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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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Baldinger-Melich P, Spies M, Bozic I, Kasper S, Rujescu D, Frey R. Perspectives in treatment-resistant depression: esketamine and electroconvulsive therapy. Wien Klin Wochenschr 2024:10.1007/s00508-024-02358-w. [PMID: 38662240 DOI: 10.1007/s00508-024-02358-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/23/2024] [Indexed: 04/26/2024]
Abstract
Modern electroconvulsive therapy (ECT) and the approval of nasal esketamine for clinical use have significantly improved the approach to treatment-resistant depression (TRD), which is defined as non-response to at least two different courses of antidepressants with verified adherence to treatment, adequate dosage, and duration of treatment. The goal of this literature review is to present the newest evidence regarding efficacy and safety. Furthermore, we aim to provide an overview of future perspectives in this field of research, for example, regarding structural and molecular effects. Both treatment methods will be critically evaluated for their individual advantages, disadvantages, and response rates. Firstly, we will discuss the well-established method of ECT and its different treatment modalities. Secondly, we will discuss the properties of ketamine, the discovery of its antidepressive effects and the route to clinical approval of the esketamine nasal spray. We will comment on research settings which have evaluated intravenous ketamine against ECT. The decision-making process between esketamine nasal spray or ECT should include the assessment of contraindications, age, severity of disease, presence of psychotic symptoms, patient preference and treatment accessibility. We conclude that both treatment options are highly effective in TRD. If both are indicated, pragmatically esketamine will be chosen before ECT; however, ECT studies in ketamine non-responders are missing.
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Affiliation(s)
- Pia Baldinger-Melich
- Department of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Vienna, Austria
| | - Marie Spies
- Department of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Vienna, Austria
| | - Ina Bozic
- Department of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Vienna, Austria
| | - Siegfried Kasper
- Department of Molecular Neurosciences, Center for Brain Research, Vienna, Austria
| | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Vienna, Austria
| | - Richard Frey
- Department of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University Vienna, Vienna, Austria.
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Vienna, Austria.
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Orrico-Sanchez A, Guiard BP, Manta S, Callebert J, Launay JM, Louis F, Paccard A, Gruszczynski C, Betancur C, Vialou V, Gautron S. Organic cation transporter 2 contributes to SSRI antidepressant efficacy by controlling tryptophan availability in the brain. Transl Psychiatry 2023; 13:302. [PMID: 37775532 PMCID: PMC10542329 DOI: 10.1038/s41398-023-02596-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 09/15/2023] [Indexed: 10/01/2023] Open
Abstract
Selective serotonin reuptake inhibitors (SSRI) are common first-line treatments for major depression. However, a significant number of depressed patients do not respond adequately to these pharmacological treatments. In the present preclinical study, we demonstrate that organic cation transporter 2 (OCT2), an atypical monoamine transporter, contributes to the effects of SSRI by regulating the routing of the essential amino acid tryptophan to the brain. Contrarily to wild-type mice, OCT2-invalidated mice failed to respond to prolonged fluoxetine treatment in a chronic depression model induced by corticosterone exposure recapitulating core symptoms of depression, i.e., anhedonia, social withdrawal, anxiety, and memory impairment. After corticosterone and fluoxetine treatment, the levels of tryptophan and its metabolites serotonin and kynurenine were decreased in the brain of OCT2 mutant mice compared to wild-type mice and reciprocally tryptophan and kynurenine levels were increased in mutants' plasma. OCT2 was detected by immunofluorescence in several structures at the blood-cerebrospinal fluid (CSF) or brain-CSF interface. Tryptophan supplementation during fluoxetine treatment increased brain concentrations of tryptophan and, more discreetly, of 5-HT in wild-type and OCT2 mutant mice. Importantly, tryptophan supplementation improved the sensitivity to fluoxetine treatment of OCT2 mutant mice, impacting chiefly anhedonia and short-term memory. Western blot analysis showed that glycogen synthase kinase-3β (GSK3β) and mammalian/mechanistic target of rapamycin (mTOR) intracellular signaling was impaired in OCT2 mutant mice brain after corticosterone and fluoxetine treatment and, conversely, tryptophan supplementation recruited selectively the mTOR protein complex 2. This study provides the first evidence of the physiological relevance of OCT2-mediated tryptophan transport, and its biological consequences on serotonin homeostasis in the brain and SSRI efficacy.
