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Lyu N, Wang H, Zhao Q, Fu B, Li J, Yue Z, Huang J, Yang F, Liu H, Zhang L, Li R. Peripheral biomarkers to differentiate bipolar depression from major depressive disorder: a real-world retrospective study. BMC Psychiatry 2024; 24:543. [PMID: 39085797 PMCID: PMC11293032 DOI: 10.1186/s12888-024-05979-7] [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: 02/19/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Bipolar depression (BPD) is often misdiagnosed as a major depressive disorder (MDD) in clinical practice, which may be attributed to a lack of robust biomarkers indicative of differentiated diagnosis. This study analysed the differences in various hormones and inflammatory markers to explore peripheral biomarkers that differentiate BPD from MDD patients. METHODS A total of 2,048 BPD and MDD patients were included. A panel of blood tests was performed to determine the levels of sex hormones, stress hormones, and immune-related indicators. Propensity score matching (PSM) was used to control for the effect of potential confounders between two groups and further a receiver operating characteristic (ROC) curve was used to analyse the potential biomarkers for differentiating BPD from MDD. RESULTS Compared to patients with MDD, patients with BPD expressed a longer duration of illness, more hospitalisations within five years, and an earlier age of onset, along with fewer comorbid psychotic symptoms. In terms of biochemical parameters, MDD patients presented higher IgA and IgM levels, while BPD patients featured more elevated neutrophil and monocyte counts. ROC analysis suggested that combined biological indicators and clinical features could moderately distinguish between BPD and MDD. In addition, different biological features exist in BPD and MDD patients of different ages and sexes. CONCLUSIONS Differential peripheral biological parameters were observed between BPD and MDD, which may be age-sex specific, and a combined diagnostic model that integrates clinical characteristics and biochemical indicators has a moderate accuracy in distinguishing BPD from MDD.
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Affiliation(s)
- Nan Lyu
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing, China
| | - Han Wang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing, China
| | - Qian Zhao
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing, China
| | - Bingbing Fu
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Jinhong Li
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Ziqi Yue
- National Center for Cardiovascular Diseases and Fuwai Hospital, Beijing, China
| | - Juan Huang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Fan Yang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Hao Liu
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Ling Zhang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Rena Li
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
- Center for Brain Disorders Research, Capital Medical University & Beijing Institute of Brain Disorders, Beijing, China.
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Liu H, Wang L, Yu H, Chen J, Sun P. Polygenic Risk Scores for Bipolar Disorder: Progress and Perspectives. Neuropsychiatr Dis Treat 2023; 19:2617-2626. [PMID: 38050614 PMCID: PMC10693760 DOI: 10.2147/ndt.s433023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/05/2023] [Indexed: 12/06/2023] Open
Abstract
Bipolar disorder (BD) is a common and highly heritable psychiatric disorder, the study of BD genetic characteristics can help with early prevention and individualized treatment. At the same time, BD is a highly heterogeneous polygenic genetic disorder with significant genetic overlap with other psychiatric disorders. In recent years, polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) data have been widely used in genetic studies of various complex diseases and can be used to explore the genetic susceptibility of diseases. This review discusses phenotypic associations and genetic correlations with other conditions of BD based on PRS, and provides ideas for genetic studies and prevention of BD.
