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Wu Z, Wang J, Zhang C, Peng D, Mellor D, Luo Y, Fang Y. Clinical distinctions in symptomatology and psychiatric comorbidities between misdiagnosed bipolar I and bipolar II disorder versus major depressive disorder. BMC Psychiatry 2024; 24:352. [PMID: 38730288 PMCID: PMC11088069 DOI: 10.1186/s12888-024-05810-3] [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: 01/23/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND To explore the demographic and clinical features of current depressive episode that discriminate patients diagnosed with major depressive disorder (MDD) from those with bipolar I (BP-I) and bipolar II (BP-II) disorder who were misdiagnosed as having MDD . METHODS The Mini-International Neuropsychiatric Interview (MINI) assessment was performed to establish DSM-IV diagnoses of MDD, and BP-I and BP-II, previously being misdiagnosed as MDD. Demographics, depressive symptoms and psychiatric comorbidities were compared between 1463 patients with BP-I, BP-II and MDD from 8 psychiatric settings in mainland China. A multinomial logistic regression model was performed to assess clinical correlates of diagnoses. RESULTS A total of 14.5% of the enrolled patients initially diagnosed with MDD were eventually diagnosed with BP. Broad illness characteristics including younger age, higher prevalence of recurrence, concurrent dysthymia, suicidal attempts, agitation, psychotic features and psychiatric comorbidities, as well as lower prevalence of insomnia, weight loss and somatic symptoms were featured by patients with BP-I and/or BP-I, compared to those with MDD. Comparisons between BP-I and BP-II versus MDD indicated distinct symptom profiles and comorbidity patterns with more differences being observed between BP-II and MDD, than between BP-I and MDD . CONCLUSION The results provide evidence of clinically distinguishing characteristics between misdiagnosed BP-I and BP- II versus MDD. The findings have implications for guiding more accurate diagnoses of bipolar disorders.
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Affiliation(s)
- Zhiguo Wu
- Department of Psychological Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China.
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Yangpu District Mental Health Center, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Jun Wang
- Shanghai Yangpu District Mental Health Center, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Chen Zhang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Daihui Peng
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - David Mellor
- School of Psychology, Deakin University, Melbourne, Australia
| | - Yanli Luo
- Department of Psychological Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China
| | - Yiru Fang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China.
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Niu Z, Wu X, Zhu Y, Yang L, Shi Y, Wang Y, Qiu H, Gu W, Wu Y, Long X, Lu Z, Hu S, Yao Z, Yang H, Liu T, Xia Y, Chen Z, Chen J, Fang Y. Early Diagnosis of Bipolar Disorder Coming Soon: Application of an Oxidative Stress Injury Biomarker (BIOS) Model. Neurosci Bull 2022; 38:979-991. [PMID: 35590012 PMCID: PMC9468206 DOI: 10.1007/s12264-022-00871-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/10/2022] [Indexed: 11/26/2022] Open
Abstract
Early distinction of bipolar disorder (BD) from major depressive disorder (MDD) is difficult since no tools are available to estimate the risk of BD. In this study, we aimed to develop and validate a model of oxidative stress injury for predicting BD. Data were collected from 1252 BD and 1359 MDD patients, including 64 MDD patients identified as converting to BD from 2009 through 2018. 30 variables from a randomly-selected subsample of 1827 (70%) patients were used to develop the model, including age, sex, oxidative stress markers (uric acid, bilirubin, albumin, and prealbumin), sex hormones, cytokines, thyroid and liver function, and glycolipid metabolism. Univariate analyses and the Least Absolute Shrinkage and Selection Operator were applied for data dimension reduction and variable selection. Multivariable logistic regression was used to construct a model for predicting bipolar disorder by oxidative stress biomarkers (BIOS) on a nomogram. Internal validation was assessed in the remaining 784 patients (30%), and independent external validation was done with data from 3797 matched patients from five other hospitals in China. 10 predictors, mainly oxidative stress markers, were shown on the nomogram. The BIOS model showed good discrimination in the training sample, with an AUC of 75.1% (95% CI: 72.9%-77.3%), sensitivity of 0.66, and specificity of 0.73. The discrimination was good both in internal validation (AUC 72.1%, 68.6%-75.6%) and external validation (AUC 65.7%, 63.9%-67.5%). In this study, we developed a nomogram centered on oxidative stress injury, which could help in the individualized prediction of BD. For better real-world practice, a set of measurements, especially on oxidative stress markers, should be emphasized using big data in psychiatry.