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Affiliation(s)
| | - Bruno P Guiard
- Université Paul Sabatier, CNRS, Research Center on Animal Cognition, Toulouse, France
| | - Stella Manta
- Université Paul Sabatier, CNRS, Research Center on Animal Cognition, Toulouse, France
| | - Jacques Callebert
- Sorbonne Paris Cité, Hôpital Lariboisière, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Jean-Marie Launay
- Sorbonne Paris Cité, Hôpital Lariboisière, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Franck Louis
- Sorbonne Université, INSERM, CNRS, Neuroscience Paris Seine, Paris, France
| | - Antoine Paccard
- Sorbonne Université, INSERM, CNRS, Neuroscience Paris Seine, Paris, France
| | | | - Catalina Betancur
- Sorbonne Université, INSERM, CNRS, Neuroscience Paris Seine, Paris, France
| | - Vincent Vialou
- Sorbonne Université, INSERM, CNRS, Neuroscience Paris Seine, Paris, France.
| | - Sophie Gautron
- Sorbonne Université, INSERM, CNRS, Neuroscience Paris Seine, Paris, France.
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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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5
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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Genetics of antidepressant response and treatment-resistant depression. PROGRESS IN BRAIN RESEARCH 2023. [DOI: 10.1016/bs.pbr.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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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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8
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Zheng Y, Zhang L, He S, Xie Z, Zhang J, Ge C, Sun G, Huang J, Li H. Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study. BMJ Open 2022; 12:e067447. [PMID: 36418119 PMCID: PMC9685190 DOI: 10.1136/bmjopen-2022-067447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes. METHODS AND ANALYSIS iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants' responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase. ETHICS AND DISSEMINATION The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT04518592.
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Affiliation(s)
- Yuzhen Zheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linna Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shen He
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zuoquan Xie
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Jing Zhang
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Changrong Ge
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Guangqiang Sun
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Jingjing Huang
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center for Mental Health, Shanghai Mental Health Center, Shanghai, China
| | - Huafang Li
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center for Mental Health, Shanghai Mental Health Center, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Bobo WV, Van Ommeren B, Athreya AP. Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder. Expert Rev Clin Pharmacol 2022; 15:927-944. [DOI: 10.1080/17512433.2022.2112949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- William V. Bobo
- Department of Psychiatry & Psychology, Mayo Clinic Florida, Jacksonville, FL, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN & Jacksonville, FL, USA
| | | | - Arjun P. Athreya
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
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10
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Fabbri C. Genetics in psychiatry: Methods, clinical applications and future perspectives. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e6. [PMID: 38868637 PMCID: PMC11114394 DOI: 10.1002/pcn5.6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/18/2022] [Accepted: 03/02/2022] [Indexed: 06/14/2024]
Abstract
Psychiatric disorders and related traits have a demonstrated genetic component, with heritability estimated by twin studies generally between 80% and 40%. Their pathogenesis is complex and multi-determined: environmental factors interact with a polygenic architecture, making difficult the development of models able to stratify patients or predict mental health outcomes. Despite this difficult challenge, relevant progress has been made in the field of psychiatric genetics in recent years. This review aims to present the main current methods in psychiatric genetics, their output, limitations, clinical applications, and possible future developments. Genome-wide association studies (GWASs) performed in increasingly large samples have led to the identification of replicated genetic loci associated with the risk of major psychiatric disorders, including schizophrenia and mood disorders. Statistical and biological approaches have been developed to improve our understanding of the etiopathogenetic mechanisms behind genome-wide significant associations, as well as for estimating the cumulative effect of risk variants at the individual level and the genetic overlap between different disorders, as pleiotropy is the rule rather than the exception. Clinical applications are available in the pharmacogenetics field. The main issues that remain to be addressed include improving ethnic diversity in genetic studies and the optimization of statistical power through methodological improvements, such as the definition of dimensional phenotypes with specific biological correlates and the integration of different types of omics data.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and Neuromotor SciencesUniversity of BolognaBolognaItaly
- Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK
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11
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Zhao M, Ma J, Li M, Zhu W, Zhou W, Shen L, Wu H, Zhang N, Wu S, Fu C, Li X, Yang K, Tang T, Shen R, He L, Huai C, Qin S. Different responses to risperidone treatment in Schizophrenia: a multicenter genome-wide association and whole exome sequencing joint study. Transl Psychiatry 2022; 12:173. [PMID: 35484098 PMCID: PMC9050705 DOI: 10.1038/s41398-022-01942-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 12/11/2022] Open
Abstract
Risperidone is routinely used in the clinical management of schizophrenia, but the treatment response is highly variable among different patients. The genetic underpinnings of the treatment response are not well understood. We performed a pharmacogenomic study of the treatment response to risperidone in patients with schizophrenia by using a SNP microarray -based genome-wide association study (GWAS) and whole exome sequencing (WES)-based GWAS. DNA samples were collected from 189 patients for the GWAS and from 222 patients for the WES after quality control in multiple centers of China. Antipsychotic response phenotypes of patients who received eight weeks of risperidone treatment were quantified with percentage change on the Positive and Negative Syndrome Scale (PANSS). The GWAS revealed a significant association between several SNPs and treatment response, such as three GRM7 SNPs (rs141134664, rs57521140, and rs73809055). Gene-based analysis in WES revealed 13 genes that were associated with antipsychotic response, such as GPR12 and MAP2K3. We did not identify shared loci or genes between GWAS and WES, but association signals tended to cluster into the GPCR gene family and GPCR signaling pathway, which may play an important role in the treatment response etiology. This study may provide a research paradigm for pharmacogenomic research, and these data provide a promising illustration of our potential to identify genetic variants underlying antipsychotic responses and may ultimately facilitate precision medicine in schizophrenia.
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Affiliation(s)
- Mingzhe Zhao
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Jingsong Ma
- grid.494629.40000 0004 8008 9315School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang Province China ,grid.494629.40000 0004 8008 9315Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang Province China
| | - Mo Li
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Wenli Zhu
- The Fourth People’s Hospital of Wuhu, No.1 East Wuxiashan Road, Wuhu, 241003 China
| | - Wei Zhou
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Lu Shen
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Hao Wu
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Na Zhang
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Shaochang Wu
- The Second People’s Hospital of Lishui, No.69 Beihua Road, Lishui, 323020 China
| | - Chunpeng Fu
- The Third People’s Hospital of Shangrao, No.1 Fenghuang East Avenue, Taokan Road, Shangrao, 334000 China
| | - Xianxi Li
- Shanghai Yangpu district mental health center, No.585 Jungong Road, Yangpu District, Shanghai, 900093 China
| | - Ke Yang
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Tiancheng Tang
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Ruoxi Shen
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Lin He
- grid.16821.3c0000 0004 0368 8293Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030 China ,grid.16821.3c0000 0004 0368 8293School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Cong Huai
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China. .,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Shengying Qin
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China. .,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
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12
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Serretti A. Precision medicine in mood disorders. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e1. [PMID: 38868801 PMCID: PMC11114272 DOI: 10.1002/pcn5.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/09/2021] [Accepted: 12/05/2021] [Indexed: 06/14/2024]
Abstract
The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome-wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.
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Affiliation(s)
- Alessandro Serretti
- Department of Biomedical and NeuroMotor SciencesUniversity of BolognaBolognaItaly
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13
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Fabbri C. An interview with Dr Chiara Fabbri: pharmacogenomics and drug repurposing for treatment-resistant depression. Pharmacogenomics 2021; 22:1107-1109. [PMID: 34761695 DOI: 10.2217/pgs-2021-0134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Chiara Fabbri
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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14
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Kendall KM, Van Assche E, Andlauer TFM, Choi KW, Luykx JJ, Schulte EC, Lu Y. The genetic basis of major depression. Psychol Med 2021; 51:2217-2230. [PMID: 33682643 DOI: 10.1017/s0033291721000441] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene-environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care.