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Affiliation(s)
- Huanxi Liu
- Qingdao Medical College, Qingdao University, Qingdao, 266071, People’s Republic of China
- Qingdao Mental Health Center, Qingdao, 266034, People’s Republic of China
| | - Ligang Wang
- Qingdao Mental Health Center, Qingdao, 266034, People’s Republic of China
| | - Hui Yu
- Qingdao Mental Health Center, Qingdao, 266034, People’s Republic of China
| | - Jun Chen
- Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Ping Sun
- Qingdao Mental Health Center, Qingdao, 266034, People’s Republic of China
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Yang R, Zhao Y, Tan Z, Lai J, Chen J, Zhang X, Sun J, Chen L, Lu K, Cao L, Liu X. Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers. Front Hum Neurosci 2023; 17:1192544. [PMID: 37780961 PMCID: PMC10540438 DOI: 10.3389/fnhum.2023.1192544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/24/2023] [Indexed: 10/03/2023] Open
Abstract
Background Mood disorders are very common among adolescents and include mainly bipolar disorder (BD) and major depressive disorder (MDD), with overlapping depressive symptoms that pose a significant challenge to realizing a rapid and accurate differential diagnosis in clinical practice. Misdiagnosis of BD as MDD can lead to inappropriate treatment and detrimental outcomes, including a poorer ultimate clinical and functional prognosis and even an increased risk of suicide. Therefore, it is of great significance for clinical management to identify clinical symptoms or features and biological markers that can accurately distinguish BD from MDD. With the aid of bibliometric analysis, we explore, visualize, and conclude the important directions of differential diagnostic studies of BD and MDD in adolescents. Materials and methods A literature search was performed for studies on differential diagnostic studies of BD and MDD among adolescents in the Web of Science Core Collection database. All studies considered for this article were published between 2004 and 2023. Bibliometric analysis and visualization were performed using the VOSviewer and CiteSpace software. Results In total, 148 publications were retrieved. The number of publications on differential diagnostic studies of BD and MDD among adolescents has been generally increasing since 2012, with the United States being an emerging hub with a growing influence in the field. Boris Birmaher is the top author in terms of the number of publications, and the Journal of Affective Disorders is the most published journal in the field. Co-occurrence analysis of keywords showed that clinical characteristics, genetic factors, and neuroimaging are current research hotspots. Ultimately, we comprehensively sorted out the current state of research in this area and proposed possible research directions in future. Conclusion This is the first-ever study of bibliometric and visual analyses of differential diagnostic studies of BD and MDD in adolescents to reveal the current research status and important directions in the field. Our research and analysis results might provide some practical sources for academic scholars and clinical practice.
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Affiliation(s)
- Ruilan Yang
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yanmeng Zhao
- Southern Medical University, Guangzhou, Guangdong, China
| | - Zewen Tan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Juan Lai
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, China
| | - Jianshan Chen
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaofei Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jiaqi Sun
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Lei Chen
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Kangrong Lu
- School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Liping Cao
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xuemei Liu
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
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Han K, Ji L, Xie Q, Liu L, Wu X, He L, Shi Y, Zhang R, He G, Dong Z, Yu T. Different roles of microbiota and genetics in the prediction of treatment response in major depressive disorder. J Psychiatr Res 2023; 161:402-411. [PMID: 37023596 DOI: 10.1016/j.jpsychires.2023.03.036] [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: 11/14/2022] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 04/08/2023]
Abstract
The roles of gut microbiota and susceptibility genes in patients with major depression disorder (MDD) are not well understood. Examining the microbiome and host genetics might be helpful for clinical decision-making. Patients with MDD were recruited in this study and subsequently treated for eight weeks. We identified the differences between the population with a response after two weeks and those with a response after eight weeks. The factors that were significantly correlated with efficacy were used to predict the treatment response. The differences in the importance of microbiota and genetics in prediction were analyzed. Our study identified rs58010457 as a potentially key locus affecting the treatment effect. Different microbiota and enriched pathways might play different roles in the response after two and eight weeks. We found that the area under the curve (AUC) value was greater than 0.8 for both random forest models. The contribution of different components to the AUC was evaluated by removing genetic information, microbiota abundance, and pathway data. The gut microbiome was an important predictor of the response after eight weeks, while genetics was an important predictor of the response after two weeks. These results suggested a dynamic effect of interaction among genetics and gut microbes on treatment. Furthermore, these results provide new guidance for clinical decisions: in cases of inadequate treatment effects after two weeks, the composition of the intestinal flora can be improved by diet therapy, which could ultimately affect the efficacy.
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Affiliation(s)
- Ke Han
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Lei Ji
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Qinglian Xie
- Out-patient Department of West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Liangjie Liu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Xi Wu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Rong Zhang
- Shanghai Center for Women and Children's Health, 339 Luding Road, Shanghai, 200062, China
| | - Guang He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
| | - Zaiquan Dong
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Tao Yu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China; Shanghai Center for Women and Children's Health, 339 Luding Road, Shanghai, 200062, China.
