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Cheng CM, Chen MH, Tsai SJ, Chang WH, Tsai CF, Lin WC, Bai YM, Su TP, Chen TJ, Li CT. Susceptibility to Treatment-Resistant Depression Within Families. JAMA Psychiatry 2024; 81:663-672. [PMID: 38568605 PMCID: PMC10993159 DOI: 10.1001/jamapsychiatry.2024.0378] [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: 11/02/2023] [Accepted: 01/22/2024] [Indexed: 04/06/2024]
Abstract
Importance Antidepressant responses and the phenotype of treatment-resistant depression (TRD) are believed to have a genetic basis. Genetic susceptibility between the TRD phenotype and other psychiatric disorders has also been established in previous genetic studies, but population-based cohort studies have not yet provided evidence to support these outcomes. Objective To estimate the TRD susceptibility and the susceptibility between TRD and other psychiatric disorders within families in a nationwide insurance cohort with extremely high coverage and comprehensive health care data. Design, Setting, and Participants This cohort study assessed data from the Taiwan national health insurance database across entire population (N = 26 554 001) between January 2003 and December 2017. Data analysis was performed from August 2021 to April 2023. TRD was defined as having experienced at least 3 distinct antidepressant treatments in the current episode, each with adequate dose and duration, based on the prescribing records. Then, we identified the first-degree relatives of individuals with TRD (n = 34 467). A 1:4 comparison group (n = 137 868) of first-degree relatives of individuals without TRD was arranged for the comparison group, matched by birth year, sex, and kinship. Main Outcomes and Measures Modified Poisson regression analyses were performed and adjusted relative risks (aRRs) and 95% CIs were calculated for the risk of TRD, the risk of other major psychiatric disorders, and different causes of mortality. Results This study included 172 335 participants (88 330 male and 84 005 female; mean [SD] age at beginning of follow-up, 22.9 [18.1] years). First-degree relatives of individuals with TRD had lower incomes, more physical comorbidities, higher suicide mortality, and increased risk of developing TRD (aRR, 9.16; 95% CI, 7.21-11.63) and higher risk of other psychiatric disorders than matched control individuals, including schizophrenia (aRR, 2.36; 95% CI, 2.10-2.65), bipolar disorder (aRR, 3.74; 95% CI, 3.39-4.13), major depressive disorder (aRR, 3.65; 95% CI, 3.44-3.87), attention-deficit/hyperactivity disorders (aRR, 2.38; 95% CI, 2.20-2.58), autism spectrum disorder (aRR, 2.26; 95% CI, 1.86-2.74), anxiety disorder (aRR, 2.71; 95% CI, 2.59-2.84), and obsessive-compulsive disorder (aRR, 3.14; 95% CI, 2.70-3.66). Sensitivity and subgroup analyses validated the robustness of the findings. Conclusions and Relevance To our knowledge, this study is the largest and perhaps first nationwide cohort study to demonstrate TRD phenotype transmission across families and coaggregation with other major psychiatric disorders. Patients with a family history of TRD had an increased risk of suicide mortality and tendency toward antidepressant resistance; therefore, more intensive treatments for depressive symptoms might be considered earlier, rather than antidepressant monotherapy.
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Affiliation(s)
- Chih-Ming Cheng
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Wen-Han Chang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Graduate Institute of Statistics National Central University, Taoyuan, Taiwan
| | - Chia-Fen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Wei-Chen Lin
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Tung-Ping Su
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Tzeng-Ji Chen
- Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Family Medicine, Taipei Veterans General Hospital, Hsinchu branch, Hsinchu, Taiwan
| | - Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
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Zhai S, Mehrotra DV, Shen J. Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities. Brief Bioinform 2023; 25:bbad470. [PMID: 38152980 PMCID: PMC10782924 DOI: 10.1093/bib/bbad470] [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: 07/14/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/29/2023] Open
Abstract
Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.
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Affiliation(s)
- Song Zhai
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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3
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Xiong Y, Karlsson R, Song J, Kowalec K, Rück C, Sigström R, Jonsson L, Clements CC, Andersson E, Boberg J, Lewis CM, Sullivan PF, Landén M, Lu Y. Polygenic risk scores of lithium response and treatment resistance in major depressive disorder. Transl Psychiatry 2023; 13:301. [PMID: 37770441 PMCID: PMC10539379 DOI: 10.1038/s41398-023-02602-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 09/30/2023] Open
Abstract
Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate the genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) and non-TRD within ~4500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores (PRS) of antidepressants and lithium response for individuals with MDD and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1778 ECT-treated MDD cases, nearly all (94%) used antidepressants before their first ECT and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We did not observe a significant difference in the mean PRS of antidepressant response between TRD and non-TRD; however, we found that TRD cases had a significantly higher PRS of lithium response compared to non-TRD cases (OR = 1.10-1.12 under various definitions). The results support the evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.
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Affiliation(s)
- Ying Xiong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jie Song
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kaarina Kowalec
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- College of Pharmacy, University of Manitoba, Winnipeg, MB, Canada
| | - Christian Rück
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Robert Sigström
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Cognition and Old Age Psychiatry, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Lina Jonsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Caitlin C Clements
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychology, University of Notre Dame, South Bend, IN, USA
| | - Evelyn Andersson
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Julia Boberg
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mikael Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
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Shah SB, Peddada TN, Song C, Mensah M, Sung H, Yavi M, Yuan P, Zarate CA, Mickey BJ, Burmeister M, Akula N, McMahon FJ. Exome-wide association study of treatment-resistant depression suggests novel treatment targets. Sci Rep 2023; 13:12467. [PMID: 37528149 PMCID: PMC10394052 DOI: 10.1038/s41598-023-38984-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 07/18/2023] [Indexed: 08/03/2023] Open
Abstract
Treatment-resistant depression (TRD) is a severe form of major depressive disorder (MDD) with substantial public health impact and poor treatment outcome. Treatment outcome in MDD is significantly heritable, but genome-wide association studies have failed to identify replicable common marker alleles, suggesting a potential role for uncommon variants. Here we investigated the hypothesis that uncommon, putatively functional genetic variants are associated with TRD. Whole-exome sequencing data was obtained from 182 TRD cases and 2021 psychiatrically healthy controls. After quality control, the remaining 149 TRD cases and 1976 controls were analyzed with tests designed to detect excess burdens of uncommon variants. At the gene level, 5 genes, ZNF248, PRKRA, PYHIN1, SLC7A8, and STK19 each carried exome-wide significant excess burdens of variants in TRD cases (q < 0.05). Analysis of 41 pre-selected gene sets suggested an excess of uncommon, functional variants among genes involved in lithium response. Among the genes identified in previous TRD studies, ZDHHC3 was also significant in this sample after multiple test correction. ZNF248 and STK19 are involved in transcriptional regulation, PHYIN1 and PRKRA are involved in immune response, SLC7A8 is associated with thyroid hormone transporter activity, and ZDHHC3 regulates synaptic clustering of GABA and glutamate receptors. These results implicate uncommon, functional alleles in TRD and suggest promising novel targets for future research.
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Affiliation(s)
- Shrey B Shah
- Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
- Rutgers New Jersey Medical School, Newark, NJ, USA.
| | - Teja N Peddada
- Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Stanford University School of Medicine, Stanford, CA, USA
| | - Christopher Song
- Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Maame Mensah
- Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Heejong Sung
- Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Mani Yavi
- Experimental Therapeutics and Pathophysiology Branch and Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peixiong Yuan
- Experimental Therapeutics and Pathophysiology Branch and Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Zarate
- Experimental Therapeutics and Pathophysiology Branch and Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Brian J Mickey
- Department of Psychiatry, Huntsman Mental Health Institute, University of Utah, Salt Lake City, UT, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Margit Burmeister
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
- Michigan Neuroscience Institute and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nirmala Akula
- Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Francis J McMahon
- Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
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Clark K, Fu W, Liu CL, Ho PC, Wang H, Lee WP, Chou SY, Wang LS, Tzeng JY. The prediction of Alzheimer's disease through multi-trait genetic modeling. Front Aging Neurosci 2023; 15:1168638. [PMID: 37577355 PMCID: PMC10416111 DOI: 10.3389/fnagi.2023.1168638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/26/2023] [Indexed: 08/15/2023] Open
Abstract
To better capture the polygenic architecture of Alzheimer's disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.
