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Wang J, Chen J, Li J, Wu Q, Sun J, Zhang X, Li X, Yang C, Cao L, Wang J. Transdiagnostic network alterations and associated neurotransmitter signatures across major psychiatric disorders in adolescents: Evidence from edge-centric analysis of time-varying functional brain networks. J Affect Disord 2025; 380:401-412. [PMID: 40154800 DOI: 10.1016/j.jad.2025.03.151] [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: 12/20/2024] [Revised: 02/20/2025] [Accepted: 03/25/2025] [Indexed: 04/01/2025]
Abstract
BACKGROUND Adolescence is a pivotal phase marked by heightened vulnerability to the onset of psychiatric disorders. However, there are few transdiagnostic studies of dynamic brain networks across major psychiatric disorders during this phase. METHODS We collected resting-state functional MRI data from 189 adolescent patients (61 with bipolar disorder, 73 with major depressive disorder, and 55 with schizophrenia) and 181 healthy adolescents. Functional networks were constructed using a state-of-art edge-centric dynamic functional connectivity (DFC) approach. RESULTS Four DFC states were identified for the healthy adolescents that were related to different behavioral and cognitive terms. Disorder-related alterations were observed in two states involving motor and somatosensory processing and one state involving various cognitive functions. Regardless of the state, the three patient groups exhibited lower FC that were mainly involved in edges between different functional subsystems and were predominantly linked to regions in the somatomotor network. The patients with major depressive disorder additionally showed increased FC that were primarily linked to default mode regions. Graph-based network analysis revealed different patterns of disrupted small-world organization and altered nodal degree in the disorders in a state-dependent manner. The nodal degree alterations were correlated with the concentration of various neurotransmitters. Intriguingly, the noradrenaline concentration was engaged in the nodal degree alterations in each patient group. Finally, decreased FC involving regions in the somatomotor network showed significant correlations with clinical variables in the major depressive disorder patients. CONCLUSION These findings may help understand the developmental pathways associated with the heightened vulnerability to major psychiatric disorders during adolescence.
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Affiliation(s)
- Jing Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Jianshan Chen
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Qiuxia Wu
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Jiaqi Sun
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Xiaofei Zhang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China
| | - Xuan Li
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Chanjuan Yang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Liping Cao
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China.
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China.
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Tusa BS, Alati R, Ayano G, Betts K, Weldesenbet AB, Dachew B. Maternal perinatal depression and the risk of disruptive behavioural disorder symptoms among offspring: A systematic review and meta-analysis. Psychiatry Res 2025; 348:116428. [PMID: 40069987 DOI: 10.1016/j.psychres.2025.116428] [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: 02/16/2024] [Revised: 01/03/2025] [Accepted: 03/03/2025] [Indexed: 05/06/2025]
Abstract
Inconsistent findings exist regarding the association between maternal perinatal depression and the risk of Disruptive Behavioural Disorder (DBD) symptoms, including Conduct Disorder (CD) and Oppositional Defiant Disorder (ODD) symptoms in children and adolescents. This study aimed to estimate the overall risk of DBD symptoms in offspring of mothers who have experienced perinatal depression. PubMed, Medline, Embase, Scopus, CINAHL, and Psych INFO were searched. A meta-analysis was conducted using inverse variance-weighted random-effects models. The odds ratios (OR) with 95 % confidence intervals (CI) were presented as summary effect estimates. Among the 4,591 publications identified, 12 studies, comprising 51,468 mother-offspring pairs were included in the final analysis. A meta-analysis showed that maternal perinatal depression was associated with a 47 % increased risk of any DBD symptoms (OR = 1.47, 95 % CI = 1.18-1.76), a 41 % increased risk of CD symptoms (OR = 1.41, 95 % CI = 1.04-1.77), and a 53 % increased risk of ODD symptoms (OR = 1.53, 95 % CI = 1.11-1.94) in offspring. This meta-analysis highlights a significant link between maternal perinatal depression and an elevated risk of DBD symptoms in children and adolescents, underscoring the importance of timely interventions and support for at-risk children and adolescents.
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Affiliation(s)
- Biruk Shalmeno Tusa
- School of Population Health, Curtin University, Perth, WA, Australia; Department of Epidemiology and Biostatistics, College of Health and Medical Sciences, Haramaya University, Haramaya, Ethiopia.
| | - Rosa Alati
- School of Population Health, Curtin University, Perth, WA, Australia; Institute for Social Science Research, The University of Queensland, Brisbane, QLD, Australia
| | - Getinet Ayano
- School of Population Health, Curtin University, Perth, WA, Australia
| | - Kim Betts
- School of Population Health, Curtin University, Perth, WA, Australia
| | - Adisu Birhanu Weldesenbet
- Department of Epidemiology and Biostatistics, College of Health and Medical Sciences, Haramaya University, Haramaya, Ethiopia
| | - Berihun Dachew
- School of Population Health, Curtin University, Perth, WA, Australia; enAble Institute, Curtin University, Perth, Western Australia, Australia
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3
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Gonzales S, Zhao JZ, Choi NY, Acharya P, Jeong S, Wang X, Lee MY. SOX7: Autism associated gene identified by analysis of multi-Omics data. PLoS One 2025; 20:e0320096. [PMID: 40373085 DOI: 10.1371/journal.pone.0320096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/12/2025] [Indexed: 05/17/2025] Open
Abstract
Genome-wide association studies and next generation sequencing data analyses based on DNA information have identified thousands of mutations associated with autism spectrum disorder (ASD). However, more than 99% of identified mutations are non-coding. Thus, it is unclear which of these mutations might be functional and thus potentially causal variants. Transcriptomic profiling using total RNA-sequencing has been one of the most utilized approaches to link protein levels to genetic information at the molecular level. The transcriptome captures molecular genomic complexity that the DNA sequence solely does not. Some mutations alter a gene's DNA sequence but do not necessarily change expression and/or protein function. To date, few common variants reliably associated with the diagnosis status of ASD despite consistently high estimates of heritability. In addition, reliable biomarkers used to diagnose ASD or molecular mechanisms to define the severity of ASD do not exist. Therefore, it is necessary to integrate DNA and RNA testing together to identify true causal genes and propose useful biomarkers for ASD. We performed gene-based association studies with adaptive test using genome-wide association studies' (GWAS) summary statistics with two large GWAS datasets (ASD 2019 data: 18,382 ASD cases and 27,969 controls [discovery data]; ASD 2017 data: 6,197 ASD cases and 7,377 controls [replication data]) which were obtained from the Psychiatric Genomics Consortium (PGC). In addition, we investigated differential expression between ASD cases and controls for genes identified in gene-based GWAS with two RNA-seq datasets (GSE211154: 20 cases and 19 controls; GSE30573: 3 cases and 3 controls). We identified 5 genes significantly associated with ASD in ASD 2019 data (KIZ-AS1, p = 8.67 × 10-10; KIZ, p = 1.16 × 10-9; XRN2, p = 7.73 × 10-9; SOX7, p = 2.22 × 10-7; LOC101929229 also known as PINX1-DT, p = 2.14 × 10-6). Among these 5 genes, gene SOX7 (p = 0.00087) and LOC101929229 (p = 0.009) were replicated in ASD 2017 data. KIZ-AS1 (p = 0.059) and KIZ (p = 0.06) were close to the boundary of replication in ASD 2017 data. Genes SOX7 (p = 0.036 in all samples; p = 0.044 in white samples) indicated significant expression differences between cases and controls in the GSE211154 RNA-seq data. Furthermore, gene SOX7 was upregulated in cases than in controls in the GSE30573 RNA-seq data (p = 0.0017; Benjamini-Hochberg adjusted p = 0.0085). SOX7 encodes a member of the SOX (SRY-related HMG-box) family of transcription factors pivotally contributing to determining of the cell fate and identity in many lineages. The encoded protein may act as a transcriptional regulator after forming a protein complex with other proteins leading to autism. Gene SOX7 in the transcription factor family could be associated with ASD. This finding may provide new diagnostic and therapeutic strategies for ASD.
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Affiliation(s)
- Samantha Gonzales
- Department of Biostatistics, Florida International University, Miami, Florida, United States of America
| | - Jane Zizhen Zhao
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Na Young Choi
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, United States of America
| | - Prabha Acharya
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, United States of America
| | - Sehoon Jeong
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, South Korea
| | - Xuexia Wang
- Department of Biostatistics, Florida International University, Miami, Florida, United States of America
| | - Moo-Yeal Lee
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, United States of America
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Kravariti E, Fragkaki AM, Georgiades A, Cardno AG, Kane F, Kalidindi S, Schulze KK, McDonald C, Picchioni MM, Hall MH, Watson CJ, Glenthøj BY, Ebdrup BH, Fagerlund B, Lemvigh CK, Van Haren NEM, Kahn R, Murray RM, Rijsdijk F, Toulopoulou T. Transdiagnostic Neurocognitive Endophenotypes for Schizophrenia, Bipolar I Disorder and a Broad Psychosis/Bipolar I Disorder Phenotype: A Mega-Analysis of Twin and Sibling Data. Schizophr Bull 2025:sbaf050. [PMID: 40341418 DOI: 10.1093/schbul/sbaf050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/10/2025]
Abstract
BACKGROUND Psychiatric research is increasingly embracing a paradigm shift from categorical diagnoses to neurobiologically meaningful dimensions that cross current diagnostic boundaries. This transposition calls for redefining endophenotypes to accommodate transdiagnostic vulnerabilities. We sought to identify shared and disorder-specific neurocognitive endophenotypes for schizophrenia, bipolar I disorder (BD-I) and a broad psychosis/BD-I phenotype in a mega-analysis of twin/sibling data. STUDY DESIGN We performed genetic model fitting to intelligence (IQ) and computerised neurocognitive data derived from 1050 twins/siblings from three research centres in the UK, Denmark and the Netherlands, affected (n = 257) or unaffected (n = 793) by schizophrenia, other primary psychoses and BD-I. We examined the endophenotypic status of IQ, spatial working memory (SWM), visual recognition, sustained attention/rapid visual processing (RVP), mental flexibility, and spatial planning/problem solving (all validated as endophenotypes for schizophrenia in previous studies) in relation to schizophrenia, BD-I and the broad phenotype. STUDY RESULTS After covarying for age, gender, education and research centre, IQ and SWM emerged as transdiagnostic endophenotypes, showing statistically significant heritabilities (h2 67-75% and 28-30%, respectively), phenotypic correlations (rph |0.14|-|0.25|) and genetic correlations (rg |0.18|-|0.42|) with all diagnostic phenotypes. Additionally, all remaining cognitive domains received validation as endophenotypes for the broad phenotype, and all, but RVP, for schizophrenia. CONCLUSIONS IQ and SWM tap into transdiagnostic elements of the genetic vulnerabilities to psychosis and BD-I. Our findings add to emergent evidence which spurs cautious optimism that a psychiatric nosology based on aetiology rather than phenotypical classifications may be feasible in the future, enabling biotyping and novel approaches to treatment.
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Affiliation(s)
- Eugenia Kravariti
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Anna-Maria Fragkaki
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, United Kingdom
- First Department of Psychiatry, School of Medicine, National and Kapodistrian University of Athens 115 27, Greece, Athens, Greece
| | - Anna Georgiades
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, United Kingdom
- Brent Early Intervention Service, CNWL, NHS Foundation Trust, 27-29 Fairlight Avenue, London NW10 8AL, United Kingdom
| | - Alastair G Cardno
- Division of Psychological and Social Medicine, Faculty of Medicine and Health, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Fergus Kane
- Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, United Kingdom
| | - Sridevi Kalidindi
- Recovery and Rehabilitation Team, Croydon Directorate, South London and Maudsley NHS Foundation Trust, London CR0 2PR, United Kingdom
| | - Katja K Schulze
- Centre for Anxiety Disorders and Trauma (CADAT), South London and Maudsley NHS Foundation Trust, London SE5 8AZ, United Kingdom
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, Galway H91 TK33, Ireland
| | - Marco M Picchioni
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, McLean Hospital, Harvard Medical School, Belmont, Massachusetts MA 02478, United States
| | - Cameron J Watson
- South London and Maudsley NHS Foundation Trust, London SE5 8AZ, United Kingdom
- Neuropsychiatry Research and Education Group Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London SE5 8AF, London, United Kingdom
| | - Birte Y Glenthøj
- Center for Neuropsychiatric Schizophrenia Research (CNSR)/Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, DK 2600, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Bjørn H Ebdrup
- Center for Neuropsychiatric Schizophrenia Research (CNSR)/Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, DK 2600, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Birgitte Fagerlund
- Center for Neuropsychiatric Schizophrenia Research (CNSR)/Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, DK 2600, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
- Child and Adolescent Mental Health Center, Copenhagen University Hospital - Mental Health Services CPH, 2900 Hellerup, Denmark
- Department of Psychology, Faculty of Social Sciences, University of Copenhagen, 1353 Copenhagen K, Denmark
| | - Cecilie K Lemvigh
- Center for Neuropsychiatric Schizophrenia Research (CNSR)/Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, DK 2600, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
| | - Neeltje E M Van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, 3000 CA Rotterdam, Netherlands
| | - Rene Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, Park Ave, New York, NY 10029, United States
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Fruhling Rijsdijk
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London SE5 8AF, United Kingdom
- Psychology Department, Faculty of Social Sciences, Anton de Kom University of Suriname, P.O.B. 9212 Paramaribo, Suriname, South America
| | - Timothea Toulopoulou
- First Department of Psychiatry, School of Medicine, National and Kapodistrian University of Athens 115 27, Greece, Athens, Greece
- Department of Psychology & National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Centre (ASBAM), Bilkent University, Ankara 06800, Turkey
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, Park Ave, New York, NY 10029, United States
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Song Y, Li L, Jiang Y, Peng B, Jiang H, Chao Z, Chang X. Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians. Schizophr Bull 2025; 51:684-695. [PMID: 39190819 PMCID: PMC12061663 DOI: 10.1093/schbul/sbae145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS While genetic correlations, pleiotropic loci, and shared genetic mechanisms of psychiatric disorders have been extensively studied in European populations, the investigation of these factors in East Asian populations has been relatively limited. STUDY DESIGN To identify novel pleiotropic risk loci for depression and schizophrenia (SCZ) in East Asians. We utilized the most comprehensive dataset available for East Asians and quantified the genetic overlap between depression, SCZ, and their related traits via a multitrait genome-wide association study. Global and local genetic correlations were estimated by LDSC and ρ-HESS. Pleiotropic loci were identified by the multitrait analysis of GWAS (MTAG). STUDY RESULTS Besides the significant correlation between depression and SCZ, our analysis revealed genetic correlations between depression and obesity-related traits, such as weight, BMI, T2D, and HDL. In SCZ, significant correlations were detected with HDL, heart diseases and use of various medications. Conventional meta-analysis of depression and SCZ identified a novel locus at 1q25.2 in East Asians. Further multitrait analysis of depression, SCZ and related traits identified ten novel pleiotropic loci for depression, and four for SCZ. CONCLUSIONS Our findings demonstrate shared genetic underpinnings between depression and SCZ in East Asians, as well as their associated traits, providing novel candidate genes for the identification and prioritization of therapeutic targets specific to this population.
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Affiliation(s)
- Yingchao Song
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Linzehao Li
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Yue Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Bichen Peng
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Hengxuan Jiang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
| | - Xiao Chang
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University, Shandong, China
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Vellucci L, Barone A, Buonaguro EF, Ciccarelli M, De Simone G, Iannotta F, Matrone M, Mazza B, Vitelli R, de Bartolomeis A, Iasevoli F. Severity of autism-related symptoms in treatment-resistant schizophrenia: associations with cognitive performance, psychosocial functioning, and neurological soft signs - Clinical evidence and ROC analysis. J Psychiatr Res 2025; 185:119-129. [PMID: 40179689 DOI: 10.1016/j.jpsychires.2025.03.040] [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: 09/10/2024] [Revised: 02/19/2025] [Accepted: 03/22/2025] [Indexed: 04/05/2025]
Abstract
Treatment-resistant schizophrenia (TRS) occurs when symptoms persist despite adequate antipsychotic treatment in terms of both timing and dosage. This severe condition is often overlooked, despite the existence of guidelines, with an average delay of 4-9 years before the introduction of clozapine, the gold standard treatment. We hypothesized that schizophrenia patients with severe autistic symptoms are more prone to develop TRS. To test this, we administered the Positive and Negative Syndrome Scale for Schizophrenia Autism Severity Scale (PAUSS) to 117 patients diagnosed with schizophrenia. Our results revealed that both TRS and clozapine non-responder (CLZ-nR) groups had higher rates of autistic symptoms than non-TRS patients. A machine learning model was developed to examine the relationship between PAUSS scores and TRS, obtaining an accuracy of 0.65 and an AUC of 0.67. Specifically, PAUSS items N6 ("lack of spontaneity and flow of conversation") and N7 ("stereotypical thinking") emerged as the most significant factors in the model. In addition, PAUSS was correlated with cognitive and social functions, as well as soft neurological signs, in TRS patients. Autism-related symptoms were found to predict significant variance in motor coordination, verbal fluency, functional ability and soft neurological signs. These results suggest that autism-related symptoms in schizophrenia may define a distinct subgroup with unique neurobiological characteristics.