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5
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Hara T, Owada Y, Takata A. Genetics of bipolar disorder: insights into its complex architecture and biology from common and rare variants. J Hum Genet 2023; 68:183-191. [PMID: 35614313 DOI: 10.1038/s10038-022-01046-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/28/2022] [Accepted: 05/11/2022] [Indexed: 11/09/2022]
Abstract
Bipolar disorder (BD) is a common mental disorder characterized by recurrent mood episodes, which causes major socioeconomic burdens globally. Though its disease pathogenesis is largely unknown, the high heritability of BD indicates strong contributions from genetic factors. In this review, we summarize the recent achievements in the genetics of BD, particularly those from genome-wide association study (GWAS) of common variants and next-generation sequencing analysis of rare variants. These include the identification of dozens of robust disease-associated loci, deepening of our understanding of the biology of BD, objective description of correlations with other psychiatric disorders and behavioral traits, formulation of methods for predicting disease risk and drug response, and the discovery of a single gene associated with bipolar disorder and schizophrenia spectrum with a large effect size. On the other hand, the findings to date have not yet made a clear contribution to the improvement of clinical psychiatry of BD. We overview the remaining challenges as well as possible paths to resolve them, referring to studies of other major neuropsychiatric disorders.
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Affiliation(s)
- Tomonori Hara
- Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan.,Department of Organ Anatomy, Tohoku University Graduate School of Medicine, Sendai, Miyagi, 980-8575, Japan
| | - Yuji Owada
- Department of Organ Anatomy, Tohoku University Graduate School of Medicine, Sendai, Miyagi, 980-8575, Japan
| | - Atsushi Takata
- Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan.
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Kamran M, Bibi F, ur. Rehman A, Morris DW. Major Depressive Disorder: Existing Hypotheses about Pathophysiological Mechanisms and New Genetic Findings. Genes (Basel) 2022; 13:646. [PMID: 35456452 PMCID: PMC9025468 DOI: 10.3390/genes13040646] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/16/2022] [Accepted: 03/23/2022] [Indexed: 01/08/2023] Open
Abstract
Major depressive disorder (MDD) is a common mental disorder generally characterized by symptoms associated with mood, pleasure and effectiveness in daily life activities. MDD is ranked as a major contributor to worldwide disability. The complex pathogenesis of MDD is not yet understood, and this is a major cause of failure to develop new therapies and MDD recurrence. Here we summarize the literature on existing hypotheses about the pathophysiological mechanisms of MDD. We describe the different approaches undertaken to understand the molecular mechanism of MDD using genetic data. Hundreds of loci have now been identified by large genome-wide association studies (GWAS). We describe these studies and how they have provided information on the biological processes, cell types, tissues and druggable targets that are enriched for MDD risk genes. We detail our understanding of the genetic correlations and causal relationships between MDD and many psychiatric and non-psychiatric disorders and traits. We highlight the challenges associated with genetic studies, including the complexity of MDD genetics in diverse populations and the need for a study of rare variants and new studies of gene-environment interactions.
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Affiliation(s)
- Muhammad Kamran
- Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan; (M.K.); (A.u.R.)
- Centre for Neuroimaging, Cognition and Genomics (NICOG), Discipline of Biochemistry, National University of Ireland Galway, H91 CF50 Galway, Ireland
| | - Farhana Bibi
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan;
| | - Asim. ur. Rehman
- Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan; (M.K.); (A.u.R.)
