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Shin M, Crouse JJ, Byrne EM, Mitchell BL, Lind P, Parker R, Tonini E, Carpenter JS, Wray NR, Colodro-Conde L, Medland SE, Hickie IB. Changes in sleep patterns in people with a history of depression during the COVID-19 pandemic: a natural experiment. BMJ MENTAL HEALTH 2024; 27:e301067. [PMID: 39362788 PMCID: PMC11459332 DOI: 10.1136/bmjment-2024-301067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 09/13/2024] [Indexed: 10/05/2024]
Abstract
BACKGROUND The COVID-19 pandemic, while a major stressor, increased flexibility in sleep-wake schedules. OBJECTIVES To investigate the impact of the pandemic on sleep patterns in people with a history of depression and identify sociodemographic, clinical or genetic predictors of those impacts. METHODS 6453 adults from the Australian Genetics of Depression Study (45±15 years; 75% women) completed surveys before (2016-2018) and during the pandemic (2020-2021). Participants were assigned to 'short sleep' (<6 hours), 'optimal sleep' (6-8 hours) or 'long sleep' (>8 hours). We focused on those having prepandemic 'optimal sleep'. FINDINGS Pre pandemic, the majority (70%, n=4514) reported optimal sleep, decreasing to 49% (n=3189) during the pandemic. Of these, 57% maintained optimal sleep, while 16% (n=725) shifted to 'short sleep' and 27% (n=1225) to 'long sleep'. In group comparisons 'optimal-to-short sleep' group had worse prepandemic mental health and increased insomnia (p's<0.001), along with an elevated depression genetic score (p=0.002). The 'optimal-to-long sleep' group were slightly younger and had higher distress (p's<0.05), a greater propensity to being evening types (p<0.001) and an elevated depression genetic score (p=0.04). Multivariate predictors for 'optimal-to-short sleep' included reported stressful life events, psychological or somatic distress and insomnia severity (false discovery rate-corrected p values<0.004), while no significant predictors were identified for 'optimal-to-long sleep'. CONCLUSION AND IMPLICATIONS The COVID-19 pandemic, a natural experiment, elicited significant shifts in sleep patterns among people with a history of depression, revealing associations with diverse prepandemic demographic and clinical characteristics. Understanding these dynamics may inform the selection of interventions for people with depression facing major challenges.
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Affiliation(s)
- Mirim Shin
- The University of Sydney Brain and Mind Centre, Camperdown, New South Wales, Australia
| | - Jacob J Crouse
- The University of Sydney Brain and Mind Centre, Camperdown, New South Wales, Australia
| | - Enda M Byrne
- The University of Queensland Child Health Research Centre, South Brisbane, Queensland, Australia
| | | | - Penelope Lind
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
- University of Queensland, School of Biomedical Sciences, St Lucia, Queensland, Australia
| | - Richard Parker
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Emiliana Tonini
- The University of Sydney Brain and Mind Centre, Camperdown, New South Wales, Australia
| | - Joanne S Carpenter
- The University of Sydney Brain and Mind Centre, Camperdown, New South Wales, Australia
| | - Naomi R Wray
- The University of Queensland Institute for Molecular Bioscience, Saint Lucia, Queensland, Australia
- University of Oxford Department of Psychiatry, Oxford, UK
| | - Lucia Colodro-Conde
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
- The University of Queensland School of Psychology, Saint Lucia, Queensland, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
- The University of Queensland School of Psychology, Saint Lucia, Queensland, Australia
| | - Ian B Hickie
- The University of Sydney Brain and Mind Centre, Camperdown, New South Wales, Australia
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2
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Adams MJ, Thorp JG, Jermy BS, Kwong ASF, Kõiv K, Grotzinger AD, Nivard MG, Marshall S, Milaneschi Y, Baune BT, Müller-Myhsok B, Penninx BWJH, Boomsma DI, Levinson DF, Breen G, Pistis G, Grabe HJ, Tiemeier H, Berger K, Rietschel M, Magnusson PK, Uher R, Hamilton SP, Lucae S, Lehto K, Li QS, Byrne EM, Hickie IB, Martin NG, Medland SE, Wray NR, Tucker-Drob EM, Lewis CM, McIntosh AM, Derks EM. Genome-wide meta-analysis of ascertainment and symptom structures of major depression in case-enriched and community cohorts. Psychol Med 2024; 54:3459-3468. [PMID: 39324397 PMCID: PMC11496230 DOI: 10.1017/s0033291724001880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 06/20/2024] [Accepted: 08/02/2024] [Indexed: 09/27/2024]
Abstract
BACKGROUND Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. METHODS We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. RESULTS The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms). CONCLUSION The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.
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Affiliation(s)
- Mark J. Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Jackson G. Thorp
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Bradley S. Jermy
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Alex S. F. Kwong
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Kadri Kõiv
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andrew D. Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michel G. Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Sally Marshall
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bernhard T. Baune
- Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Department of Psychiatry, University of Münster, Münster, NRW, Germany
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, BY, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, BY, Germany
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Brenda W. J. H. Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology & Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Douglas F. Levinson
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
| | - Giorgio Pistis
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, VD, Switzerland
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, MV, Germany
| | - Henning Tiemeier
- Child and Adolescent Psychiatry, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
- Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, NRW, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - Patrik K. Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Rudolf Uher
- Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Steven P. Hamilton
- Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, USA
| | - Susanne Lucae
- Max Planck Institute of Psychiatry, Munich, BY, Germany
| | - Kelli Lehto
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Qingqin S. Li
- Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, USA
| | - Enda M. Byrne
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
| | - Ian B. Hickie
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Nicholas G. Martin
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sarah E Medland
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Elliot M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | | | | | - 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
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Institute for Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Eske M. Derks
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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3
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Crouse JJ, Park SH, Byrne EM, Mitchell BL, Scott J, Medland SE, Lin T, Wray NR, Martin NG, Hickie IB. Patterns of stressful life events and polygenic scores for five mental disorders and neuroticism among adults with depression. Mol Psychiatry 2024; 29:2765-2773. [PMID: 38575805 PMCID: PMC11420070 DOI: 10.1038/s41380-024-02492-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 02/08/2024] [Accepted: 02/20/2024] [Indexed: 04/06/2024]
Abstract
The dominant ('general') version of the diathesis-stress theory of depression views stressors and genetic vulnerability as independent risks. In the Australian Genetics of Depression Study (N = 14,146; 75% female), we tested whether polygenic scores (PGS) for major depression, bipolar disorder, schizophrenia, anxiety, ADHD, and neuroticism were associated with reported exposure to 32 childhood, past-year, lifetime, and accumulated stressful life events (SLEs). In false discovery rate-corrected models, the clearest PGS-SLE relationships were for the ADHD- and depression-PGSs, and to a lesser extent, the anxiety- and schizophrenia-PGSs. We describe the associations for childhood and accumulated SLEs, and the 2-3 strongest past-year/lifetime SLE associations. Higher ADHD-PGS was associated with all childhood SLEs (emotional abuse, emotional neglect, physical neglect; ORs = 1.09-1.14; p's < 1.3 × 10-5), more accumulated SLEs, and reported exposure to sudden violent death (OR = 1.23; p = 3.6 × 10-5), legal troubles (OR = 1.15; p = 0.003), and sudden accidental death (OR = 1.14; p = 0.006). Higher depression-PGS was associated with all childhood SLEs (ORs = 1.07-1.12; p's < 0.013), more accumulated SLEs, and severe human suffering (OR = 1.17; p = 0.003), assault with a weapon (OR = 1.12; p = 0.003), and living in unpleasant surroundings (OR = 1.11; p = 0.001). Higher anxiety-PGS was associated with childhood emotional abuse (OR = 1.08; p = 1.6 × 10-4), more accumulated SLEs, and serious accident (OR = 1.23; p = 0.004), physical assault (OR = 1.08; p = 2.2 × 10-4), and transportation accident (OR = 1.07; p = 0.001). Higher schizophrenia-PGS was associated with all childhood SLEs (ORs = 1.12-1.19; p's < 9.3-8), more accumulated SLEs, and severe human suffering (OR = 1.16; p = 0.003). Higher neuroticism-PGS was associated with living in unpleasant surroundings (OR = 1.09; p = 0.007) and major financial troubles (OR = 1.06; p = 0.014). A reversed pattern was seen for the bipolar-PGS, with lower odds of reported physical assault (OR = 0.95; p = 0.014), major financial troubles (OR = 0.93; p = 0.004), and living in unpleasant surroundings (OR = 0.92; p = 0.007). Genetic risk for several mental disorders influences reported exposure to SLEs among adults with moderately severe, recurrent depression. Our findings emphasise that stressors and diatheses are inter-dependent and challenge diagnosis and subtyping (e.g., reactive/endogenous) based on life events.
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Affiliation(s)
- Jacob J Crouse
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Shin Ho Park
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Brittany L Mitchell
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jan Scott
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Academic Psychiatry, Institute of Neuroscience, Newcastle University, Newcastle, UK
- Norwegian University of Science and Technology, Trondheim, Norway
- Université de Paris, Paris, France
| | - Sarah E Medland
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nicholas G Martin
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B Hickie
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
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4
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Mosley PE, van der Meer JN, Hamilton LHW, Fripp J, Parker S, Jeganathan J, Breakspear M, Parker R, Holland R, Mitchell BL, Byrne E, Hickie IB, Medland SE, Martin NG, Cocchi L. Markers of positive affect and brain state synchrony discriminate melancholic from non-melancholic depression using naturalistic stimuli. Mol Psychiatry 2024:10.1038/s41380-024-02699-y. [PMID: 39191867 DOI: 10.1038/s41380-024-02699-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 08/11/2024] [Accepted: 08/14/2024] [Indexed: 08/29/2024]
Abstract
Melancholia has been proposed as a qualitatively distinct depressive subtype associated with a characteristic symptom profile (psychomotor retardation, profound anhedonia) and a better response to biological therapies. Existing work has suggested that individuals with melancholia are blunted in their display of positive emotions and differ in their neural response to emotionally evocative stimuli. Here, we unify these brain and behavioural findings amongst a carefully phenotyped group of seventy depressed participants, drawn from an established Australian database (the Australian Genetics of Depression Study) and further enriched for melancholia (high ratings of psychomotor retardation and anhedonia). Melancholic (n = 30) or non-melancholic status (n = 40) was defined using a semi-structured interview (the Sydney Melancholia Prototype Index). Complex facial expressions were captured whilst participants watched a movie clip of a comedian and classified using a machine learning algorithm. Subsequently, the dynamics of sequential changes in brain activity were modelled during the viewing of an emotionally evocative movie in the MRI scanner. We found a quantitative reduction in positive facial expressivity amongst participants with melancholia, combined with differences in the synchronous expression of brain states during positive epochs of the movie. In non-melancholic depression, the display of positive affect was inversely related to the activity of cerebellar regions implicated in the processing of affect. However, this relationship was reduced in those with a melancholic phenotype. Our multimodal findings show differences in evaluative and motoric domains between melancholic and non-melancholic depression through engagement in ecologically valid tasks that evoke positive emotion. These findings provide new markers to stratify depression and an opportunity to support the development of targeted interventions.