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Affiliation(s)
- Licia Vellucci
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy; Department of Translational Medical Sciences, University of Naples "Federico II", Naples, Italy
| | - Annarita Barone
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy
| | - Elisabetta Filomena Buonaguro
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy; Unità Operativa di Salute Mentale Terzigno, ASL NAPOLI 3 SUD, Naples, Italy
| | - Mariateresa Ciccarelli
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy
| | - Giuseppe De Simone
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy
| | - Federica Iannotta
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy
| | - Marta Matrone
- NESMOS (Neurosciences, Mental Health, and Sensory Organs) Department, Sapienza University of Rome, Faculty of Medicine and Psychology, Via di Grottarossa 1035-1039, 00189, Rome, Italy; Department of Mental Health Protection and Promotion, Unit of Addiction Pathology, Via Salaria per Roma, 36, 02100, Rieti, Italy
| | - Benedetta Mazza
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy
| | - Roberto Vitelli
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy
| | - Andrea de Bartolomeis
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy; UNESCO Staff Chair on Health Education and Sustainable Development, University "Federico II", Naples, Italy.
| | - Felice Iasevoli
- Unit for Treatment-Resistant Psychoses, Section of Psychiatry, Department of Neuroscience, Reproductive Sciences and Dentistry - University of Naples "Federico II", Naples, Italy
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Llach CD, Le GH, Badulescu S, Anmella G, Hasan HA, Giménez-Palomo A, Pacchiarotti I, Vieta E, McIntyre RS, Rosenblat JD, Mansur RB. Extracellular vesicles in mood disorders: A systematic review of human studies. Eur Neuropsychopharmacol 2025; 94:59-75. [PMID: 40057988 DOI: 10.1016/j.euroneuro.2025.02.009] [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: 12/18/2024] [Revised: 02/13/2025] [Accepted: 02/15/2025] [Indexed: 05/02/2025]
Abstract
Extracellular vesicles (EVs) are small, membrane-bound particles that are naturally released by nearly all cell types in the body. They serve as molecular biosignatures, reflecting the state of their cells of origin and providing a non-invasive peripheral marker of central nervous system (CNS) activity under physiological and pathological conditions. We conducted a systematic review (ID: CRD42024528824) of studies investigating the use of EVs in mood disorders within clinical populations. We screened articles indexed in PubMed, EMBASE, Scopus, ISI Web of Science, and APA PsycInfo from January 2010 to October 2024. Available research has focused on four key areas: (1) EV cargo as mechanistic and diagnostic biomarkers; (2) EV cargo as predictive or tracking biomarkers for antidepressant response; (3) EV cargo and neuroimaging correlates; and (4) EV physical properties. Most studies examined major depressive disorder (MDD), with others addressing bipolar disorder (BD), adolescent depression, postpartum depression, and late-life depression. Notably, only 35,55 % of the studies utilized brain-derived EVs. Through analyses of EV-derived miRNA, proteins, mtDNA, and metabolites, these studies have explored neural mitochondrial function, brain insulin resistance, neurogenesis, neuroinflammation, and blood-brain barrier permeability in the context of mood disorders. Some EV-derived markers demonstrated diagnostic and predictive potential. This review discusses key findings, limitations of current research, and future directions for leveraging EVs in the study of mood disorders.
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Affiliation(s)
- Cristian-Daniel Llach
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada.
| | - Gia Han Le
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Sebastian Badulescu
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Gerard Anmella
- Bipolar and Depressive Disorders Unit, Hospital Clínic de Barcelona, Barcelona, Spain; Institute of Neurosciences (UBNeuro); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII);; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Hayder Ali Hasan
- Department of Neurosciences, Psychiatry and Pediatric Psychiatry, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj, Napoca, Romania
| | - Anna Giménez-Palomo
- Bipolar and Depressive Disorders Unit, Hospital Clínic de Barcelona, Barcelona, Spain; Institute of Neurosciences (UBNeuro); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Isabella Pacchiarotti
- Bipolar and Depressive Disorders Unit, Hospital Clínic de Barcelona, Barcelona, Spain; Institute of Neurosciences (UBNeuro); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII);; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clínic de Barcelona, Barcelona, Spain; Institute of Neurosciences (UBNeuro); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Biomedical Research Networking Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III (ISCIII);; Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Roger S McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Joshua D Rosenblat
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Rodrigo B Mansur
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
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8
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Bhattacharyya U, John J, Lam M, Fisher J, Sun B, Baird D, Burgess S, Chen CY, Lencz T. Circulating Blood-Based Proteins in Psychopathology and Cognition: A Mendelian Randomization Study. JAMA Psychiatry 2025; 82:481-491. [PMID: 40072421 PMCID: PMC11904806 DOI: 10.1001/jamapsychiatry.2025.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Accepted: 12/11/2024] [Indexed: 03/15/2025]
Abstract
Importance Peripheral (blood-based) biomarkers for psychiatric illness could benefit diagnosis and treatment, but research to date has typically been low throughput, and traditional case-control studies are subject to potential confounds of treatment and other exposures. Large-scale 2-sample mendelian randomization (MR) can examine the potentially causal impact of circulating proteins on neuropsychiatric phenotypes without these confounds. Objective To identify circulating proteins associated with risk for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) as well as cognitive task performance (CTP). Design, Setting, and Participants In a 2-sample MR design, significant proteomic quantitative trait loci were used as candidate instruments, obtained from 2 large-scale plasma proteomics datasets: the UK Biobank Pharma Proteomics Project (2923 proteins per 34 557 UK individuals) and deCODE Genetics (4719 proteins per 35 559 Icelandic individuals). Data analysis was performed from November 2023 to November 2024. Exposure Genetic influence on circulating levels of proteins in plasma. Main Outcomes and Measures Outcome measures were summary statistics drawn from recent large-scale genome-wide association studies for SCZ (67 323 cases and 93 456 controls), BD (40 463 cases and 313 436 controls), MDD (166 773 cases and 507 679 controls), and CTP (215 333 individuals). MR was carried out for each phenotype, and proteins that showed statistically significant (Bonferroni-corrected P < .05) associations from MR analysis were used for pathway, protein-protein interaction, drug target enrichment, and potential druggability analysis for each outcome phenotype separately. Results MR analysis revealed 113 Bonferroni-corrected associations (46 novel) involving 91 proteins across the 4 outcome phenotypes. Immune-related proteins, such as interleukins and complement factors, showed pleiotropic effects across multiple outcome phenotypes. Drug target enrichment analysis provided support for repurposing of anti-inflammatory agents for SCZ, amantadine for BD, retinoic acid for MDD, and duloxetine for CTP. Conclusions and Relevance Identifying potentially causal effects of circulating proteins on neuropsychiatric phenotypes suggests potential biomarkers and offers insights for the development of innovative therapeutic strategies. The study also reveals pleiotropic effects of many proteins across different phenotypes, indicating shared etiology among serious psychiatric conditions and cognition.
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Affiliation(s)
- Upasana Bhattacharyya
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
| | - Jibin John
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
| | - Max Lam
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
- Institute of Mental Health, Hougang, Singapore
- Lee Kong Chian School of Medicine, Population and Global Health, Nanyang Technological University, Singapore, Singapore
| | - Jonah Fisher
- Biogen Inc, Cambridge, Massachusetts
- Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts
| | - Benjamin Sun
- Biogen Inc, Cambridge, Massachusetts
- now with Bristol Myers Squibb, Princeton, New Jersey
| | | | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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9
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Kendler KS, Ohlsson H, Sundquist J, Sundquist K. Exploring the implications of case selection methods for psychiatric molecular genetic studies. Mol Psychiatry 2025:10.1038/s41380-025-03015-y. [PMID: 40254710 DOI: 10.1038/s41380-025-03015-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2024] [Revised: 03/19/2025] [Accepted: 04/02/2025] [Indexed: 04/22/2025]
Abstract
Researchers selecting probands for molecular genetic studies confront a range of sampling issues with modest empirical guidance. In this paper, using cases of major depression (MD), anxiety disorders (AD) alcohol use disorder (AUD), drug use disorder (DUD), bipolar disorder (BD) and schizophrenia (SZ) from a large population cohort of all native Swedes born 1940-2003, we examine the implications of three proband selection decisions by exploring profiles of genetic risks assessed using the validated family genetic risk scores. The impact of censoring cases with comorbid diagnoses is quite variable, depending on the frequency of that disorder in the case sample and the genetic relationship of the censored to the primary disorder. In an MD cohort, censoring SZ cases produces only a focal small decrease in schizophrenia genetic risk while censoring AD cases produces a wide-spread reduction in genetic risk for MD and most other disorders. We examine the value of censoring cases of SZ, BD and MD whose onset was preceded by one to two years by first episodes of DUD or AUD. We do not see any increase in genetic risk for these "screened" cohorts. Secondary ascertainment, where disorder A is ascertained as a comorbid diagnosis in a sample collected for disorder B, can, in certain situations, produces large increases in the genetic risk for disorder B and associated disorders in cases of A. However, if disorder B is closely genetically related to disorder A (as seen with MD/AD and DUD/AUD pairings), the pattern differs dramatically and produces a general moderate elevation across the genetic risk profile. These findings provide guidelines for future investigators and suggest caution when screening out comorbid disorders and when utilizing secondary ascertainment.
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Affiliation(s)
- Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.
| | - Henrik Ohlsson
- Center for Primary Health Care Research, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Jan Sundquist
- Center for Primary Health Care Research, Department of Clinical Sciences, Lund University, Malmö, Sweden
- University Clinic Primary Care Skåne, Region Skåne, Sweden
| | - Kristina Sundquist
- Center for Primary Health Care Research, Department of Clinical Sciences, Lund University, Malmö, Sweden
- University Clinic Primary Care Skåne, Region Skåne, Sweden
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10
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De la Serna E, Moreno D, Sugranyes G, Camprodon-Boadas P, Ilzarbe D, Bigorra A, Mora-Maltas B, Baeza I, Flamarique I, Parrilla S, Díaz-Caneja CM, Moreno C, Borras R, Torrent C, Garcia-Rizo C, Castro-Fornieles J. Effects of parental characteristics on the risk of psychopathology in offspring: a 4-year follow-up study. Eur Child Adolesc Psychiatry 2025:10.1007/s00787-025-02719-4. [PMID: 40237842 DOI: 10.1007/s00787-025-02719-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2024] [Accepted: 04/08/2025] [Indexed: 04/18/2025]
Abstract
Offspring of patients diagnosed with schizophrenia (SZoff) or bipolar disorder (BDoff) have double the risk of developing a psychiatric disorder. Here we report the effects of some parental characteristics on the offspring risk of psychopathology at 4-year follow-up. At baseline, 90 BDoff, 41 SZoff and 107 Community Control offspring (CCoff) aged 6 to 17 were included. At 4-year follow-up, 71% of the sample was assessed. Parents' and offspring's psychiatric diagnoses as well as socio-economic status (SES) and global functioning were assessed in addition to parents' ages at childbirth and offspring subclinical psychotic/bipolar symptoms. Kaplan-Meier method and Cox regression analysis were used to assess between-group differences in the cumulative incidence of psychiatric disorders and subclinical psychotic/bipolar symptoms and the association of some offspring and parents' variables with risk of psychopathology and subclinical psychotic/bipolar symptoms. SZoff and BDoff had a higher risk of psychopathology than CCoff at 4-year follow-up. SZoff showed a higher risk for attention deficit hyperactivity disorder (ADHD), disruptive disorders and subclinical psychotic symptoms, whereas BDoff displayed a heightened risk for mood disorders, ADHD and subclinical bipolar symptoms when compared to CCoff. Higher parental psychosocial functioning and SES were associated with a lower prevalence of psychopathology. Both SZoff and BDoff samples have a higher risk for psychopathology but the pattern of this psychopathology seems to be group specific. Longer follow-up studies and larger sample sizes are needed to assess the capacity of psychopathological disorder and subclinical psychotic or bipolar symptoms to predict progression to fully-fledged disorders.
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Grants
- PI07/00853, PI11/02283, PI15/00810, PI17/01066, PI17/00741, PI17/00481, PI18/01119, PI20/00344, PI20/00721, PI21/00519, PI21/01694, PI23/00625, JR19/00024 Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III
- 202210-10 Fundació Marató TV3
- S2022/BMD-7216 AGES 3-CM Madrid Regional Government
- FRCB-IPB2-2023 Pons-Bartran legacy
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Affiliation(s)
- E De la Serna
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain.
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain.
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - D Moreno
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - G Sugranyes
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - P Camprodon-Boadas
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - D Ilzarbe
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - A Bigorra
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - B Mora-Maltas
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - I Baeza
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
| | - I Flamarique
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - S Parrilla
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - C M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - C Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - R Borras
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - C Torrent
- Department of Adult Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Barcelona, Spain
| | - C Garcia-Rizo
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Adult Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Barcelona, Spain
| | - J Castro-Fornieles
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, CIBER, C/ Villarroel, 170, Barcelona, 08036, Spain
- Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
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11
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Zai CC, Dimick MK, Young LT, Kennedy JL, Goldstein BI. Polygenic risk scores in relation to suicidality among youth with or at risk for bipolar disorder. J Affect Disord 2025; 375:44-48. [PMID: 39800071 DOI: 10.1016/j.jad.2025.01.032] [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: 12/18/2023] [Revised: 01/06/2025] [Accepted: 01/08/2025] [Indexed: 01/15/2025]
Abstract
PURPOSE The risk of suicide among individuals with bipolar disorder (BD) is among the highest of all psychiatric disorders. The etiology of suicidality is complex and multifactorial, with genetic factors playing a prominent role according to twin-, family-, and molecular genetic studies. This study examines polygenic risk scores from adult studies in relation to suicidality in youth with or at risk for BD. METHODS Primary analyses examined the association of polygenic risk scores for suicide attempt, based on adult genome-wide association study data, with suicidal ideation, self-harm, and suicide attempt in 232 youth (mean age 16.7 years), including 125 with, and 107 at high-risk for, BD. We also tested polygenic risk scores for risk tolerance, schizophrenia, major depressive disorder, BD, and attention-deficit hyperactivity disorder in secondary analyses. RESULTS Polygenic risk scores for suicide attempt were not significantly associated with suicidal ideation, self-harm, or suicide attempt. Higher polygenic risk scores for major depressive disorder were nominally associated with increased risk of suicidal ideation in the overall sample (beta = 0.36, se(beta) = 0.16, p = 0.017), controlling for covariates. IMPLICATIONS Our finding that polygenic risk for depression is associated with suicidal ideation converges with prior findings in youth and adults. While present findings are constrained by sample size, they underscore the importance of undertaking genome-wide association studies in youth, rather than relying solely on prior adult genome-wide association studies.
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Affiliation(s)
- Clement C Zai
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; Tanenbaum Centre for Pharmacogenetics, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Canada; Institute of Medical Science, University of Toronto, Canada; Laboratory Medicine and Pathobiology, University of Toronto, Canada; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - L Trevor Young
- Department of Psychiatry, University of Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Canada; Pharmacology and Toxicology, University of Toronto, Canada; Centre for Addiction and Mental Health, Toronto, Canada
| | - James L Kennedy
- Tanenbaum Centre for Pharmacogenetics, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Canada; Institute of Medical Science, University of Toronto, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Canada; Institute of Medical Science, University of Toronto, Canada; Pharmacology and Toxicology, University of Toronto, Canada.