| | - Derek W. Morris
- Centre for Neuroimaging, Cognition and Genomics (NICOG), Discipline of Biochemistry, National University of Ireland Galway, H91 CF50 Galway, Ireland
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Han M, Yuan L, Huang Y, Wang G, Du C, Wang Q, Zhang G. Integrated co-expression network analysis uncovers novel tissue-specific genes in major depressive disorder and bipolar disorder. Front Psychiatry 2022; 13:980315. [PMID: 36081461 PMCID: PMC9445988 DOI: 10.3389/fpsyt.2022.980315] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Tissue-specific gene expression has been found to be associated with multiple complex diseases including cancer, metabolic disease, aging, etc. However, few studies of brain-tissue-specific gene expression patterns have been reported, especially in psychiatric disorders. In this study, we performed joint analysis on large-scale transcriptome multi-tissue data to investigate tissue-specific expression patterns in major depressive disorder (MDD) and bipolar disorder (BP). We established the strategies of identifying tissues-specific modules, annotated pathways for elucidating biological functions of tissues, and tissue-specific genes based on weighted gene co-expression network analysis (WGCNA) and robust rank aggregation (RRA) with transcriptional profiling data from different human tissues and genome wide association study (GWAS) data, which have been expanded into overlapping tissue-specific modules and genes sharing with MDD and BP. Nine tissue-specific modules were identified and distributed across the four tissues in the MDD and six modules in the BP. In general, the annotated biological functions of differentially expressed genes (DEGs) in blood were mainly involved in MDD and BP progression through immune response, while those in the brain were in neuron and neuroendocrine response. Tissue-specific genes of the prefrontal cortex (PFC) in MDD-, such as IGFBP2 and HTR1A, were involved in disease-related functions, such as response to glucocorticoid, taste transduction, and tissue-specific genes of PFC in BP-, such as CHRM5 and LTB4R2, were involved in neuroactive ligand-receptor interaction. We also found PFC tissue-specific genes including SST and CRHBP were shared in MDD-BP, SST was enriched in neuroactive ligand-receptor interaction, and CRHBP shown was related to the regulation of hormone secretion and hormone transport.
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Affiliation(s)
- Mengyao Han
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.,CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liyun Yuan
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuwei Huang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guiying Wang
- Shanghai Key Laboratory of Signaling and Disease Research, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, National Stem Cell Translational Resource Center, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Changsheng Du
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Qingzhong Wang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Shanghai Key Laboratory of Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guoqing Zhang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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Su L, Shuai Y, Mou S, Shen Y, Shen X, Shen Z, Zhang X. Development and validation of a nomogram based on lymphocyte subsets to distinguish bipolar depression from major depressive disorder. Front Psychiatry 2022; 13:1017888. [PMID: 36276314 PMCID: PMC9583168 DOI: 10.3389/fpsyt.2022.1017888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/13/2022] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE Bipolar depression (BD) and major depressive disorder (MDD) are both common affective disorders. The common depression episodes make it difficult to distinguish between them, even for experienced clinicians. Failure to properly diagnose them in a timely manner leads to inappropriate treatment strategies. Therefore, it is important to distinguish between BD and MDD. The aim of this study was to develop and validate a nomogram model that distinguishes BD from MDD based on the characteristics of lymphocyte subsets. MATERIALS AND METHODS A prospective cross-sectional study was performed. Blood samples were obtained from participants who met the inclusion criteria. The least absolute shrinkage and selection operator (LASSO) regression model was used for factor selection. A differential diagnosis nomogram for BD and MDD was developed using multivariable logistic regression and the area under the curve (AUC) with 95% confidence interval (CI) was calculated, as well as the internal validation using a bootstrap algorithm with 1,000 repetitions. Calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical utility of the nomogram, respectively. RESULTS A total of 166 participants who were diagnosed with BD (83 cases) or MDD (83 cases), as well as 101 healthy controls (HCs) between June 2018 and January 2022 were enrolled in this study. CD19+ B cells, CD3+ T cells, CD3-CD16/56+ NK cells, and total lymphocyte counts were strong predictors of the diagnosis of BD and MDD and were included in the differential diagnosis nomogram. The AUC of the nomogram and internal validation were 0.922 (95%; CI, 0.879-0.965), and 0.911 (95% CI, 0.838-0.844), respectively. The calibration curve used to discriminate BD from MDD showed optimal agreement between the nomogram and the actual diagnosis. The results of DCA showed that the net clinical benefit was significant. CONCLUSION This is an easy-to-use, repeatable, and economical nomogram for differential diagnosis that can help clinicians in the individual diagnosis of BD and MDD patients, reduce the risk of misdiagnosis, facilitate the formulation of appropriate treatment strategies and intervention plans.