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Affiliation(s)
- Zhiang Niu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xiaohui Wu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Yuncheng Zhu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Division of Mood Disorders, Shanghai Hongkou Mental Health Center, Shanghai, 200083, China
| | - Lu Yang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Yifan Shi
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Yun Wang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Hong Qiu
- Information and Statistics Department, Shanghai Mental Health Center, Shanghai, 200030, China
| | - Wenjie Gu
- Information and Statistics Department, Shanghai Mental Health Center, Shanghai, 200030, China
| | - Yina Wu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xiangyun Long
- Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200333, China
| | - Zheng Lu
- Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200333, China
| | - Shaohua Hu
- Department of Psychiatry, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Zhijian Yao
- Nanjing Brain Hospital, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Haichen Yang
- Shenzhen Mental Health Center, Shenzhen, 518003, China
| | - Tiebang Liu
- Shenzhen Mental Health Center, Shenzhen, 518003, China
| | - Yong Xia
- Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou Seventh People's Hospital, Hangzhou, 310013, China
| | - Zhiyu Chen
- Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou Seventh People's Hospital, Hangzhou, 310013, China
| | - Jun Chen
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
| | - Yiru Fang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China.
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 201108, China.
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Phenotypes, mechanisms and therapeutics: insights from bipolar disorder GWAS findings. Mol Psychiatry 2022; 27:2927-2939. [PMID: 35351989 DOI: 10.1038/s41380-022-01523-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/02/2022] [Accepted: 03/10/2022] [Indexed: 12/25/2022]
Abstract
Genome-wide association studies (GWAS) have reported substantial genomic loci significantly associated with clinical risk of bipolar disorder (BD), and studies combining techniques of genetics, neuroscience, neuroimaging, and pharmacology are believed to help tackle clinical problems (e.g., identifying novel therapeutic targets). However, translating findings of psychiatric genetics into biological mechanisms underlying BD pathogenesis remains less successful. Biological impacts of majority of BD GWAS risk loci are obscure, and the involvement of many GWAS risk genes in this illness is yet to be investigated. It is thus necessary to review the progress of applying BD GWAS risk genes in the research and intervention of the disorder. A comprehensive literature search found that a number of such risk genes had been investigated in cellular or animal models, even before they were highlighted in BD GWAS. Intriguingly, manipulation of many BD risk genes (e.g., ANK3, CACNA1C, CACNA1B, HOMER1, KCNB1, MCHR1, NCAN, SHANK2 etc.) resulted in altered murine behaviors largely restoring BD clinical manifestations, including mania-like symptoms such as hyperactivity, anxiolytic-like behavior, as well as antidepressant-like behavior, and these abnormalities could be attenuated by mood stabilizers. In addition to recapitulating phenotypic characteristics of BD, some GWAS risk genes further provided clues for the neurobiology of this illness, such as aberrant activation and functional connectivity of brain areas in the limbic system, and modulated dendritic spine morphogenesis as well as synaptic plasticity and transmission. Therefore, BD GWAS risk genes are undoubtedly pivotal resources for modeling this illness, and might be translational therapeutic targets in the future clinical management of BD. We discuss both promising prospects and cautions in utilizing the bulk of useful resources generated by GWAS studies. Systematic integrations of findings from genetic and neuroscience studies are called for to promote our understanding and intervention of BD.