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12
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Milosavljevic S, Piroli MV, Sandago EJ, Piroli GG, Smith HH, Beggiato S, Frizzell N, Pocivavsek A. Parental kynurenine 3-monooxygenase genotype in mice directs sex-specific behavioral outcomes in offspring. Biol Sex Differ 2025; 16:22. [PMID: 40176166 PMCID: PMC11967062 DOI: 10.1186/s13293-025-00703-w] [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: 12/18/2024] [Accepted: 03/13/2025] [Indexed: 04/04/2025] Open
Abstract
BACKGROUND Disruptions in brain development can impact behavioral traits and increase the risk of neurodevelopmental conditions such as autism spectrum disorder, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder, often in sex-specific ways. Dysregulation of the kynurenine pathway (KP) of tryptophan metabolism has been implicated in cognitive and neurodevelopmental disorders. Increased brain kynurenic acid (KYNA), a neuroactive metabolite implicated in cognition and sleep homeostasis, and variations in the Kmo gene, which encodes kynurenine 3-monooxygenase (KMO), have been identified in these patients. We hypothesize that parental Kmo genetics influence KP biochemistry, sleep behavior and brain energy demands, contributing to impairments in cognition and sleep in offspring through the influence of parental genotype and genetic nurture mechanisms. METHODS A mouse model of partial Kmo deficiency, Kmo heterozygous (HET-Kmo+/-), was used to examine brain KMO activity, KYNA levels, and sleep behavior in HET-Kmo+/- compared to wild-type control (WT-Control) mice. Brain mitochondrial respiration was assessed, and KP metabolites and corticosterone levels were measured in breast milk. Behavioral assessments were conducted on wild-type offspring from two parental groups: (i) WT-Control from WT-Control parents, (ii) wild-type Kmo (WT-Kmo+/+) from Kmo heterozygous parents (HET-Kmo+/-). All mice were C57Bl/6J background strain. Adult female and male offspring underwent behavioral testing for learning, memory, anxiety-like behavior and sleep-wake patterns. RESULTS HET-Kmo+/- mice exhibited reduced brain KMO activity, increased KYNA levels, and disrupted sleep architecture and electroencephalogram (EEG) power spectra. Mitochondrial respiration (Complex I and Complex II activity) and electron transport chain protein levels were impaired in the hippocampus of HET-Kmo+/- females. Breast milk from HET-Kmo+/- mothers increased kynurenine exposure during lactation but corticosterone levels were unchanged. Compared to WT-Control offspring, WT-Kmo+/+ females showed impaired spatial learning, heightened anxiety, reduced sleep and abnormal EEG spectral power. WT-Kmo+/+ males had deficits in reversal learning but no sleep disturbances or anxiety-like behaviors. CONCLUSIONS These findings suggest that Kmo deficiency impacts KP biochemistry, sleep behavior, and brain mitochondrial function. Even though WT-Kmo+/+ inherit identical genetic material as WT-Control, their development might be shaped by the parent's physiology, behavior, or metabolic state influenced by their Kmo genotype, leading to phenotypic sex-specific differences in offspring.
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Affiliation(s)
- Snezana Milosavljevic
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Building 1, D26, 6311 Garners Ferry Rd, Columbia, SC, 29209, USA
| | - Maria V Piroli
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Building 1, D26, 6311 Garners Ferry Rd, Columbia, SC, 29209, USA
| | - Emma J Sandago
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Building 1, D26, 6311 Garners Ferry Rd, Columbia, SC, 29209, USA
| | - Gerardo G Piroli
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Building 1, D26, 6311 Garners Ferry Rd, Columbia, SC, 29209, USA
| | - Holland H Smith
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Building 1, D26, 6311 Garners Ferry Rd, Columbia, SC, 29209, USA
| | - Sarah Beggiato
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Norma Frizzell
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Building 1, D26, 6311 Garners Ferry Rd, Columbia, SC, 29209, USA
| | - Ana Pocivavsek
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Building 1, D26, 6311 Garners Ferry Rd, Columbia, SC, 29209, USA.
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13
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Miura K, Yoshida M, Morita K, Fujimoto M, Yasuda Y, Yamamori H, Takahashi J, Miyata S, Okazaki K, Matsumoto J, Toyomaki A, Makinodan M, Hashimoto N, Onitsuka T, Kasai K, Ozaki N, Hashimoto R. Gaze behaviors during free viewing revealed differences in visual salience processing across four major psychiatric disorders: a mega-analysis study of 1012 individuals. Mol Psychiatry 2025; 30:1594-1600. [PMID: 39394456 PMCID: PMC11919774 DOI: 10.1038/s41380-024-02773-5] [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: 03/04/2024] [Revised: 09/20/2024] [Accepted: 09/26/2024] [Indexed: 10/13/2024]
Abstract
Aberrant salience processing has been proposed as a pathophysiological mechanism underlying psychiatric symptoms in patients with schizophrenia. The gaze trajectories of individuals with schizophrenia have been reported to be abnormal when viewing an image, suggesting anomalous visual salience as one possible pathophysiological mechanism associated with psychiatric diseases. This study was designed to determine whether visual salience is affected in individuals with schizophrenia, and whether this abnormality is unique to patients with schizophrenia. We examined the gaze behaviors of 1012 participants recruited from seven institutes (550 healthy individuals and 238, 41, 50 and 133 individuals with schizophrenia, bipolar disorder, major depressive disorder and autism spectrum disorder, respectively) when they looked at stationary images as they liked, i.e., free-viewing condition. We used an established computational model of salience maps derived from low-level visual features to measure the degree to which the gaze trajectories of individuals were guided by visual salience. The analysis revealed that the saliency at the gaze of individuals with schizophrenia were higher than healthy individuals, suggesting that patients' gazes were guided more by low-level image salience. Among the low-level image features, orientation salience was most affected. Furthermore, a general linear model analysis of the data for the four psychiatric disorders revealed a significant effect of disease. This abnormal salience processing depended on the disease and was strongest in patients with schizophrenia, followed by patients with bipolar disorder, major depressive disorder, and autism spectrum disorder, suggesting a link between abnormalities in salience processing and strength/frequency for psychosis of these disorders.
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Affiliation(s)
- Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 184-8553, Japan.
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan.
| | - Masatoshi Yoshida
- Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Sapporo, 060-0812, Japan.
| | - Kentaro Morita
- Department of Rehabilitation, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Michiko Fujimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 184-8553, Japan
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Yuka Yasuda
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 184-8553, Japan
- Medical Corporation Foster, Life Grow Brilliant Mental Clinic, Osaka, 531-0075, Japan
| | - Hidenaga Yamamori
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 184-8553, Japan
- Japan Community Health Care Organization, Osaka Hospital, Osaka, 553-0003, Japan
| | - Junichi Takahashi
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | - Seiko Miyata
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Kosuke Okazaki
- Department of Psychiatry, Nara Medical University School of Medicine, Kashihara, 634-8521, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 184-8553, Japan
| | - Atsuto Toyomaki
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, 060-8638, Japan
| | - Manabu Makinodan
- Department of Psychiatry, Nara Medical University School of Medicine, Kashihara, 634-8521, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, 060-8638, Japan
| | | | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, 113-0033, Japan
| | - Norio Ozaki
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
- Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, 464-8601, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 184-8553, Japan
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Gasalla P, Thomas KL, Wilkinson L, Hall J, Dwyer DM. Reduced Cacna1c Expression Produces Anhedonic Reactions to Palatable Sucrose in Rats: No Interactions With Juvenile or Adult Stress. GENES, BRAIN, AND BEHAVIOR 2025; 24:e70021. [PMID: 40263772 PMCID: PMC12014513 DOI: 10.1111/gbb.70021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Revised: 03/10/2025] [Accepted: 03/23/2025] [Indexed: 04/24/2025]
Abstract
Genetic variation in CACNA1C, which encodes the alpha-1 subunit of Cav1.2 L-type voltage-gated calcium channels, is strongly linked to risk for psychiatric disorders including schizophrenia, bipolar disorder, and major depression. Here we investigated the impact of mutations of one copy of Cacna1c (leading to low gene dosage of Cacna1c) on rats' hedonic responses to palatable sucrose (assessed using the analysis of consumption microstructure). In addition, we also investigated the effects of combining either juvenile or adult stress with the manipulation of Cacna1c. Across three experiments, Cacna1c+/- rats displayed attenuated hedonic reactions to sucrose compared to wild-type littermate controls, despite the Cacna1c+/- rats retaining sensitivity to sucrose concentration in terms of the amount of consumption. Unexpectedly, juvenile stress enhanced rather than reduced hedonic reactions to sucrose, while adult stress did not have clear hedonic effects. The effects of Cacna1c manipulation did not interact with either juvenile or adult stress. The fact that Cacna1c+/- rats display a clear analogue of anhedonia-a reduction in the positive hedonic reactions normally elicited by highly palatable sucrose-a symptom observed trans-diagnostically across psychiatric disorders linked to CACNA1C, suggests this model may play a valuable role in the translational investigation of anhedonia.
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Affiliation(s)
- Patricia Gasalla
- Neuroscience & Mental Health Innovation Institute, School of MedicineCardiff UniversityCardiffUK
| | - Kerrie L. Thomas
- Neuroscience & Mental Health Innovation Institute, School of MedicineCardiff UniversityCardiffUK
| | - Lawrence Wilkinson
- Neuroscience & Mental Health Innovation Institute, School of MedicineCardiff UniversityCardiffUK
| | - Jeremy Hall
- Neuroscience & Mental Health Innovation Institute, School of MedicineCardiff UniversityCardiffUK
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15
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Guineau MG, Ikani N, Rinck M, Collard RM, van Eijndhoven P, Tendolkar I, Schene AH, Becker ES, Vrijsen JN. Anhedonia as a Transdiagnostic Symptom Across Psychological Disorders: A Network Approach. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2025; 23:257-269. [PMID: 40235614 PMCID: PMC11995902 DOI: 10.1176/appi.focus.25023012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2025]
Abstract
Background Anhedonia is apparent in different mental disorders and is suggested to be related to dysfunctions in the reward system and/or affect regulation. It may hence be a common underlying feature associated with symptom severity of mental disorders. Methods We constructed a cross-sectional graphical Least Absolute Shrinkage and Selection Operator (LASSO) network and a relative importance network to estimate the relationships between anhedonia severity and the severity of symptom clusters of major depressive disorder (MDD), anxiety sensitivity (AS), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) in a sample of Dutch adult psychiatric patients (N = 557). Results Both these networks revealed anhedonia severity and depression symptom severity as central to the network. Results suggest that anhedonia severity may be predictive of the severity of symptom clusters of MDD, AS, ADHD, and ASD. MDD symptom severity may be predictive of AS and ADHD symptom severity. Conclusions The results suggest that anhedonia may serve as a common underlying transdiagnostic psychopathology feature, predictive of the severity of symptom clusters of depression, AS, ADHD, and ASD. Thus, anhedonia may be associated with the high comorbidity between these symptom clusters and disorders. If our results will be replicated in future studies, it is recommended for clinicians to be more vigilant about screening for anhedonia and/or depression severity in individuals diagnosed with an anxiety disorder, ADHD and/or ASD.Appeared originally in Psychol Med 2023; 53:3908-3919.
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Affiliation(s)
- Melissa G Guineau
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - N Ikani
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - M Rinck
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - R M Collard
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - P van Eijndhoven
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - I Tendolkar
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - A H Schene
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - E S Becker
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
| | - J N Vrijsen
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands (Guineau, Ikani, Rinck, Becker, Vrijsen); Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care, Nijmegen, The Netherlands (Guineau, Ikani); Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands (Ikani, Collard, van Eijndhoven, Tendolkar, Schene, Vrijsen); Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands (Ikani, van Eijndhoven, Tendolkar, Schene, Vrijsen); Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, The Netherlands (Ikani, Vrijsen); Department of Psychiatry and Psychotherapy, University Hospital Essen, Essen, Germany (Tendolkar)
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16
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Cai N, Verhulst B, Andreassen OA, Buitelaar J, Edenberg HJ, Hettema JM, Gandal M, Grotzinger A, Jonas K, Lee P, Mallard TT, Mattheisen M, Neale MC, Nurnberger JI, Peyrot WJ, Tucker-Drob EM, Smoller JW, Kendler KS. Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research. Mol Psychiatry 2025; 30:1627-1638. [PMID: 39730880 PMCID: PMC11919726 DOI: 10.1038/s41380-024-02878-x] [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: 06/19/2024] [Revised: 11/07/2024] [Accepted: 12/16/2024] [Indexed: 12/29/2024]
Abstract
Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.
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Affiliation(s)
- Na Cai
- Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany
- Computational Health Centre, Helmholtz Munich, Neuherberg, Germany
- School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Brad Verhulst
- Department of Psychiatry and Behavioral Sciences, Texas A&M University, College Station, TX, USA
| | - Ole A Andreassen
- Centre of Precision Psychiatry, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Karakter Child and Adolescent University Center, Nijmegen, The Netherlands
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John M Hettema
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael Gandal
- Departments of Psychiatry and Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Katherine Jonas
- Department of Psychiatry & Behavioral Health, Stony Brook University, Stony Brook, NY, USA
| | - Phil Lee
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Travis T Mallard
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Manuel Mattheisen
- Department of Community Health and Epidemiology and Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital of Munich, Munich, Germany
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - John I Nurnberger
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Wouter J Peyrot
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.
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17
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Lee PH, Jung JY, Sanzo BT, Duan R, Ge T, Waldman I, Smoller JW, Schwaba T, Tucker-Drob EM, Grotzinger AD. Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in the All of Us Research Program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.03.26.25324720. [PMID: 40196240 PMCID: PMC11974969 DOI: 10.1101/2025.03.26.25324720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
Psychiatric disorders exhibit substantial genetic overlap, raising questions about the utility of transdiagnostic genetic risk models. Using data from the All of Us Research Program (N=102,091), we evaluated common psychiatric genetic (CPG) factor-based polygenic risk scores (PRSs) compared to standard disorder-specific PRSs. The CPG PRS consistently outperformed disorder-specific scores in predicting individual disorder risk, explaining 1.07 to 24.6 times more phenotypic variance across 11 psychiatric conditions. Meanwhile, many disorder-specific PRSs retained independent but smaller contributions, highlighting the complementary nature of shared and disorder-specific genetic risk. While alternative multi-factor models improved model fit, the CPG PRS provided comparable or superior predictive performance across most disorders, including overall comorbidity burden. Cross-ancestry analyses however revealed notable limitations of European-centric GWAS datasets for other populations due to ancestral differences in genetic architecture. These findings underscore the potential value of transdiagnostic PRSs for psychiatric genetics while highlighting the need for more equitable genetic risk models.
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Affiliation(s)
- Phil H. Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Mass General Brigham, Boston, MA, USA
- Department of Psychiatry, Mass General Brigham and Harvard Medical School, Boston, MA, USA
- Stanly Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jae-Yoon Jung
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brandon T. Sanzo
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Mass General Brigham, Boston, MA, USA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Mass General Brigham, Boston, MA, USA
- Department of Psychiatry, Mass General Brigham and Harvard Medical School, Boston, MA, USA
- Stanly Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Irwin Waldman
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Mass General Brigham, Boston, MA, USA
- Department of Psychiatry, Mass General Brigham and Harvard Medical School, Boston, MA, USA
- Stanly Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ted Schwaba
- Department of Psychology, Michigan State University, MI, USA
| | | | - Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado at Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado at Boulder, CO, USA
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18
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Tang B, Lin N, Liang J, Yi G, Zhang L, Peng W, Xue C, Jiang H, Li M. Leveraging pleiotropic clustering to address high proportion correlated horizontal pleiotropy in Mendelian randomization studies. Nat Commun 2025; 16:2817. [PMID: 40118820 PMCID: PMC11928562 DOI: 10.1038/s41467-025-57912-5] [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: 04/28/2024] [Accepted: 03/05/2025] [Indexed: 03/24/2025] Open
Abstract
Mendelian randomization harnesses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. However, certain genetic variants can affect both the exposure and the outcome through a shared factor. This phenomenon, called correlated horizontal pleiotropy, may result in false-positive causal findings. Here, we propose a Pleiotropic Clustering framework for Mendelian randomization, PCMR. PCMR detects correlated horizontal pleiotropy and extends the zero modal pleiotropy assumption to enhance causal inference in trait pairs with correlated horizontal pleiotropic variants. Simulations show that PCMR can effectively detect correlated horizontal pleiotropy and avoid false positives in the presence of correlated horizontal pleiotropic variants, even when they constitute a high proportion of the variants connecting both traits (e.g., 30-40%). In datasets consisting of 48 exposure-common disease pairs, PCMR detects horizontal correlated pleiotropy in 7 out of the exposure-common disease pairs, and avoids detecting false positive causal links. Additionally, PCMR can facilitate the integration of biological information to exclude correlated horizontal pleiotropic variants, enhancing causal inference. We apply PCMR to study causal relationships between three common psychiatric disorders as examples.