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Affiliation(s)
- Philip E Mosley
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia.
- Australian eHealth Research Centre, CSIRO Health and Biosecurity, Herston, QLD, Australia.
- Faculty of Medicine, School of Biomedical Sciences, University of Queensland, St Lucia, QLD, Australia.
| | - Johan N van der Meer
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- School of Information Systems, Queensland University of Technology, Kelvin Grove, QLD, Australia
| | | | - Jurgen Fripp
- Australian eHealth Research Centre, CSIRO Health and Biosecurity, Herston, QLD, Australia
| | - Stephen Parker
- Faculty of Medicine, School of Biomedical Sciences, University of Queensland, St Lucia, QLD, Australia
- Metro North Mental Health, Royal Brisbane & Women's Hospital, Herston, QLD, Australia
| | - Jayson Jeganathan
- School of Psychology, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia
- Brain Neuromodulation Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, College of Medicine, Health and Wellbeing, University of Newcastle, Newcastle, NSW, Australia
| | - Michael Breakspear
- School of Psychology, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia
- Brain Neuromodulation Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, College of Medicine, Health and Wellbeing, University of Newcastle, Newcastle, NSW, Australia
| | - Richard Parker
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Rebecca Holland
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Brittany L Mitchell
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- Faculty of Medicine, School of Biomedical Sciences, University of Queensland, St Lucia, QLD, Australia
| | - Enda Byrne
- Child Health Research Centre, University of Queensland, South Brisbane, QLD, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- School of Psychology, University of Queensland, St Lucia, QLD, Australia
- School of Psychology and Counselling, Queensland University of Technology, Kelvin Grove, QLD, Australia
| | | | - Luca Cocchi
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- Faculty of Medicine, School of Biomedical Sciences, University of Queensland, St Lucia, QLD, Australia
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5
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Crouse JJ, Park SH, Byrne EM, Mitchell BL, Chan K, Scott J, Medland SE, Martin NG, Wray NR, Hickie IB. Evening Chronotypes With Depression Report Poorer Outcomes of Selective Serotonin Reuptake Inhibitors: A Survey-Based Study of Self-Ratings. Biol Psychiatry 2024; 96:4-14. [PMID: 38185236 DOI: 10.1016/j.biopsych.2023.12.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND Preliminary evidence suggests that evening chronotype is related to poorer efficacy of selective serotonin reuptake inhibitors. It is unknown whether this is specific to particular medications, self-rated chronotype, or efficacy. METHODS In the Australian Genetics of Depression Study (n = 15,108; 75% women; 18-90 years; 68% with ≥1 other lifetime diagnosis), a survey recorded experiences with 10 antidepressants, and the reduced Morningness-Eveningness Questionnaire was used to estimate chronotype. A chronotype polygenic score was calculated. Age- and sex-adjusted regression models (Bonferroni-corrected) estimated associations among antidepressant variables (how well the antidepressant worked [efficacy], duration of symptom improvement, side effects, discontinuation due to side effects) and self-rated and genetic chronotypes. RESULTS The chronotype polygenic score explained 4% of the variance in self-rated chronotype (r = 0.21). Higher self-rated eveningness was associated with poorer efficacy of escitalopram (odds ratio [OR] = 1.04; 95% CI, 1.02 to 1.06; p = .000035), citalopram (OR = 1.03; 95% CI, 1.01 to 1.05; p = .004), fluoxetine (OR = 1.03; 95% CI, 1.01 to 1.05; p = .001), sertraline (OR = 1.02; 95% CI, 1.01 to 1.04; p = .0008), and desvenlafaxine (OR = 1.03; 95% CI, 1.01 to 1.05; p = .004), and a profile of increased side effects (80% of those recorded; ORs = 0.93-0.98), with difficulty getting to sleep the most common. Self-rated chronotype was unrelated to duration of improvement or discontinuation. The chronotype polygenic score was only associated with suicidal thoughts and attempted suicide (self-reported). While our measures are imperfect, and not of circadian phase under controlled conditions, the model coefficients suggest that dysregulation of the phenotypic chronotype relative to its genetic proxy drove relationships with antidepressant outcomes. CONCLUSIONS The idea that variation in circadian factors influences response to antidepressants was supported and encourages exploration of circadian mechanisms of depressive disorders and antidepressant treatments.
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Affiliation(s)
- Jacob J Crouse
- Brain and Mind Centre, the University of Sydney, Sydney, New South Wales, Australia.
| | - Shin Ho Park
- Brain and Mind Centre, the University of Sydney, Sydney, New South Wales, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, the University of Queensland, Brisbane, Queensland, Australia; Child Health Research Centre, the University of Queensland, Brisbane, Queensland, Australia
| | - Brittany L Mitchell
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Karina Chan
- Brain and Mind Centre, the University of Sydney, Sydney, New South Wales, Australia
| | - Jan Scott
- Brain and Mind Centre, the University of Sydney, Sydney, New South Wales, Australia; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sarah E Medland
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, the University of Queensland, Brisbane, Queensland, Australia; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Ian B Hickie
- Brain and Mind Centre, the University of Sydney, Sydney, New South Wales, Australia
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6
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Huider F, Milaneschi Y, Hottenga JJ, Bot M, Rietman ML, Kok AAL, Galesloot TE, 't Hart LM, Rutters F, Blom MT, Rhebergen D, Visser M, Brouwer I, Feskens E, Hartman CA, Oldehinkel AJ, de Geus EJC, Kiemeney LA, Huisman M, Picavet HSJ, Verschuren WMM, van Loo HM, Penninx BWJH, Boomsma DI. Genomics Research of Lifetime Depression in the Netherlands: The BIObanks Netherlands Internet Collaboration (BIONIC) Project. Twin Res Hum Genet 2024; 27:1-11. [PMID: 38497097 DOI: 10.1017/thg.2024.4] [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: 03/19/2024]
Abstract
In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.
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Affiliation(s)
- Floris Huider
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
| | - Yuri Milaneschi
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
| | - Mariska Bot
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - M Liset Rietman
- Center for Prevention, Lifestyle and Health, Dutch National Institute for Public Health and the Environment, 3721 Bilthoven, the Netherlands
| | - Almar A L Kok
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit, 1081 Amsterdam, the Netherlands
| | | | | | | | | | - Didi Rhebergen
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Mental health Institute GGZ Centraal, Amersfoort, the Netherlands
| | - Marjolein Visser
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Ingeborg Brouwer
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Edith Feskens
- Division of Human Nutrition and Health, Wageningen University & Research, 6700 Wageningen, the Netherlands
| | - Catharina A Hartman
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, 9713 Groningen, the Netherlands
| | - Albertine J Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, 9713 Groningen, the Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
| | | | - Martijn Huisman
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit, 1081 Amsterdam, the Netherlands
- Department of Sociology, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - H Susan J Picavet
- Center for Prevention, Lifestyle and Health, Dutch National Institute for Public Health and the Environment, 3721 Bilthoven, the Netherlands
| | - W M Monique Verschuren
- Center for Prevention, Lifestyle and Health, Dutch National Institute for Public Health and the Environment, 3721 Bilthoven, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 Utrecht, the Netherlands
| | - Hanna M van Loo
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, 9713 Groningen, the Netherlands
| | - Brenda W J H Penninx
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
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Tio ES, Misztal MC, Felsky D. Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning. Front Psychiatry 2024; 14:1294666. [PMID: 38274429 PMCID: PMC10808719 DOI: 10.3389/fpsyt.2023.1294666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024] Open
Abstract
Background Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide. Methods We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide. Results Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance. Conclusion Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.
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Affiliation(s)
- Earvin S. Tio
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Melissa C. Misztal
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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8
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Kiewa J, Meltzer-Brody S, Milgrom J, Guintivano J, Hickie IB, Whiteman DC, Olsen CM, Medland SE, Martin NG, Wray NR, Byrne EM. Comprehensive Sex-Stratified Genetic Analysis of 28 Blood Biomarkers and Depression Reveals a Significant Association between Depression and Low Levels of Total Protein in Females. Complex Psychiatry 2024; 10:19-34. [PMID: 38584764 PMCID: PMC10997320 DOI: 10.1159/000538058] [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: 09/13/2023] [Accepted: 02/14/2024] [Indexed: 04/09/2024] Open
Abstract
Introduction Major depression (MD) is more common amongst women than men, and MD episodes have been associated with fluctuations in reproductive hormones amongst women. To investigate biological underpinnings of heterogeneity in MD, the associations between depression, stratified by sex and including perinatal depression (PND), and blood biomarkers, using UK Biobank (UKB) data, were evaluated, and extended to include the association of depression with biomarker polygenic scores (PGS), generated as proxy for each biomarker. Method Using female (N = 39,761) and male (N = 38,821) UKB participants, lifetime MD and PND were tested for association with 28 blood biomarkers. A GWAS was conducted for each biomarker and genetic correlations with depression subgroups were estimated. Using independent data from the Australian Genetics of Depression Study, PGS were constructed for each biomarker, and tested for association with depression status (n [female cases/controls] = 9,006/6,442; n [male cases/controls] = 3,106/6,222). Regions of significant local genetic correlation between depression subgroups and biomarkers highlighted by the PGS analysis were identified. Results Depression in females was significantly associated with levels of twelve biomarkers, including total protein (OR = 0.90, CI = [0.86, 0.94], p = 3.9 × 10-6) and vitamin D (OR = 0.94, CI = [0.90, 0.97], p = 2.6 × 10-4), and PND with five biomarker levels, also including total protein (OR = 0.88, CI = [0.81, 0.96], p = 4.7 × 10-3). Depression in males was significantly associated with levels of eleven biomarkers. In the independent Australian Genetics of Depression Study, PGS analysis found significant associations for female depression and PND with total protein (female depression: OR = 0.93, CI = [0.88, 0.98], p = 3.6 × 10-3; PND: OR = 0.91, CI = [0.86, 0.96], p = 1.1 × 10-3), as well as with vitamin D (female depression: OR = 0.93, CI = [0.89, 0.97], p = 2.0 × 10-3; PND: OR = 0.92, CI = [0.87, 0.97], p = 1.4 × 10-3). The male depression sample did not report any significant results, and the point estimate of total protein (OR = 0.98, CI = [0.92-1.04], p = 4.7 × 10-1) did not indicate any association. Local genetic correlation analysis highlighted significant genetic correlation between PND and total protein, located in 5q13.3 (rG = 0.68, CI = [0.33, 1.0], p = 3.6 × 10-4). Discussion and Conclusion Multiple lines of evidence from genetic analysis highlight an association between total serum protein levels and depression in females. Further research involving prospective measurement of total protein and depressive symptoms is warranted.