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Affiliation(s)
- Liming Su
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China
| | - Yibing Shuai
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China
| | - Shaoqi Mou
- Department of Psychiatry, Wenzhou Medical University, Wenzhou, China
| | - Yue Shen
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China
| | - Xinhua Shen
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China
| | - Zhongxia Shen
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China
| | - Xiaomei Zhang
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China
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9
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Fang X, Wang D, Tang W, Liu H, Zhang X, Zhang C. Anhedonia difference between major depressive disorder and bipolar disorder II. BMC Psychiatry 2021; 21:531. [PMID: 34706699 PMCID: PMC8555067 DOI: 10.1186/s12888-021-03548-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 10/14/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE This study aims to explore the difference in anhedonia between Major Depressive Disorder (MDD) and Bipolar Disorder II (BD-II), and attempt to distinguish the two diseases through Snaith-Hamilton Pleasure Scale (SHAPS). METHODS A total of 164 drug-free depressive patients (98 MDD patients, 66 BD-II patients) completed the investigation. 17-item Hamilton Depression Scale (HAMD-17) and Hamilton Anxiety Scale (HAMA) and SHAPS were assessed in all participants. RESULTS Our results showed that BD-II patients had higher SHAPS scores than MDD patients. The stepwise logistic regression analysis further revealed that SHAPS score, drinking habit, and extroversion as influencing factors for the identification of BD-II. The ROC curve analysis indicated that SHAPS could differentiate BD-II from MDD patients (AUC = 0.655, P = 0.001, 95% CI = 0.568 to 0.742), with the best screening cutoff at 26, and the corresponding sensitivity and specificity was 0.788 and 0.520, respectively. CONCLUSION Our results suggest that BD-II patients had more severe anhedonia compared to MDD patients, and the difference in anhedonia may help clinicians preliminary identify BD patients from MDD patients. The preliminary findings are worthly of further exploration.
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Affiliation(s)
- Xinyu Fang
- grid.89957.3a0000 0000 9255 8984Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, People’s Republic of China ,grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Dandan Wang
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Wei Tang
- grid.268099.c0000 0001 0348 3990The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China
| | - Hongyang Liu
- grid.268099.c0000 0001 0348 3990The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, People's Republic of China.
| | - Chen Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
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10
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Rao S, Yin L, Xiang Y, So HC. Analysis of genetic differences between psychiatric disorders: exploring pathways and cell types/tissues involved and ability to differentiate the disorders by polygenic scores. Transl Psychiatry 2021; 11:426. [PMID: 34389699 PMCID: PMC8363629 DOI: 10.1038/s41398-021-01545-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 07/13/2021] [Accepted: 08/02/2021] [Indexed: 02/07/2023] Open
Abstract
Although displaying genetic correlations, psychiatric disorders are clinically defined as categorical entities as they each have distinguishing clinical features and may involve different treatments. Identifying differential genetic variations between these disorders may reveal how the disorders differ biologically and help to guide more personalized treatment. Here we presented a statistical framework and comprehensive analysis to identify genetic markers differentially associated with various psychiatric disorders/traits based on GWAS summary statistics, covering 18 psychiatric traits/disorders and 26 comparisons. We also conducted comprehensive analysis to unravel the genes, pathways and SNP functional categories involved, and the cell types and tissues implicated. We also assessed how well one could distinguish between psychiatric disorders by polygenic risk scores (PRS). SNP-based heritabilities (h2snp) were significantly larger than zero for most comparisons. Based on current GWAS data, PRS have mostly modest power to distinguish between psychiatric disorders. For example, we estimated that AUC for distinguishing schizophrenia from major depressive disorder (MDD), bipolar disorder (BPD) from MDD and schizophrenia from BPD were 0.694, 0.602 and 0.618, respectively, while the maximum AUC (based on h2snp) were 0.763, 0.749 and 0.726, respectively. We also uncovered differences in each pair of studied traits in terms of their differences in genetic correlation with comorbid traits. For example, clinically defined MDD appeared to more strongly genetically correlated with other psychiatric disorders and heart disease, when compared to non-clinically defined depression in UK Biobank. Our findings highlight genetic differences between psychiatric disorders and the mechanisms involved. PRS may help differential diagnosis of selected psychiatric disorders in the future with larger GWAS samples.