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Affiliation(s)
- Kaylyn Clark
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Wei Fu
- Department of Health Management and Systems Sciences, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, United States
| | - Chia-Lun Liu
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Pei-Chuan Ho
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
| | - Hui Wang
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Shin-Yi Chou
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Economics, Lehigh University, Bethlehem, PA, United States
- National Bureau of Economic Research, Cambridge, MA, United States
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jung-Ying Tzeng
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Statistics, North Carolina State University, Raleigh, NC, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States
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Gómez-Carrillo A, Kirmayer LJ. A cultural-ecosocial systems view for psychiatry. Front Psychiatry 2023; 14:1031390. [PMID: 37124258 PMCID: PMC10133725 DOI: 10.3389/fpsyt.2023.1031390] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 03/08/2023] [Indexed: 05/02/2023] Open
Abstract
While contemporary psychiatry seeks the mechanisms of mental disorders in neurobiology, mental health problems clearly depend on developmental processes of learning and adaptation through ongoing interactions with the social environment. Symptoms or disorders emerge in specific social contexts and involve predicaments that cannot be fully characterized in terms of brain function but require a larger social-ecological view. Causal processes that result in mental health problems can begin anywhere within the extended system of body-person-environment. In particular, individuals' narrative self-construal, culturally mediated interpretations of symptoms and coping strategies as well as the responses of others in the social world contribute to the mechanisms of mental disorders, illness experience, and recovery. In this paper, we outline the conceptual basis and practical implications of a hierarchical ecosocial systems view for an integrative approach to psychiatric theory and practice. The cultural-ecosocial systems view we propose understands mind, brain and person as situated in the social world and as constituted by cultural and self-reflexive processes. This view can be incorporated into a pragmatic approach to clinical assessment and case formulation that characterizes mechanisms of pathology and identifies targets for intervention.
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Affiliation(s)
- Ana Gómez-Carrillo
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, QC, Canada
- Culture and Mental Health Research Unit, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
| | - Laurence J. Kirmayer
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, QC, Canada
- Culture and Mental Health Research Unit, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
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Lu Y, Xiong Y, Karlsson R, Song J, Kowalec K, Rück C, Sigstrom R, Jonsson L, Clements C, Andersson E, Boberg J, Lewis C, Sullivan P, Landén M. Investigating genetic overlap between antidepressant and lithium response and treatment resistance in major depressive disorder. RESEARCH SQUARE 2023:rs.3.rs-2556941. [PMID: 36865283 PMCID: PMC9980196 DOI: 10.21203/rs.3.rs-2556941/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) within ~ 4 500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores of antidepressant and lithium response for individuals with MDD, and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1 778 ECT-treated MDD cases, nearly all (94%) used antidepressants before first ECT, and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We found that TRD cases tend to have lower genetic load of antidepressant response than non-TRD, although the difference was not significant; furthermore, TRD cases had significantly higher genetic load of lithium response (OR = 1.10-1.12 under different definitions). The results support evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.
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Tsermpini EE, Serretti A, Dolžan V. Precision Medicine in Antidepressants Treatment. Handb Exp Pharmacol 2023; 280:131-186. [PMID: 37195310 DOI: 10.1007/164_2023_654] [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] [Indexed: 05/18/2023]
Abstract
Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.
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Affiliation(s)
- Evangelia Eirini Tsermpini
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Vita Dolžan
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
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Amasi-Hartoonian N, Pariante CM, Cattaneo A, Sforzini L. Understanding treatment-resistant depression using "omics" techniques: A systematic review. J Affect Disord 2022; 318:423-455. [PMID: 36103934 DOI: 10.1016/j.jad.2022.09.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/26/2022] [Accepted: 09/07/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Treatment-resistant depression (TRD) results in huge healthcare costs and poor patient clinical outcomes. Most studies have adopted a "candidate mechanism" approach to investigate TRD pathogenesis, however this is made more challenging due to the complex and heterogeneous nature of this condition. High-throughput "omics" technologies can provide a more holistic view and further insight into the underlying mechanisms involved in TRD development, expanding knowledge beyond already-identified mechanisms. This systematic review assessed the information from studies that examined TRD using hypothesis-free omics techniques. METHODS PubMed, MEDLINE, Embase, APA PsycInfo, Scopus and Web of Science databases were searched on July 2022. 37 human studies met the eligibility criteria, totalling 17,518 TRD patients, 571,402 healthy controls and 62,279 non-TRD depressed patients (including antidepressant responders and untreated MDD patients). RESULTS Significant findings were reported that implicate the role in TRD of various molecules, including polymorphisms, genes, mRNAs and microRNAs. The pathways most commonly reported by the identified studies were involved in immune system and inflammation, neuroplasticity, calcium signalling and neurotransmitters. LIMITATIONS Small sample sizes, variability in defining TRD, and heterogeneity in study design and methodology. CONCLUSIONS These findings provide insight into TRD pathophysiology, proposing future research directions for novel drug targets and potential biomarkers for clinical staging and response to antidepressants (citalopram/escitalopram in particular) and electroconvulsive therapy (ECT). Further validation is warranted in large prospective studies using standardised TRD criteria. A multi-omics and systems biology strategy with a collaborative effort will likely deliver robust findings for translation into the clinic.
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Affiliation(s)
- Nare Amasi-Hartoonian
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Department of Psychological Medicine, London, UK.
| | - Carmine Maria Pariante
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Department of Psychological Medicine, London, UK; National Institute for Health and Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, UK
| | - Annamaria Cattaneo
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy; Laboratory of Biological Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Luca Sforzini
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Department of Psychological Medicine, London, UK
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10
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Fusar-Poli L, Rutten BPF, van Os J, Aguglia E, Guloksuz S. Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype? Int Rev Psychiatry 2022; 34:663-675. [PMID: 36786114 DOI: 10.1080/09540261.2022.2101352] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Over the last years, the decreased costs and enhanced accessibility to large genome-wide association studies datasets have laid the foundations for the development of polygenic risk scores (PRSs). A PRS is calculated on the weighted sum of single nucleotide polymorphisms and measures the individual genetic predisposition to develop a certain phenotype. An increasing number of studies have attempted to utilize the PRSs for risk stratification and prognostic evaluation. The present narrative review aims to discuss the potential clinical utility of PRSs in predicting outcomes and treatment response in psychiatry. After summarizing the evidence on major mental disorders, we have discussed the advantages and limitations of currently available PRSs. Although PRSs represent stable trait features with a normal distribution in the general population and can be relatively easily calculated in terms of time and costs, their real-world applicability is reduced by several limitations, such as low predictive power and lack of population diversity. Even with the rapid expansion of the psychiatric genetic knowledge base, pure genetic prediction in clinical psychiatry appears to be out of reach in the near future. Therefore, combining genomic and exposomic vulnerabilities for mental disorders with a detailed clinical characterization is needed to personalize care.
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Affiliation(s)
- Laura Fusar-Poli
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands.,UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Eugenio Aguglia
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy
| | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands.,Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
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11
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Polygenic heterogeneity in antidepressant treatment and placebo response. Transl Psychiatry 2022; 12:456. [PMID: 36309483 PMCID: PMC9617908 DOI: 10.1038/s41398-022-02221-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/05/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
The genetic architecture of antidepressant response is poorly understood. Polygenic risk scores (PRS), exploration of placebo response and the use of sub-scales might provide insights. Here, we investigate the association between PRSs for relevant complex traits and response to vortioxetine treatment and placebo using clinical scales, including sub-scales and self-reported assessments. We collected a clinical test sample of Major Depressive Disorder (MDD) patients treated with vortioxetine (N = 907) or placebo (N = 455) from seven randomized, double-blind, clinical trials. In parallel, we obtained data from an observational web-based study of vortioxetine-treated patients (N = 642) with self-reported response. PRSs for antidepressant response, psychiatric disorders, and symptom traits were derived using summary statistics from well-powered genome-wide association studies (GWAS). Association tests were performed between the PRSs and treatment response in each of the two test samples and empirical p-values were evaluated. In the clinical test sample, no PRSs were significantly associated with response to vortioxetine treatment or placebo following Bonferroni correction. However, clinically assessed treatment response PRS was nominally associated with vortioxetine treatment and placebo response given by several secondary outcome scales (improvement on HAM-A, HAM-A Psychic Anxiety sub-scale, CPFQ & PDQ), (P ≤ 0.026). Further, higher subjective well-being PRS (P ≤ 0.033) and lower depression PRS (P = 0.01) were nominally associated with higher placebo response. In the self-reported test sample, higher schizophrenia PRS was significantly associated with poorer self-reported response (P = 0.0001). The identified PRSs explain a low proportion of the variance (1.2-5.3%) in placebo and treatment response. Although the results were limited, we believe that PRS associations bear unredeemed potential as a predictor for treatment response, as more well-powered and phenotypically similar GWAS bases become available.