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Affiliation(s)
- Jacqueline Kiewa
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | | | - Jeannette Milgrom
- Parent-Infant Research Institute, Austin Health, Melbourne, VIC, Australia
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Jerry Guintivano
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Ian B. Hickie
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | | | | | - Sarah E. Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Enda M. Byrne
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
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9
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Perna G, Spiti A, Torti T, Daccò S, Caldirola D. Biomarker-Guided Tailored Therapy in Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1456:379-400. [PMID: 39261439 DOI: 10.1007/978-981-97-4402-2_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.
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Affiliation(s)
- Giampaolo Perna
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, Como, Italy.
- Humanitas SanpioX, Milan, Italy.
| | - Alessandro Spiti
- IRCCS Humanitas Research Hospital, Milan, Italy
- Psicocare, Humanitas Medical Care, Monza, Italy
| | - Tatiana Torti
- ASIPSE School of Cognitive-Behavioral-Therapy, Milan, Italy
| | - Silvia Daccò
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Humanitas SanpioX, Milan, Italy
- Psicocare, Humanitas Medical Care, Monza, Italy
| | - Daniela Caldirola
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, Como, Italy
- Humanitas SanpioX, Milan, Italy
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10
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Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, Lind PA, Farajzadeh L, Wang L, Hatoum AS, Johnson J, Lee H, Mallard TT, Xu J, Johnston KJA, Johnson EC, Nielsen TT, Galimberti M, Dao C, Levey DF, Overstreet C, Byrne EM, Gillespie NA, Gordon S, Hickie IB, Whitfield JB, Xu K, Zhao H, Huckins LM, Davis LK, Sanchez-Roige S, Madden PAF, Heath AC, Medland SE, Martin NG, Ge T, Smoller JW, Hougaard DM, Børglum AD, Demontis D, Krystal JH, Gaziano JM, Edenberg HJ, Agrawal A, Justice AC, Stein MB, Kranzler HR, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nat Med 2023; 29:3184-3192. [PMID: 38062264 PMCID: PMC10719093 DOI: 10.1038/s41591-023-02653-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 10/18/2023] [Indexed: 12/17/2023]
Abstract
Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
| | - Rachel L Kember
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joseph D Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Yuan
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Lu Wang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, Saint Louis, MO, USA
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T Mallard
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Trine Tollerup Nielsen
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Marco Galimberti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cecilia Dao
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Enda M Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Nathan A Gillespie
- Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - John B Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Tian Ge
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W Smoller
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Anders D Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Medicine, Divisions of Aging and Preventative Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA.
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11
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Garcia-Marin LM, Mulcahy A, Byrne EM, Medland SE, Wray NR, Chafota F, Lind PA, Martin NG, Hickie IB, Rentería ME, Campos AI. Discontinuation of antidepressant treatment: a retrospective cohort study on more than 20,000 participants. Ann Gen Psychiatry 2023; 22:49. [PMID: 38001492 PMCID: PMC10668351 DOI: 10.1186/s12991-023-00480-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Factors influencing antidepressant treatment discontinuation are poorly understood. In the present study, we aimed to estimate the prevalence of antidepressant treatment discontinuation and identify demographic characteristics, psychiatric comorbidities, and specific side effects associated with treatment discontinuation. METHODS We leveraged data from the Australian Genetics of Depression Study (AGDS; N = 20,941) to perform a retrospective cohort study on antidepressant treatment discontinuation. Participants were eligible if they were over 18 years of age, had taken antidepressants in the past 4 years, and provided informed consent. RESULTS Among the ten antidepressants studied, the highest discontinuation rates were observed for Mirtazapine (57.3%) and Amitriptyline (51.6%). Discontinuation rates were comparable across sexes except for Mirtazapine, for which women were more likely to discontinue. The two most common side effects, reduced sexual function and weight gain, were not associated with increased odds of treatment discontinuation. Anxiety, agitation, suicidal thoughts, vomiting, and rashes were associated with higher odds for treatment discontinuation, as were lifetime diagnoses of PTSD, ADHD, and a higher neuroticism score. Educational attainment showed a negative (protective) association with discontinuation across medications. CONCLUSIONS Our study suggests that not all side effects contribute equally to discontinuation. Common side effects such as reduced sexual function and weight gain may not necessarily increase the risk of treatment discontinuation. Side effects linked to discontinuation can be divided into two groups, psychopathology related and allergy/intolerance.
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Affiliation(s)
- Luis M Garcia-Marin
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
| | - Aoibhe Mulcahy
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Freddy Chafota
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Penelope A Lind
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Miguel E Rentería
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Adrian I Campos
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
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12
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Lind PA, Siskind DJ, Hickie IB, Colodro-Conde L, Cross S, Parker R, Martin NG, Medland SE. Preliminary results from the Australian Genetics of Bipolar Disorder Study: A nation-wide cohort. Aust N Z J Psychiatry 2023; 57:1428-1442. [PMID: 37655588 PMCID: PMC10619176 DOI: 10.1177/00048674231195571] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
OBJECTIVE The Australian Genetics of Bipolar Disorder Study is a nation-wide cohort of adults living with bipolar disorder. The study aims to detect the relationships between genetic risk, symptom severity, and the lifetime prevalence of bipolar disorder, treatment response and medication side effects, and patterns and costs of health care usage. METHODS A total of 6682 participants (68.3% female; aged 44.8 ± 13.6 years [range = 18-90]) were recruited in three waves: a nation-wide media campaign, a mail-out based on prescriptions for lithium carbonate and through the Australian Genetics of Depression Study. Participants completed a self-report questionnaire. A total of 4706 (70%) participants provided a saliva sample and were genotyped and 5506 (82%) consented to record linkage of their Pharmaceutical and Medicare Benefits Schedule data. RESULTS Most participants were living with bipolar I disorder (n = 4068) while 1622 participants were living with bipolar II disorder and 992 with sub-threshold bipolar disorder. The mean age of bipolar disorder diagnosis was 32.7 ± 11.6 years but was younger in bipolar I (p = 2.0E-26) and females (p = 5.7E-23). Excluding depression with onset prior to bipolar disorder diagnosis, 64.5% of participants reported one or more co-occurring psychiatric disorders: most commonly generalised anxiety disorder (43.5%) and posttraumatic stress disorder (20.7%). Adverse drug reactions were common and resulted in discontinuation rates ranging from 33.4% for lithium to 63.0% for carbamazepine. CONCLUSION Our findings highlight the high rate of comorbidities and adverse drug reactions among adults living with bipolar disorder in the general Australian population. Future genomic analyses focus on identifying genetic variants influencing pharmacotherapy treatment response and side effects.
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Affiliation(s)
- Penelope A Lind
- Psychiatric Genetics, Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Dan J Siskind
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Metro South Addiction and Mental Health Service, Brisbane, QLD, Australia
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Lucía Colodro-Conde
- Psychiatric Genetics, Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Simone Cross
- Psychiatric Genetics, Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Richard Parker
- Psychiatric Genetics, Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sarah E Medland
- Psychiatric Genetics, Mental Health and Neuroscience Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
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13
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Ong JS, Seviiri M, Dusingize JC, Wu Y, Han X, Shi J, Olsen CM, Neale RE, Thompson JF, Saw RPM, Shannon KF, Mann GJ, Martin NG, Medland SE, Gordon SD, Scolyer RA, Long GV, Iles MM, Landi MT, Whiteman DC, MacGregor S, Law MH. Uncovering the complex relationship between balding, testosterone and skin cancers in men. Nat Commun 2023; 14:5962. [PMID: 37789011 PMCID: PMC10547720 DOI: 10.1038/s41467-023-41231-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 08/24/2023] [Indexed: 10/05/2023] Open
Abstract
Male-pattern baldness (MPB) is related to dysregulation of androgens such as testosterone. A previously observed relationship between MPB and skin cancer may be due to greater exposure to ultraviolet radiation or indicate a role for androgenic pathways in the pathogenesis of skin cancers. We dissected this relationship via Mendelian randomization (MR) analyses, using genetic data from recent male-only meta-analyses of cutaneous melanoma (12,232 cases; 20,566 controls) and keratinocyte cancers (KCs) (up to 17,512 cases; >100,000 controls), followed by stratified MR analysis by body-sites. We found strong associations between MPB and the risk of KC, but not with androgens, and multivariable models revealed that this relationship was heavily confounded by MPB single nucleotide polymorphisms involved in pigmentation pathways. Site-stratified MR analyses revealed strong associations between MPB with head and neck squamous cell carcinoma and melanoma, suggesting that sun exposure on the scalp, rather than androgens, is the main driver. Men with less hair covering likely explains, at least in part, the higher incidence of melanoma in men residing in countries with high ambient UV.
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Affiliation(s)
- Jue-Sheng Ong
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
| | - Mathias Seviiri
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Australia
| | - Jean Claude Dusingize
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Yeda Wu
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Xikun Han
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Catherine M Olsen
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Herston, QLD, Australia
| | - Rachel E Neale
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - John F Thompson
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
- Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | - Robyn P M Saw
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
- Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | - Kerwin F Shannon
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
- Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | - Graham J Mann
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Nicholas G Martin
- Department of Mental Health & Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Sarah E Medland
- Department of Mental Health & Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Scott D Gordon
- Department of Mental Health & Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Richard A Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital & NSW Health Pathology, Sydney, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Georgina V Long
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Department of Medical Oncology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Mark M Iles
- Leeds Institute of Medical Research & Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - David C Whiteman
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Stuart MacGregor
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Matthew H Law
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Australia.