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Affiliation(s)
- K M Kendall
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - E Van Assche
- Department of Psychiatry, University of Muenster, Muenster, Germany
| | - T F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - K W Choi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA02114, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA02114, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA02115, USA
| | - J J Luykx
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Outpatient Second Opinion Clinic, GGNet Mental Health, Warnsveld, The Netherlands
| | - E C Schulte
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Y Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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15
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Sforzini L. Lost in translation. The quest for definitions of treatment-resistant depression with a focus on inflammation-related gene expression. Brain Behav Immun Health 2021; 16:100331. [PMID: 34514443 PMCID: PMC7611643 DOI: 10.1016/j.bbih.2021.100331] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Approximately one third of individuals with major depressive disorder (MDD) do not respond to antidepressant treatments; but what does treatment-resistant depression (TRD) mean? With this article, I aim to provide an overview of the clinical and operational criteria currently used to define TRD, highlighting core gaps in knowledge and open questions to be addressed in order to drive future research in the field. Importantly, a better definition of TRD must include a better characterization of the biological and molecular correlates of non-response. Among these potential biomarkers, compelling evidence reveals a potential role of inflammation-related gene expression signatures. A more accurate clinical and etiopathological characterization of TRD subjects may help to identify biologically based MDD clinical phenotypes to be targeted in future research and finally achieve better outcomes.
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Affiliation(s)
- Luca Sforzini
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
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16
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Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing. Neuropsychopharmacology 2021; 46:1821-1829. [PMID: 34158615 PMCID: PMC8357803 DOI: 10.1038/s41386-021-01059-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/19/2021] [Accepted: 06/03/2021] [Indexed: 12/17/2022]
Abstract
Major depressive disorder (MDD) is the single largest contributor to global disability and up to 20-30% of patients do not respond to at least two antidepressants (treatment-resistant depression, TRD). This study leveraged imputed gene expression in TRD to perform a drug repurposing analysis. Among those with MDD, we defined TRD as having at least two antidepressant switches according to primary care records in UK Biobank (UKB). We performed a transcriptome-wide association study (TWAS) of TRD (n = 2165) vs healthy controls (n = 11,188) using FUSION and gene expression levels from 21 tissues. We identified compounds with opposite gene expression signatures (ConnectivityMap data) compared to our TWAS results using the Kolmogorov-Smirnov test, Spearman and Pearson correlation. As symptom patterns are routinely assessed in clinical practice and could be used to provide targeted treatments, we identified MDD subtypes associated with TRD in UKB and analysed them using the same pipeline described for TRD. Anxious MDD (n = 14,954) and MDD with weight gain (n = 4697) were associated with TRD. In the TWAS, two genes were significantly dysregulated (TMEM106B and ATP2A1 for anxious and weight gain MDD, respectively). A muscarinic receptor antagonist was identified as top candidate for repurposing in TRD; inhibition of heat shock protein 90 was the main mechanism of action identified for anxious MDD, while modulators of metabolism such as troglitazone showed promising results for MDD with weight gain. This was the first TWAS of TRD and associated MDD subtypes. Our results shed light on possible pharmacological approaches in individuals with difficult-to-treat depression.
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17
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Shalimova A, Babasieva V, Chubarev VN, Tarasov VV, Schiöth HB, Mwinyi J. Therapy response prediction in major depressive disorder: current and novel genomic markers influencing pharmacokinetics and pharmacodynamics. Pharmacogenomics 2021; 22:485-503. [PMID: 34018822 DOI: 10.2217/pgs-2020-0157] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Major depressive disorder is connected with high rates of functional disability and mortality. About a third of the patients are at risk of therapy failure. Several pharmacogenetic markers especially located in CYP450 genes such as CYP2D6 or CYP2C19 are of relevance for therapy outcome prediction in major depressive disorder but a further optimization of predictive tools is warranted. The article summarizes the current knowledge on pharmacogenetic variants, therapy effects and side effects of important antidepressive therapeutics, and sheds light on new methodological approaches for therapy response estimation based on genetic markers with relevance for pharmacokinetics, pharmacodynamics and disease pathology identified in genome-wide association study analyses, highlighting polygenic risk score analysis as a tool for further optimization of individualized therapy outcome prediction.