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12
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Bai Y, Dai G, Song L, Gu X, Ba N, Ju W, Zhang W. Potential Anti-Depressive Effects and Mechanisms of Zhi-Zi Hou-Po Decoction Using Behavioral Despair Tests Combined With in Vitro Approaches. Front Pharmacol 2022; 13:918776. [PMID: 35873590 PMCID: PMC9298739 DOI: 10.3389/fphar.2022.918776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Zhi-Zi Hou-Po Decoction (ZHD) has been widely used in the treatment of depression for centuries. This study aimed to investigate the antidepressant effects of the water extract of ZHD (ZHD-WE) and ethanol extract of ZHD (ZHD-EE) using behavioral despair tests in mice, and to further explore the neuroprotective effects in a PC12 cell injury model induced by corticosterone (CORT). Mice were divided into a control group (normal saline), ZHD-WE groups (4, 8, and 16 g kg-1), ZHD-EE groups (4, 8, and 16 g kg-1) and the fluoxetine group (20 mg kg-1). The forced swimming test (FST) and tail suspension test (TST) were used to screen the antidepressant effects of ZHD-WE and ZHD-EE after oral administration for seven consecutive days. The level of brain-derived neurotrophic factor (BDNF) in the hippocampus was determined by ELISA. The MTT, lactate dehydrogenase (LDH) and flow cytometry analysis were performed to elucidate the neuroprotective effect of ZHD-EE on a PC12 cell injury model. Additionally, the mRNA and proteins expression of apoptotic molecules Bax, Bcl-2 and BDNF were detected by RT-PCR and western blot assay. It showed that ZHD-EE at concentrations of 8 and 16 g kg-1 significantly decreased the immobility time in the TST and FST, and increased the BDNF levels in the hippocampus. While ZHD-WE at concentrations of 4, 8, and 16 g kg-1 had no significant effect on the immobility time in the TST, and only the 16 g kg-1 of extract group significantly decreased the immobility time in the FST. In vitro, the obtained results showed that PC12 cells pre-incubated with ZHD-EE at concentrations of 100 and 400 μg ml-1 improved cell viability, decreased LDH release, and reduced apoptosis rate of PC12 cells. Moreover, ZHD-EE significantly increased the mRNA and proteins expression of Bcl-2 and BDNF, while decreased the mRNA and protein expression of Bax. ZHD-EE significantly improved despair-like behavior in mice, and its mechanism may be related to BDNF upregulation in the hippocampus. This study also showed that ZHD-EE had a protective effect on CORT-induced injury in PC12 cells by upregulating the expression of BDNF and restoring Bcl-2/Bax balance.
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Affiliation(s)
- Yongtao Bai
- Department of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China.,Clinical Research Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Guoliang Dai
- Department of Clinical Pharmacology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Lihua Song
- Department of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Xiaolei Gu
- Clinical Research Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Ning Ba
- Clinical Research Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Wenzheng Ju
- Department of Clinical Pharmacology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenzhou Zhang
- Department of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
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13
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García-Marín LM, Rabinowitz JA, Ceja Z, Alcauter S, Medina-Rivera A, Rentería ME. The pharmacogenomics of selective serotonin reuptake inhibitors. Pharmacogenomics 2022; 23:597-607. [PMID: 35673953 DOI: 10.2217/pgs-2022-0037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.
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Affiliation(s)
- Luis M García-Marín
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.,Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Zuriel Ceja
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Miguel E Rentería
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
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14
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The Potential of Polygenic Risk Scores to Predict Antidepressant Treatment Response in Major Depression: A Systematic Review. J Affect Disord 2022; 304:1-11. [PMID: 35151671 DOI: 10.1016/j.jad.2022.02.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 12/29/2021] [Accepted: 02/09/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND Understanding the genetic underpinnings of antidepressant treatment response in unipolar major depressive disorder (MDD) can be useful in identifying patients at risk for poor treatment response or treatment resistant depression. A polygenic risk score (PRS) is a useful tool to explore genetic liability of a complex trait such as antidepressant treatment response. Here, we review studies that use PRSs to examine genetic overlap between any trait and antidepressant treatment response in unipolar MDD. METHODS A systematic search of literature was conducted in PubMed, Embase, and PsycINFO. Our search included studies examining associations between PRSs of psychiatric as well as non-psychiatric traits and antidepressant treatment response in patients with unipolar MDD. A quality assessment of the included studies was performed. RESULTS In total, eleven articles were included which contained PRSs for 30 traits. Studies varied in sample size and endpoints used for antidepressant treatment response. Overall, PRSs for attention-deficit hyperactivity disorder, the personality trait openness, coronary artery disease, obesity, and stroke have been associated with antidepressant treatment response in patients with unipolar MDD. LIMITATIONS The endpoints used by included studies differed significantly, therefore it was not possible to perform a meta-analysis. CONCLUSIONS Associations between a PRS and antidepressant treatment response have been reported for a number of traits in patients with unipolar MDD. PRSs could be informative to predict antidepressant treatment response in this population, given advances in the field. Most importantly, there is a need for larger study cohorts and the use of standardized outcome measures.
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15
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Pain O, Hodgson K, Trubetskoy V, Ripke S, Marshe VS, Adams MJ, Byrne EM, Campos AI, Carrillo-Roa T, Cattaneo A, Als TD, Souery D, Dernovsek MZ, Fabbri C, Hayward C, Henigsberg N, Hauser J, Kennedy JL, Lenze EJ, Lewis G, Müller DJ, Martin NG, Mulsant BH, Mors O, Perroud N, Porteous DJ, Rentería ME, Reynolds CF, Rietschel M, Uher R, Wigmore EM, Maier W, Wray NR, Aitchison KJ, Arolt V, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Li QS, Liu YL, Serretti A, Tsai SJ, Turecki G, Weinshilboum R, McIntosh AM, Lewis CM, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TF, Bacanu SA, Bækvad-Hansen M, Beekman AT, Bigdeli TB, Binder EB, Bryois J, Buttenschøn HN, Bybjerg-Grauholm J, Cai N, Castelao E, Christensen JH, Clarke TK, Coleman JR, Colodro-Conde L, Couvy-Duchesne B, Craddock N, Crawford GE, Davies G, Deary IJ, Degenhardt F, Derks EM, Direk N, Dolan CV, Dunn EC, Eley TC, Escott-Price V, Hassan Kiadeh FF, Finucane HK, Foo JC, Forstner AJ, Frank J, Gaspar HA, Gill M, Goes FS, Gordon SD, Grove J, Hall LS, Hansen CS, Hansen TF, Herms S, Hickie IB, Hoffmann P, Homuth G, Horn C, Hottenga JJ, Hougaard DM, Howard DM, Ising M, Jansen R, Jones I, Jones LA, Jorgenson E, Knowles JA, Kohane IS, Kraft J, Kretzschmar WW, Kutalik Z, Li Y, Lind PA, MacIntyre DJ, MacKinnon DF, Maier RM, Maier W, Marchini J, Mbarek H, McGrath P, McGuffin P, Medland SE, Mehta D, Middeldorp CM, Mihailov E, Milaneschi Y, Milani L, Mondimore FM, Montgomery GW, Mostafavi S, Mullins N, Nauck M, Ng B, Nivard MG, Nyholt DR, O’Reilly PF, Oskarsson H, Owen MJ, Painter JN, Pedersen CB, Pedersen MG, Peterson RE, Peyrot WJ, Pistis G, Posthuma D, Quiroz JA, Qvist P, Rice JP, Riley BP, Rivera M, Mirza SS, Schoevers R, Schulte EC, Shen L, Shi J, Shyn SI, Sigurdsson E, Sinnamon GC, Smit JH, Smith DJ, Stefansson H, Steinberg S, Streit F, Strohmaier J, Tansey KE, Teismann H, Teumer A, Thompson W, Thomson PA, Thorgeirsson TE, Traylor M, Treutlein J, Trubetskoy V, Uitterlinden AG, Umbricht D, Van der Auwera S, van Hemert AM, Viktorin A, Visscher PM, Wang Y, Webb BT, Weinsheimer SM, Wellmann J, Willemsen G, Witt SH, Wu Y, Xi HS, Yang J, Zhang F, Arolt V, Baune BT, Berger K, Boomsma DI, Cichon S, Dannlowski U, de Geus E, DePaulo JR, Domenici E, Domschke K, Esko T, Grabe HJ, Hamilton SP, Hayward C, Heath AC, Kendler KS, Kloiber S, Lewis G, Li QS, Lucae S, Madden PA, Magnusson PK, Martin NG, McIntosh AM, Metspalu A, Mors O, Mortensen PB, Müller-Myhsok B, Nordentoft M, Nöthen MM, O’Donovan MC, Paciga SA, Pedersen NL, Penninx BW, Perlis RH, Porteous DJ, Potash JB, Preisig M, Rietschel M, Schaefer C, Schulze TG, Smoller JW, Stefansson K, Tiemeier H, Uher R, Völzke H, Weissman MM, Werge T, Lewis CM, Levinson DF, Breen G, Børglum AD, Sullivan PF. Identifying the Common Genetic Basis of Antidepressant Response. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 2:115-126. [PMID: 35712048 PMCID: PMC9117153 DOI: 10.1016/j.bpsgos.2021.07.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 01/20/2023] Open
Abstract
Background Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.