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Affiliation(s)
- Bin Tang
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Nan Lin
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Junhao Liang
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Guorong Yi
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Liubin Zhang
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Wenjie Peng
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Chao Xue
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Hui Jiang
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
| | - Miaoxin Li
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China.
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China.
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19
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Gonzales S, Zhao JZ, Choi NY, Acharya P, Jeong S, Wang X, Lee MY. SOX7: Autism Associated Gene Identified by Analysis of Multi-Omics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2023.05.26.542456. [PMID: 37292933 PMCID: PMC10245991 DOI: 10.1101/2023.05.26.542456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-wide association studies and next generation sequencing data analyses based on DNA information have identified thousands of mutations associated with autism spectrum disorder (ASD). However, more than 99% of identified mutations are non-coding. Thus, it is unclear which of these mutations might be functional and thus potentially causal variants. Transcriptomic profiling using total RNA-sequencing has been one of the most utilized approaches to link protein levels to genetic information at the molecular level. The transcriptome captures molecular genomic complexity that the DNA sequence solely does not. Some mutations alter a gene's DNA sequence but do not necessarily change expression and/or protein function. To date, few common variants reliably associated with the diagnosis status of ASD despite consistently high estimates of heritability. In addition, reliable biomarkers used to diagnose ASD or molecular mechanisms to define the severity of ASD do not exist. Therefore, it is necessary to integrate DNA and RNA testing together to identify true causal genes and propose useful biomarkers for ASD. We performed gene-based association studies with adaptive test using genome-wide association studies (GWAS) summary statistics with two large GWAS datasets (ASD 2019 data: 18,382 ASD cases and 27,969 controls [discovery data]; ASD 2017 data: 6,197 ASD cases and 7,377 controls [replication data]) which were obtained from the Psychiatric Genomics Consortium (PGC). In addition, we investigated differential expression between ASD cases and controls for genes identified in gene-based GWAS with two RNA-seq datasets (GSE211154: 20 cases and 19 controls; GSE30573: 3 cases and 3 controls). We identified 5 genes significantly associated with ASD in ASD 2019 data (KIZ-AS1, p=8.67×10-10; KIZ, p=1.16×10-9; XRN2, p=7.73×10-9; SOX7, p=2.22×10-7; LOC101929229 also known as PINX1-DT, p=2.14×10-6). Among these 5 genes, gene SOX7 (p=0.00087) and LOC101929229 (p=0.009) were replicated in ASD 2017 data. KIZ-AS1 (p=0.059) and KIZ (p=0.06) were close to the boundary of replication in ASD 2017 data. Genes SOX7 (p=0.036 in all samples; p=0.044 in white samples) indicated significant expression differences between cases and controls in the GSE211154 RNA-seq data. Furthermore, gene SOX7 was upregulated in cases than in controls in the GSE30573 RNA-seq data (p=0.0017; Benjamini-Hochberg adjusted p=0.0085). SOX7 encodes a member of the SOX (SRY-related HMG-box) family of transcription factors pivotally contributing to determining of the cell fate and identity in many lineages. The encoded protein may act as a transcriptional regulator after forming a protein complex with other proteins leading to autism. Gene SOX7 in the transcription factor family could be associated with ASD. This finding may provide new diagnostic and therapeutic strategies for ASD.
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Affiliation(s)
- Samantha Gonzales
- Department of Biostatistics, Florida International University, Miami, FL 33199
| | - Jane Zizhen Zhao
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Na Young Choi
- Department of Biomedical Engineering, University of North Texas, Denton, TX 76207
| | - Prabha Acharya
- Department of Biomedical Engineering, University of North Texas, Denton, TX 76207
| | - Sehoon Jeong
- Department of Healthcare Information Technology Inje University, Gimhae, South Korea, 50834
| | - Xuexia Wang
- Department of Biostatistics, Florida International University, Miami, FL 33199
| | - Moo-Yeal Lee
- Department of Biomedical Engineering, University of North Texas, Denton, TX 76207
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Boquet-Pujadas A, Zeng J, Tian YE, Yang Z, Shen L, Zalesky A, Davatzikos C, Wen J. MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics. Brief Bioinform 2025; 26:bbaf125. [PMID: 40135505 PMCID: PMC11938998 DOI: 10.1093/bib/bbaf125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Revised: 02/09/2025] [Accepted: 03/03/2025] [Indexed: 03/27/2025] Open
Abstract
Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that bridge the genetics and clinical manifestations of human disease. However, the genetic architecture of these AI endophenotypes remains largely unexplored in the context of human multiorgan system diseases. Using publicly available genome-wide association study summary statistics from the UK Biobank (UKBB), FinnGen, and the Psychiatric Genomics Consortium, we comprehensively depicted the genetic architecture of 2024 multiorgan AI endophenotypes (MAEs). We comparatively assessed the single-nucleotide polymorphism-based heritability, polygenicity, and natural selection signatures of 2024 MAEs using methods commonly used in the field. Genetic correlation and Mendelian randomization analyses reveal both within-organ relationships and cross-organ interconnections. Bi-directional causal relationships were established between chronic human diseases and MAEs across multiple organ systems, including Alzheimer's disease for the brain, diabetes for the metabolic system, asthma for the pulmonary system, and hypertension for the cardiovascular system. Finally, we derived polygenic risk scores for the 2024 MAEs for individuals not used to calculate MAEs and returned these to the UKBB. Our findings underscore the promise of the MAEs as new instruments to ameliorate overall human health. All results are encapsulated into the MUlTiorgan AI endophenoTypE genetic atlas and are publicly available at https://labs-laboratory.com/mutate.
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Affiliation(s)
- Aleix Boquet-Pujadas
- Laboratory of AI and Biomedical Science (LABS), Columbia University, 530 W 166th St, New York, NY 10032, United States
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Alan Gilbert Building, Level 3/161 Barry St, Carlton VIC 3053, Australia
| | - Zhijian Yang
- GE Healthcare, 1040 12th Ave NW, Issaquah, WA 98027, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 N Service Dr, Philadelphia, PA 19104, United States
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Alan Gilbert Building, Level 3/161 Barry St, Carlton VIC 3053, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk Richards Building, 7th Floor Philadelphia, PA 19104, United States
| | | | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Columbia University, 530 W 166th St, New York, NY 10032, United States
- New York Genome Center (NYGC), 101 6th Ave, New York, NY 10013, United States
- Department of Biomedical Engineering, Columbia University, 1210 Amsterdam Ave, New York, NY 10027, United States
- Data Science Institute (DSI), Columbia University, Mudd Building, W 120th St, New York, NY 10027, United States
- Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Department of Radiology, Columbia University, 530 W 166th St, New York, NY 10032, United States
- Zuckerman Institute, Columbia University, New York, NY, United States
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21
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Scott J, Crouse JJ, Medland SE, Mitchell BL, Gillespie NA, Martin NG, Hickie IB. Polygenic risk scores and help-seeking behaviour in young people with recent onset of mood and psychotic disorders. J Affect Disord 2025; 372:40-47. [PMID: 39615756 DOI: 10.1016/j.jad.2024.11.067] [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: 09/16/2024] [Revised: 11/18/2024] [Accepted: 11/21/2024] [Indexed: 01/15/2025]
Abstract
OBJECTIVES We examined associations between polygenic risk scores (PRS) for depression (PRS-MDD), psychosis (PRS-SCZ), bipolar disorders (PRS-BD) and neuroticism (PRS-NEU) and (i) help-seeking, and (ii) new onset cases of full-threshold mood or psychotic disorders in youth. METHODS Help-seeking for mental health problems was assessed by self-report and mood and psychotic disorders were identified using the Composite International Diagnostic Interview. A principal component analysis of the four selected PRS identified two dimensions (BD-SCZ; MDD-NEU) that accounted for 69.9 % of the explained variance. We explored the associations between these PRS dimensions and help-seeking and diagnostic subgroup using analyses of co-variance (ANCOVA) adjusted for variables of influence (such as age, sex, twin status). RESULTS Almost 30 % (409 of 1473) of study participants met CIDI criteria for ≥ 1 mood or psychotic disorder. Overall, 60 % (n = 245) of CIDI cases sought help, ranging from 35 % for psychosis to 77 % for mania. Furthermore, 143 help-seekers did not have a CIDI diagnosis of mood or psychotic disorders. The BD-SCZ dimension showed associations with help-seeking behaviour and diagnostic groups, but the MDD-NEU dimension only showed associations with help-seeking. LIMITATIONS Some diagnoses could not be studied in detail (i.e., schizophreniform disorders) due to the small size of subgroups and planned analyses needed to be adjusted for the presence of twins and non-twin siblings. CONCLUSIONS Signals of genetic liability are higher in young people who seek help from health services whether or not the problem they are seeking help for meets full-threshold diagnostic criteria for a major mental disorder.
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Affiliation(s)
- Jan Scott
- Brain and Mind Centre, The University of Sydney, Sydney, Australia; Institute of Neuroscience, Newcastle University, Newcastle, United Kingdom.
| | - Jacob J Crouse
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Sarah E Medland
- Brain and Mental Health Program, QIMR Berghofer Institute of Medical Research, Brisbane, Australia; Institute of Molecular Bioscience, The University of Queensland, Brisbane, Australia; School of Psychology, The University of Queensland, Brisbane, Queensland, Australia; School of Psychology and Counselling, Queensland University of Techonology, Brisbane, Queensland, Australia
| | - Brittany L Mitchell
- Brain and Mental Health Program, QIMR Berghofer Institute of Medical Research, Brisbane, Australia; Institute of Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Nicholas G Martin
- Brain and Mental Health Program, QIMR Berghofer Institute of Medical Research, Brisbane, Australia
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
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22
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Arbona-Lampaya A, Sung H, D'Amico A, Knowles EEM, Besançon EK, Freifeld A, Lacbawan L, Lopes F, Kassem L, Nardi AE, McMahon FJ. Heritability, phenotypic, and genetic correlations across dimensional and categorical models of bipolar disorder in a family sample. J Affect Disord 2025; 372:394-401. [PMID: 39667704 DOI: 10.1016/j.jad.2024.12.030] [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: 05/09/2024] [Revised: 09/13/2024] [Accepted: 12/07/2024] [Indexed: 12/14/2024]
Abstract
BACKGROUND Bipolar disorder (BD) presents with a wide range of symptoms that vary among relatives, casting doubt on categorical illness models. To address this uncertainty, we investigated the heritability and genetic relationships between categorical and dimensional models of BD in a family sample. METHODS This retrospective study included participants (n = 397 Females, n = 329 Males, mean age 47 yr) in the Amish-Mennonite Bipolar Genetics (AMBiGen) study from North and South America that were assigned categorical mood disorder diagnoses ("narrow" or "broad") by structured psychiatric interview and completed the Mood Disorder Questionnaire (MDQ), which assesses lifetime history of manic symptoms and associated impairment. MDQ-dimensions were analyzed by Principal Component Analysis (PCA). Heritability and genetic overlaps between categorical diagnoses and MDQ-dimensions were estimated with SOLAR-ECLIPSE within 432 genotyped participants. RESULTS Individuals diagnosed with BD (n = 124) endorsed more MDQ items (61 %) than those with other mood disorders (26 %) or with no mood disorder (9 %), as expected. PCA suggested a three-component model for the MDQ, capturing 60 % of the variance. Heritability of the MDQ and its principal components was significant but modest (20-30 %, p < 0.001). Genetic correlations between MDQ measures and categorical diagnoses (ρG = 0.62-1.0; p < 0.001) were stronger than phenotypic correlations (ρP = 0.11-0.58; p < 0.001). LIMITATIONS Recruitment through probands with BD resulted in increased prevalence of BD in this sample, limiting generalizability. Unavailable genetic data reduced sample size for some analyses. CONCLUSION Findings support a genetic continuity between dimensional and categorical models of BD and suggest that the MDQ is a useful phenotype measure for genetic studies of BD.
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Affiliation(s)
- Alejandro Arbona-Lampaya
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; School of Medicine, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico.
| | - Heejong Sung
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alexander D'Amico
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Emma E M Knowles
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emily K Besançon
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ally Freifeld
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ley Lacbawan
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Fabiana Lopes
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Layla Kassem
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Antonio E Nardi
- Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Francis J McMahon
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
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23
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Anglin DM, Selloni AT. When the Structural, Social, and Biological Domains Converge: The Case of Neighborhood Ethnic Density and Psychosis. Harv Rev Psychiatry 2025; 33:78-82. [PMID: 40036025 DOI: 10.1097/hrp.0000000000000420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
ABSTRACT Biological mechanisms associated with psychosis risk are often formed by generations of social-environmental experiences within families, communities, and neighborhoods, and further shaped by structural policies. This column first describes conceptual work that integrates macrolevel structural, individual-level social, and biological domains to better understand psychosis risk. It then highlights the interconnection of low neighborhood ethnic density and racial exclusion as an example of how social determinants connect to social and biological consequences associated with psychosis outcomes. Neighborhood ethnoracial diversity may be protective against social and biological mechanisms connected to psychosis outcomes among minoritized groups at risk for psychosis. This is particularly salient during childhood because such diversity attenuates stress processes associated with social exclusion and discrimination. Moreover, ethnoracially diverse communities foster close relationships and social connection. We provide supportive literature to illustrate the importance of multilevel/multifactorial approaches for identifying psychosis risk and protective factors. Investing further in integrative approaches for understanding psychosis risk and prognosis may translate into more substantial improvements for individuals with these lived experiences.
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Affiliation(s)
- Deidre M Anglin
- From Department of Psychology, The City College of New York, City University of New York (Dr. Anglin and Ms. Selloni); The Graduate Center, City University of New York (Dr. Anglin)
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24
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Li Y, Gui Q, Ren S, Liu Z, Zhang A, Liu P, Zhou X, Sun N, Yang C. Mendelian Randomization Analysis of the Possible Causal Relationships Between Neurodevelopment-Related Proteins and Bipolar Disorder. Brain Behav 2025; 15:e70442. [PMID: 40123161 PMCID: PMC11930852 DOI: 10.1002/brb3.70442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2024] [Revised: 03/03/2025] [Accepted: 03/06/2025] [Indexed: 03/25/2025] Open
Abstract
BACKGROUND Bipolar disorder (BD) is a complex mental condition of which the mechanism of onset remains unclear. Mendelian randomization (MR) allows evaluation of the causal effects of biomarkers by minimizing the risks of reverse causation and confounding factors. In this study, MR was used to assess the causal relationships between neurodevelopment-related proteins and BD, thereby providing potential evidence for the neurodevelopmental hypothesis of this mental disorder. METHODS Leveraging data from large-scale genome-wide association studies (GWASs), the associations between six neurodevelopment-related proteins and BD were analyzed using five MR approaches; namely, inverse-variance weighted, weighted median, MR-Egger, simple mode, and weighted mode methods. The neurodevelopment-related proteins were selected in the study with 5368 European descents. GWAS of BD come from the Psychiatric Genomics Consortium (NCase = 41,917, NControl = 371,549). RESULTS The analyses identified robust causal relationships between BD and the proteins inter-alpha-trypsin inhibitor heavy chain (ITIH)5 (OR = 1.08, 95% CI = 1.00-1.17, p = 0.04) and neurofascin (NFASC) (OR = 0.96, 95% CI = 0.92-1.00, p = 0.042). Initial findings for ITIH1 and ITIH3 were deemed unreliable due to pleiotropy (ITIH1: MR-Egger intercept p = 0.025) or heterogeneity (ITIH3: Cochran's Q p = 0.001). Furthermore, the MR analyses failed to yield evidence supporting a causal effect of liability to BD on neurodevelopment-related proteins. CONCLUSION The MR analysis indicated potential causal relationships between two neurodevelopment-related proteins (NFASC and ITIH5) and BD. Further studies are required to validate these results and elucidate the specific functions of these proteins in the development of this mental disorder.