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Affiliation(s)
- Alena Shalimova
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, 751 24, Sweden.,Department of Pharmacology, Institute of Pharmacy, I. M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Viktoria Babasieva
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, 751 24, Sweden.,Department of Pharmacology, Institute of Pharmacy, I. M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Vladimir N Chubarev
- Department of Pharmacology, Institute of Pharmacy, I. M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Vadim V Tarasov
- Department of Pharmacology, Institute of Pharmacy, I. M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia.,Institute of Translational Medicine & Biotechnology, I. M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Helgi B Schiöth
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, 751 24, Sweden.,Institute of Translational Medicine & Biotechnology, I. M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Jessica Mwinyi
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, 751 24, Sweden
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18
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Wendt FR, Koller D, Pathak GA, Jacoby D, Miller EJ, Polimanti R. Biobank Scale Pharmacogenomics Informs the Genetic Underpinnings of Simvastatin Use. Clin Pharmacol Ther 2021; 110:777-785. [PMID: 33837531 DOI: 10.1002/cpt.2260] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/01/2021] [Indexed: 12/31/2022]
Abstract
Studying drug-metabolizing enzymes, encoded by pharmacogenes, may inform biological mechanisms underlying the diseases for which a medication is prescribed. Until recently, pharmacogenes could not be studied at biobank scale. In 7,649 unrelated African-ancestry (AFR) and 326,214 unrelated European-ancestry (EUR) participants from the UK Biobank, we associated pharmacogene haplotypes from 50 genes with 265 (EUR) and 17 (AFR) medication use phenotypes using generalized linear models. In EUR, N-acetyltransferase 2 (NAT2) metabolizer phenotype and activity score were associated with simvastatin use. The dose of NAT2*1 was associated with simvastatin use when compared with NAT2*5 (the most common haplotype). This association was robust to effects of low-density lipoprotein cholesterol (LDL-C) concentration (NAT2*1 odds ratio (OR) = 1.07, 95% CI: 1.05-1.09, P = 1.14 × 10-8 ) and polygenic risk for LDL-C concentration (NAT2*1 OR = 1.09, 95% CI: 1.04-1.14, P = 2.26 × 10-4 ). Interactive effects between NAT2*1 and simvastatin use on LDL-C concentration (OR = 0.957, 95% CI: 0.916-0.998, P = 0.045) were replicated in the electronic Medical Records and Genomics Pharmacogenetic Sequencing Pilot (eMERGE-PGx) cohort (OR = 0.987, 95% CI: 0.976-0.998, P = 0.029). We used biobank-scale data to uncover and replicate an association between NAT2 locus variation and better response to statin therapy. Testing NAT2 alleles may be useful for making clinical decisions regarding the potential benefit (e.g., absolute risk reduction) in LDL-C concentration prior to statin treatment.
