanglement of the Symptom Heterogeneity in Asian Patients with Schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics. J Pers Med 2022; 12:jpm12010033. [PMID: 35055348 PMCID: PMC8779246 DOI: 10.3390/jpm12010033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/25/2021] [Accepted: 12/29/2021] [Indexed: 02/02/2023] Open
Abstract
The symptom heterogeneity of schizophrenia is consistent with Wittgenstein's analogy of a language game. From the perspective of precision medicine, this study aimed to estimate the symptom presentation and identify the psychonectome in Asian patients, using data obtained from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics. We constructed a network structure of the Brief Psychiatric Rating Scale (BPRS) items in 1438 Asian patients with schizophrenia. Furthermore, all the BPRS items were considered to be an ordered categorical variable ranging in value from 1-7. Motor retardation was situated most centrally within the BPRS network structure, followed by depressive mood and unusual thought content. Contrastingly, hallucinatory behavior was situated least centrally within the network structure. Using a community detection algorithm, the BPRS items were organized into positive, negative, and general symptom clusters. Overall, DSM symptoms were not more central than non-DSM symptoms within the symptom network of Asian patients with schizophrenia. Thus, motor retardation, which results from the unmet needs associated with current antipsychotic medications for schizophrenia, may be a tailored treatment target for Asian patients with schizophrenia. Based on these findings, targeting non-dopamine systems (glutamate, γ-aminobutyric acid) may represent an effective strategy with respect to precision medicine for psychosis.
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14
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Ucok A, Karakaş B, Şahin OŞ. Formal thought disorder in patients with first-episode schizophrenia: Results of a one-year follow-up study. Psychiatry Res 2021; 301:113972. [PMID: 33979765 DOI: 10.1016/j.psychres.2021.113972] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 04/24/2021] [Indexed: 11/19/2022]
Abstract
Formal thought disorder (FTD) refers to abnormal speech patterns that can be characterized by deficiencies in thought organization and direction. The present study aimed to assess the factor structure of FTD and to examine its relationship with cognition and clinical features at first admission in patients with first-episode schizophrenia. We also examined the course of FTD during the twelve months after first admission. We assessed FTD using the alogia items of the Scale for the Assessment of Negative Symptoms and FTD items of the Scale for the Assessment of Positive Symptoms in 160 drug-naïve patients. A three-factor structure as a disorganization factor, poverty factor, and verbosity factor were found in principal component analysis. The poverty factor was correlated negatively with executive functions, attention, and global cognition. The poverty factor was also correlated with global functioning. Admission FTD factor scores were not related to global functioning and work/study status at one year. The positive-FTD score decreased from admission to the third month, but no change occurred from the third to the twelfth month. The negative-FTD score did not differ throughout the follow-up. Our findings showed that FTD had three factors. Each factor had a different relationship with cognition and functioning.
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Affiliation(s)
- Alp Ucok
- Department of Psychiatry, Faculty of Medicine, Istanbul University, Istanbul, Turkey.
| | - Begüm Karakaş
- Department of Psychiatry, Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Olcay Şenay Şahin
- Department of Psychiatry, Faculty of Medicine, Istanbul University, Istanbul, Turkey
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15
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Bryll A, Krzyściak W, Karcz P, Śmierciak N, Kozicz T, Skrzypek J, Szwajca M, Pilecki M, Popiela TJ. The Relationship between the Level of Anterior Cingulate Cortex Metabolites, Brain-Periphery Redox Imbalance, and the Clinical State of Patients with Schizophrenia and Personality Disorders. Biomolecules 2020; 10:E1272. [PMID: 32899276 PMCID: PMC7565827 DOI: 10.3390/biom10091272] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/17/2020] [Accepted: 08/28/2020] [Indexed: 01/10/2023] Open
Abstract
Schizophrenia is a complex mental disorder whose course varies with periods of deterioration and symptomatic improvement without diagnosis and treatment specific for the disease. So far, it has not been possible to clearly define what kinds of functional and structural changes are responsible for the onset or recurrence of acute psychotic decompensation in the course of schizophrenia, and to what extent personality disorders may precede the appearance of the appropriate symptoms. The work combines magnetic resonance spectroscopy imaging with clinical evaluation and laboratory tests to determine the likely pathway of schizophrenia development by identifying peripheral cerebral biomarkers compared to personality disorders. The relationship between the level of metabolites in the brain, the clinical status of patients according to International Statistical Classification of Diseases and Related Health Problems, 10th Revision ICD-10, duration of untreated psychosis (DUP), and biochemical indices related to redox balance (malondialdehyde), the efficiency of antioxidant systems (FRAP), and bioenergetic metabolism of mitochondria, were investigated. There was a reduction in the level of brain N-acetyl-aspartate and glutamate in the anterior cingulate gyrus of patients with schisophrenia compared to the other groups that seems more to reflect a biological etiopathological factor of psychosis. Decreased activity of brain metabolites correlated with increased peripheral oxidative stress (increased malondialdehyde MDA) associated with decreased efficiency of antioxidant systems (FRAP) and the breakdown of clinical symptoms in patients with schizophrenia in the course of psychotic decompensation compared to other groups. The period of untreated psychosis correlated negatively with glucose value in the brain of people with schizophrenia, and positively with choline level. The demonstrated differences between two psychiatric units, such as schizophrenia and personality disorders in relation to healthy people, may be used to improve the diagnosis and prognosis of schizophrenia compared to other heterogenous psychopathology in the future. The collapse of clinical symptoms of patients with schizophrenia in the course of psychotic decompensation may be associated with the occurrence of specific schizotypes, the determination of which is possible by determining common relationships between changes in metabolic activity of particular brain structures and peripheral parameters, which may be an important biological etiopathological factor of psychosis. Markers of peripheral redox imbalance associated with disturbed bioenergy metabolism in the brain may provide specific biological factors of psychosis however, they need to be confirmed in further studies.
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Affiliation(s)
- Amira Bryll
- Department of Radiology, Jagiellonian University Medical College, Kopernika 19, 31-501 Krakow, Poland;
| | - Wirginia Krzyściak
- Department of Medical Diagnostics, Jagiellonian University, Medical College, Medyczna 9, 30-688 Krakow, Poland;
| | - Paulina Karcz
- Department of Electroradiology, Jagiellonian University Medical College, Michałowskiego 12, 31-126 Krakow, Poland;
| | - Natalia Śmierciak
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Jagiellonian University, Medical College, Kopernika 21a, 31-501 Krakow, Poland; (N.Ś.); (M.S.); (M.P.)
| | - Tamas Kozicz
- Department of Clinical Genomics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Justyna Skrzypek
- Department of Medical Diagnostics, Jagiellonian University, Medical College, Medyczna 9, 30-688 Krakow, Poland;
| | - Marta Szwajca
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Jagiellonian University, Medical College, Kopernika 21a, 31-501 Krakow, Poland; (N.Ś.); (M.S.); (M.P.)
| | - Maciej Pilecki
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, Jagiellonian University, Medical College, Kopernika 21a, 31-501 Krakow, Poland; (N.Ś.); (M.S.); (M.P.)
| | - Tadeusz J. Popiela
- Department of Radiology, Jagiellonian University Medical College, Kopernika 19, 31-501 Krakow, Poland;
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