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16
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Fanelli G, Domschke K, Minelli A, Gennarelli M, Martini P, Bortolomasi M, Maron E, Squassina A, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, De Ronchi D, Baune BT, Serretti A, Fabbri C. A meta-analysis of polygenic risk scores for mood disorders, neuroticism, and schizophrenia in antidepressant response. Eur Neuropsychopharmacol 2022; 55:86-95. [PMID: 34844152 DOI: 10.1016/j.euroneuro.2021.11.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/04/2021] [Accepted: 11/09/2021] [Indexed: 12/28/2022]
Abstract
About two-thirds of patients with major depressive disorder (MDD) fail to achieve symptom remission after the initial antidepressant treatment. Despite a role of genetic factors was proven, the specific underpinnings are not fully understood yet. Polygenic risk scores (PRSs), which summarise the additive effect of multiple risk variants across the genome, might provide insights into the underlying genetics. This study aims to investigate the possible association of PRSs for bipolar disorder, MDD, neuroticism, and schizophrenia (SCZ) with antidepressant non-response or non-remission in patients with MDD. PRSs were calculated at eight genome-wide P-thresholds based on publicly available summary statistics of the largest genome-wide association studies. Logistic regressions were performed between PRSs and non-response or non-remission in six European clinical samples, adjusting for age, sex, baseline symptom severity, recruitment sites, and population stratification. Results were meta-analysed across samples, including up to 3,637 individuals. Bonferroni correction was applied. In the meta-analysis, no result was significant after Bonferroni correction. The top result was found for MDD-PRS and non-remission (p = 0.004), with patients in the highest vs. lowest PRS quintile being more likely not to achieve remission (OR=1.5, 95% CI=1.11-1.98, p = 0.007). Nominal associations were also found between MDD-PRS and non-response (p = 0.013), as well as between SCZ-PRS and non-remission (p = 0.035). Although PRSs are still not able to predict non-response or non-remission, our results are in line with previous works; methodological improvements in PRSs calculation may improve their predictive performance and have a meaningful role in precision psychiatry.
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Affiliation(s)
- Giuseppe Fanelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy; Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Paolo Martini
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Psychiatric Clinic, West Tallinn Central Hospital, Tallinn, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, UK
| | - Alessio Squassina
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University Vienna, Vienna, Austria
| | - Joseph Zohar
- Department of Psychiatry, Sheba Medical Center, Tel Hashomer, and Sackler School of Medicine, Tel Aviv University, Tel Hashomer, Israel
| | - Daniel Souery
- Laboratoire de Psychologie Médicale, Université Libre de Bruxelles and Psy Pluriel, Centre Européen de Psychologie Médicale, Brussels, Belgium
| | | | - Diego Albani
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Gianluigi Forloni
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Medical University Vienna, Vienna, Austria
| | | | - Diana De Ronchi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Parkville, VIC, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy; Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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Kasyanov E, Rakitko A, Rukavishnikov G, Golimbet V, Shmukler A, Iliinsky V, Neznanov N, Kibitov A, Mazo G. Contemporary GWAS studies of depression: the critical role of phenotyping. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:50-61. [DOI: 10.17116/jnevro202212201150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Understanding genetic risk factors for common side effects of antidepressant medications. COMMUNICATIONS MEDICINE 2021; 1:45. [PMID: 35602235 PMCID: PMC9053224 DOI: 10.1038/s43856-021-00046-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023] Open
Abstract
Background Major depression is one of the most disabling health conditions internationally. In recent years, new generation antidepressant medicines have become very widely prescribed. While these medicines are efficacious, side effects are common and frequently result in discontinuation of treatment. Compared with specific pharmacological properties of the different medications, the relevance of individual vulnerability is understudied. Methods We used data from the Australian Genetics of Depression Study to gain insights into the aetiology and genetic risk factors to antidepressant side effects. To this end, we employed structural equation modelling, polygenic risk scoring and regressions. Results Here we show that participants reporting a specific side effect for one antidepressant are more likely to report the same side effect for other antidepressants, suggesting the presence of shared individual or pharmacological factors. Polygenic risk scores (PRS) for depression associated with side effects that overlapped with depressive symptoms, including suicidality and anxiety. Body Mass Index PRS are strongly associated with weight gain from all medications. PRS for headaches are associated with headaches from sertraline. Insomnia PRS show some evidence of predicting insomnia from amitriptyline and escitalopram. Conclusions Our results suggest a set of common factors underlying the risk for antidepressant side effects. These factors seem to be partly explained by genetic liability related to depression severity and the nature of the side effect. Future studies on the genetic aetiology of side effects will enable insights into their underlying mechanisms and the possibility of risk stratification and prophylaxis strategies. Antidepressants are commonly prescribed medications, but adverse side effects are cause for treatment discontinuation. We analysed data from a large group of adults who have taken antidepressants to understand why some people experience specific side effects. Our results suggest that a person’s genetic characteristics play a role. For example, participants genetically predisposed to a higher body mass index were more likely to report weight gain from antidepressants. These results open up the possibility of predicting adverse side effects as we increase our knowledge on the genetics of related complex traits. Future studies can focus on performing large-scale genetic studies of antidepressant side effects to gain further insights into the mechanisms underlying antidepressant side effects and to identify genetic markers of side effects that could be used in the clinic. Campos et al. study the genetic aetiology of antidepressant side effects. Using data from the Australian Genetics of Depression study, the authors show that polygenic risk scores for traits such as BMI, insomnia and headaches have a shared genetic basis with side effects to commonly used antidepressant drugs.
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Ask H, Cheesman R, Jami ES, Levey DF, Purves KL, Weber H. Genetic contributions to anxiety disorders: where we are and where we are heading. Psychol Med 2021; 51:2231-2246. [PMID: 33557968 DOI: 10.1017/s0033291720005486] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Anxiety disorders are among the most common psychiatric disorders worldwide. They often onset early in life, with symptoms and consequences that can persist for decades. This makes anxiety disorders some of the most debilitating and costly disorders of our time. Although much is known about the synaptic and circuit mechanisms of fear and anxiety, research on the underlying genetics has lagged behind that of other psychiatric disorders. However, alongside the formation of the Psychiatric Genomic Consortium Anxiety workgroup, progress is rapidly advancing, offering opportunities for future research.Here we review current knowledge about the genetics of anxiety across the lifespan from genetically informative designs (i.e. twin studies and molecular genetics). We include studies of specific anxiety disorders (e.g. panic disorder, generalised anxiety disorder) as well as those using dimensional measures of trait anxiety. We particularly address findings from large-scale genome-wide association studies and show how such discoveries may provide opportunities for translation into improved or new therapeutics for affected individuals. Finally, we describe how discoveries in anxiety genetics open the door to numerous new research possibilities, such as the investigation of specific gene-environment interactions and the disentangling of causal associations with related traits and disorders.We discuss how the field of anxiety genetics is expected to move forward. In addition to the obvious need for larger sample sizes in genome-wide studies, we highlight the need for studies among young people, focusing on specific underlying dimensional traits or components of anxiety.
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Affiliation(s)
- Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Eshim S Jami
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, UK
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, Connecticut
| | - Kirstin L Purves
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Heike Weber
- Department of Psychology, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany
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20
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Skelton M, Rayner C, Purves KL, Coleman JRI, Gaspar HA, Glanville KP, Hunjan AK, Hübel C, Breen G, Eley TC. Self-reported medication use as an alternate phenotyping method for anxiety and depression in the UK Biobank. Am J Med Genet B Neuropsychiatr Genet 2021; 186:389-398. [PMID: 34658127 DOI: 10.1002/ajmg.b.32878] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 06/03/2021] [Accepted: 09/21/2021] [Indexed: 01/22/2023]
Abstract
The requirement for large sample sizes for psychiatric genetic analyses necessitates novel approaches to derive cases. Anxiety and depression show substantial genetic overlap and share pharmacological treatments. Data on prescribed medication could be effective for inferring case status when other indicators of mental health are unavailable. We investigated self-reported current medication use in UK Biobank participants of European ancestry. Medication Status cases reported using antidepressant or anxiolytic medication (n = 22,218), controls did not report psychotropic medication use (n = 168,959). A subset, "Medication Only," additionally did not meet criteria for any other mental health indicator (case n = 2,643, control n = 107,029). We assessed genetic overlap between these phenotypes and two published genetic association studies of anxiety and depression, and an internalizing disorder trait derived from symptom-based questionnaires in UK Biobank. Genetic correlations between Medication Status and the three anxiety and depression phenotypes were significant (rg = 0.60-0.73). In the Medication Only subset, the genetic correlation with depression was significant (rg = 0.51). The three polygenic scores explained 0.33% - 0.80% of the variance in Medication Status and 0.07% - 0.19% of the variance in Medication Only. This study provides evidence that self-reported current medication use offers an alternate or supplementary anxiety or depression phenotype in genetic studies where diagnostic information is sparse or unavailable.