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14
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Gomez L, Díaz-Torres S, Colodro-Conde L, Garcia-Marin LM, Yap CX, Byrne EM, Yengo L, Lind PA, Wray NR, Medland SE, Hickie IB, Lupton MK, Rentería ME, Martin NG, Campos AI. Phenotypic and genetic factors associated with donation of DNA and consent to record linkage for prescription history in the Australian Genetics of Depression Study. Eur Arch Psychiatry Clin Neurosci 2023; 273:1359-1368. [PMID: 36422680 DOI: 10.1007/s00406-022-01527-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 11/15/2022] [Indexed: 11/27/2022]
Abstract
Samples can be prone to ascertainment and attrition biases. The Australian Genetics of Depression Study is a large publicly recruited cohort (n = 20,689) established to increase the understanding of depression and antidepressant treatment response. This study investigates differences between participants who donated a saliva sample or agreed to linkage of their records compared to those who did not. We observed that older, male participants with higher education were more likely to donate a saliva sample. Self-reported bipolar disorder, ADHD, panic disorder, PTSD, substance use disorder, and social anxiety disorder were associated with lower odds of donating a saliva sample, whereas anorexia was associated with higher odds of donation. Male and younger participants showed higher odds of agreeing to record linkage. Participants with higher neuroticism scores and those with a history of bipolar disorder were also more likely to agree to record linkage whereas participants with a diagnosis of anorexia were less likely to agree. Increased likelihood of consent was associated with increased genetic susceptibility to anorexia and reduced genetic risk for depression, and schizophrenia. Overall, our results show moderate differences among these subsamples. Most current epidemiological studies do not search for attrition biases at the genetic level. The possibility to do so is a strength of samples such as the AGDS. Our results suggest that analyses can be made more robust by identifying attrition biases both on the phenotypic and genetic level, and either contextualising them as a potential limitation or performing sensitivity analyses adjusting for them.
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Affiliation(s)
- Lina Gomez
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Santiago Díaz-Torres
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Statistical Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Lucía Colodro-Conde
- Psychiatric Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Luis M Garcia-Marin
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Chloe X Yap
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Penelope A Lind
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Psychiatric Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Queensland Institute of Technology, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- Psychiatric Genetics Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Michelle K Lupton
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Adrian I Campos
- Genetic Epidemiology Lab, Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
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15
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Li X, Tian Y, Phillips MR, Xiao S, Zhang X, Li Z, Liu J, Li L, Zhou J, Wang X. Protocol of a prospective community-based study about the onset and course of depression in a nationally representative cohort of adults in China: the China Depression Cohort Study-I. BMC Public Health 2023; 23:1617. [PMID: 37620799 PMCID: PMC10463817 DOI: 10.1186/s12889-023-16542-6] [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: 04/23/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Depression is the second most important cause of disability worldwide. Reducing this major burden on global health requires a better understanding of the etiology, risk factors, and course of the disorder. With the goal of improving the prevention, recognition, and appropriate management of depressive disorders in China, the China Depression Cohort Study will establish a nationally representative sample of at least 85,000 adults (the China Depression Cohort Study-I) and 15,000 middle school students (the China Depression Cohort Study-II) and follow them over time to identify factors that influence the onset, characteristics, and course of depressive disorders. This protocol describes the China Depression Cohort Study-I. METHODS A multistage stratified random sampling method will be used to identify a nationally representative community-based cohort of at least 85,000 adults (i.e., ≥ 18 years of age) from 34 communities in 17 of mainland China's 31 provincial-level administrative regions. Baseline data collection includes 1) demographic, social and clinical data, 2) diagnostic information, 3) biological samples (i.e., blood, urine, hair), 4) brain MRI scans, and 5) environmental data (e.g., community-level metrics of climate change, air pollution, and socio-economic characteristics). Baseline findings will identify participants with or without depressive disorders. Annual reassessments will monitor potential risk factors for depression and identify incident cases of depression. Cox Proportional-Hazards Regression, Network analysis, Disease trajectory modelling, and Machine learning prediction models will be used to analyze the collected data. The study's main outcomes are the occurrence of depressive disorders; secondary outcomes include adverse behaviors (e.g., self-harm, suicide), the recurrence of depression and the incidence other mental disorders. DISCUSSION The China Depression Cohort Study-I will collect a comprehensive, nationally representative set of individual-level and community-level variables over time. The findings will reframe the understanding of depression from a 'biology-psychology-society' perspective. This perspective will improve psychiatrists' understanding of depression and, thus, promote the development of more effective subgroup-specific antidepressant drugs and other interventions based on the new biomarkers and relationships identified in the study. TRAIL REGISTRATION The protocol has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2200059016).
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Affiliation(s)
- Xuting Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yusheng Tian
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Michael R Phillips
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuiyuan Xiao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiaojie Zhang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zongchang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Lingjiang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jiansong Zhou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China.
| | - Xiaoping Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China.
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16
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Gharahkhani P, He W, Diaz Torres S, Wu Y, Ingold N, Yu R, Seviiri M, Ong JS, Law MH, Craig JE, Mackey DA, Hewitt AW, MacGregor S. Study profile: the Genetics of Glaucoma Study. BMJ Open 2023; 13:e068811. [PMID: 37536973 PMCID: PMC10401214 DOI: 10.1136/bmjopen-2022-068811] [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: 09/30/2022] [Accepted: 07/18/2023] [Indexed: 08/05/2023] Open
Abstract
PURPOSE Glaucoma, a major cause of irreversible blindness, is a highly heritable human disease. Currently, the majority of the risk genes for glaucoma are unknown. We established the Genetics of Glaucoma Study (GOGS) to identify disease genes and improve genetic prediction of glaucoma risk and response to treatment. PARTICIPANTS More than 5700 participants with glaucoma or a family history of glaucoma were recruited through a media campaign and the Australian Government healthcare service provider, Services Australia, making GOGS one of the largest genetic studies of glaucoma globally. The mean age of the participants was 65.30±9.36 years, and 62% were female. Participants completed a questionnaire obtaining information about their glaucoma-related medical history such as family history, glaucoma status and subtypes, surgical procedures, and prescriptions. The questionnaire also obtained information about other eye and systemic diseases. Approximately 80% of the participants provided a DNA sample and ~70% consented to data linkage to their Australian Government Medicare and Pharmaceutical Benefits Scheme schedules. FINDINGS TO DATE 4336 GOGS participants reported that an optometrist or ophthalmologist has diagnosed them with glaucoma and 3639 participants reported having a family history of glaucoma. The vast majority of the participants (N=4393) had used at least one glaucoma-related medication; latanoprost was the most commonly prescribed drug (54% of the participants who had a glaucoma prescription). A subset of the participants reported a surgical treatment for glaucoma including a laser surgery in 2008 participants and a non-laser operation in 803 participants. Several comorbid eye and systemic diseases were also observed; the most common reports were ocular hypertension (53% of the participants), cataract (48%), hypertension (40%), nearsightedness (31%), astigmatism (22%), farsightedness (16%), diabetes (12%), sleep apnoea (11%) and migraines (10%). FUTURE PLANS GOGS will contribute to the global gene-mapping efforts as one of the largest genetic studies for glaucoma. We will also use GOGS to develop or validate genetic risk prediction models to stratify glaucoma risk, particularly in individuals with a family history of glaucoma, and to predict clinical outcomes (eg, which medication works better for an individual and whether glaucoma surgery is required). GOGS will also help us answer various research questions about genetic overlap and causal relationships between glaucoma and its comorbidities.
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Affiliation(s)
- Puya Gharahkhani
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Weixiong He
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Santiago Diaz Torres
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Yeda Wu
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nathan Ingold
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Regina Yu
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Mathias Seviiri
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jue-Sheng Ong
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Matthew H Law
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jamie E Craig
- Department of Ophthalmology, Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia
| | - David A Mackey
- Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Perth, Western Australia, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Alex W Hewitt
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
| | - Stuart MacGregor
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
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17
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Adams MJ, Thorp JG, Jermy BS, Kwong ASF, Kõiv K, Grotzinger AD, Nivard MG, Marshall S, Milaneschi Y, Baune BT, Müller-Myhsok B, Penninx BW, Boomsma DI, Levinson DF, Breen G, Pistis G, Grabe HJ, Tiemeier H, Berger K, Rietschel M, Magnusson PK, Uher R, Hamilton SP, Lucae S, Lehto K, Li QS, Byrne EM, Hickie IB, Martin NG, Medland SE, Wray NR, Tucker-Drob EM, Lewis CM, McIntosh AM, Derks EM. Genetic structure of major depression symptoms across clinical and community cohorts. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.05.23292214. [PMID: 37461564 PMCID: PMC10350129 DOI: 10.1101/2023.07.05.23292214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.
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Affiliation(s)
- Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Jackson G Thorp
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
| | - Bradley S Jermy
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, FI
| | - Alex S F Kwong
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Kadri Kõiv
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, EE
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, US
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, US
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Sally Marshall
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Bernhard T Baune
- Department of Psychiatry, University of Melbourne, Melbourne, VIC, AU
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, AU
- Department of Psychiatry, University of Münster, Münster, NRW, DE
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, BY, DE
- Munich Cluster for Systems Neurology (SyNergy), Munich, BY, DE
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Dorret I Boomsma
- Department of Biological Psychology & Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, NL
| | - Douglas F Levinson
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, US
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
| | - Giorgio Pistis
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, VD, CH
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald MV, DE
| | - Henning Tiemeier
- Child and Adolescent Psychiatry, Erasmus University Medical Center Rotterdam, Rotterdam, NL
- Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, MA, US
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, NRW, DE
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, DE
| | - Patrik K Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE
| | - Rudolf Uher
- Psychiatry, Dalhousie University, Halifax, NS, CA
| | - Steven P Hamilton
- Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, US
| | | | - Kelli Lehto
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, EE
| | - Qingqin S Li
- Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, US
| | - Enda M Byrne
- Child Health Research Centre, University of Queensland, Brisbane, QLD, AU
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, NSW, AU
| | - Nicholas G Martin
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
| | - Sarah E Medland
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, AU
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, AU
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, US
- Population Research Center, University of Texas at Austin, Austin, TX, US
| | - 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
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Institute for Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Eske M Derks
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, AU
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18
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Flint J. The genetic basis of major depressive disorder. Mol Psychiatry 2023; 28:2254-2265. [PMID: 36702864 PMCID: PMC10611584 DOI: 10.1038/s41380-023-01957-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, Billy and Audrey Wilder Endowed Chair in Psychiatry and Neuroscience, Center for Neurobehavioral Genetics, 695 Charles E. Young Drive South, 3357B Gonda, Box 951761, Los Angeles, CA, 90095-1761, USA.
