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Farooq S, Hattle M, Kingstone T, Ajnakina O, Dazzan P, Demjaha A, Murray RM, Di Forti M, Jones PB, Doody GA, Shiers D, Andrews G, Milner A, Nettis MA, Lawrence AJ, van der Windt DA, Riley RD. Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT). Br J Psychiatry 2024:1-10. [PMID: 39101211 DOI: 10.1192/bjp.2024.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
BACKGROUND A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication. AIMS To develop and evaluate a model that could predict the risk of TRS in routine clinical practice. METHOD We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model. RESULTS We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723-0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism. CONCLUSIONS We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.
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Affiliation(s)
- Saeed Farooq
- School of Medicine, Keele University, Newcastle-under-Lyme, UK; National Institute for Health and Care Research (NIHR), UK; and St George's Hospital, Midlands Partnership University NHS Foundation Trust, Stafford, UK
| | - Miriam Hattle
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Tom Kingstone
- School of Medicine, Keele University, Newcastle-under-Lyme, UK; National Institute for Health and Care Research (NIHR), UK; and St George's Hospital, Midlands Partnership University NHS Foundation Trust, Stafford, UK
| | - Olesya Ajnakina
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; and Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Arsime Demjaha
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; and Department of Psychiatry, Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Palermo, Italy
| | - Marta Di Forti
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Gillian A Doody
- Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, UK
| | - David Shiers
- School of Medicine, Keele University, Newcastle-under-Lyme, UK; Psychosis Research Unit, Greater Manchester Mental Health NHS Trust, Manchester, UK; and University of Manchester, Manchester, UK
| | - Gabrielle Andrews
- St George's Hospital, Midlands Partnership University NHS Foundation Trust, Stafford, UK
| | - Abbie Milner
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Maria Antonietta Nettis
- South London and Maudsley NHS Foundation Trust, London, UK; and Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Andrew J Lawrence
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Danielle A van der Windt
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
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2
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Koehler JC, Dong MS, Song DY, Bong G, Koutsouleris N, Yoo H, Falter-Wagner CM. Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews. Sci Rep 2024; 14:5663. [PMID: 38453972 PMCID: PMC10920641 DOI: 10.1038/s41598-024-56098-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.
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Affiliation(s)
- Jana Christina Koehler
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.
| | - Mark Sen Dong
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Da-Yea Song
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Guiyoung Bong
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Heejeong Yoo
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
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3
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Kirkbride JB, Anglin DM, Colman I, Dykxhoorn J, Jones PB, Patalay P, Pitman A, Soneson E, Steare T, Wright T, Griffiths SL. The social determinants of mental health and disorder: evidence, prevention and recommendations. World Psychiatry 2024; 23:58-90. [PMID: 38214615 PMCID: PMC10786006 DOI: 10.1002/wps.21160] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
People exposed to more unfavourable social circumstances are more vulnerable to poor mental health over their life course, in ways that are often determined by structural factors which generate and perpetuate intergenerational cycles of disadvantage and poor health. Addressing these challenges is an imperative matter of social justice. In this paper we provide a roadmap to address the social determinants that cause mental ill health. Relying as far as possible on high-quality evidence, we first map out the literature that supports a causal link between social determinants and later mental health outcomes. Given the breadth of this topic, we focus on the most pervasive social determinants across the life course, and those that are common across major mental disorders. We draw primarily on the available evidence from the Global North, acknowledging that other global contexts will face both similar and unique sets of social determinants that will require equitable attention. Much of our evidence focuses on mental health in groups who are marginalized, and thus often exposed to a multitude of intersecting social risk factors. These groups include refugees, asylum seekers and displaced persons, as well as ethnoracial minoritized groups; lesbian, gay, bisexual, transgender and queer (LGBTQ+) groups; and those living in poverty. We then introduce a preventive framework for conceptualizing the link between social determinants and mental health and disorder, which can guide much needed primary prevention strategies capable of reducing inequalities and improving population mental health. Following this, we provide a review of the evidence concerning candidate preventive strategies to intervene on social determinants of mental health. These interventions fall broadly within the scope of universal, selected and indicated primary prevention strategies, but we also briefly review important secondary and tertiary strategies to promote recovery in those with existing mental disorders. Finally, we provide seven key recommendations, framed around social justice, which constitute a roadmap for action in research, policy and public health. Adoption of these recommendations would provide an opportunity to advance efforts to intervene on modifiable social determinants that affect population mental health.