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Affiliation(s)
- Megan Skelton
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK.,National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Christopher Rayner
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK
| | - Kirstin L Purves
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK
| | - Jonathan R I Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK.,National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Héléna A Gaspar
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK
| | - Kylie P Glanville
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK
| | - Avina K Hunjan
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK.,National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Christopher Hübel
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK.,National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK.,National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Thalia C Eley
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, University of London, London, UK.,National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
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21
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Fabbri C, Hagenaars SP, John C, Williams AT, Shrine N, Moles L, Hanscombe KB, Serretti A, Shepherd DJ, Free RC, Wain LV, Tobin MD, Lewis CM. Genetic and clinical characteristics of treatment-resistant depression using primary care records in two UK cohorts. Mol Psychiatry 2021; 26:3363-3373. [PMID: 33753889 PMCID: PMC8505242 DOI: 10.1038/s41380-021-01062-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/12/2021] [Accepted: 02/24/2021] [Indexed: 01/08/2023]
Abstract
Treatment-resistant depression (TRD) is a major contributor to the disability caused by major depressive disorder (MDD). Primary care electronic health records provide an easily accessible approach to investigate TRD clinical and genetic characteristics. MDD defined from primary care records in UK Biobank (UKB) and EXCEED studies was compared with other measures of depression and tested for association with MDD polygenic risk score (PRS). Using prescribing records, TRD was defined from at least two switches between antidepressant drugs, each prescribed for at least 6 weeks. Clinical-demographic characteristics, SNP-based heritability (h2SNP) and genetic overlap with psychiatric and non-psychiatric traits were compared in TRD and non-TRD MDD cases. In 230,096 and 8926 UKB and EXCEED participants with primary care data, respectively, the prevalence of MDD was 8.7% and 14.2%, of which 13.2% and 13.5% was TRD, respectively. In both cohorts, MDD defined from primary care records was strongly associated with MDD PRS, and in UKB it showed overlap of 71-88% with other MDD definitions. In UKB, TRD vs healthy controls and non-TRD vs healthy controls h2SNP was comparable (0.25 [SE = 0.04] and 0.19 [SE = 0.02], respectively). TRD vs non-TRD was positively associated with the PRS of attention deficit hyperactivity disorder, with lower socio-economic status, obesity, higher neuroticism and other unfavourable clinical characteristics. This study demonstrated that MDD and TRD can be reliably defined using primary care records and provides the first large scale population assessment of the genetic, clinical and demographic characteristics of TRD.
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Affiliation(s)
- Chiara Fabbri
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Saskia P Hagenaars
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Catherine John
- Department of Health Sciences, University of Leicester, Leicester, UK
| | | | - Nick Shrine
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Louise Moles
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ken B Hanscombe
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - David J Shepherd
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Robert C Free
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.,Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Louise V Wain
- Department of Health Sciences, University of Leicester, Leicester, UK.,NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Martin D Tobin
- Department of Health Sciences, University of Leicester, Leicester, UK.,NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. .,Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK.
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22
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Fanelli G, Benedetti F, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, Serretti A, Fabbri C. Higher polygenic risk scores for schizophrenia may be suggestive of treatment non-response in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 108:110170. [PMID: 33181205 DOI: 10.1016/j.pnpbp.2020.110170] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/22/2020] [Accepted: 11/04/2020] [Indexed: 02/06/2023]
Abstract
Up to 60% of patients with major depressive disorder (MDD) do not respond to the first treatment with antidepressants. Response to antidepressants is a polygenic trait, although its underpinning genetics has not been fully clarified. This study aimed to investigate if polygenic risk scores (PRSs) for major psychiatric disorders and trait neuroticism (NEU) were associated with non-response or resistance to antidepressants in MDD. PRSs for bipolar disorder, MDD, NEU, and schizophrenia (SCZ) were computed in 1,148 patients with MDD. Summary statistics from the largest meta-analyses of genome-wide association studies were used as base data. Patients were classified as responders, non-responders to one treatment, non-responders to two or more treatments (treatment-resistant depression or TRD). Regression analyses were adjusted for population stratification and recruitment sites. PRSs did not predict either non-response vs response or TRD vs response after Bonferroni correction. However, SCZ-PRS was nominally associated with non-response (p = 0.003). Patients in the highest SCZ-PRS quintile were more likely to be non-responders than those in the lowest quintile (OR = 2.23, 95% CI = 1.21-4.10, p = 0.02). Patients in the lowest SCZ-PRS quintile showed higher response rates when they did not receive augmentation with second-generation antipsychotics (SGAs), while those in the highest SCZ-PRS quintile had a poor response independently from the treatment strategy (p = 0.009). A higher genetic liability to SCZ may reduce treatment response in MDD, and patients with low SCZ-PRSs may show higher response rates without SGA augmentation. Multivariate approaches and methodological refinements will be necessary before clinical implementations of PRSs.
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Affiliation(s)
- Giuseppe Fanelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Francesco Benedetti
- Vita-Salute San Raffaele University, Milan, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Joseph Zohar
- Department of Psychiatry, Sheba Medical Center, Tel Hashomer, and Sackler School of Medicine, Tel Aviv University, Tel Hashomer, Israel
| | - Daniel Souery
- Laboratoire de Psychologie Médicale, Université Libre de Bruxelles and Psy Pluriel, Centre Européen de Psychologie Médicale, Brussels, Belgium
| | | | - Diego Albani
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, IRCCS Mario Negri Institute for Pharmacological Research, Milan, Italy
| | - Gianluigi Forloni
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, IRCCS Mario Negri Institute for Pharmacological Research, Milan, Italy
| | | | - Dan Rujescu
- University Clinic for Psychiatry, Psychotherapy and Psychosomatic, Martin-Luther-University, Halle-Wittenberg, Germany
| | | | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy; Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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23
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Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021; 20:154-170. [PMID: 34002503 PMCID: PMC8129866 DOI: 10.1002/wps.20882] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
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Affiliation(s)
- Adam M Chekroud
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Spring Health, New York City, NY, USA
| | | | - Jaime Delgadillo
- Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK
| | - Gavin Doherty
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Akash Wasil
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marjolein Fokkema
- Department of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Zachary Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Robert DeRubeis
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Karmel Choi
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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24
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Morningstar-Kywi N, Haworth IS, Mosley SA. Ligand-specific pharmacogenetic effects of nonsynonymous mutations. Pharmacogenet Genomics 2021; 31:75-82. [PMID: 33395026 DOI: 10.1097/fpc.0000000000000424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
In pharmacogenomics, variable receptor phenotypes, resulting from genetic polymorphisms, are often described as a change in protein function or regulation observed upon exposure to a drug. However, in some instances, phenotypes are defined using a class of medications rather than individual drugs. This paradigm assumes that a variation associated with a drug response phenotype will retain the magnitude and direction of the effect for other drugs with the same mechanism of action. However, nonsynonymous polymorphisms may have ligand-specific effects. The purpose of this study was to investigate the potential for point mutations to asymmetrically affect the binding of different drugs to a common target. Ligand binding data from site-directed mutagenesis studies on five G-protein coupled receptors (beta-1 and -2 adrenergic, dopamine D2, angiotensin II and mu-opioid receptor) were collected and analyzed. Binding data from 81 studies for 253 ligands with 447 mutant proteins, including 10 naturally occurring human variants, were analyzed, yielding 1989 mutation-ligand pairs. Fold change in binding affinity for mutant proteins, relative to the wild-type, for different drugs was examined for ligand-specific effects, with a fold-change difference of one or more orders of magnitude between agents considered significant. Of the mutations examined, 49% were associated with ligand-specific effects. One human variant (T164I, beta-2 adrenergic receptor) showed ligand-specific effects for antiasthmatic agents. These results indicate that ligand-specific changes in binding are a possible consequence of missense mutations. This implies that caution needs to be exercised when grouping drugs together during design or interpretation of genotype-phenotype association studies.