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Affiliation(s)
- Shitao Rao
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Liangying Yin
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Yong Xiang
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Hon-Cheong So
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong.
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Kunming, China.
- CUHK Shenzhen Research Institute, Shenzhen, China.
- Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong.
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Shatin, Hong Kong.
- Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong.
- Hong Kong Branch of the Chinese Academy of Sciences (CAS) Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Shatin, Hong Kong.
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11
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Coleman JR. The Validity of Brief Phenotyping in Population Biobanks for Psychiatric Genome-Wide Association Studies on the Biobank Scale. Complex Psychiatry 2021; 7:11-15. [PMID: 34883499 PMCID: PMC8443942 DOI: 10.1159/000516837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 04/14/2021] [Indexed: 11/19/2022] Open
Affiliation(s)
- Jonathan R.I. Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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12
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Tomasik J, Han SYS, Barton-Owen G, Mirea DM, Martin-Key NA, Rustogi N, Lago SG, Olmert T, Cooper JD, Ozcan S, Eljasz P, Thomas G, Tuytten R, Metcalfe T, Schei TS, Farrag LP, Friend LV, Bell E, Cowell D, Bahn S. A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data. Transl Psychiatry 2021; 11:41. [PMID: 33436544 PMCID: PMC7804187 DOI: 10.1038/s41398-020-01181-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 12/17/2022] Open
Abstract
The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.
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Affiliation(s)
- Jakub Tomasik
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
| | - Sung Yeon Sarah Han
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | | | - Dan-Mircea Mirea
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA
| | - Nayra A Martin-Key
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Nitin Rustogi
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Santiago G Lago
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Tony Olmert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- University of California San Diego School of Medicine, San Diego, California, USA
| | - Jason D Cooper
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Owlstone Medical Ltd, Cambridge, UK
| | - Sureyya Ozcan
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Department of Chemistry, Middle East Technical University, Ankara, Turkey
| | - Pawel Eljasz
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | | | - Robin Tuytten
- Metabolomic Diagnostics, Little Island, Cork, Ireland
| | | | | | | | | | | | | | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
- Psyomics Ltd, Cambridge, UK.
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13
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O'Donovan C, Alda M. Depression Preceding Diagnosis of Bipolar Disorder. Front Psychiatry 2020; 11:500. [PMID: 32595530 PMCID: PMC7300293 DOI: 10.3389/fpsyt.2020.00500] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 05/18/2020] [Indexed: 12/18/2022] Open
Abstract
This paper focuses on depression that precedes an onset of manifest bipolar disorder as early stage bipolar disorder. First, we review how to pragmatically identify the clinical characteristics of patients presenting with an episode of depression who subsequently go on to develop episodes of mania or hypomania. The existing literature shows a strong consensus: accurate identification of depression with early onset and recurrent course with multiple episodes, subthreshold hypomanic and/or mixed symptoms, and family history of bipolar disorder or completed suicide have been shown by multiple authors as signs pointing to bipolar diagnosis. This contrasts with relatively limited information available to guide management of such "pre-bipolar" (pre-declared bipolar) patients, especially those in the adult age range. Default assumption of unipolar depression at this stage carries significant risk. Antidepressants are still the most common pharmacological treatment used, but clinicians need to be aware of their potential harm. In some patients with unrecognized bipolar depression, antidepressants can not only produce switch to (hypo)mania, but also mixed symptoms, or worsening of depression with an increased risk of suicide. We review pragmatic management strategies in the literature beyond clinical guidelines that can be considered for this at-risk group encompassing the more recent child and adolescent literature. In the future, genetic research could make the early identification of bipolar depression easier by generating informative markers and polygenic risk scores.
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Affiliation(s)
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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