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Affiliation(s)
- Frank R Wendt
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Dora Koller
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Gita A Pathak
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Daniel Jacoby
- Section of Cardiovascular Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Edward J Miller
- Section of Cardiovascular Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Renato Polimanti
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
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19
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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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20
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Fabbri C, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, Serretti A, Lewis CM. Cost-effectiveness of genetic and clinical predictors for choosing combined psychotherapy and pharmacotherapy in major depression. J Affect Disord 2021; 279:722-729. [PMID: 33217644 DOI: 10.1016/j.jad.2020.10.049] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 09/09/2020] [Accepted: 10/25/2020] [Indexed: 01/02/2023]
Abstract
BACKGROUND Predictors of treatment outcome in major depressive disorder (MDD) could contribute to evidence-based therapeutic choices. Combined pharmacotherapy and psychotherapy show increased efficacy but higher cost compared with antidepressant pharmacotherapy; baseline predictors of pharmacotherapy resistance could be used to identify patients more likely to benefit from combined treatment. METHODS We performed a proof-of-principle study of the cost-effectiveness of using previously identified pharmacogenetic and clinical risk factors (PGx-CL-R) of antidepressant resistance or clinical risk factors alone (CL-R) to guide the prescription of combined pharmacotherapy and psychotherapy vs pharmacotherapy. The cost-effectiveness of these two strategies was compared with standard care (ST, pharmacotherapy to all subjects) using a three-year Markov model. Model parameters were literature-based estimates of response to pharmacotherapy and combined treatment, costs (UK National Health System) and benefits (quality-adjusted life years [QALYs], one QALY=one year lived in perfect health). RESULTS CL-R was more cost-effective than PGx-CL-R: the cost of one-QALY improvement was £2341 for CL-R and £3937 for PGx-CL-R compared to ST. PGx-CL-R had similar or better cost-effectiveness compared to CL-R when 1) the cost of genotyping was £100 per subject or less or 2) the PGx-CL-R test had sensitivity ≥ 0.90 and specificity ≥ 0.85. The cost of one-QALY improvement for CL-R was £3664 and of £4110 in two independent samples. LIMITATIONS lack of validation in large samples from the general population. CONCLUSIONS Using clinical risk factors to predict pharmacotherapy resistance and guide the prescription of pharmacotherapy combined with psychotherapy could be a cost-effective strategy.
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Affiliation(s)
- Chiara Fabbri
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University Vienna, Austria
| | - Joseph Zohar
- Department of Psychiatry, Sheba Medical Center, Tel Hashomer, and Sackler School of Medicine, Tel Aviv University, Israel
| | - Daniel Souery
- Laboratoire de Psychologie Medicale, Universitè Libre de Bruxelles and Psy Pluriel, Centre Européen de Psychologie Medicale, Brussels
| | | | - Diego Albani
- Laboratory of Biology of Neurodegenerative Disorders, Neuroscience Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Gianluigi Forloni
- Laboratory of Biology of Neurodegenerative Disorders, Neuroscience Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 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, Italy
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
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21
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Drug repositioning for treatment-resistant depression: Hypotheses from a pharmacogenomic study. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:110050. [PMID: 32738352 DOI: 10.1016/j.pnpbp.2020.110050] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/20/2020] [Accepted: 07/23/2020] [Indexed: 02/06/2023]
Abstract
About 20-30% of patients with major depressive disorder (MDD) develop treatment-resistant depression (TRD) and finding new effective treatments for TRD has been a challenge. This study aimed to identify new possible pharmacological options for TRD. Genes in pathways included in predictive models of TRD in a previous whole exome sequence study were compared with those coding for targets of drugs in any phase of development, nutraceuticals, proteins and peptides from Drug repurposing Hub, Drug-Gene Interaction database and DrugBank database. We tested if known gene targets were enriched in TRD-associated genes by a hypergeometric test. Compounds enriched in TRD-associated genes after false-discovery rate (FDR) correction were annotated and compared with those showing enrichment in genes associated with MDD in the last Psychiatric Genomics Consortium genome-wide association study. Among a total of 15,475 compounds, 542 were enriched in TRD-associated genes (FDR p < .05). Significant results included drugs which are currently used in TRD (e.g. lithium and ketamine), confirming the rationale of this approach. Interesting molecules included modulators of inflammation, renin-angiotensin system, proliferator-activated receptor agonists, glycogen synthase kinase 3 beta inhibitors and the rho associated kinase inhibitor fasudil. Nutraceuticals, mostly antioxidant polyphenols, were also identified. Drugs showing enrichment for TRD-associated genes had a higher probability of enrichment for MDD-associated genes compared to those having no TRD-genes enrichment (p = 6.21e-55). This study suggested new potential treatments for TRD using a in silico approach. These analyses are exploratory only but can contribute to the identification of drugs to study in future clinical trials.