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MESH Headings
- Angiotensin Receptor Antagonists/pharmacology
- Genetic Association Studies
- Humans
- Ligands
- Mutagenesis, Site-Directed
- Pharmacogenomic Testing
- Polymorphism, Genetic/genetics
- Receptors, Adrenergic, beta-1/genetics
- Receptors, Adrenergic, beta-2/genetics
- Receptors, Angiotensin/genetics
- Receptors, Dopamine D2/genetics
- Receptors, G-Protein-Coupled/antagonists & inhibitors
- Receptors, G-Protein-Coupled/genetics
- Receptors, Opioid, mu/antagonists & inhibitors
- Receptors, Opioid, mu/genetics
- Signal Transduction/drug effects
- Silent Mutation/genetics
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Affiliation(s)
| | - Ian S Haworth
- Departments of Pharmacology and Pharmaceutical Sciences
| | - Scott A Mosley
- Departments of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, California, USA
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25
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Borczyk M, Piechota M, Rodriguez Parkitna J, Korostynski M. Prospects for personalization of depression treatment with genome sequencing. Br J Pharmacol 2021; 179:4220-4232. [PMID: 33786859 DOI: 10.1111/bph.15470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/22/2021] [Accepted: 03/24/2021] [Indexed: 12/20/2022] Open
Abstract
The effectiveness of antidepressants in the treatment of major depressive disorder varies considerably between patients. With these interindividual differences and a number of antidepressants to choose from, the first choice of treatment often fails to produce improvement in the patient's condition. A substantial part of the variation in response to antidepressants can be explained by genetic factors. Accordingly, variants related to drug metabolism in two pharmacogenes, CYP2D6 and CYP2C19, have already been translated into guidelines for antidepressant prescriptions. The role of variants in other genes that influence antidepressant responses is not yet understood. Furthermore, rare and individual variants account for a substantial part of genetic differences in antidepressant efficacy. Recent years have brought a tremendous increase in the accessibility of genome sequencing in terms of data availability and its clinical use. In this review, we summarize recent developments and current issues in the personalization of major depressive disorder treatment through pharmacogenomics.
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Affiliation(s)
- Malgorzata Borczyk
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Marcin Piechota
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Jan Rodriguez Parkitna
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Michal Korostynski
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
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26
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Herzog DP, Pascual Cuadrado D, Treccani G, Jene T, Opitz V, Hasch A, Lutz B, Lieb K, Sillaber I, van der Kooij MA, Tiwari VK, Müller MB. A distinct transcriptional signature of antidepressant response in hippocampal dentate gyrus granule cells. Transl Psychiatry 2021; 11:4. [PMID: 33414410 PMCID: PMC7791134 DOI: 10.1038/s41398-020-01136-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 10/27/2020] [Accepted: 12/01/2020] [Indexed: 11/09/2022] Open
Abstract
Major depressive disorder is the most prevalent mental illness worldwide, still its pharmacological treatment is limited by various challenges, such as the large heterogeneity in treatment response and the lack of insight into the neurobiological pathways underlying this phenomenon. To decode the molecular mechanisms shaping antidepressant response and to distinguish those from general paroxetine effects, we used a previously established approach targeting extremes (i.e., good vs poor responder mice). We focused on the dentate gyrus (DG), a subregion of major interest in the context of antidepressant mechanisms. Transcriptome profiling on micro-dissected DG granule cells was performed to (i) reveal cell-type-specific changes in paroxetine-induced gene expression (paroxetine vs vehicle) and (ii) to identify molecular signatures of treatment response within a cohort of paroxetine-treated animals. We identified 112 differentially expressed genes associated with paroxetine treatment. The extreme group comparison (good vs poor responder) yielded 211 differentially expressed genes. General paroxetine effects could be distinguished from treatment response-associated molecular signatures, with a differential gene expression overlap of only 4.6% (15 genes). Biological pathway enrichment and cluster analyses identified candidate mechanisms associated with good treatment response, e.g., neuropeptide signaling, synaptic transmission, calcium signaling, and regulation of glucocorticoid secretion. Finally, we examined glucocorticoid receptor (GR)-dependent regulation of selected response-associated genes to analyze a hypothesized interplay between GR signaling and good antidepressant treatment response. Among the most promising candidates, we suggest potential targets such as the developmental gene Otx2 or Htr2c for further investigations into antidepressant treatment response in the future.
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Affiliation(s)
- David P. Herzog
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany ,grid.410607.4Focus Program Translational Neurosciences, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Diego Pascual Cuadrado
- grid.410607.4Institute of Physiological Chemistry, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Giulia Treccani
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany ,grid.410607.4Institute of Microscopic Anatomy and Neurobiology, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Tanja Jene
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany ,grid.410607.4Focus Program Translational Neurosciences, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Verena Opitz
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Annika Hasch
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Beat Lutz
- grid.410607.4Focus Program Translational Neurosciences, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany ,grid.410607.4Institute of Physiological Chemistry, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Klaus Lieb
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany ,grid.410607.4Focus Program Translational Neurosciences, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | | | - Michael A. van der Kooij
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany ,grid.410607.4Focus Program Translational Neurosciences, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Vijay K. Tiwari
- grid.5802.f0000 0001 1941 7111Institute of Molecular Biology, Johannes Gutenberg University Mainz, Mainz, Germany ,grid.4777.30000 0004 0374 7521Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University Belfast, Belfast, UK
| | - Marianne B. Müller
- grid.410607.4Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany ,grid.410607.4Focus Program Translational Neurosciences, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
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27
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Morris J, Leung SSY, Bailey ME, Cullen B, Ferguson A, Graham N, Johnston KJA, Lyall DM, Lyall LM, Ward J, Smith DJ, Strawbridge RJ. Exploring the Role of Contactins across Psychological, Psychiatric and Cardiometabolic Traits within UK Biobank. Genes (Basel) 2020; 11:E1326. [PMID: 33182605 PMCID: PMC7697406 DOI: 10.3390/genes11111326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/05/2020] [Accepted: 11/07/2020] [Indexed: 12/17/2022] Open
Abstract
Individuals with severe mental illness have an increased risk of cardiometabolic diseases compared to the general population. Shared risk factors and medication effects explain part of this excess risk; however, there is growing evidence to suggest that shared biology (including genetic variation) is likely to contribute to comorbidity between mental and physical illness. Contactins are a family of genes involved in development of the nervous system and implicated, though genome-wide association studies, in a wide range of psychological, psychiatric and cardiometabolic conditions. Contactins are plausible candidates for shared pathology between mental and physical health. We used data from UK Biobank to systematically assess how genetic variation in contactin genes was associated with a wide range of psychological, psychiatric and cardiometabolic conditions. We also investigated whether associations for cardiometabolic and psychological traits represented the same or distinct signals and how the genetic variation might influence the measured traits. We identified: A novel genetic association between variation in CNTN1 and current smoking; two independent signals in CNTN4 for BMI; and demonstrated that associations between CNTN5 and neuroticism were distinct from those between CNTN5 and blood pressure/HbA1c. There was no evidence that the contactin genes contributed to shared aetiology between physical and mental illness.
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Affiliation(s)
- Julia Morris
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Soddy Sau Yu Leung
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Mark E.S. Bailey
- School of Life Sciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK;
| | - Breda Cullen
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Amy Ferguson
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Nicholas Graham
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Keira J. A. Johnston
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
- School of Life Sciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK;
- Deanery of Molecular, Genetic and Population Health Sciences, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Donald M. Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Laura M. Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Joey Ward
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Daniel J. Smith
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
| | - Rona J. Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 8RZ, UK; (J.M.); (S.S.Y.L.); (B.C.); (A.F.); (N.G.); (K.J.A.J.); (D.M.L.); (L.M.L.); (J.W.); (D.J.S.)
- Health Data Research UK, Glasgow G12 8RZ, UK
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, 171 77 Stockholm, Sweden
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28
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Li D, Choque-Olsson N, Jiao H, Norgren N, Jonsson U, Bölte S, Tammimies K. The influence of common polygenic risk and gene sets on social skills group training response in autism spectrum disorder. NPJ Genom Med 2020; 5:45. [PMID: 33083014 PMCID: PMC7550579 DOI: 10.1038/s41525-020-00152-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022] Open
Abstract
Social skills group training (SSGT) is a frequently used behavioral intervention in autism spectrum disorder (ASD), but the effects are moderate and heterogeneous. Here, we analyzed the effect of polygenic risk score (PRS) and common variants in gene sets on the intervention outcome. Participants from the largest randomized clinical trial of SSGT in ASD to date were selected (N = 188, 99 from SSGT, 89 from standard care) to calculate association between the outcomes in the SSGT trial and PRSs for ASD, attention-deficit hyperactivity disorder (ADHD), and educational attainment. In addition, specific gene sets were selected to evaluate their role on intervention outcomes. Among all participants in the trial, higher PRS for ADHD was associated with significant improvement in the outcome measure, the parental-rated Social Responsiveness Scale. The significant association was due to better outcomes in the standard care group for individuals with higher PRS for ADHD (post-intervention: β = −4.747, P = 0.0129; follow-up: β = −5.309, P = 0.0083). However, when contrasting the SSGT and standard care group, an inferior outcome in the SSGT group was associated with higher ADHD PRS at follow-up (β = 6.67, P = 0.016). Five gene sets within the synaptic category showed a nominal association with reduced response to interventions. We provide preliminary evidence that genetic liability calculated from common variants could influence the intervention outcomes. In the future, larger cohorts should be used to investigate how genetic contribution affects individual response to ASD interventions.