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19
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Valkovskaya M, Hassan A, Zartaloudi E, Hussain F, Umar M, Khizar B, Khattak I, Gill SA, Khan SUDA, Dogar IA, Mustafa AB, Ansari MA, Qalb I Hyder S, Ali M, Ilyas N, Channar P, Mughal N, Channa S, Mufti K, Mufti AA, Hussain MI, Shafiq S, Tariq M, Khan MK, Chaudhry ST, Choudhary AR, Ali MN, Ali G, Hussain A, Rehman M, Ahmad N, Farooq S, Naeem F, Nasr T, Lewis G, Knowles JA, Ayub M, Kuchenbaecker K. Study protocol of DIVERGE, the first genetic epidemiological study of major depressive disorder in Pakistan. Psychiatr Genet 2023; 33:69-78. [PMID: 36538573 PMCID: PMC9997631 DOI: 10.1097/ypg.0000000000000333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Globally, 80% of the burdenof major depressive disorder (MDD) pertains to low- and middle-income countries. Research into genetic and environmental risk factors has the potential to uncover disease mechanisms that may contribute to better diagnosis and treatment of mental illness, yet has so far been largely limited to participants with European ancestry from high-income countries. The DIVERGE study was established to help overcome this gap and investigate genetic and environmental risk factors for MDD in Pakistan. METHODS DIVERGE aims to enrol 9000 cases and 4000 controls in hospitals across the country. Here, we provide the rationale for DIVERGE, describe the study protocol and characterise the sample using data from the first 500 cases. Exploratory data analysis is performed to describe demographics, socioeconomic status, environmental risk factors, family history of mental illness and psychopathology. RESULTS AND DISCUSSION Many participants had severe depression with 74% of patients who experienced multiple depressive episodes. It was a common practice to seek help for mental health struggles from faith healers and religious leaders. Socioeconomic variables reflected the local context with a large proportion of women not having access to any education and the majority of participants reporting no savings. CONCLUSION DIVERGE is a carefully designed case-control study of MDD in Pakistan that captures diverse risk factors. As the largest genetic study in Pakistan, DIVERGE helps address the severe underrepresentation of people from South Asian countries in genetic as well as psychiatric research.
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Affiliation(s)
| | - Arsalan Hassan
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | - Eirini Zartaloudi
- Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Fahad Hussain
- Lahore Institute of Research and Development, Lahore
| | - Muhammad Umar
- Lahore Institute of Research and Development, Lahore
| | - Bakht Khizar
- Lahore Institute of Research and Development, Lahore
| | | | | | | | | | - Ali Burhan Mustafa
- Department of Psychiatry and Behavioural Sciences, Sheikh Zayed Medical College/Hospital, Rahim Yar Khan
| | - Moin Ahmed Ansari
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Syed Qalb I Hyder
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Muhammad Ali
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Nilofar Ilyas
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Parveen Channar
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Nazish Mughal
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | - Sumera Channa
- Sir Cowasjee Jehangir Institute of Psychiatric and Behavioral Sciences, Hyderabad
| | | | | | | | | | | | | | | | | | | | - Gohar Ali
- Department of Psychiatry, Saidu Teaching Hospital
| | | | | | - Noman Ahmad
- Punjab Institute of Mental Health (PIMH), Lahore, Pakistan
| | - Saeed Farooq
- School of Medicine, Keele University, Keele
- Innovation Department, Midlands Partnership NHS Foundation Trust, Staffotdshire, UK
| | - Farooq Naeem
- Department of Psychiatry, University of Toronto
- Center for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Tanveer Nasr
- Lahore Institute of Research and Development, Lahore
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, UK
| | - James A. Knowles
- Human Genetics Institute of New Jersey (HGINJ), Rutgers University, New Brunswick, New Jersey, USA
| | - Muhammad Ayub
- Division of Psychiatry, University College London, London, UK
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
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20
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Campos AI, Ingold N, Huang Y, Mitchell BL, Kho PF, Han X, García-Marín LM, Ong JS, Law MH, Yokoyama JS, Martin NG, Dong X, Cuellar-Partida G, MacGregor S, Aslibekyan S, Rentería ME. Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring. Sleep 2023; 46:6918774. [PMID: 36525587 PMCID: PMC9995783 DOI: 10.1093/sleep/zsac308] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 11/11/2022] [Indexed: 12/23/2022] Open
Abstract
STUDY OBJECTIVES Despite its association with severe health conditions, the etiology of sleep apnea (SA) remains understudied. This study sought to identify genetic variants robustly associated with SA risk. METHODS We performed a genome-wide association study (GWAS) meta-analysis of SA across five cohorts (NTotal = 523 366), followed by a multi-trait analysis of GWAS (multi-trait analysis of genome-wide association summary statistics [MTAG]) to boost power, leveraging the high genetic correlation between SA and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional and joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal = 1 477 352; Ncases = 175 522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method. RESULTS Our SA meta-analysis identified five independent variants with evidence of association beyond genome-wide significance. After adjustment for BMI, only one genome-wide significant variant was identified. MTAG analyses uncovered 49 significant independent loci associated with SA risk. Twenty-nine variants were replicated in the 23andMe GWAS adjusting for BMI. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions. CONCLUSION Our study uncovered multiple genetic loci associated with SA risk, thus increasing our understanding of the etiology of this condition and its relationship with other complex traits.
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Affiliation(s)
- Adrian I Campos
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.,Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Nathan Ingold
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Brittany L Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Pik-Fang Kho
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Xikun Han
- Program in Genetic Epidemiology and Statistical Genetics, Harvard University T.H. Chan School of Public Health, Boston, MA, USA
| | - Luis M García-Marín
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Jue-Sheng Ong
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Matthew H Law
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Jennifer S Yokoyama
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.,Weill Institute of Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Xianjun Dong
- Genomics and Bioinformatics Hub, Brigham and Women's Hospital, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Miguel E Rentería
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
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21
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Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, Lind PA, Farajzadeh L, Wang L, Hatoum AS, Johnson J, Lee H, Mallard TT, Xu J, Johnston KJ, Johnson EC, Galimberti M, Dao C, Levey DF, Overstreet C, Byrne EM, Gillespie NA, Gordon S, Hickie IB, Whitfield JB, Xu K, Zhao H, Huckins LM, Davis LK, Sanchez-Roige S, Madden PAF, Heath AC, Medland SE, Martin NG, Ge T, Smoller JW, Hougaard DM, Børglum AD, Demontis D, Krystal JH, Gaziano JM, Edenberg HJ, Agrawal A, Justice AC, Stein MB, Kranzler HR, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.24.23284960. [PMID: 36747741 PMCID: PMC9901058 DOI: 10.1101/2023.01.24.23284960] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. To improve our understanding of the genetics of PAU, we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals. We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine-mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and/or chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by drug repurposing analysis. Cross-ancestry polygenic risk scores (PRS) showed better performance in independent sample than single-ancestry PRS. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. The analysis of diverse ancestries contributed significantly to the findings, and fills an important gap in the literature.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- These authors contributed equally
| | - Rachel L. Kember
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- These authors contributed equally
| | - Joseph D. Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Yuan
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Penelope A. Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Lu Wang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S. Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, Saint Louis, MO, USA
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T. Mallard
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Emma C. Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Marco Galimberti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cecilia Dao
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA
| | - Daniel F. Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Enda M. Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Nathan A. Gillespie
- Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B. Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - John B. Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Laura M. Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pamela A. F. Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C. Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G. Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Tian Ge
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W. Smoller
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David M. Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Anders D. Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - John H. Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Medicine, Divisions of Aging and Preventative Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Amy C. Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Murray B. Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R. Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- These authors jointly supervised this work
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- These authors jointly supervised this work
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22
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Zhao J, Chapman E, Houghton S. Key Predictive Factors in the Mental Health of Chinese University Students at Home and Abroad. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16103. [PMID: 36498176 PMCID: PMC9739269 DOI: 10.3390/ijerph192316103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
The prevalence of reported mental health problems among university students has increased at alarming rates in recent years. While various negative life events (from personal events such as relationship breakdowns to more global events such as COVID-19 [SARS-CoV-2] pandemic) have been found to be important predictors of poor mental health in this population, some individuals have been found robustly to fare better than others in confronting such events. Identifying factors that predict these individuals' mental health, along with the specific coping strategies they utilize may have significant practical implications when confronted by adverse events such as COVID-19. This study investigated relationships between the impact of the COVID-19 pandemic on 828 (453 females, 374 males, and one "Other") Chinese university students' mental health, and their internal strengths, personality characteristics, and demographic profiles. We also investigated whether students' use of specific coping strategies mediated these relationships. Stepwise multiple regression analyses (MRAs) and a path analysis revealed that students who resided in their home country, had higher levels of internal strengths, a lower level of neuroticism and a higher level of agreeableness and reported fewer negative mental health changes than did other respondents during COVID-19 in the second half of 2020. Self-regulation and withdrawal coping strategies were both important mediators of these relationships. These findings have important implications for universities in identifying and assisting students in the face of adverse events such as COVID-19.
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Affiliation(s)
| | - Elaine Chapman
- Graduate School of Education, The University of Western Australia, Crawley 6009, Australia
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23
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Boyce JO, Jackson VE, van Reyk O, Parker R, Vogel AP, Eising E, Horton SE, Gillespie NA, Scheffer IE, Amor DJ, Hildebrand MS, Fisher SE, Martin NG, Reilly S, Bahlo M, Morgan AT. Self-reported impact of developmental stuttering across the lifespan. Dev Med Child Neurol 2022; 64:1297-1306. [PMID: 35307825 DOI: 10.1111/dmcn.15211] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 02/24/2022] [Accepted: 02/28/2022] [Indexed: 02/05/2023]
Abstract
AIM To examine the phenomenology of stuttering across the lifespan in the largest prospective cohort to date. METHOD Participants aged 7 years and older with a history of developmental stuttering were recruited. Self-reported phenotypic data were collected online including stuttering symptomatology, co-occurring phenotypes, genetic predisposition, factors associated with stuttering severity, and impact on anxiety, education, and employment. RESULTS A total of 987 participants (852 adults: 590 males, 262 females, mean age 49 years [SD = 17 years 10 months; range = 18-93 years] and 135 children: 97 males, 38 females, mean age 11 years 4 months [SD = 3 years; range = 7-17 years]) were recruited. Stuttering onset occurred at age 3 to 6 years in 64.0%. Blocking (73.2%) was the most frequent phenotype; 75.9% had sought stuttering therapy and 15.5% identified as having recovered. Half (49.9%) reported a family history. There was a significant negative correlation with age for both stuttering frequency and severity in adults. Most were anxious due to stuttering (90.4%) and perceived stuttering as a barrier to education and employment outcomes (80.7%). INTERPRETATION The frequent persistence of stuttering and the high proportion with a family history suggest that stuttering is a complex trait that does not often resolve, even with therapy. These data provide new insights into the phenotype and prognosis of stuttering, information that is critically needed to encourage the development of more effective speech therapies. WHAT THIS PAPER ADDS Half of the study cohort had a family history of stuttering. While 75.9% of participants had sought stuttering therapy, only 15.5% identified as having recovered. There was a significant negative correlation between age and stuttering frequency and severity in adults.