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Demissie M, Hanlon C, Ng L, Fekadu A, Mayston R. Why doesn't God say "enough"? Experiences of living with bipolar disorder in rural Ethiopia. Soc Sci Med 2021; 270:113625. [PMID: 33373775 DOI: 10.1016/j.socscimed.2020.113625] [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] [Revised: 11/04/2020] [Accepted: 12/14/2020] [Indexed: 10/22/2022]
Abstract
RATIONALE Little is known about the specific experience people living with bipolar disorder in rural, low resource settings, where conditions that disrupt normal social interactions are often highly stigmatized and evidence-based treatments are rare. OBJECTIVE To explore illness experience, coping strategies, help-seeking practices, and consequences of illness among people with bipolar disorder (PBD) and their family members in rural Ethiopia as an initial step for developing psychosocial intervention grounded by the experiences of PBD. METHOD A qualitative methods using in-depth interviews were carried out with 27 individuals (15 PBD and 12 caregivers). The participants were identified on the basis of previous community-based research among people with severe mental illness. Interviews were carried out in Amharic, audio-recorded, transcribed, and translated into English. Data were analyzed using thematic analysis. Our approach was informed by phenomenological theory. RESULT Three major themes emerged: expressions and experiences of illness, managing self and living with otherness, and the costs of affliction. PBD and caregivers were concerned by different forewarnings of illness. Stigma and social exclusion were entwined in a vicious cycle that shaped both illness experience and the economic health and social life of the household. Nonetheless, PBD and caregivers learned from their experiences, developed coping strategies, and sought relief from trusted relationships, spirituality, and medication. CONCLUSION Our findings suggest that psychosocial intervention could be used to strengthen existing resources, in order to improve the lives of PBD and their family members. However, pervasive stigma may be a barrier to group and peer support approaches.
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Affiliation(s)
- Mekdes Demissie
- Department of Psychiatry, Addis Ababa University, Ethiopia; School of Nursing and Midwifery, College of Health and medical Sciences, Haramaya University, Ethiopia.
| | - Charlotte Hanlon
- Department of Psychiatry, Addis Ababa University, Ethiopia; Centre for Global Mental Health, Institute of Psychiatry, Psychology and Neuroscience, Health Services and Population Research, King's College London, UK
| | - Lauren Ng
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Abebaw Fekadu
- Department of Psychiatry, Addis Ababa University, Ethiopia; Centre for Innovative Drug Development and Therapeutic Studies for Africa (CDT-Africa), Addis Ababa University, Addis Ababa, Ethiopia; Department of Global Health and Infection, Brighton and Sussex Medical School, Brighton, UK; Center for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, King's College London, UK
| | - Rosie Mayston
- Global Health & Social Medicine/King's Global Health Institute, King's College London, NE Wing Bush House, 30 Aldwych, London WC2B 4BJ, UK
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Zhang ZQ, Wu WW, Chen JD, Zhang GY, Lin JY, Wu YK, Zhang Y, Su YA, Li JT, Si TM. Weighted Gene Coexpression Network Analysis Reveals Essential Genes and Pathways in Bipolar Disorder. Front Psychiatry 2021; 12:553305. [PMID: 33815158 PMCID: PMC8010671 DOI: 10.3389/fpsyt.2021.553305] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 02/24/2021] [Indexed: 11/13/2022] Open
Abstract
Bipolar disorder (BD) is a major and highly heritable mental illness with severe psychosocial impairment, but its etiology and pathogenesis remains unclear. This study aimed to identify the essential pathways and genes involved in BD using weighted gene coexpression network analysis (WGCNA), a bioinformatic method studying the relationships between genes and phenotypes. Using two available BD gene expression datasets (GSE5388, GSE5389), we constructed a gene coexpression network and identified modules related to BD. The analyses of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways were performed to explore functional enrichment of the candidate modules. A protein-protein interaction (PPI) network was further constructed to identify the potential hub genes. Ten coexpression modules were identified from the top 5,000 genes in 77 samples and three modules were significantly associated with BD, which were involved in several biological processes (e.g., the actin filament-based process) and pathways (e.g., MAPK signaling). Four genes (NOTCH1, POMC, NGF, and DRD2) were identified as candidate hub genes by PPI analysis and CytoHubba. Finally, we carried out validation analyses in a separate dataset, GSE12649, and verified NOTCH1 as a hub gene and the involvement of several biological processes such as actin filament-based process and axon development. Taken together, our findings revealed several candidate pathways and genes (NOTCH1) in the pathogenesis of BD and call for further investigation for their potential research values in BD diagnosis and treatment.
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Affiliation(s)
- Zhen-Qing Zhang
- Xiamen Xianyue Hospital, Xiamen, China.,Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | | | | | - Guang-Yin Zhang
- Department of Psychosomatic Medicine, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jing-Yu Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yan-Kun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yu Zhang
- Institute of Mental Health, Hebei North University, Hebei, China
| | - Yun-Ai Su
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Ji-Tao Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
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