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Affiliation(s)
- Danyang Li
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm County Council, Stockholm, Sweden.,Astrid Lindgren Children's Hospital, Karolinska University Hospital, Region Stockholm, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Nora Choque-Olsson
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm County Council, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Hong Jiao
- Department of Biosciences and Nutrition, Karolinska Institutet, and Clinical Research Centre, Karolinska University Hospital, Huddinge, Sweden
| | - Nina Norgren
- Department of Molecular Biology, National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Umeå University, 901 87 Umeå, Sweden
| | - Ulf Jonsson
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm County Council, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Department of Neuroscience, Child and Adolescent Psychiatry, Uppsala University, Uppsala, Sweden
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm County Council, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Curtin Autism Research Group, School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA Australia
| | - Kristiina Tammimies
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm County Council, Stockholm, Sweden.,Astrid Lindgren Children's Hospital, Karolinska University Hospital, Region Stockholm, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
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29
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Cai N, Choi KW, Fried EI. Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies. Hum Mol Genet 2020; 29:R10-R18. [PMID: 32568380 PMCID: PMC7530517 DOI: 10.1093/hmg/ddaa115] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 02/06/2023] Open
Abstract
With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.
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Affiliation(s)
- Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Karmel W Choi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute, Boston, MA 02142, USA
| | - Eiko I Fried
- Department of Psychology, Leiden University, Leiden 2333 AK, Netherlands
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30
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Shoaib M, Giacopuzzi E, Pain O, Fabbri C, Magri C, Minelli A, Lewis CM, Gennarelli M. Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression. THE PHARMACOGENOMICS JOURNAL 2020; 21:85-93. [PMID: 32943772 DOI: 10.1038/s41397-020-00186-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 08/13/2020] [Accepted: 09/08/2020] [Indexed: 11/09/2022]
Abstract
In clinical practice, an antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy. We predicted the transcriptomic profile of citalopram remitters by performing an in silico transcriptomic-wide association study on STAR*D GWAS data (N = 1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity-map database. Spearman correlation, Pearson correlation, and the Kolmogorov-Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p value for each rank by using a permutation procedure. The drugs with the top ranks were those having a high positive correlation with the expression profiles of remitters and that may have higher chances of efficacy in the tested patients. In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p = 0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second-highest ranked antidepressants, respectively (p = 0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants. The correlation between citalopram remitters' and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements, it can be applicable at the individual level to tailor treatment prescription.
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Affiliation(s)
- Muhammad Shoaib
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.,Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Edoardo Giacopuzzi
- National Institute for Health Research (NIHR), Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.,IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Oliver Pain
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Chiara Fabbri
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Chiara Magri
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK. .,Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK.
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.,IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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31
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Barakat AK, Scholl C, Steffens M, Brandenburg K, Ising M, Lucae S, Holsboer F, Laje G, Kalayda GV, Jaehde U, Stingl JC. Citalopram-induced pathways regulation and tentative treatment-outcome-predicting biomarkers in lymphoblastoid cell lines from depression patients. Transl Psychiatry 2020; 10:210. [PMID: 32612257 PMCID: PMC7329820 DOI: 10.1038/s41398-020-00900-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 06/08/2020] [Accepted: 06/16/2020] [Indexed: 12/17/2022] Open
Abstract
Antidepressant therapy is still associated with delays in symptomatic improvement and low response rates. Incomplete understanding of molecular mechanisms underlying antidepressant effects hampered the identification of objective biomarkers for antidepressant response. In this work, we studied transcriptome-wide expression followed by pathway analysis in lymphoblastoid cell lines (LCLs) derived from 17 patients documented for response to SSRI antidepressants from the Munich Antidepressant Response Signatures (MARS) study upon short-term incubation (24 and 48 h) with citalopram. Candidate transcripts were further validated with qPCR in MARS LCLs from responders (n = 33) vs. non-responders (n = 36) and afterward in an independent cohort of treatment-resistant patients (n = 20) vs. first-line responders (n = 24) from the STAR*D study. In MARS cohort we observed significant associations of GAD1 (glutamate decarboxylase 1; p = 0.045), TBC1D9 (TBC1 Domain Family Member 9; p = 0.014-0.021) and NFIB (nuclear factor I B; p = 0.015-0.025) expression with response status, remission status and improvement in depression scale, respectively. Pathway analysis of citalopram-altered gene expression indicated response-status-dependent transcriptional reactions. Whereas in clinical responders neural function pathways were primarily up- or downregulated after incubation with citalopram, deregulated pathways in non-responders LCLs mainly involved cell adhesion and immune response. Results from the STAR*D study showed a marginal association of treatment-resistant depression with NFIB (p = 0.068) but not with GAD1 (p = 0.23) and TBC1D9 (p = 0.27). Our results propose the existence of distinct pathway regulation mechanisms in responders vs. non-responders and suggest GAD1, TBC1D9, and NFIB as tentative predictors for clinical response, full remission, and improvement in depression scale, respectively, with only a weak overlap in predictors of different therapy outcome phenotypes.
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Affiliation(s)
- Abdul Karim Barakat
- grid.414802.b0000 0000 9599 0422Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany ,grid.10388.320000 0001 2240 3300Department of Clinical Pharmacy, University of Bonn, Bonn, Germany
| | - Catharina Scholl
- grid.414802.b0000 0000 9599 0422Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - Michael Steffens
- grid.414802.b0000 0000 9599 0422Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - Kerstin Brandenburg
- grid.414802.b0000 0000 9599 0422Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - Marcus Ising
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Susanne Lucae
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Florian Holsboer
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Gonzalo Laje
- Washington Behavioral Medicine Associates LLC, Chevy Chase, MD USA
| | - Ganna V. Kalayda
- grid.10388.320000 0001 2240 3300Department of Clinical Pharmacy, University of Bonn, Bonn, Germany
| | - Ulrich Jaehde
- grid.10388.320000 0001 2240 3300Department of Clinical Pharmacy, University of Bonn, Bonn, Germany
| | - Julia Carolin Stingl
- Institute of Clinical Pharmacology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
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32
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Genetic stratification of depression in UK Biobank. Transl Psychiatry 2020; 10:163. [PMID: 32448866 PMCID: PMC7246256 DOI: 10.1038/s41398-020-0848-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/28/2020] [Accepted: 04/30/2020] [Indexed: 11/18/2022] Open
Abstract
Depression is a common and clinically heterogeneous mental health disorder that is frequently comorbid with other diseases and conditions. Stratification of depression may align sub-diagnoses more closely with their underling aetiology and provide more tractable targets for research and effective treatment. In the current study, we investigated whether genetic data could be used to identify subgroups within people with depression using the UK Biobank. Examination of cross-locus correlations were used to test for evidence of subgroups using genetic data from seven other complex traits and disorders that were genetically correlated with depression and had sufficient power (>0.6) for detection. We found no evidence for subgroups within depression for schizophrenia, bipolar disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, anorexia nervosa, inflammatory bowel disease or obesity. This suggests that for these traits, genetic correlations with depression were driven by pleiotropic genetic variants carried by everyone rather than by a specific subgroup.
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33
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Fu CHY, Fan Y, Davatzikos C. Widespread Morphometric Abnormalities in Major Depression: Neuroplasticity and Potential for Biomarker Development. Neuroimaging Clin N Am 2020; 30:85-95. [PMID: 31759575 PMCID: PMC7106506 DOI: 10.1016/j.nic.2019.09.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Major depression is common and debilitating. Identifying neurobiological subtypes that comprise the disorder and predict clinical outcome are key challenges. Genetic and environmental factors leading to major depression are expressed in neural structure and function. Volumetric decreases in gray matter have been demonstrated in corticolimbic circuits involved in emotion regulation. MR imaging observable abnormalities reflect cytoarchitectonic alterations within a local neuroendocrine milieu with systemic effects. Multivariate pattern analysis offers the potential to identify the neurobiological subtypes and predictors of clinical outcome. It is essential to characterize disease heterogeneity by incorporating data-driven inductive and symptom-based deductive approaches in an iterative process.
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Affiliation(s)
- Cynthia H Y Fu
- School of Psychology, University of East London, Arthur Edwards Building, Water Lane, London E15 4LZ, UK; Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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34
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Corponi F, Fabbri C, Serretti A. Pharmacogenetics and Depression: A Critical Perspective. Psychiatry Investig 2019; 16:645-653. [PMID: 31455064 PMCID: PMC6761796 DOI: 10.30773/pi.2019.06.16] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 06/16/2019] [Indexed: 12/17/2022] Open
Abstract
Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.