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Affiliation(s)
- Jessica O Boyce
- Speech and Language, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Audiology and Speech Pathology, University of Melbourne, Parkville, VIC, Australia
| | - Victoria E Jackson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
| | - Olivia van Reyk
- Speech and Language, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Richard Parker
- Queensland Institute for Medical Research, Berghofer Medical Research Institute, Brisbane, Australia
| | - Adam P Vogel
- Department of Audiology and Speech Pathology, University of Melbourne, Parkville, VIC, Australia.,Centre for Neuroscience of Speech, University of Melbourne, Parkville, VIC, Australia.,Redenlab Inc, Melbourne, VIC, Australia
| | - Else Eising
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Sarah E Horton
- Speech and Language, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Audiology and Speech Pathology, University of Melbourne, Parkville, VIC, Australia
| | - Nathan A Gillespie
- Queensland Institute for Medical Research, Berghofer Medical Research Institute, Brisbane, Australia.,Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, VA, USA
| | - Ingrid E Scheffer
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Heidelberg, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - David J Amor
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia.,Royal Children's Hospital
| | - Michael S Hildebrand
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Heidelberg, VIC, Australia
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Nicholas G Martin
- Queensland Institute for Medical Research, Berghofer Medical Research Institute, Brisbane, Australia
| | - Sheena Reilly
- Menzies Health Institute Queensland, Griffith University, Southport, Australia
| | - Melanie Bahlo
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
| | - Angela T Morgan
- Speech and Language, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Audiology and Speech Pathology, University of Melbourne, Parkville, VIC, Australia.,Royal Children's Hospital
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24
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Campos AI, Ngo TT, Medland SE, Wray NR, Hickie IB, Byrne EM, Martin NG, Rentería ME. Genetic risk for chronic pain is associated with lower antidepressant effectiveness: Converging evidence for a depression subtype. Aust N Z J Psychiatry 2022; 56:1177-1186. [PMID: 34266302 DOI: 10.1177/00048674211031491] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Chronic pain and depression are highly comorbid and difficult-to-treat disorders. We previously showed this comorbidity is associated with higher depression severity, lower antidepressant treatment effectiveness and poorer prognosis in the Australian Genetics of Depression Study. OBJECTIVE The current study aimed to assess whether a genetic liability to chronic pain is associated with antidepressant effectiveness over and above the effect of genetic factors for depression in a sample of 12,863 Australian Genetics of Depression Study participants. METHODS Polygenic risk scores were calculated using summary statistics from genome-wide association studies of multisite chronic pain and major depression. Cumulative linked regressions were employed to assess the association between polygenic risk scores and antidepressant treatment effectiveness across 10 different medications. RESULTS Mixed-effects logistic regressions showed that individual genetic propensity for chronic pain, but not major depression, was significantly associated with patient-reported chronic pain (PainPRS OR = 1.17 [1.12, 1.22]; MDPRS OR = 1.01 [0.98, 1.06]). Significant associations were also found between lower antidepressant effectiveness and genetic risk for chronic pain or for major depression. However, a fully adjusted model showed the effect of PainPRS (adjOR = 0.93 [0.90, 0.96]) was independent of MDPRS (adjOR = 0.96 [0.93, 0.99]). Sensitivity analyses were performed to assess the robustness of these results. After adjusting for depression severity measures (i.e. age of onset; number of depressive episodes; interval between age at study participation and at depression onset), the associations between PainPRS and patient-reported chronic pain with lower antidepressant effectiveness remained significant (0.95 [0.92, 0.98] and 0.84 [0.78, 0.90], respectively). CONCLUSION These results suggest genetic risk for chronic pain accounted for poorer antidepressant effectiveness, independent of the genetic risk for major depression. Our results, along with independent converging evidence from other studies, point towards a difficult-to-treat depression subtype characterised by comorbid chronic pain. This finding warrants further investigation into the implications for biologically based nosology frameworks in pain medicine and psychiatry.
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Affiliation(s)
- Adrián I Campos
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Trung Thanh Ngo
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.,UQ Diamantina Institute, The University of Queensland and Translational Research Institute, Woolloongabba, QLD, Australia
| | - Sarah E Medland
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Miguel E Rentería
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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25
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Campos AI, Byrne EM, Iorfino F, Fabbri C, Hickie IB, Lewis CM, Wray NR, Medland SE, Rentería ME, Martin NG. Clinical, demographic, and genetic risk factors of treatment-attributed suicidality in >10,000 Australian adults taking antidepressants. Am J Med Genet B Neuropsychiatr Genet 2022; 189:196-206. [PMID: 35833543 PMCID: PMC9544797 DOI: 10.1002/ajmg.b.32913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 04/07/2022] [Accepted: 06/28/2022] [Indexed: 11/09/2022]
Abstract
Emergence of suicidal symptoms has been reported as a potential antidepressant adverse drug reaction. Identifying risk factors associated could increase our understanding of this phenomenon and stratify individuals at higher risk. Logistic regressions were used to identify risk factors of self-reported treatment-attributed suicidal ideation (TASI). We then employed classifiers to test the predictive ability of the variables identified. A TASI GWAS, as well as SNP-based heritability estimation, were performed. GWAS replication was sought from an independent study. Significant associations were found for age and comorbid conditions, including bipolar and personality disorders. Participants reporting TASI from one antidepressant were more likely to report TASI from other antidepressants. No genetic loci associated with TAS I (p < 5e-8) were identified. Of 32 independent variants with suggestive association (p < 1e-5), 27 lead SNPs were available in a replication dataset from the GENDEP study. Only one variant showed a consistent effect and nominal association in the independent replication sample. Classifiers were able to stratify non-TASI from TASI participants (AUC = 0.77) and those reporting treatment-attributed suicide attempts (AUC = 0.85). The pattern of TASI co-occurrence across participants suggest nonspecific factors underlying its etiology. These findings provide insights into the underpinnings of TASI and serve as a proof-of-concept of the use of classifiers for risk stratification.
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Affiliation(s)
- Adrian I. Campos
- Department of Genetics and Computational BiologyQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia,School of Biomedical Sciences, Faculty of MedicineThe University of QueenslandBrisbaneQueenslandAustralia,Institute for Molecular BioscienceThe University of QueenslandBrisbaneQueenslandAustralia
| | - Enda M. Byrne
- Institute for Molecular BioscienceThe University of QueenslandBrisbaneQueenslandAustralia,Child Health Research CentreThe University of QueenslandBrisbaneQueenslandAustralia
| | - Frank Iorfino
- Brain and Mind CentreThe University of SydneyCamperdownNew South WalesAustralia
| | - Chiara Fabbri
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK,Department of Biomedical and Neuromotor SciencesUniversity of BolognaBolognaItaly
| | - Ian B. Hickie
- Brain and Mind CentreThe University of SydneyCamperdownNew South WalesAustralia
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Naomi R. Wray
- Institute for Molecular BioscienceThe University of QueenslandBrisbaneQueenslandAustralia,Queensland Brain InstituteThe University of QueenslandBrisbaneQueenslandAustralia
| | - Sarah E. Medland
- Department of Genetics and Computational BiologyQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
| | - Miguel E. Rentería
- Department of Genetics and Computational BiologyQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia,School of Biomedical Sciences, Faculty of MedicineThe University of QueenslandBrisbaneQueenslandAustralia
| | - Nicholas G. Martin
- Department of Genetics and Computational BiologyQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
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26
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Kiewa J, Meltzer-Brody S, Milgrom J, Bennett E, Mackle T, Guintivano J, Hickie IB, Colodro-Conde L, Medland SE, Martin N, Wray N, Byrne E. Lifetime prevalence and correlates of perinatal depression in a case-cohort study of depression. BMJ Open 2022; 12:e059300. [PMID: 35973706 PMCID: PMC9621163 DOI: 10.1136/bmjopen-2021-059300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES This study sought to evaluate the prevalence, timing of onset and duration of symptoms of depression in the perinatal period (PND) in women with depression, according to whether they had a history of depression prior to their first perinatal period. We further sought to identify biopsychosocial correlates of perinatal symptoms in women with depression. DESIGN AND SETTING The Australian Genetics of Depression Study is an online case cohort study of the aetiology of depression. For a range of variables, women with depression who report significant perinatal depressive symptoms were compared with women with lifetime depression who did not experience perinatal symptoms. PARTICIPANTS In a large sample of parous women with major depressive disorder (n=7182), we identified two subgroups of PND cases with and without prior depression history (n=2261; n=878, respectively). PRIMARY AND SECONDARY OUTCOME MEASURES The primary outcome measure was a positive screen for PND on the lifetime version of the Edinburgh Postnatal Depression Scale. Descriptive measures reported lifetime prevalence, timing of onset and duration of PND symptoms. There were no secondary outcome measures. RESULTS The prevalence of PND among parous women was 70%. The majority of women reported at least one perinatal episode with symptoms both antenatally and postnatally. Of women who experienced depression prior to first pregnancy, PND cases were significantly more likely to report more episodes of depression (OR=1.15 per additional depression episode, 95% CI 1.13 to 1.17, p<0.001), non-European ancestry (OR 1.5, 95% CI 1.0 to 2.1, p=0.03), severe nausea during pregnancy (OR 1.3, 95% CI 1.1 to 1.6, p=0.006) and emotional abuse (OR 1.4, 95% CI 1.1 to 1.7, p=0.005). CONCLUSIONS The majority of parous women with lifetime depression in this study experienced PND, associated with more complex, severe depression. Results highlight the importance of perinatal assessments of depressive symptoms, particularly for women with a history of depression or childhood adverse experiences.