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Affiliation(s)
- Yanyan Li
- Shanxi Medical UniversityTaiyuanChina
| | | | | | - Zhifen Liu
- Department of PsychiatryFirst Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Aixia Zhang
- Department of PsychiatryFirst Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Penghong Liu
- Department of PsychiatryFirst Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Xueping Zhou
- Department of PsychiatryFirst Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Ning Sun
- Department of PsychiatryFirst Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Chunxia Yang
- Department of PsychiatryFirst Hospital of Shanxi Medical UniversityTaiyuanChina
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25
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Akkouh IA, Osete JR, Szabo A, Andreassen OA, Djurovic S. Neurobiological Perturbations in Bipolar Disorder Compared With Schizophrenia: Evidence From Cell Cultures and Brain Organoids. Biol Psychiatry 2025:S0006-3223(25)00110-6. [PMID: 39983953 DOI: 10.1016/j.biopsych.2025.02.012] [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: 10/16/2024] [Revised: 01/06/2025] [Accepted: 02/13/2025] [Indexed: 02/23/2025]
Abstract
Bipolar disorder (BD) and schizophrenia (SCZ) are uniquely human disorders with a complex pathophysiology that involves adverse neuropathological events in brain development. High disease polygenicity and limited access to live human brain tissue make these disorders exceedingly challenging to study mechanistically. Cellular cultures and brain organoids generated from human-derived pluripotent stem cells preserve the genetic background of the donor cells and recapitulate some of the defining characteristics of human brain architecture and early spatiotemporal development. These model systems have already proven successful in deciphering some of the neuropathological perturbations in BD and SCZ, and methodological advancements, such as the functional integration of 2 or more region-specific organoids and organoid transplantation in animals, promise to deliver increasingly refined insights. Here, we review a selection of recent discoveries achieved by stem cell-based models, with a particular focus on patterns of cellular and molecular convergence and divergence between BD and SCZ. First, we provide a brief overview of the evidence from glial and neuronal cell cultures and brain organoids, centering our discussion on several key functional domains, including neuroinflammation, neuronal excitability, and mitochondrial function. Then, we review recent findings demonstrating the power of integrating stem cell-based systems with gene editing technologies to elucidate the functional consequences of risk variants identified through genetic association studies. We end with a discussion of current challenges and some promising avenues for future research.
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Affiliation(s)
- Ibrahim A Akkouh
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Jordi Requena Osete
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Attila Szabo
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Department of Clinical Science, University of Bergen, Bergen, Norway.
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26
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Manns M, Juckel G, Freund N. The Balance in the Head: How Developmental Factors Explain Relationships Between Brain Asymmetries and Mental Diseases. Brain Sci 2025; 15:169. [PMID: 40002502 PMCID: PMC11852682 DOI: 10.3390/brainsci15020169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2024] [Revised: 01/29/2025] [Accepted: 02/07/2025] [Indexed: 02/27/2025] Open
Abstract
Cerebral lateralisation is a core organising principle of the brain that is characterised by a complex pattern of hemispheric specialisations and interhemispheric interactions. In various mental disorders, functional and/or structural hemispheric asymmetries are changed compared to healthy controls, and these alterations may contribute to the primary symptoms and cognitive impairments of a specific disorder. Since multiple genetic and epigenetic factors influence both the pathogenesis of mental illness and the development of brain asymmetries, it is likely that the neural developmental pathways overlap or are even causally intertwined, although the timing, magnitude, and direction of interactions may vary depending on the specific disorder. However, the underlying developmental steps and neuronal mechanisms are still unclear. In this review article, we briefly summarise what we know about structural, functional, and developmental relationships and outline hypothetical connections, which could be investigated in appropriate animal models. Altered cerebral asymmetries may causally contribute to the development of the structural and/or functional features of a disorder, as neural mechanisms that trigger neuropathogenesis are embedded in the asymmetrical organisation of the developing brain. Therefore, the occurrence and severity of impairments in neural processing and cognition probably cannot be understood independently of the development of the lateralised organisation of intra- and interhemispheric neuronal networks. Conversely, impaired cellular processes can also hinder favourable asymmetry development and lead to cognitive deficits in particular.
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Affiliation(s)
- Martina Manns
- Research Division Experimental and Molecular Psychiatry, Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, 44809 Bochum, Germany;
| | - Georg Juckel
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, 44791 Bochum, Germany;
| | - Nadja Freund
- Research Division Experimental and Molecular Psychiatry, Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, 44809 Bochum, Germany;
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27
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Chen X, Lu Y, Cue JM, Han MV, Nimgaonkar VL, Weinberger DR, Han S, Zhao Z, Chen J. Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2025; 11:14. [PMID: 39910091 PMCID: PMC11799204 DOI: 10.1038/s41537-025-00564-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2024] [Accepted: 01/17/2025] [Indexed: 02/07/2025]
Abstract
Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders.
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Affiliation(s)
- Xiangning Chen
- Center for Precision Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houton, Houston, Texas, USA.
| | - Yimei Lu
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Joan Manuel Cue
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Mira V Han
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | | | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Zhongming Zhao
- Center for Precision Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houton, Houston, Texas, USA.
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV, USA.
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
- Interdisciplinary Neuroscience Program, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, USA.
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28
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Schowe AM, Godara M, Czamara D, Adli M, Singer T, Binder EB. Genetic predisposition for negative affect predicts mental health burden during the COVID-19 pandemic. Eur Arch Psychiatry Clin Neurosci 2025; 275:61-73. [PMID: 38587666 PMCID: PMC11799032 DOI: 10.1007/s00406-024-01795-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/09/2024] [Indexed: 04/09/2024]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic was accompanied by an increase in mental health challenges including depression, stress, loneliness, and anxiety. Common genetic variants can contribute to the risk for psychiatric disorders and may present a risk factor in times of crises. However, it is unclear to what extent polygenic risk played a role in the mental health response to the COVID-19 pandemic. In this study, we investigate whether polygenic scores (PGSs) for mental health-related traits can distinguish between four resilience-vulnerability trajectories identified during the COVID-19 pandemic and associated lockdowns in 2020/21. We used multinomial regression in a genotyped subsample (n = 1316) of the CovSocial project. The most resilient trajectory characterized by the lowest mental health burden and the highest recovery rates served as the reference group. Compared to this most resilient trajectory, a higher value on the PGS for the well-being spectrum decreased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.3%). Conversely, a higher value on the PGS for neuroticism increased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.2%). Latent change in mental health burden extracted from the resilience-vulnerability trajectories was not associated with any PGS. Although our findings support an influence of PGS on mental health during COVID-19, the small added explained variance suggests limited utility of such genetic markers for the identification of vulnerable individuals in the general population.
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Affiliation(s)
- Alicia M Schowe
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany.
- Graduate School of Systemic Neuroscience, Ludwig Maximilian University, Munich, Germany.
| | - Malvika Godara
- Social Neuroscience Lab, Max Planck Society, 10557, Berlin, Germany.
| | - Darina Czamara
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | - Mazda Adli
- Department of Psychiatry and Neurosciences, CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Center for Psychiatry, Psychotherapy and Psychosomatic Medicine, Fliedner Klinik Berlin, Berlin, Germany
| | - Tania Singer
- Social Neuroscience Lab, Max Planck Society, 10557, Berlin, Germany
| | - Elisabeth B Binder
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
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29
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Mahajan P, Patil D, Nair N, Musmade N, Apte P. Mapping the Landscape of Autism Research: A Scientometric Review (2011-2023). Int J Dev Neurosci 2025; 85:e10406. [PMID: 39723621 DOI: 10.1002/jdn.10406] [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: 09/10/2024] [Revised: 11/13/2024] [Accepted: 12/09/2024] [Indexed: 12/28/2024] Open
Abstract
This scientometric analysis maps the landscape of autism spectrum disorder (ASD) research between 2011 and 2023. By exploring patterns in publication growth, geographic distribution and institutional involvement, this study highlights evolving research themes, key contributors and collaborative networks. Our findings reveal a marked rise in ASD publications, particularly from 2020 onwards, with the United States, United Kingdom and China leading in contributions and collaborations. Scientometric analysis identifies a shift towards advanced machine learning techniques and neuroimaging in ASD studies, reflecting technological integration in research. Institutional analysis uncovers Vanderbilt University and Yale University as major contributors, with significant citation impacts across their publications. Furthermore, prominent funding sources, including the National Institutes of Health, underscore the critical role of funding in shaping research priorities. This comprehensive scientometric overview not only consolidates current knowledge but also serves as a resource to inform future research directions, enhancing interdisciplinary approaches to ASD understanding and intervention.
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Affiliation(s)
- Pratibha Mahajan
- Department of Artificial Intelligence, Vishwakarma University, Pune, India
| | - Deven Patil
- Department of Artificial Intelligence, Vishwakarma University, Pune, India
| | - Nidhi Nair
- Department of Artificial Intelligence, Vishwakarma University, Pune, India
| | - Nishant Musmade
- Department of Artificial Intelligence, Vishwakarma University, Pune, India
| | - Preet Apte
- Department of Artificial Intelligence, Vishwakarma University, Pune, India
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Arnatkeviciute A, Fornito A, Tong J, Pang K, Fulcher BD, Bellgrove MA. Linking Genome-Wide Association Studies to Pharmacological Treatments for Psychiatric Disorders. JAMA Psychiatry 2025; 82:151-160. [PMID: 39661350 PMCID: PMC11800018 DOI: 10.1001/jamapsychiatry.2024.3846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 10/02/2024] [Indexed: 12/12/2024]
Abstract
Importance Large-scale genome-wide association studies (GWAS) should ideally inform the development of pharmacological treatments, but whether GWAS-identified mechanisms of disease liability correspond to the pathophysiological processes targeted by current pharmacological treatments is unclear. Objective To investigate whether functional information from a range of open bioinformatics datasets can elucidate the relationship between GWAS-identified genetic variation and the genes targeted by current treatments for psychiatric disorders. Design, Setting, and Participants Associations between GWAS-identified genetic variation and pharmacological treatment targets were investigated across 4 psychiatric disorders-attention-deficit/hyperactivity disorder, bipolar disorder, schizophrenia, and major depressive disorder. Using a candidate set of 2232 genes listed as targets for all approved treatments in the DrugBank database, each gene was independently assigned 2 scores for each disorder-one based on its involvement as a treatment target and the other based on the mapping between GWAS-implicated single-nucleotide variants (SNVs) and genes according to 1 of 4 bioinformatic data modalities: SNV position, gene distance on the protein-protein interaction (PPI) network, brain expression quantitative trail locus (eQTL), and gene expression patterns across the brain. Study data were analyzed from November 2023 to September 2024. Main Outcomes and Measures Gene scores for pharmacological treatments and GWAS-implicated genes were compared using a measure of weighted similarity applying a stringent null hypothesis-testing framework that quantified the specificity of the match by comparing identified associations for a particular disorder with a randomly selected set of treatments. Results Incorporating information derived from functional bioinformatics data in the form of a PPI network revealed links for bipolar disorder (P permutation [P-perm] = 7 × 10-4; weighted similarity score, empirical [ρ-emp] = 0.1347; mean [SD] weighted similarity score, random [ρ-rand] = 0.0704 [0.0163]); however, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeded null expectations. Exploratory analysis assessing the overlap between the GWAS-identified genetic architecture and treatment targets across disorders identified that most disorder pairs and mapping methods did not show a significant correspondence. Conclusions and Relevance In this bioinformatic study, the relatively low degree of correspondence across modalities suggests that the genetic architecture driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms currently used for targeting symptom manifestations through pharmacological treatments. Novel approaches incorporating insights derived from GWAS based on refined phenotypes including treatment response may assist in mapping disorder risk genes to pharmacological treatments in the long term.
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Affiliation(s)
- Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Janette Tong
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Ken Pang
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Sydney, New South Wales, Australia
| | - Mark A. Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
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Burstein D, Tomasi S, Venkatesh S, Rizk M, Roussos P, Voloudakis G. Modeling diagnostic code dropout of schizophrenia in electronic health records improves phenotypic data quality and cross-ancestry transferability of polygenic scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.01.19.25320806. [PMID: 39974071 PMCID: PMC11838988 DOI: 10.1101/2025.01.19.25320806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Importance Researchers commonly use counts of diagnostic codes from EHR-linked biobanks to infer phenotypic status. However, these approaches overlook temporal changes in EHR data, such as the discontinuation or "dropout" of diagnostic codes, which may exacerbate disparities in genomics research, as EHR data quality can be confounded with demographic attributes. Objective To address this, we propose modeling diagnostic code dropout in EHR data to inform phenotyping for schizophrenia in genomic analyses. Design We develop and test our diagnostic dropout model by analyzing EHR data from individuals with prior schizophrenia diagnoses. We further validate model performance on a subset of patients whose diagnoses were attained through chart review. Using PRS-CS and existing GWAS summary statistics, we first extrapolate polygenic weights. Then, we apply our dropout model's outputs to construct a data-driven filter defining our target cohort for measuring polygenic score performance. Setting Our analysis utilizes EHR and genomic data from the Million Veteran Program. Participants To model diagnostic dropout in schizophrenia, we leverage data from 12,739 patients with a history of schizophrenia, after excluding outliers. For polygenic score analyses, we incorporate data from a potential pool of 8,385 European ancestry and 6,806 African ancestry patients with a history of schizophrenia. Main outcomes and measures We compare the performance of our diagnostic dropout model with alternative methodologies both in predicting diagnostic dropout on a holdout set, as well as on chart review labeled data. Using the top differential diagnosis predictors in our model, we select relevant cases by filtering out patients with a prior history of mood or anxiety disorders. We then test the impact of applying different filters for measuring polygenic score performance. Results When evaluated on chart review-labeled data, our model improves the area under the precision-recall curve (AUPRC) by 9.6% compared to competing methods. By applying our data-driven filter for schizophrenia, we achieve a 62% increase in the association effect size when transferring a European polygenic score to an African ancestry target cohort. Conclusions and Relevance These findings highlight the potential of modeling diagnostic code dropout to enhance the phenotypic quality of EHR-linked biobank data, advancing more equitable and accurate genomics research across diverse populations.
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Affiliation(s)
- David Burstein
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone Tomasi
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sanan Venkatesh
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Mina Rizk
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Georgios Voloudakis
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Askelund AD, Hegemann L, Allegrini AG, Corfield EC, Ask H, Davies NM, Andreassen OA, Havdahl A, Hannigan LJ. The genetic architecture of differentiating behavioral and emotional problems in early life. Biol Psychiatry 2025:S0006-3223(25)00022-8. [PMID: 39793691 DOI: 10.1016/j.biopsych.2024.12.021] [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: 11/27/2023] [Revised: 11/29/2024] [Accepted: 12/24/2024] [Indexed: 01/13/2025]
Abstract
BACKGROUND Early in life, behavioral and cognitive traits associated with risk for developing a psychiatric condition are broad and undifferentiated. As children develop, these traits differentiate into characteristic clusters of symptoms and behaviors that ultimately form the basis of diagnostic categories. Understanding this differentiation process - in the context of genetic risk for psychiatric conditions, which is highly generalized - can improve early detection and intervention. METHODS We modeled the differentiation of behavioral and emotional problems from age 1.5-5 years (behavioral problems - emotional problems = differentiation score) in a pre-registered study of ∼79,000 children from the population-based Norwegian Mother, Father, and Child Cohort Study. We used genomic structural equation modeling to identify genetic signal in differentiation and total problems, investigating their links with 11 psychiatric and neurodevelopmental conditions. We examined associations of polygenic scores (PGS) with both outcomes and assessed the relative contributions of direct and indirect genetic effects in ∼33,000 family trios. RESULTS Differentiation was primarily genetically correlated with psychiatric conditions via a "neurodevelopmental" factor. Total problems were primarily associated with the "neurodevelopmental" factor and "p"-factor. PGS analyses revealed an association between liability to ADHD and differentiation (β=0.11 [0.10,0.12]), and a weaker association with total problems (β=0.06 [0.04,0.07]). Trio-PGS analyses showed predominantly direct genetic effects on both outcomes. CONCLUSIONS We uncovered genomic signal in the differentiation process, mostly related to common variants associated with neurodevelopmental conditions. Investigating the differentiation of early life behavioral and emotional problems may enhance our understanding of the developmental emergence of different psychiatric and neurodevelopmental conditions.