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Affiliation(s)
- Filippo Corponi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Chiara Fabbri
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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35
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Kessler RC, Bossarte RM, Luedtke A, Zaslavsky AM, Zubizarreta JR. Machine learning methods for developing precision treatment rules with observational data. Behav Res Ther 2019; 120:103412. [PMID: 31233922 DOI: 10.1016/j.brat.2019.103412] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 05/15/2019] [Accepted: 05/26/2019] [Indexed: 12/28/2022]
Abstract
Clinical trials have identified a variety of predictor variables for use in precision treatment protocols, ranging from clinical biomarkers and symptom profiles to self-report measures of various sorts. Although such variables are informative collectively, none has proven sufficiently powerful to guide optimal treatment selection individually. This has prompted growing interest in the development of composite precision treatment rules (PTRs) that are constructed by combining information across a range of predictors. But this work has been hampered by the generally small samples in randomized clinical trials and the use of suboptimal analysis methods to analyze the resulting data. In this paper, we propose to address the sample size problem by: working with large observational electronic medical record databases rather than controlled clinical trials to develop preliminary PTRs; validating these preliminary PTRs in subsequent pragmatic trials; and using ensemble machine learning methods rather than individual algorithms to carry out statistical analyses to develop the PTRs. The major challenges in this proposed approach are that treatment are not randomly assigned in observational databases and that these databases often lack measures of key prescriptive predictors and mental disorder treatment outcomes. We proposed a tiered case-cohort design approach that uses innovative methods for measuring and balancing baseline covariates and estimating PTRs to address these challenges.
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Affiliation(s)
- Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
| | - Robert M Bossarte
- West Virginia University Injury Control Research Center, Morgantown, WV, USA; Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA; VISN 2 Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Alan M Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Jose R Zubizarreta
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA; Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
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36
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Rayner C, Coleman JRI, Purves KL, Hodsoll J, Goldsmith K, Alpers GW, Andersson E, Arolt V, Boberg J, Bögels S, Creswell C, Cooper P, Curtis C, Deckert J, Domschke K, El Alaoui S, Fehm L, Fydrich T, Gerlach AL, Grocholewski A, Hahlweg K, Hamm A, Hedman E, Heiervang ER, Hudson JL, Jöhren P, Keers R, Kircher T, Lang T, Lavebratt C, Lee SH, Lester KJ, Lindefors N, Margraf J, Nauta M, Pané-Farré CA, Pauli P, Rapee RM, Reif A, Rief W, Roberts S, Schalling M, Schneider S, Silverman WK, Ströhle A, Teismann T, Thastum M, Wannemüller A, Weber H, Wittchen HU, Wolf C, Rück C, Breen G, Eley TC. A genome-wide association meta-analysis of prognostic outcomes following cognitive behavioural therapy in individuals with anxiety and depressive disorders. Transl Psychiatry 2019; 9:150. [PMID: 31123309 PMCID: PMC6533285 DOI: 10.1038/s41398-019-0481-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 03/01/2019] [Accepted: 03/23/2019] [Indexed: 01/04/2023] Open
Abstract
Major depressive disorder and the anxiety disorders are highly prevalent, disabling and moderately heritable. Depression and anxiety are also highly comorbid and have a strong genetic correlation (rg ≈ 1). Cognitive behavioural therapy is a leading evidence-based treatment but has variable outcomes. Currently, there are no strong predictors of outcome. Therapygenetics research aims to identify genetic predictors of prognosis following therapy. We performed genome-wide association meta-analyses of symptoms following cognitive behavioural therapy in adults with anxiety disorders (n = 972), adults with major depressive disorder (n = 832) and children with anxiety disorders (n = 920; meta-analysis n = 2724). We estimated the variance in therapy outcomes that could be explained by common genetic variants (h2SNP) and polygenic scoring was used to examine genetic associations between therapy outcomes and psychopathology, personality and learning. No single nucleotide polymorphisms were strongly associated with treatment outcomes. No significant estimate of h2SNP could be obtained, suggesting the heritability of therapy outcome is smaller than our analysis was powered to detect. Polygenic scoring failed to detect genetic overlap between therapy outcome and psychopathology, personality or learning. This study is the largest therapygenetics study to date. Results are consistent with previous, similarly powered genome-wide association studies of complex traits.
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Affiliation(s)
- Christopher Rayner
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jonathan R I Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Trust, NIHR Biomedical Research Centre for Mental Health, London, UK
| | - Kirstin L Purves
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John Hodsoll
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Kimberley Goldsmith
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Georg W Alpers
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Evelyn Andersson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Volker Arolt
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Julia Boberg
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Susan Bögels
- Research Institute Child Development and Education, University of Amsterdam, Amsterdam, The Netherlands
| | - Cathy Creswell
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
| | - Peter Cooper
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
| | - Charles Curtis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Trust, NIHR Biomedical Research Centre for Mental Health, London, UK
| | - Jürgen Deckert
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University of Würzburg, Würzburg, 97078, Germany
| | - Katharina Domschke
- Faculty of Medicine, Department of Psychiatry and Psychotherapy, Medical Center, University of Freiburg, Freiburg, Germany
- Center for NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Samir El Alaoui
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Lydia Fehm
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Thomas Fydrich
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Alexander L Gerlach
- Clinical Psychology and Psychotherapy, University of Cologne, Cologne, Germany
| | - Anja Grocholewski
- Department of Psychology, University of Braunschweig, Braunschweig, Germany
| | - Kurt Hahlweg
- Department of Psychology, University of Braunschweig, Braunschweig, Germany
| | - Alfons Hamm
- Department of Biological and Clinical Psychology, University of Greifswald, Greifswald, Germany
| | - Erik Hedman
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Einar R Heiervang
- Division of Mental Health and Addiction, Department of Child and Adolescent Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Jennifer L Hudson
- Centre for Emotional Health, Department of Psychology, Macquarie University, Sydney, Australia
| | - Peter Jöhren
- Mental Health Research and Treatment Center, Ruhr-Universität Bochum, Bochum, Germany
| | - Robert Keers
- Department of Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Thomas Lang
- Christoph-Dornier-Stiftung für Klinische Psychologie, Institut für Klinische Psychologie und Psychotherapie, Bremen, Germany
| | - Catharina Lavebratt
- Neurogenetics Unit, Center for Molecular Medicine, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Sang-Hyuck Lee
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Trust, NIHR Biomedical Research Centre for Mental Health, London, UK
| | - Kathryn J Lester
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Psychology, University of Sussex, Brighton, UK
| | - Nils Lindefors
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Jürgen Margraf
- Mental Health Research and Treatment Center, Ruhr-Universität Bochum, Bochum, Germany
| | - Maaike Nauta
- Department of Clinical Psychology and Experimental Psychopathology, University of Groningen, Groningen, The Netherlands
| | - Christiane A Pané-Farré
- Department of Biological and Clinical Psychology, University of Greifswald, Greifswald, Germany
| | - Paul Pauli
- Department of Psychology (Biological Psychology, Clinical Psychology, and Psychotherapy), University of Würzburg, Würzburg, Germany
| | - Ronald M Rapee
- Centre for Emotional Health, Department of Psychology, Macquarie University, Sydney, Australia
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Winfried Rief
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Susanna Roberts
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Martin Schalling
- Neurogenetics Unit, Center for Molecular Medicine, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Silvia Schneider
- Mental Health Research and Treatment Center, Ruhr-Universität Bochum, Bochum, Germany
| | - Wendy K Silverman
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Andreas Ströhle
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Teismann
- Mental Health Research and Treatment Center, Ruhr-Universität Bochum, Bochum, Germany
| | - Mikael Thastum
- Department of Psychology and Behavioural Sciences, Aarhus University, Aarhus, Denmark
| | - Andre Wannemüller
- Mental Health Research and Treatment Center, Ruhr-Universität Bochum, Bochum, Germany
- Dental Clinic Bochum, Bochum, Germany
| | - Heike Weber
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University of Würzburg, Würzburg, 97078, Germany
| | - Hans-Ulrich Wittchen
- Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Christiane Wolf
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University of Würzburg, Würzburg, 97078, Germany
| | - Christian Rück
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- South London and Maudsley NHS Trust, NIHR Biomedical Research Centre for Mental Health, London, UK.
| | - Thalia C Eley
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- South London and Maudsley NHS Trust, NIHR Biomedical Research Centre for Mental Health, London, UK.
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McIntosh AM, Sullivan PF, Lewis CM. Uncovering the Genetic Architecture of Major Depression. Neuron 2019; 102:91-103. [PMID: 30946830 PMCID: PMC6482287 DOI: 10.1016/j.neuron.2019.03.022] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 03/05/2019] [Accepted: 03/14/2019] [Indexed: 12/21/2022]
Abstract
There have been several recent studies addressing the genetic architecture of depression. This review serves to take stock of what is known now about the genetics of depression, how it has increased our knowledge and understanding of its mechanisms, and how the information and knowledge can be leveraged to improve the care of people affected. We identify four priorities for how the field of MD genetics research may move forward in future years, namely by increasing the sample sizes available for genome-wide association studies (GWASs), greater inclusion of diverse ancestries and low-income countries, the closer integration of psychiatric genetics with electronic medical records, and the development of the neuroscience toolkit for polygenic disorders.
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Affiliation(s)
- Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
| | - Patrick F Sullivan
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK; Department of Medical and Molecular Genetics, King's College London, London UK
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