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Affiliation(s)
- Jacqueline Kiewa
- Child Health Research Centre, The University of Queensland Faculty of Medicine and Biomedical Sciences, Brisbane, Queensland, Australia
| | | | - Jeannette Milgrom
- Parent-Infant Research Institute, Heidelberg Heights, Victoria, Australia
- University of Melbourne, Melbourne, Victoria, Australia
| | | | | | - Jerry Guintivano
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | | | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Nick Martin
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Naomi Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Enda Byrne
- Child Health Research Centre, The University of Queensland Faculty of Medicine and Biomedical Sciences, Brisbane, Queensland, Australia
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Polimanti R. Not Only Gene Discovery: Genome-wide Association Studies and Polygenic Risk Scores as Tools to Dissect the Heterogeneity of Major Depressive Disorder. Biol Psychiatry 2022; 92:177-178. [PMID: 35835505 PMCID: PMC9514509 DOI: 10.1016/j.biopsych.2022.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022]
Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, West Haven, Connecticut; VA Connecticut Health Care Center, West Haven, Connecticut.
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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: 36] [Impact Index Per Article: 18.0] [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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Kiewa J, Meltzer-Brody S, Milgrom J, Guintivano J, Hickie IB, Whiteman DC, Olsen CM, Colodro-Conde L, Medland SE, Martin NG, Wray NR, Byrne EM. Perinatal depression is associated with a higher polygenic risk for major depressive disorder than non-perinatal depression. Depress Anxiety 2022; 39:182-191. [PMID: 34985809 DOI: 10.1002/da.23232] [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: 06/18/2021] [Revised: 11/19/2021] [Accepted: 12/12/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Distinctions between major depressive disorder (MDD) and perinatal depression (PND) reflect varying views of PND, from a unique etiological subtype of MDD to an MDD episode that happens to coincide with childbirth. This case-control study investigated genetic differences between PND and MDD outside the perinatal period (non-perinatal depression or NPD). METHODS We conducted a genome-wide association study using PND cases (Edinburgh Postnatal Depression Scale score ≥ 13) from the Australian Genetics of Depression Study 2018 data (n = 3804) and screened controls (n = 6134). Results of gene-set enrichment analysis were compared with those of women with non-PND. For six psychiatric disorders/traits, genetic correlations with PND were evaluated, and logistic regression analysis reported polygenic score (PGS) association with both PND and NPD. RESULTS Genes differentially expressed in ovarian tissue were significantly enriched (stdBeta = 0.07, p = 3.3e-04), but were not found to be associated with NPD. The genetic correlation between PND and MDD was 0.93 (SE = 0.07; p = 3.5e-38). Compared with controls, PGS for MDD are higher for PND cases (odds ratio [OR] = 1.8, confidence interval [CI] = [1.7-1.8], p = 9.5e-140) than for NPD cases (OR = 1.6, CI = [1.5-1.7], p = 1.2e-49). Highest risk is for those reporting both antenatal and postnatal depression, irrespective of prior MDD history. CONCLUSIONS PND has a high genetic overlap with MDD, but points of distinction focus on differential expression in ovarian tissue and higher MDD PGS, particularly for women experiencing both antenatal and postpartum PND.
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Affiliation(s)
- Jacqueline Kiewa
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Samantha Meltzer-Brody
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jeanette Milgrom
- Parent-Infant Research Institute, Austin Health, Melbourne, Victoria, Australia.,Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jerry Guintivano
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - David C Whiteman
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Catherine M Olsen
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
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30
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Campos AI, Byrne EM, Mitchell BL, Wray NR, Lind PA, Licinio J, Medland SE, Martin NG, Hickie IB, Rentería ME. Impact of CYP2C19 metaboliser status on SSRI response: a retrospective study of 9500 participants of the Australian Genetics of Depression Study. THE PHARMACOGENOMICS JOURNAL 2022; 22:130-135. [PMID: 35094016 PMCID: PMC8975743 DOI: 10.1038/s41397-022-00267-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND Variation within the CYP2C19 gene has been linked to differential metabolism of selective serotonin reuptake inhibitors (SSRIs). Pharmacogenetic recommendations based on the effect of CYP2C19 variants have been made available and are used increasingly by clinical practitioners. Nonetheless, the underlying assumption linking differential metabolism to efficacy or adverse side effects remains understudied. Here, we aim to fill this gap by studying CYP2C19 polymorphisms and inferred metabolism and patient-reported antidepressant response in a sample of 9531 Australian adults who have taken SSRIs. METHODS Metaboliser status was inferred for participants based on CYP2C19 alleles. Primary analysis consisted of assessing differences in treatment efficacy and tolerability between normal (reference) and: ultrarapid, rapid, intermediate and poor metabolisers. RESULTS Across medications, poor metabolisers reported a higher efficacy, whereas rapid metabolisers reported higher tolerability. When stratified by drug, associations between metaboliser status and efficacy did not survive multiple testing correction. Intermediate metabolisers were at greater odds of reporting any side effect for sertraline and higher number of side effects across medications and for sertraline. CONCLUSIONS The effects between metaboliser status and treatment efficacy, tolerability and side effects were in the expected direction. Our power analysis suggests we would detect moderate to large effects, at least nominally. Reduced power may also be explained by heterogeneity in antidepressant dosages or concomitant medications, which we did not measure. The fact that we identify slower metabolisers to be at higher risk of side effects even without adjusting for clinical titration, and the nominally significant associations consistent with the expected metabolic effects provide new evidence for the link between CYP2C19 metabolism and SSRI response. Nonetheless, longitudinal and interventional designs such as randomized clinical trials that stratify by metaboliser status are necessary to establish the effects of CYP2C19 metabolism on SSRI treatment efficacy or adverse effects.
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Affiliation(s)
- Adrian I Campos
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Brittany L Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Penelope A Lind
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Julio Licinio
- Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, NY, USA
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Miguel E Rentería
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
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31
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Bivol S, Mellick GD, Gratten J, Parker R, Mulcahy A, Mosley PE, Poortvliet PC, Campos AI, Mitchell BL, Garcia-Marin LM, Cross S, Ferguson M, Lind PA, Loesch DZ, Visscher PM, Medland SE, Scherzer CR, Martin NG, Rentería ME. Australian Parkinson's Genetics Study (APGS): pilot (n=1532). BMJ Open 2022; 12:e052032. [PMID: 35217535 PMCID: PMC8883215 DOI: 10.1136/bmjopen-2021-052032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 01/31/2022] [Indexed: 11/04/2022] Open
Abstract
PURPOSE Parkinson's disease (PD) is a neurodegenerative disorder associated with progressive disability. While the precise aetiology is unknown, there is evidence of significant genetic and environmental influences on individual risk. The Australian Parkinson's Genetics Study seeks to study genetic and patient-reported data from a large cohort of individuals with PD in Australia to understand the sociodemographic, genetic and environmental basis of PD susceptibility, symptoms and progression. PARTICIPANTS In the pilot phase reported here, 1819 participants were recruited through assisted mailouts facilitated by Services Australia based on having three or more prescriptions for anti-PD medications in their Pharmaceutical Benefits Scheme records. The average age at the time of the questionnaire was 64±6 years. We collected patient-reported information and sociodemographic variables via an online (93% of the cohort) or paper-based (7%) questionnaire. One thousand five hundred and thirty-two participants (84.2%) met all inclusion criteria, and 1499 provided a DNA sample via traditional post. FINDINGS TO DATE 65% of participants were men, and 92% identified as being of European descent. A previous traumatic brain injury was reported by 16% of participants and was correlated with a younger age of symptom onset. At the time of the questionnaire, constipation (36% of participants), depression (34%), anxiety (17%), melanoma (16%) and diabetes (10%) were the most reported comorbid conditions. FUTURE PLANS We plan to recruit sex-matched and age-matched unaffected controls, genotype all participants and collect non-motor symptoms and cognitive function data. Future work will explore the role of genetic and environmental factors in the aetiology of PD susceptibility, onset, symptoms, and progression, including as part of international PD research consortia.
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Affiliation(s)
- Svetlana Bivol
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - George D Mellick
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, QLD, Australia
| | - Jacob Gratten
- Mater Research, Translational Research Institute, Brisbane, QLD, Australia
| | - Richard Parker
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Aoibhe Mulcahy
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Philip E Mosley
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Peter C Poortvliet
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, QLD, Australia
| | - Adrian I Campos
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Brittany L Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Luis M Garcia-Marin
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Simone Cross
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Mary Ferguson
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Penelope A Lind
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Danuta Z Loesch
- School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Clemens R Scherzer
- Center for Advanced Parkinson Research, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Precision Neurology Program, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA, USA
| | | | - Miguel E Rentería
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Center for Advanced Parkinson Research, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
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Davies MR, Buckman JEJ, Adey BN, Armour C, Bradley JR, Curzons SCB, Davies HL, Davis KAS, Goldsmith KA, Hirsch CR, Hotopf M, Hübel C, Jones IR, Kalsi G, Krebs G, Lin Y, Marsh I, McAtarsney-Kovacs M, McIntosh AM, Mundy J, Monssen D, Peel AJ, Rogers HC, Skelton M, Smith DJ, Ter Kuile A, Thompson KN, Veale D, Walters JTR, Zahn R, Breen G, Eley TC. Comparison of symptom-based versus self-reported diagnostic measures of anxiety and depression disorders in the GLAD and COPING cohorts. J Anxiety Disord 2022; 85:102491. [PMID: 34775166 DOI: 10.1016/j.janxdis.2021.102491] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 10/13/2021] [Accepted: 10/20/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Understanding and improving outcomes for people with anxiety or depression often requires large sample sizes. To increase participation and reduce costs, such research is typically unable to utilise "gold-standard" methods to ascertain diagnoses, instead relying on remote, self-report measures. AIMS Assess the comparability of remote diagnostic methods for anxiety and depression disorders commonly used in research. METHOD Participants from the UK-based GLAD and COPING NBR cohorts (N = 58,400) completed an online questionnaire between 2018 and 2020. Responses to detailed symptom reports were compared to DSM-5 criteria to generate symptom-based diagnoses of major depressive disorder (MDD), generalised anxiety disorder (GAD), specific phobia, social anxiety disorder, panic disorder, and agoraphobia. Participants also self-reported any prior diagnoses from health professionals, termed self-reported diagnoses. "Any anxiety" included participants with at least one anxiety disorder. Agreement was assessed by calculating accuracy, Cohen's kappa, McNemar's chi-squared, sensitivity, and specificity. RESULTS Agreement between diagnoses was moderate for MDD, any anxiety, and GAD, but varied by cohort. Agreement was slight to fair for the phobic disorders. Many participants with self-reported GAD did not receive a symptom-based diagnosis. In contrast, symptom-based diagnoses of the phobic disorders were more common than self-reported diagnoses. CONCLUSIONS Agreement for MDD, any anxiety, and GAD was higher for cases in the case-enriched GLAD cohort and for controls in the general population COPING NBR cohort. For anxiety disorders, self-reported diagnoses classified most participants as having GAD, whereas symptom-based diagnoses distributed participants more evenly across the anxiety disorders. Further validation against gold standard measures is required.