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Affiliation(s)
- Adrian Dahl Askelund
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway.
| | - Laura Hegemann
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway.
| | - Andrea G Allegrini
- Division of Psychology and Language Sciences, Department of Clinical, Educational and Health Psychology, University College London, London, UK; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Elizabeth C Corfield
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway.
| | - Helga Ask
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Neil M Davies
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK; Division of Psychiatry, University College London, United Kingdom; Department of Statistical Sciences, University College London, London WC1E 6BT, UK; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway.
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway; KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
| | - Alexandra Havdahl
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Laurie J Hannigan
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
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Zhu K, Xie X, Hou F, Chen Y, Wang H, Jiang Q, Feng Y, Xiao P, Zhang Q, Xiang Z, Fan Y, Wu X, Li L, Song R. The Association Between Functional Variants in Long Non-coding RNAs and the Risk of Autism Spectrum Disorder Was Not Mediated by Gut Microbiota. Mol Neurobiol 2025; 62:412-420. [PMID: 38861233 DOI: 10.1007/s12035-024-04276-4] [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: 02/03/2023] [Accepted: 05/27/2024] [Indexed: 06/12/2024]
Abstract
The effect of functional variants in long non-coding RNA (lncRNA) gene regions on autism spectrum disorder (ASD) remains unclear. The present study aimed to investigate the association of functional variants located in lncRNA genes with the risk of ASD and explore whether gut microbiota would mediate the relationship. A total of 87 cases and 71 healthy controls were enrolled in the study. MassARRAY platform and 16S rRNA sequencing were respectively applied to assess the genotype of candidate SNPs and gut microbiota of children. The logistic regression models showed that the association between rs2295412 and the risk of ASD was statistically significant after Bonferroni adjustments. The risk of ASD decreased by 19% for each additional C allele carried by children in multiplicative models (OR = 0.81, 95% CI, 0.69-0.94, P = 0.007). Although we identified significant correlations between rs8113922 polymorphisms, Bifidobacteriales, and ASD, the mediating effect of gut microbiota on the relationship of the polymorphisms with the risk of ASD was not significant. The findings demonstrated that functional variants in lncRNA genes play an important role in ASD and gut microbiota could not mediate the association. Future studies are warranted to verify the results and search for more possible mechanisms of variants located in lncRNA genes implicated in ASD.
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Affiliation(s)
- Kaiheng Zhu
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Xinyan Xie
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Fang Hou
- Maternity and Children, Health Care Hospital of Luohu District, Shenzhen, China
| | - Yanlin Chen
- Maternity and Children, Health Care Hospital of Luohu District, Shenzhen, China
| | - Haoxue Wang
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Qi Jiang
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Yanan Feng
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Pei Xiao
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Quan Zhang
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Zhen Xiang
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Yixi Fan
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Xufang Wu
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China
| | - Li Li
- Maternity and Children, Health Care Hospital of Luohu District, Shenzhen, China.
| | - Ranran Song
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No 13 Hangkong Road, Wuhan, China.
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Zhu T, Mu D, Hu Y, Cao Y, Yuan M, Xu J, Ye HQ, Zhang W. Association of clinical phenotypes of depression with comorbid conditions, treatment patterns and outcomes: a 10-year region-based cohort study. Transl Psychiatry 2024; 14:504. [PMID: 39719438 DOI: 10.1038/s41398-024-03213-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 12/05/2024] [Accepted: 12/16/2024] [Indexed: 12/26/2024] Open
Abstract
Depression is a heterogeneous and complex psychological syndrome with highly variable manifestations, which poses difficulties for treatment and prognosis. Depression patients are prone to developing various comorbidities, which stem from different pathophysiological mechanisms, remaining largely understudied. The current study focused on identifying comorbidity-specific phenotypes, and whether these clustered phenotypes are associated with different treatment patterns, clinical manifestations, physiological characteristics, and prognosis. We have conducted a 10-year retrospective observational cohort study using electronic medical records (EMR) for 11,818 patients diagnosed with depression and hospitalized at a large academic medical center in Chengdu, China. K-means clustering and visualization methods were performed to identify phenotypic categories. The association between phenotypic categories and clinical outcomes was evaluated using adjusted Cox proportional hazards model. We classified patients with depression into five stable phenotypic categories, including 15 statistically driven clusters in the discovery cohort (n = 9925) and the validation cohort (n = 1893), respectively. The categories include: (Category A) the lowest incidence of comorbidity, with prominent suicide, psychotic, and somatic symptoms (n = 3493/9925); (Category B) moderate comorbidity rate, with prominent anhedonia and anxious symptoms (n = 1795/9925); (Category C) the highest incidence of comorbidity of endocrine/metabolic and digestive system diseases (n = 1702/9925); (Category D) the highest incidence of comorbidity of neurological, mental and behavioral diseases (n = 881/9925); (Category E) other diseases comorbid with depression (n = 2054/9925). Patients in Category E had the lowest risk of psychiatric rehospitalization within 60-day follow-up, followed by Category C (HR, 1.57; 95% CI, 1.07-2.30), Category B (HR, 1.61; 95% CI, 1.10-2.40), Category A (HR, 1.82; 95% CI, 1.28-2.60), and Category D (HR, 2.38; 95% CI, 1.59-3.60) with P < 0.05, after adjustment for comorbidities, medications, and age. Regarding other longer observation windows (90-day, 180-day and 365-day), patients in Category D showed the highest rehospitalization risk all the time while there were notable shifts in rankings observed for Categories A, B and C over time. The results indicate that the higher the severity of mental illness in patients with five phenotypic categories, the greater the risk of rehospitalization. These phenotypes are associated with various pathways, including the cardiometabolic system, chronic inflammation, digestive system, neurological system, and mental and behavioral disorders. These pathways play a crucial role in connecting depression with other psychiatric and somatic diseases. The identified phenotypes exhibit notable distinctions in terms of comorbidity patterns, symptomology, biological characteristics, treatment approaches, and clinical outcomes.
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Affiliation(s)
- Ting Zhu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Di Mu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3053, Australia
| | - Yao Hu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Yang Cao
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Minlan Yuan
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jia Xu
- The First Psychiatric Hospital of Harbin, Harbin, 150056, China
| | - Heng-Qing Ye
- Faculty of Business, Hong Kong Polytechnic University, Hong Kong, 100872, China.
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, 610041, China.
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Liu C, Gershon ES. Endophenotype 2.0: updated definitions and criteria for endophenotypes of psychiatric disorders, incorporating new technologies and findings. Transl Psychiatry 2024; 14:502. [PMID: 39719446 PMCID: PMC11668880 DOI: 10.1038/s41398-024-03195-1] [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: 11/25/2024] [Revised: 11/28/2024] [Accepted: 12/05/2024] [Indexed: 12/26/2024] Open
Abstract
Recent genetic studies have linked numerous loci to psychiatric disorders. However, the biological pathways that connect these genetic associations to psychiatric disorders' specific pathophysiological processes are largely unclear. Endophenotypes, first defined over five decades ago, are heritable traits, independent of disease state that are associated with a disease, encompassing a broad range of neurophysiological, biochemical, endocrinological, neuroanatomical, cognitive, and neuropsychological characteristics. Considering the advancements in genetics and genomics over recent decades, we propose a revised definition of endophenotypes as 'genetically influenced phenotypes linked to disease or treatment characteristics and their related events.' We also updated endophenotype criteria to include (1) reliable measurement, (2) association with the disease or its related events, and (3) genetic mediation. 'Genetic mediation' is introduced to differentiate between causality and pleiotropic effects and allows non-linear relationships. Furthermore, this updated Endophenotype 2.0 framework expands to encompass genetically regulated responses to disease-related factors, including environmental risks, illness progression, treatment responses, and resilience phenotypes, which may be state-dependent. This broadened definition paves the way for developing new endophenotypes crucial for genetic analyses in psychiatric disorders. Integrating genetics, genomics, and diverse endophenotypes into multi-dimensional mechanistic models is vital for advancing our understanding of psychiatric disorders. Crucially, elucidating the biological underpinnings of endophenotypes will enhance our grasp of psychiatric genetics, thereby improving disease risk prediction and treatment approaches.
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Affiliation(s)
- Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
- School of Life Sciences, Central South University, Changsha, China.
| | - Elliot S Gershon
- Departments of Psychiatry and Human Genetics, The University of Chicago, Chicago, IL, USA.
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36
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Xia C, Alliey-Rodriguez N, Tamminga CA, Keshavan MS, Pearlson GD, Keedy SK, Clementz B, McDowell JE, Parker D, Lencer R, Hill SK, Bishop JR, Ivleva EI, Wen C, Dai R, Chen C, Liu C, Gershon ES. Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations. Mol Psychiatry 2024:10.1038/s41380-024-02876-z. [PMID: 39709506 DOI: 10.1038/s41380-024-02876-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 11/22/2024] [Accepted: 12/11/2024] [Indexed: 12/23/2024]
Abstract
The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.
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Affiliation(s)
- Cuihua Xia
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA
- Institute of Neuroscience, University of Texas Rio Grande Valley, Harlingen, TX, USA
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Institute of Living, Hartford Healthcare Corp, Hartford, CT, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA
| | - Brett Clementz
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - David Parker
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Rebekka Lencer
- Institute for Translational Psychiatry, Münster University, Münster, Germany
- Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cindy Wen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Furong Laboratory, Changsha, Hunan, China.
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Chunyu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA.
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA.
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Xia C, Alliey-Rodriguez N, Tamminga CA, Keshavan MS, Pearlson GD, Keedy SK, Clementz B, McDowell JE, Parker D, Lencer R, Hill SK, Bishop JR, Ivleva EI, Wen C, Dai R, Chen C, Liu C, Gershon ES. Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.12.05.24318404. [PMID: 39677452 PMCID: PMC11643284 DOI: 10.1101/2024.12.05.24318404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/17/2024]
Abstract
The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes and isoforms showed significant genomic associations with specific Biotypes in Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in adult brain and fetal brain. TWAS inflation was addressed by inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.
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Affiliation(s)
- Cuihua Xia
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410000, China
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL 60637, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL 60637, USA
- Institute of Neuroscience, University of Texas Rio Grande Valley, Harlingen, TX 78550, USA
| | - Carol A. Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Matcheri S. Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Godfrey D. Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA
- Institute of Living, Hartford Healthcare Corp, Hartford, CT 06106, USA
| | - Sarah K. Keedy
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL 60637, USA
| | - Brett Clementz
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA 30602, USA
| | - Jennifer E. McDowell
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA 30602, USA
| | - David Parker
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Rebekka Lencer
- Institute for Translational Psychiatry, Münster University, Münster 48149, Germany
- Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck 23538, Germany
| | - S. Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL 60064, USA
| | - Jeffrey R. Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elena I. Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Cindy Wen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles; Los Angeles, CA 90095, USA
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410000, China
- Furong Laboratory, Changsha, Hunan 410000, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan 410000, China
| | - Chunyu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410000, China
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
| | - Elliot S. Gershon
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL 60637, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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Ohi K, Fujikane D, Shioiri T. Genetic overlap between schizophrenia spectrum disorders and Alzheimer's disease: Current evidence and future directions - An integrative review. Neurosci Biobehav Rev 2024; 167:105900. [PMID: 39298993 DOI: 10.1016/j.neubiorev.2024.105900] [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: 07/25/2024] [Revised: 09/15/2024] [Accepted: 09/16/2024] [Indexed: 09/22/2024]
Abstract
Schizophrenia and Alzheimer's disease (AD) are distinct neurodegenerative disorders characterized by progressive cognitive deficits and structural alterations in the brain. Schizophrenia typically emerges in adolescence or early adulthood with symptoms such as hallucinations, delusions, and cognitive impairments, whereas AD primarily affects elderly individuals, causing progressive memory loss, cognitive decline, and behavioral changes. Delusional disorder, which often emerges later in life, shares some features with schizophrenia and is considered a schizophrenia spectrum disorder. Patients with schizophrenia or delusional disorder, particularly women and those aged 65 years or older, have an increased risk of developing AD later in life. In contrast, approximately 30 % of AD patients exhibit psychotic symptoms, which accelerate cognitive decline and worsen health outcomes. This integrative review explored the genetic overlap between schizophrenia spectrum disorders and AD to identify potential shared genetic factors. The genetic correlations between schizophrenia and AD were weak but positive (rg=0.03-0.10). Polygenic risk scores (PRSs) for schizophrenia and AD indicate some genetic predisposition, although findings are inconsistent among studies; e.g., PRS-schizophrenia or PRS-AD were associated with the risk of developing psychosis in patients with AD. A higher PRS for various developmental and psychiatric disorders was correlated with an earlier age at onset of schizophrenia. Research gaps include the need for studies on the impacts of PRS-AD on the risk of schizophrenia, genetic correlations between later-onset delusional disorder and AD, and genetic relationships between AD and late-onset schizophrenia (LOS) with a greater risk of progressing to AD. Further investigation into these genetic overlaps is crucial to enhance prevention, treatment, and prognosis for affected patients.
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Affiliation(s)
- Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan; Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan.
| | - Daisuke Fujikane
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Toshiki Shioiri
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
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Tan P, Shen X, Zeng L, Weng X, Geng H. Pharmacotherapy for the core symptoms of autism spectrum disorder. J Zhejiang Univ Sci B 2024; 25:956-971. [PMID: 39626879 PMCID: PMC11634452 DOI: 10.1631/jzus.b2300864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/09/2024] [Indexed: 12/13/2024]
Abstract
Autism spectrum disorder (ASD) is a range of neurodevelopmental diseases characterized by social dysfunction and stereotypic behaviors. The etiology of ASD remains largely unexplored, resulting in a diverse array of described clinical manifestations and varying degrees of severity. Currently, there are no drugs approved by a supervisory organization that can effectively treat the core symptoms of ASD. Childhood and adolescence are crucial stages for making significant achievements in ASD treatment, necessitating the development of drugs specifically for these periods. Based on the drug targets and mechanisms of action, it can be found that atypical psychotropic medications, anti-inflammatory and antioxidant medications, hormonal medications, ion channel medications, and gastrointestinal medications have shown significant improvement in treating the core symptoms of ASD in both children and adolescents. In addition, comparisons of drugs within the same category regarding efficacy and safety have been made to identify better alternatives and promote drug development. While further evaluation of the effectiveness and safety of these medications is needed, they hold great potential for widespread application in the clinical treatment of the principal symptoms of ASD.
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Affiliation(s)
- Peiying Tan
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Xiaolin Shen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Lizhang Zeng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Xuchu Weng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Hongyan Geng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou 510631, China.
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.
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40
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Fortea L, Ortuño M, De Prisco M, Oliva V, Albajes-Eizagirre A, Fortea A, Madero S, Solanes A, Vilajosana E, Yao Y, Del Fabro L, Solé E, Verdolini N, Farré-Colomés A, Serra-Blasco M, Picó-Pérez M, Lukito S, Wise T, Carlisi C, Arnone D, Kempton MJ, Hauson AO, Wollman S, Soriano-Mas C, Rubia K, Norman L, Fusar-Poli P, Mataix-Cols D, Valentí M, Via E, Cardoner N, Solmi M, Zhang J, Pan P, Shin JI, Fullana MA, Vieta E, Radua J. Atlas of Gray Matter Volume Differences Across Psychiatric Conditions: A Systematic Review With a Novel Meta-Analysis That Considers Co-Occurring Disorders. Biol Psychiatry 2024:S0006-3223(24)01729-3. [PMID: 39491638 DOI: 10.1016/j.biopsych.2024.10.020] [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: 02/06/2024] [Revised: 10/04/2024] [Accepted: 10/21/2024] [Indexed: 11/05/2024]
Abstract
BACKGROUND Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correlates, we conducted a large-scale multidisorder meta-analysis using a novel approach that explicitly models co-occurring disorders. METHODS We systematically reviewed voxel-based morphometry studies indexed in PubMed and Scopus up to January 2023 that compared adults with major mental disorders (anorexia nervosa, schizophrenia spectrum, anxiety, bipolar, major depressive, obsessive-compulsive, and posttraumatic stress disorders plus attention-deficit/hyperactivity, autism spectrum, and borderline personality disorders) with comparison participants. Two authors independently extracted data and assessed quality using the Newcastle-Ottawa Scale. We derived GMV correlates for each disorder using: 1) a multidisorder meta-analysis that accounted for all co-occurring mental disorders simultaneously and 2) separate standard meta-analyses for each disorder in which co-occurring disorders were ignored. We assessed the alterations' extent, intensity (effect size), and specificity (interdisorder correlations and transdiagnostic alterations) for both approaches. RESULTS We included 433 studies (499 datasets) involving 19,718 patients and 16,441 comparison participants (51% female, ages 20-67 years). We provide GMV correlate maps for each disorder using both approaches. The novel approach, which accounted for co-occurring disorders, produced GMV correlates that were more focal and disorder specific (less correlated across disorders and fewer transdiagnostic abnormalities). CONCLUSIONS This work offers the most comprehensive atlas of GMV correlates across major mental disorders. Modeling co-occurring disorders yielded more specific correlates, supporting this approach's validity. The atlas NIfTI maps are available online.