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22
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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: 74] [Impact Index Per Article: 18.5] [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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23
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van Schaik RHN, Müller DJ, Serretti A, Ingelman-Sundberg M. Pharmacogenetics in Psychiatry: An Update on Clinical Usability. Front Pharmacol 2020; 11:575540. [PMID: 33041820 PMCID: PMC7518035 DOI: 10.3389/fphar.2020.575540] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 08/25/2020] [Indexed: 12/15/2022] Open
Abstract
Using pharmacogenetics in guiding drug therapy experiences a steady increase in uptake, although still leads to discussions as to its clinical use. Psychiatry constitutes a field where pharmacogenomic testing might help in guiding drug therapy. To address current challenges, this minireview provides an update regarding genotyping (SNP analysis/arrays/NGS), structural variant detection (star-alleles/CNVs/hybrid alleles), genotype-to-phenotype translations, cost-effectiveness, and actionability of results (FDA/CPIC/PharmGKB) regarding clinical importance of pre-emptive pharmacogenomic testing for prescription of antidepressants and antipsychotics.
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Affiliation(s)
- Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus MC, University Medical Center, Rotterdam, Netherlands
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Magnus Ingelman-Sundberg
- Pharmacogenetics Section, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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24
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Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint. J Hum Genet 2020; 66:53-60. [PMID: 32770057 DOI: 10.1038/s10038-020-0814-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/21/2020] [Accepted: 07/19/2020] [Indexed: 01/06/2023]
Abstract
Genome-wide association studies (GWASs) have detected many susceptible variants for common diseases, including psychiatric disorders. However, because of the small effect size of each variant, clinical utility that aims for risk prediction and/or diagnostic assistance based on the individual "variants" is difficult to use. Therefore, to improve the statistical power, polygenic risk score (PRS) has been established and applied in the GWAS as a robust analytic tool. Although PRS has potential predictive ability, because of its current "insufficient" discriminative power at the individual level for clinical use, it remains limited solely in the research area, specifically in the psychiatric field. For a better understanding of the PRS, in this review, we (1) introduce the clinical features of psychiatric disorders, (2) summarize the recent GWAS/PRS findings in the psychiatric disorders, (3) evaluate the problems of PRS, and (4) propose its possible utility to apply PRS into the psychiatric clinical setting.
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25
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Protti M, Mandrioli R, Marasca C, Cavalli A, Serretti A, Mercolini L. New‐generation, non‐SSRI antidepressants: Drug‐drug interactions and therapeutic drug monitoring. Part 2: NaSSAs, NRIs, SNDRIs, MASSAs, NDRIs, and others. Med Res Rev 2020; 40:1794-1832. [DOI: 10.1002/med.21671] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 02/18/2020] [Accepted: 03/29/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Michele Protti
- Department of Pharmacy and Biotechnology (FaBiT), Pharmaco‐Toxicological Analysis Laboratory (PTA Lab)Alma Mater Studiorum ‐ University of Bologna Bologna Italy
| | - Roberto Mandrioli
- Department for Life Quality Studies (QuVi)Alma Mater Studiorum ‐ University of BolognaRimini Italy
| | - Camilla Marasca
- Department of Pharmacy and Biotechnology (FaBiT), Pharmaco‐Toxicological Analysis Laboratory (PTA Lab)Alma Mater Studiorum ‐ University of Bologna Bologna Italy
- Computational and Chemical BiologyFondazione Istituto Italiano di Tecnologia (IIT) Genoa Italy
| | - Andrea Cavalli
- Department of Pharmacy and Biotechnology (FaBiT), Pharmaco‐Toxicological Analysis Laboratory (PTA Lab)Alma Mater Studiorum ‐ University of Bologna Bologna Italy
- Computational and Chemical BiologyFondazione Istituto Italiano di Tecnologia (IIT) Genoa Italy
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences (DIBINEM)Alma Mater Studiorum ‐ University of Bologna Bologna Italy
| | - Laura Mercolini
- Department of Pharmacy and Biotechnology (FaBiT), Pharmaco‐Toxicological Analysis Laboratory (PTA Lab)Alma Mater Studiorum ‐ University of Bologna Bologna Italy
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