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Affiliation(s)
- Molly R Davies
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Joshua E J Buckman
- Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational and Health Psychology, University College London, Gower Street, London WC1E 7HB, UK; iCope - Camden and Islington Psychological Therapies Services, Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, UK
| | - Brett N Adey
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Chérie Armour
- Stress, Trauma & Related Conditions (STARC) research lab, School of Psychology, Queens University Belfast (QUB), Belfast, Northern Ireland, UK
| | - John R Bradley
- NIHR BioResource, Cambridge University Hospitals, Cambridge Biomedical Campus, Cambridge, UK
| | - Susannah C B Curzons
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Helena L Davies
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Katrina A S Davis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Kimberley A Goldsmith
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Colette R Hirsch
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Christopher Hübel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; National Centre for Register-based Research, Aarhus Business and Social Sciences, Aarhus University, Aarhus, Denmark
| | - Ian R Jones
- National Centre for Mental Health, Division of Psychiatry and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | - Gursharan Kalsi
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Georgina Krebs
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - Yuhao Lin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Ian Marsh
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Monika McAtarsney-Kovacs
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinurgh, UK
| | - Jessica Mundy
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Dina Monssen
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Alicia J Peel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK
| | - Henry C Rogers
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Megan Skelton
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Daniel J Smith
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Abigail Ter Kuile
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Katherine N Thompson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - David Veale
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK; South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
| | - James T R Walters
- National Centre for Mental Health, Division of Psychiatry and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | - Roland Zahn
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Gerome Breen
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK
| | - Thalia C Eley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, Camberwell, London, UK; National Institute for Health Research (NIHR) Biomedical Research Centre, South London and Maudsley Hospital, London, UK.
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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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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: 66] [Impact Index Per Article: 22.0] [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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Mitchell BL, Thorp JG, Wu Y, Campos AI, Nyholt DR, Gordon SD, Whiteman DC, Olsen CM, Hickie IB, Martin NG, Medland SE, Wray NR, Byrne EM. Polygenic Risk Scores Derived From Varying Definitions of Depression and Risk of Depression. JAMA Psychiatry 2021; 78:1152-1160. [PMID: 34379077 PMCID: PMC8358814 DOI: 10.1001/jamapsychiatry.2021.1988] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Genetic studies with broad definitions of depression may not capture genetic risk specific to major depressive disorder (MDD), raising questions about how depression should be operationalized in future genetic studies. OBJECTIVE To use a large, well-phenotyped single study of MDD to investigate how different definitions of depression used in genetic studies are associated with estimation of MDD and phenotypes of MDD, using polygenic risk scores (PRSs). DESIGN, SETTING, AND PARTICIPANTS In this case-control polygenic risk score analysis, patients meeting diagnostic criteria for a diagnosis of MDD were drawn from the Australian Genetics of Depression Study, a cross-sectional, population-based study of depression, and controls and patients with self-reported depression were drawn from QSkin, a population-based cohort study. Data analyzed herein were collected before September 2018, and data analysis was conducted from September 10, 2020, to January 27, 2021. MAIN OUTCOME AND MEASURES Polygenic risk scores generated from genome-wide association studies using different definitions of depression were evaluated for estimation of MDD in and within individuals with MDD for an association with age at onset, adverse childhood experiences, comorbid psychiatric and somatic disorders, and current physical and mental health. RESULTS Participants included 12 106 (71% female; mean age, 42.3 years; range, 18-88 years) patients meeting criteria for MDD and 12 621 (55% female; mean age, 60.9 years; range, 43-87 years) control participants with no history of psychiatric disorders. The effect size of the PRS was proportional to the discovery sample size, with the largest study having the largest effect size with the odds ratio for MDD (1.75; 95% CI, 1.73-1.77) per SD of PRS and the PRS derived from ICD-10 codes documented in hospitalization records in a population health cohort having the lowest odds ratio (1.14; 95% CI, 1.12-1.16). When accounting for differences in sample size, the PRS from a genome-wide association study of patients meeting diagnostic criteria for MDD and control participants was the best estimator of MDD, but not in those with self-reported depression, and associations with higher odds ratios with childhood adverse experiences and measures of somatic distress. CONCLUSIONS AND RELEVANCE These findings suggest that increasing sample sizes, regardless of the depth of phenotyping, may be most informative for estimating risk of depression. The next generation of genome-wide association studies should, like the Australian Genetics of Depression Study, have both large sample sizes and extensive phenotyping to capture genetic risk factors for MDD not identified by other definitions of depression.
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Affiliation(s)
- Brittany L. Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, Australia,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Jackson G. Thorp
- QIMR Berghofer Medical Research Institute, Brisbane, Australia,Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Adrian I. Campos
- QIMR Berghofer Medical Research Institute, Brisbane, Australia,Faculty of Medicine, The University of Queensland, Brisbane, Australia,School of Biomedical Sciences, The University of Queensland, Brisbane, Australia
| | - Dale R. Nyholt
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia,Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Australia
| | - Scott D. Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | | | - Ian B. Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | | | | | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Enda M. Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia,Child Health Research Centre, The University of Queensland, Brisbane, Australia
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Genetic Susceptibility to Pneumonia: A GWAS Meta-Analysis Between the UK Biobank and FinnGen. Twin Res Hum Genet 2021; 24:145-154. [PMID: 34340725 DOI: 10.1017/thg.2021.27] [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] [Indexed: 12/31/2022]
Abstract
Pneumonia is a respiratory condition with complex etiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation. Here, we analyze pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9980 cases and 86,519 controls) and perform a genomewide association study meta-analysis. We use gene-based tests, colocalization, genetic correlation, latent causal variable (LCV) and polygenic prediction in an independent Australian sample (N = 5595) to draw insights into the etiology of pneumonia risk. We identify two independent loci on chromosome 15 (lead single-nucleotide polymorphisms rs2009746 and rs76474922) to be associated with pneumonia (p < 5e-8). Gene-based tests revealed 18 genes in chromosomes 15, 16 and 9, including IL127, PBX3, ApoB receptor (APOBR) and smoking related genes CHRNA3/5, statistically associated with pneumonia. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. LCV analysis suggests a strong genetic causal relationship with cardiovascular health phenotypes. Polygenic risk scores for pneumonia significantly predicted self-reported pneumonia in an independent sample, albeit with a small effect size (OR = 1.11 95% CI [1.04, 1.19], p < .05). Sensitivity analyses suggested the associations in chromosome 15 are mediated by smoking history, but the associations in chromosomes 16 and 9, and polygenic prediction were robust to adjustment for smoking. Altogether, our results highlight common genetic variants, genes and potential pathways that contribute to individual differences in susceptibility to pneumonia, and advance our understanding of the genetic factors underlying heterogeneity in respiratory medical outcomes.
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Coleman JR. The Validity of Brief Phenotyping in Population Biobanks for Psychiatric Genome-Wide Association Studies on the Biobank Scale. Complex Psychiatry 2021; 7:11-15. [PMID: 34883499 PMCID: PMC8443942 DOI: 10.1159/000516837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 04/14/2021] [Indexed: 11/19/2022] Open
Affiliation(s)
- Jonathan R.I. Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Roughan WH, Campos AI, García-Marín LM, Cuéllar-Partida G, Lupton MK, Hickie IB, Medland SE, Wray NR, Byrne EM, Ngo TT, Martin NG, Rentería ME. Comorbid Chronic Pain and Depression: Shared Risk Factors and Differential Antidepressant Effectiveness. Front Psychiatry 2021; 12:643609. [PMID: 33912086 PMCID: PMC8072020 DOI: 10.3389/fpsyt.2021.643609] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/12/2021] [Indexed: 02/06/2023] Open
Abstract
The bidirectional relationship between depression and chronic pain is well-recognized, but their clinical management remains challenging. Here we characterize the shared risk factors and outcomes for their comorbidity in the Australian Genetics of Depression cohort study (N = 13,839). Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health. Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for 10 different antidepressants. Chronic pain was associated with an increased risk of depression (OR = 1.86 [1.37-2.54]), recent suicide attempt (OR = 1.88 [1.14-3.09]), higher use of tobacco (OR = 1.05 [1.02-1.09]) and misuse of painkillers (e.g., opioids; OR = 1.31 [1.06-1.62]). Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR = 0.75 [0.68-0.83]), escitalopram (OR = 0.75 [0.67-0.85]) and venlafaxine (OR = 0.78 [0.68-0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR = 0.45 [0.30-0.67]), escitalopram (OR = 0.45 [0.27-0.74]) and citalopram (OR = 0.32 [0.15-0.67]) specifically for chronic pain (among other indications) reported lower benefits compared to other participants taking these same medications but not for chronic pain. These findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further examination is warranted in targeted interventional clinical trials, which also include neuroimaging genetics and pharmacogenomics protocols. This work will advance the delineation of disease risk indicators and novel aetiological pathways for therapeutic intervention in comorbid pain and depression as well as other psychiatric comorbidities.
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Affiliation(s)
- William H. Roughan
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Adrián I. Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Luis M. García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Gabriel Cuéllar-Partida
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- UQ Diamantina Institute, The University of Queensland and Translational Research Institute, Brisbane, QLD, Australia
| | - Michelle K. Lupton
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B. Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - Sarah E. Medland
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Enda M. Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Trung Thanh Ngo
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- UQ Diamantina Institute, The University of Queensland and Translational Research Institute, Brisbane, QLD, Australia
| | - Nicholas G. Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Miguel E. Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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