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Affiliation(s)
- Lydia Fortea
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain.
| | - Maria Ortuño
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain
| | - Michele De Prisco
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clínic, Barcelona, Spain
| | - Vincenzo Oliva
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain; Bipolar and Depressive Disorders Unit, Hospital Clínic, Barcelona, Spain; Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | | | - Adriana Fortea
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Psychiatric and Psychology Service, Hospital Clínic, Barcelona, Spain
| | - Santiago Madero
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Schizophrenia Unit, Hospital Clínic, Barcelona, Spain
| | - Aleix Solanes
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain
| | - Enric Vilajosana
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain
| | - Yuanwei Yao
- Department of Psychology, The University of Hong Kong, Hong Kong SAR, China; Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lorenzo Del Fabro
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Eduard Solé
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain
| | - Norma Verdolini
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clínic, Barcelona, Spain; Local Health Unit Umbria 1, Department of Mental Health, Mental Health Center of Perugia, Perugia, Italy
| | - Alvar Farré-Colomés
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, Mannheim, Germany
| | - Maria Serra-Blasco
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; eHealth ICOnnecta't Program and Psycho-Oncology Service, Institut Català d'Oncologia, L'Hospitalet de Llobregat, Spain; Psycho-oncology and Digital Health Group, Health Services Research in Cancer, Institut d'Investigació Biomèdica de Bellvitge, L'Hospitalet del Llobregat, Spain
| | - Maria Picó-Pérez
- Live and Health Sciences Research Institute, University of Minho, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga, Guimarães, Portugal; Departamento de Psicología Básica, Universitat Jaume I, Castelló de la Plana, Spain
| | - Steve Lukito
- Department of Child and Adolescent Psychiatry, Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neurosciences, King's College London, London, United Kingdom
| | - Christina Carlisi
- Department of Child and Adolescent Psychiatry, Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom; Division of Psychology and Language Sciences, University College London, London, United Kingdom
| | - Danilo Arnone
- Centre for Affective Disorders, Psychological Medicine, King's College London, London, United Kingdom; Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada; Department of Mental Health, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Matthew J Kempton
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neurosciences, King's College London, London, United Kingdom; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alexander Omar Hauson
- Clinical Psychology PhD Program, California School of Professional Psychology, San Diego, California; Department of Psychiatry, University of California, San Diego, California
| | - Scott Wollman
- Clinical Psychology PhD Program, California School of Professional Psychology, San Diego, California
| | - Carles Soriano-Mas
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Department of Psychiatry, Bellvitge Biomedical Research Institute, Barcelona, Spain; Department of Social Psychology and Quantitative Psychology, Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry, Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom
| | - Luke Norman
- Department of Child and Adolescent Psychiatry, Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab., Department of Psychosis Studies, Institute of Psychiatry, Psychology, London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Outreach and Support in South London Service, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - David Mataix-Cols
- Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden; Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Marc Valentí
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clínic, Barcelona, Spain
| | - Esther Via
- Child and Adolescent Mental Health Research Group, Institut de Recerca Sant Joan de Déu, Barcelona, Spain; Child and Adolescent Psychiatry and Psychology Department, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Narcis Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Sant Pau Mental Health Group, Institut d'Investigació Biomèdica Sant Pau, Hospital de la Sant Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Marco Solmi
- Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada; Regional Centre for the Treatment of Eating Disorders and On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ottawa, Ontario, Canada; Ottawa Hospital Research Institute Clinical Epidemiology Program, University of Ottawa, Ottawa, Ontario, Canada; Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin, Berlin, Germany
| | - Jintao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Pinglei Pan
- Department of Neurology, Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Affiliated Yancheng Hospital of Southeast University, Yancheng, China
| | - Jae Il Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea; Severance Underwood Meta-Research Center, Institute of Convergence Science, Yonsei University, Seoul, South Korea
| | - Miquel A Fullana
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Psychiatric and Psychology Service, Hospital Clínic, Barcelona, Spain
| | - Eduard Vieta
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clínic, Barcelona, Spain; Psychiatric and Psychology Service, Hospital Clínic, Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain.
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Jiang X, Zai CC, Dimick MK, Kennedy JL, Young LT, Birmaher B, Goldstein BI. Psychiatric Polygenic Risk Scores Across Youth With Bipolar Disorder, Youth at High Risk for Bipolar Disorder, and Controls. J Am Acad Child Adolesc Psychiatry 2024; 63:1149-1157. [PMID: 38340895 DOI: 10.1016/j.jaac.2023.12.009] [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: 07/05/2023] [Revised: 11/23/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE There is a pronounced gap in knowledge regarding polygenic underpinnings of youth bipolar disorder (BD). This study aimed to compare polygenic risk scores (PRSs) in youth with BD, youth at high clinical and/or familial risk for BD (HR), and controls. METHOD Participants were 344 youths of European ancestry (13-20 years old), including 136 youths with BD, 121 HR youths, and 87 controls. PRSs for BD, schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were constructed using independent genome-wide summary statistics from adult cohorts. Multinomial logistic regression was used to examine the association between each PRS and diagnostic status (BD vs HR vs controls). All genetic analyses controlled for age, sex, and 2 genetic principal components. RESULTS The BD group showed significantly higher BD-PRS than the control group (odds ratio = 1.54, 95% CI = 1.13-2.10, p = .006), with the HR group numerically intermediate. BD-PRS explained 7.9% of phenotypic variance. PRSs for schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were not significantly different among groups. In the BD group, BD-PRS did not significantly differ in relation to BD subtype, age of onset, psychosis, or family history of BD. CONCLUSION BD-PRS derived from adult genome-wide summary statistics is elevated in youth with BD. Absence of significant between-group differences in PRSs for other psychiatric disorders supports the specificity of BD-PRS in youth. These findings add to the biological validation of BD in youth and could have implications for early identification and diagnosis. To enhance clinical utility, future genome-wide association studies that focus specifically on early-onset BD are warranted, as are studies integrating additional genetic and environmental factors. PLAIN LANGUAGE SUMMARY Polygenic risk scores estimate an individual's genetic susceptibility to develop a disorder, such as bipolar disorder (BD). In this study, the authors constructed polygenic risk scores from previous adult studies. Youth with BD had elevated polygenic risk scores for BD compared to youth without bipolar disorder. Youth at high risk for BD had intermediate polygenic risk scores. To evaluate the specificity of polygenic risk scores for BD, the authors estimated risk scores for other mental health disorders including schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder. These other polygenic risk scores did not differ between youth with and without BD. These findings support the biological validation of BD in youth, with potential implications for early identification and diagnosis. DIVERSITY & INCLUSION STATEMENT We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.
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Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada
| | - Clement C Zai
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - James L Kennedy
- University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - L Trevor Young
- University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Boris Birmaher
- Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada.
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42
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Rommel AS, Semark BD, Liu X, Madsen KB, Agerbo E, Munk-Olsen T, Petersen LV, Bergink V. Prenatal antidepressant exposure and the risk of decreased gestational age and lower birthweight: A polygenic score approach to investigate confounding by indication. Acta Psychiatr Scand 2024; 150:344-354. [PMID: 37990478 PMCID: PMC11106214 DOI: 10.1111/acps.13636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/13/2023] [Accepted: 11/11/2023] [Indexed: 11/23/2023]
Abstract
INTRODUCTION Prenatal antidepressant exposure has been associated with lower gestational age and birthweight. Yet, unmeasured residual confounding may inflate this association. We explored if maternal genetic liability for major depression explains part of the association of antidepressant use in pregnancy with lower gestational age and birthweight. MATERIAL AND METHODS We employed the maternal polygenic score (PGS) for major depression as a measure of genetic liability. We used generalised linear models to estimate the differences in gestational age and birthweight at each PGS quintile between children whose mothers continued antidepressant use during pregnancy (continuation group), children whose mothers discontinued antidepressant use during pregnancy (discontinuation group) and unexposed children. RESULTS After adjusting for confounders, we found significant differences in birthweight between PGS quintiles in the continuation and unexposed group. Yet, this relationship was not linear. Furthermore, at the lowest and highest PGS quintiles, the continuation group had significantly reduced mean gestational ages (adjusted β ranges: 1.7-4.5 days, p < 0.001-0.008) and lower mean birthweights (adjusted β ranges: 58.6-165.4 g, p = 0.001-0.008) than the discontinuation and unexposed groups. CONCLUSION We confirmed that antidepressant use in pregnancy was associated with small reductions in gestational age and birthweight but found that genetic liability for depression was not linearly associated with this risk. The causality of the observed associations could not be established due to the observational nature of the study. Residual confounding linked to the underlying disease was likely still present.
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Affiliation(s)
- Anna-Sophie Rommel
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Birgitte Dige Semark
- NCRR - The National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Xiaoqin Liu
- NCRR - The National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Kathrine Bang Madsen
- NCRR - The National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Esben Agerbo
- NCRR - The National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
- CIRRAU – Centre for Integrated Register-based Research at Aarhus University, Aarhus, Denmark
- iPSYCH – Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Trine Munk-Olsen
- NCRR - The National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Liselotte Vogdrup Petersen
- NCRR - The National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
- iPSYCH – Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Veerle Bergink
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Erasmus Medical Centre Rotterdam, Rotterdam, The Netherlands
- Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Luo J, Li L, Niu M, Kong D, Jiang Y, Poudel S, Shieh AW, Cheng L, Giase G, Grennan K, White KP, Chen C, Wang SH, Pinto D, Wang Y, Liu C, Peng J, Wang X. Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders. Mol Psychiatry 2024; 29:3330-3343. [PMID: 38724566 PMCID: PMC11540848 DOI: 10.1038/s41380-024-02576-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 11/08/2024]
Abstract
Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.
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Affiliation(s)
- Jie Luo
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou, Zhejiang, 310021, China
| | - Ling Li
- Department of Genetics, Genomics & Informatics, University of Tennessee Health Science Center, Memphis, TN, 38103, USA
| | - Mingming Niu
- Department of Structural Biology and Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Dehui Kong
- Department of Genetics, Genomics & Informatics, University of Tennessee Health Science Center, Memphis, TN, 38103, USA
| | - Yi Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Suresh Poudel
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Annie W Shieh
- Knapp Center for Biomedical Discovery, University of Chicago, Chicago, IL, 60637, USA
| | - Lijun Cheng
- Knapp Center for Biomedical Discovery, University of Chicago, Chicago, IL, 60637, USA
| | - Gina Giase
- Knapp Center for Biomedical Discovery, University of Chicago, Chicago, IL, 60637, USA
| | - Kay Grennan
- Knapp Center for Biomedical Discovery, University of Chicago, Chicago, IL, 60637, USA
| | - Kevin P White
- Department of Biochemistry and Precision Medicine, National University, Singapore, 119077, Singapore
| | - Chao Chen
- Center for Medical Genetics and Human Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410083, China
| | - Sidney H Wang
- Center for Human Genetics, Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, 77225, USA
| | - Dalila Pinto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
| | - Junmin Peng
- Department of Structural Biology and Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
| | - Xusheng Wang
- Department of Genetics, Genomics & Informatics, University of Tennessee Health Science Center, Memphis, TN, 38103, USA.
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
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44
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Willis TW, Gkrania-Klotsas E, Wareham NJ, McKinney EF, Lyons PA, Smith KGC, Wallace C. Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency. Clin Immunol 2024; 268:110356. [PMID: 39241920 DOI: 10.1016/j.clim.2024.110356] [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: 06/27/2024] [Revised: 08/22/2024] [Accepted: 08/31/2024] [Indexed: 09/09/2024]
Abstract
Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.
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Affiliation(s)
- Thomas W Willis
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK.
| | - Effrossyni Gkrania-Klotsas
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK; Department of Infectious Diseases, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | - Nicholas J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eoin F McKinney
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
| | - Paul A Lyons
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
| | - Kenneth G C Smith
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Department of Medical Biology, University of Melbourne, Melbourne, Australia
| | - Chris Wallace
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
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Moctezuma B, Santiago Á, Burguete-García A, Martínez-Barnetche J, Morales-Gómez C, Hernandez-Chavez C, Gil G, Peterson KE, Tellez-Rojo MM, Lamadrid-Figueroa H. Single nucleotide polymorphisms of ANKK1, DDR4, and GRIN2B genes predict behavior in a prospective cohort of Mexican children and adolescents. Int J Dev Neurosci 2024; 84:638-650. [PMID: 38530142 DOI: 10.1002/jdn.10326] [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: 11/07/2023] [Revised: 01/10/2024] [Accepted: 02/13/2024] [Indexed: 03/27/2024] Open
Abstract
Numerous studies have established associations between single nucleotide polymorphisms (SNPs) and various behavioral and neurodevelopmental conditions. This study explores the links between SNPs in candidate genes involved in central nervous system (CNS) physiology and their implications for the behavioral and emotional aspects in children and teenagers. A total of 590 participants, aged 7-15 years, from the Early Life Exposures In Mexico To Environmental Toxicants (ELEMENT) cohort study in Mexico City, underwent genotyping for at least one of 15 CNS gene-related SNPs at different timepoints. We employed multiple linear regression models to assess the potential impact of genetic variations on behavioral and cognitive traits, as measured by the Behavioral Assessment System for Children (BASC) and Conners parent rating scales. Significant associations were observed, including the rs1800497 TC genotype (ANKK1) with the Cognitive Problems/Inattention variable (p value = 0.003), the rs1800955 CT genotype (DDR4) with the Emotional Lability Global index variable (p value = 0.01), and the rs10492138 GA and rs7970177 TC genotypes (GRIN2B) with the Depression variable (p values 0.007 and 0.012, respectively). These finds suggest potential genetic profiles associated with "risk" and "protective" behaviors for these SNPs. Our results provide valuable insights into the role of genetic variations in neurobehavior and highlight the need for further research in the early identification and intervention in individuals at risk for these conditions.
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Affiliation(s)
- Barbara Moctezuma
- School of Public Health of Mexico, National Institute of Public Health, Cuernavaca, Mexico
| | - Ángel Santiago
- Department of Perinatal Health, National Institute of Public Health, Cuernavaca, Mexico
| | - Ana Burguete-García
- Center for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico
| | | | - Claudia Morales-Gómez
- Epidemiologic Surveillance, Mexican Institute of Social Security-Bienestar, Mexico City, Mexico
| | - Carmen Hernandez-Chavez
- Department of Developmental Neurobiology, National Institute of Perinatology, Mexico City, Mexico
| | - Gabriela Gil
- Department of Developmental Neurobiology, National Institute of Perinatology, Mexico City, Mexico
| | - Karen E Peterson
- Nutritional Sciences Department, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Martha M Tellez-Rojo
- Center for Research in Nutrition and Health, National Institute of Public Health, Cuernavaca, Mexico
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46
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Dong H, Luo T, Yang C, Liu M, Shen Y, Hao W. Psychotic symptoms associated increased CpG methylation of metabotropic glutamate receptor 8 gene in Chinese Han males with schizophrenia and methamphetamine induced psychotic disorder: a longitudinal study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:91. [PMID: 39384625 PMCID: PMC11464599 DOI: 10.1038/s41537-024-00506-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 09/05/2024] [Indexed: 10/11/2024]
Abstract
Methamphetamine use can produce psychotic symptoms almost indistinguishable from schizophrenia (SCZ). Variation in DNA methylation may be closely implicated in the etiology and longitudinal development of psychiatric disorders. However, the relationship between psychotic symptoms, functional disability, and DNA methylation is still unclear. This study consists of three periods: discovery, validation, and follow-up. In the discovery stage, we employed genome-wide DNA methylation profiling (Illumina 450K) in peripheral blood mononuclear cells to test whether DNA methylation associates with psychotic symptoms and function state in representative SCZ and methamphetamine-induced psychotic disorder (MIP) patients. Then, we found seven differentially methylated regions/genes (DMRs, in UBA6, APOL3, KIF17, MLLT3, GRM8, CSNK1E, SETDB1) overlapping with genetic variants reported in previous studies of psychosis. In the validation stage, we compared the above-mentioned seven genes by MethLight qPCR method in Chinese Han males (N = 109 SCZ patients, N = 99 methamphetamine use disorder with MIP patients, N = 150 methamphetamine use disorder without MIP patients, N = 282 normal controls, age range: 18-50 years). GRM8 showed robustly altered methylation, which has passed rigorous filtration in subsequent validation, suggesting a remarkable contribution to SCZ and MIP. In addition, hypermethylation of GRM8 showed a significant association with the total scores of the Positive Negative Syndrome Scale and WHO disability assessment schedule II in both baseline and follow-up periods. Our findings suggest that increased CpG methylation in the promoter of GRM8 is a potential candidate epigenetic biomarker of psychotic symptoms in transdiagnostic samples of SCZ and MIP.
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Affiliation(s)
- Huixi Dong
- Mental Health Center of Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Tao Luo
- Department of Psychology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Cheng Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Mengqi Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yidong Shen
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
| | - Wei Hao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
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47
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Tanaka SC, Kasai K, Okamoto Y, Koike S, Hayashi T, Yamashita A, Yamashita O, Johnstone T, Pestilli F, Doya K, Okada G, Shinzato H, Itai E, Takahara Y, Takamiya A, Nakamura M, Itahashi T, Aoki R, Koizumi Y, Shimizu M, Miyata J, Son S, Aki M, Okada N, Morita S, Sawamoto N, Abe M, Oi Y, Sajima K, Kamagata K, Hirose M, Aoshima Y, Hamatani S, Nohara N, Funaba M, Noda T, Inoue K, Hirano J, Mimura M, Takahashi H, Hattori N, Sekiguchi A, Kawato M, Hanakawa T. The status of MRI databases across the world focused on psychiatric and neurological disorders. Psychiatry Clin Neurosci 2024; 78:563-579. [PMID: 39162256 PMCID: PMC11804910 DOI: 10.1111/pcn.13717] [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: 01/22/2024] [Revised: 05/13/2024] [Accepted: 07/02/2024] [Indexed: 08/21/2024]
Abstract
Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.
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Affiliation(s)
- Saori C. Tanaka
- Brain Information Communication Research Laboratory GroupAdvanced Telecommunications Research Institutes InternationalKyotoJapan
- Division of Information ScienceNara Institute of Science and TechnologyNaraJapan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of MedicineThe University of TokyoTokyoJapan
- The International Research Center for Neurointelligence (WPI‐IRCN)The University of Tokyo Institutes for Advanced Study (UTIAS)TokyoJapan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM)TokyoJapan
- Center for Brain Imaging in Health and Diseases (CBHD)The University of Tokyo HospitalTokyoJapan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health ScienceHiroshima UniversityHiroshimaJapan
| | - Shinsuke Koike
- The International Research Center for Neurointelligence (WPI‐IRCN)The University of Tokyo Institutes for Advanced Study (UTIAS)TokyoJapan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM)TokyoJapan
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and SciencesThe University of TokyoTokyoJapan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics ImagingRIKEN Center for Biosystems Dynamics ResearchHyogoJapan
- Department of Brain ConnectomicsKyoto University Graduate School of MedicineKyotoJapan
| | - Ayumu Yamashita
- Brain Information Communication Research Laboratory GroupAdvanced Telecommunications Research Institutes InternationalKyotoJapan
- Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory GroupAdvanced Telecommunications Research Institutes InternationalKyotoJapan
- Center for Advanced Intelligence ProjectRIKENTokyoJapan
| | - Tom Johnstone
- School of Health SciencesSwinburne University of TechnologyMelbourneVictoriaAustralia
| | - Franco Pestilli
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and MemoryThe University of Texas at AustinAustinTexasUSA
| | - Kenji Doya
- Neural Computation UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health ScienceHiroshima UniversityHiroshimaJapan
| | - Hotaka Shinzato
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health ScienceHiroshima UniversityHiroshimaJapan
- Department of Neuropsychiatry, Graduate School of MedicineUniversity of the RyukyusOkinawaJapan
| | - Eri Itai
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health ScienceHiroshima UniversityHiroshimaJapan
| | - Yuji Takahara
- Brain Information Communication Research Laboratory GroupAdvanced Telecommunications Research Institutes InternationalKyotoJapan
- Biomarker R&D departmentSHIONOGI & CO., LtdOsakaJapan
| | - Akihiro Takamiya
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
- Hills Joint Research Laboratory for Future Preventive Medicine and WellnessKeio University School of MedicineTokyoJapan
- Neuropsychiatry, Department of NeurosciencesLeuven Brain Institute, KU LeuvenLeuvenBelgium
- Geriatric PsychiatryUniversity Psychiatric Center KU LeuvenLeuvenBelgium
| | - Motoaki Nakamura
- Medical Institute of Developmental Disabilities ResearchShowa UniversityTokyoJapan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities ResearchShowa UniversityTokyoJapan
| | - Ryuta Aoki
- Medical Institute of Developmental Disabilities ResearchShowa UniversityTokyoJapan
- Graduate School of HumanitiesTokyo Metropolitan UniversityTokyoJapan
| | - Yukiaki Koizumi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental SciencesTokyo Medical and Dental UniversityTokyoJapan
- Department of PsychiatryHaryugaoka HospitalFukushimaJapan
| | - Masaaki Shimizu
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental SciencesTokyo Medical and Dental UniversityTokyoJapan
| | - Jun Miyata
- Department of PsychiatryAichi Medical UniversityAichiJapan
- Department of Psychiatry, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Shuraku Son
- Department of Psychiatry, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Morio Aki
- Department of Psychiatry, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of MedicineThe University of TokyoTokyoJapan
- The International Research Center for Neurointelligence (WPI‐IRCN)The University of Tokyo Institutes for Advanced Study (UTIAS)TokyoJapan
| | - Susumu Morita
- Department of Neuropsychiatry, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Nobukatsu Sawamoto
- Department of Human Health SciencesKyoto University Graduate School of MedicineKyotoJapan
| | - Mitsunari Abe
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
| | - Yuki Oi
- Department of Neurology, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Kazuaki Sajima
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
| | - Koji Kamagata
- Department of RadiologyJuntendo University School of MedicineTokyoJapan
| | - Masakazu Hirose
- Department of Integrated Neuroanatomy and NeuroimagingKyoto University Graduate School of MedicineKyotoJapan
| | - Yohei Aoshima
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
| | - Sayo Hamatani
- Research Center for Child Mental DevelopmentChiba UniversityChibaJapan
- Research Center for Child Mental DevelopmentUniversity of FukuiFukuiJapan
| | - Nobuhiro Nohara
- Department of Stress Sciences and Psychosomatic Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Misako Funaba
- Department of Behavioral Medicine, National Institute of Mental HealthNational Center of Neurology and PsychiatryTokyoJapan
- Student Counseling CenterMeiji Gakuin UniversityTokyoJapan
| | - Tomomi Noda
- Department of Psychiatry, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Kana Inoue
- Brain Information Communication Research Laboratory GroupAdvanced Telecommunications Research Institutes InternationalKyotoJapan
| | - Jinichi Hirano
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Masaru Mimura
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental SciencesTokyo Medical and Dental UniversityTokyoJapan
- Center for Brain Integration ResearchTokyo Medical and Dental UniversityTokyoJapan
| | - Nobutaka Hattori
- Department of NeurologyJuntendo University Graduate School of MedicineTokyoJapan
- Neurodegenerative Disorders Collaborative LaboratoryRIKEN Center for Brain ScienceSaitamaJapan
| | - Atsushi Sekiguchi
- Department of Behavioral Medicine, National Institute of Mental HealthNational Center of Neurology and PsychiatryTokyoJapan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory GroupAdvanced Telecommunications Research Institutes InternationalKyotoJapan
| | - Takashi Hanakawa
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
- Department of Integrated Neuroanatomy and NeuroimagingKyoto University Graduate School of MedicineKyotoJapan
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Gargari OK, Fatehi F, Mohammadi I, Firouzabadi SR, Shafiee A, Habibi G. Diagnostic accuracy of large language models in psychiatry. Asian J Psychiatr 2024; 100:104168. [PMID: 39111087 DOI: 10.1016/j.ajp.2024.104168] [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: 05/27/2024] [Revised: 07/20/2024] [Accepted: 07/22/2024] [Indexed: 09/13/2024]
Abstract
INTRODUCTION Medical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large Language Models (LLMs) like GPT, has emerged as a promising tool to enhance diagnostic accuracy in psychiatry. This comparative study explores the diagnostic capabilities of several AI models, including Aya, GPT-3.5, GPT-4, GPT-3.5 clinical assistant (CA), Nemotron, and Nemotron CA, using clinical cases from the DSM-5. METHODS We curated 20 clinical cases from the DSM-5 Clinical Cases book, covering a wide range of psychiatric diagnoses. Four advanced AI models (GPT-3.5 Turbo, GPT-4, Aya, Nemotron) were tested using prompts to elicit detailed diagnoses and reasoning. The models' performances were evaluated based on accuracy and quality of reasoning, with additional analysis using the Retrieval Augmented Generation (RAG) methodology for models accessing the DSM-5 text. RESULTS The AI models showed varied diagnostic accuracy, with GPT-3.5 and GPT-4 performing notably better than Aya and Nemotron in terms of both accuracy and reasoning quality. While models struggled with specific disorders such as cyclothymic and disruptive mood dysregulation disorders, others excelled, particularly in diagnosing psychotic and bipolar disorders. Statistical analysis highlighted significant differences in accuracy and reasoning, emphasizing the superiority of the GPT models. DISCUSSION The application of AI in psychiatry offers potential improvements in diagnostic accuracy. The superior performance of the GPT models can be attributed to their advanced natural language processing capabilities and extensive training on diverse text data, enabling more effective interpretation of psychiatric language. However, models like Aya and Nemotron showed limitations in reasoning, indicating a need for further refinement in their training and application. CONCLUSION AI holds significant promise for enhancing psychiatric diagnostics, with certain models demonstrating high potential in interpreting complex clinical descriptions accurately. Future research should focus on expanding the dataset and integrating multimodal data to further enhance the diagnostic capabilities of AI in psychiatry.
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Affiliation(s)
- Omid Kohandel Gargari
- Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran
| | - Farhad Fatehi
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Ida Mohammadi
- Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran
| | - Shahryar Rajai Firouzabadi
- Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran
| | - Arman Shafiee
- Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran
| | - Gholamreza Habibi
- Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran.
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Edwards AC, Singh M, Peterson RE, Webb BT, Gentry AE. Associations between polygenic liability to psychopathology and non-suicidal versus suicidal self-injury. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32982. [PMID: 38551161 PMCID: PMC11438949 DOI: 10.1002/ajmg.b.32982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/01/2024] [Accepted: 03/19/2024] [Indexed: 09/30/2024]
Abstract
Little is known about how non-suicidal and suicidal self-injury are differentially genetically related to psychopathology and related measures. This research was conducted using the UK Biobank Resource, in participants of European ancestry (N = 2320 non-suicidal self-injury [NSSI] only; N = 2648 suicide attempt; 69.18% female). We compared polygenic scores (PGS) for psychopathology and other relevant measures within self-injuring individuals. Logistic regressions and likelihood ratio tests (LRT) were used to identify PGS that were differentially associated with these outcomes. In a multivariable model, PGS for anorexia nervosa (odds ratio [OR] = 1.07; 95% confidence intervals [CI] 1.01; 1.15) and suicidal behavior (OR = 1.06; 95% CI 1.00; 1.12) both differentiated between NSSI and suicide attempt, while the PGS for other phenotypes did not. The LRT between the multivariable and base models was significant (Chi square = 11.38, df = 2, p = 0.003), and the multivariable model explained a larger proportion of variance (Nagelkerke's pseudo-R2 = 0.028 vs. 0.025). While NSSI and suicidal behavior are similarly genetically related to a range of mental health and related outcomes, genetic liability to anorexia nervosa and suicidal behavior is higher among those reporting a suicide attempt than those reporting NSSI-only. Further elucidation of these distinctions is necessary, which will require a nuanced assessment of suicidal versus non-suicidal self-injury in large samples.
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Affiliation(s)
- Alexis C. Edwards
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, US 23298
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US 23298
| | - Madhurbain Singh
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US 23298
- Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, US 23298
| | - Roseann E. Peterson
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, US 23298
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US 23298
- Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, US 11205
| | - Bradley T. Webb
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, US 23298
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US 23298
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, US
| | - Amanda E. Gentry
- Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, US 23298
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US 23298
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Guerini FR, Bolognesi E, Mensi MM, Zanette M, Agliardi C, Zanzottera M, Chiappedi M, Annunziata S, García-García F, Cavallini A, Clerici M. HLA-A, -B, -C and -DRB1 Association with Autism Spectrum Disorder Risk: A Sex-Related Analysis in Italian ASD Children and Their Siblings. Int J Mol Sci 2024; 25:9879. [PMID: 39337366 PMCID: PMC11431861 DOI: 10.3390/ijms25189879] [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: 08/12/2024] [Revised: 09/10/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
Autism Spectrum disorders (ASD) are diagnosed more often in males than in females, by a ratio of about 3:1; this is likely to be due to a difference in risk burden between the sexes and/or to "compensatory skills" in females, that may delay the diagnosis of ASD. Identifying specific risk factors for ASD in females may be important in facilitating early diagnosis. We investigated whether HLA- class I: -A, -B, -C and class II -DRB1 alleles, which have been suggested to play a role in the development of ASD, can be considered as sex-related risk/protective markers towards the ASD. We performed HLA allele genotyping in 178 Italian children with ASD, 94 healthy siblings, and their parents. HLA allele distribution was compared between children with ASD, sex-matched healthy siblings, and a cohort of healthy controls (HC) enrolled in the Italian bone marrow donor registry. Allele transmission from parents to children with ASD and their siblings was also assessed. Our findings suggest that HLA-A*02, B*38, and C*12 alleles are more frequently carried by females with ASD compared to both HC and healthy female siblings, indicating these alleles as potential risk factors for ASD in females. Conversely, the HLA-A*03 allele was more commonly transmitted to healthy female siblings, suggesting it might have a protective effect. Additionally, the HLA-B*44 allele was found to be more prevalent in boys with ASD, indicating it is a potential risk factor for male patients. This is the first Italian study of sex-related HLA association with ASD. If confirmed, these results could facilitate early ASD diagnosis in female patients, allowing earlier interventions, which are crucial in the management of neurodevelopmental disorders.
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Affiliation(s)
- Franca Rosa Guerini
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
| | - Elisabetta Bolognesi
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
| | - Martina Maria Mensi
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy
- IRCCS Fondazione Mondino, 27100 Pavia, Italy
| | - Michela Zanette
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
| | - Cristina Agliardi
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
| | - Milena Zanzottera
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
| | - Matteo Chiappedi
- Child Neurology and Psychiatry Unit, ASST Pavia, 27029 Vigevano, Italy
| | - Silvia Annunziata
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
| | - Francisco García-García
- Computational Biomedicine Laboratory, Principe Felipe Research Center (CIPF), C/Eduardo Primo Yúfera 3, 46012 Valencia, Spain
| | - Anna Cavallini
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
| | - Mario Clerici
- Laboratory of Molecular Medicine and Biotechnologies, IRCCS Fondazione Don Carlo Gnocchi, Via Capecelatro 66, 20148 Milan, Italy
- Pathophysiology and Transplantation Department, University of Milan, 20122 Milan, Italy
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