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Affiliation(s)
| | - Deidre M Anglin
- City College, City University of New York, New York, NY, USA
- Graduate Center, City University of New York, New York, NY, USA
| | - Ian Colman
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | | | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
| | - Praveetha Patalay
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Alexandra Pitman
- Division of Psychiatry, University College London, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
| | - Emma Soneson
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Thomas Steare
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Talen Wright
- Division of Psychiatry, University College London, London, UK
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4
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Arold D, Bernardoni F, Geisler D, Doose A, Uen V, Boehm I, Roessner V, King JA, Ehrlich S. Predicting long-term outcome in anorexia nervosa: a machine learning analysis of brain structure at different stages of weight recovery. Psychol Med 2023; 53:7827-7836. [PMID: 37554008 PMCID: PMC10758339 DOI: 10.1017/s0033291723001861] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/31/2023] [Accepted: 06/15/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Anorexia nervosa (AN) is characterized by sizable, widespread gray matter (GM) reductions in the acutely underweight state. However, evidence for persistent alterations after weight-restoration has been surprisingly scarce despite high relapse rates, frequent transitions to other psychiatric disorders, and generally unfavorable outcome. While most studies investigated brain regions separately (univariate analysis), psychiatric disorders can be conceptualized as brain network disorders characterized by multivariate alterations with only subtle local effects. We tested for persistent multivariate structural brain alterations in weight-restored individuals with a history of AN, investigated their putative biological substrate and relation with 1-year treatment outcome. METHODS We trained machine learning models on regional GM measures to classify healthy controls (HC) (N = 289) from individuals at three stages of AN: underweight patients starting intensive treatment (N = 165, used as baseline), patients after partial weight-restoration (N = 115), and former patients after stable and full weight-restoration (N = 89). Alterations after weight-restoration were related to treatment outcome and characterized both anatomically and functionally. RESULTS Patients could be classified from HC when underweight (ROC-AUC = 0.90) but also after partial weight-restoration (ROC-AUC = 0.64). Alterations after partial weight-restoration were more pronounced in patients with worse outcome and were not detected in long-term weight-recovered individuals, i.e. those with favorable outcome. These alterations were more pronounced in regions with greater functional connectivity, not merely explained by body mass index, and even increases in cortical thickness were observed (insula, lateral orbitofrontal, temporal pole). CONCLUSIONS Analyzing persistent multivariate brain structural alterations after weight-restoration might help to develop personalized interventions after discharge from inpatient treatment.
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Affiliation(s)
- Dominic Arold
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Fabio Bernardoni
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Daniel Geisler
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Arne Doose
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Volkan Uen
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ilka Boehm
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Veit Roessner
- Eating Disorder Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Joseph A. King
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- Eating Disorder Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
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5
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Monteith S, Glenn T, Geddes JR, Achtyes ED, Whybrow PC, Bauer M. Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review. PHARMACOPSYCHIATRY 2023; 56:209-213. [PMID: 37643732 DOI: 10.1055/a-2142-9325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools.
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Affiliation(s)
- Scott Monteith
- Department of Psychiatry, Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - John R Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Eric D Achtyes
- Department of Psychiatry, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, USA
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
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6
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Abstract
The field of psychiatry is facing an important paradigm shift in the provision of clinical care and mental health service organization toward personalization and integration of multimodal data science. This approach, termed precision psychiatry, aims at identifying subgroups of patients more prone to the development of a certain phenotype, such as symptoms or severe mental disorders (risk detection), and/or to guide treatment selection. Pharmacogenomics and computational psychiatry are two fundamental tools of precision psychiatry, which have seen increasing levels of integration in clinical settings. Here we present a brief overview of these two applications of precision psychiatry in clinical settings.
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Affiliation(s)
- Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, 09127, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, 09127,Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Martino Belvederi Murri
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, 44121, Italy
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7
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Jensen KHR, Dam VH, Ganz M, Fisher PM, Ip CT, Sankar A, Marstrand-Joergensen MR, Ozenne B, Osler M, Penninx BWJH, Pinborg LH, Frokjaer VG, Knudsen GM, Jørgensen MB. Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort. BMC Psychiatry 2023; 23:151. [PMID: 36894940 PMCID: PMC9999625 DOI: 10.1186/s12888-023-04618-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/19/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging. METHODS All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [11C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome. DISCUSSION The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients. TRIAL REGISTRATION Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).
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Affiliation(s)
- Kristian Høj Reveles Jensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Cheng-Teng Ip
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Anjali Sankar
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Maja Rou Marstrand-Joergensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Merete Osler
- Center for Clinical Research and Prevention, Bispebjerg & Frederiksberg Hospitals, Copenhagen, Denmark.,Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Lars H Pinborg
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibe Gedsø Frokjaer
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin Balslev Jørgensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. .,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. .,Psychiatric Centre Copenhagen, Copenhagen, Denmark.
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8
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Gómez-Carrillo A, Paquin V, Dumas G, Kirmayer LJ. Restoring the missing person to personalized medicine and precision psychiatry. Front Neurosci 2023; 17:1041433. [PMID: 36845417 PMCID: PMC9947537 DOI: 10.3389/fnins.2023.1041433] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 01/09/2023] [Indexed: 02/11/2023] Open
Abstract
Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute of Mental Health Research Domain Criteria (RDoC), multilevel biological "omics" data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.
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Affiliation(s)
- Ana Gómez-Carrillo
- Culture, Mind, and Brain Program, Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC, Canada
- Culture and Mental Health Research Unit, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
| | - Vincent Paquin
- Culture, Mind, and Brain Program, Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Guillaume Dumas
- Culture, Mind, and Brain Program, Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC, Canada
- Precision Psychiatry and Social Physiology Laboratory at the CHU Sainte-Justine Research Center, Université de Montréal, Montreal, QC, Canada
- Mila–Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Laurence J. Kirmayer
- Culture, Mind, and Brain Program, Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC, Canada
- Culture and Mental Health Research Unit, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
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9
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Marwaha S, Palmer E, Suppes T, Cons E, Young AH, Upthegrove R. Novel and emerging treatments for major depression. Lancet 2023; 401:141-153. [PMID: 36535295 DOI: 10.1016/s0140-6736(22)02080-3] [Citation(s) in RCA: 126] [Impact Index Per Article: 126.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/09/2022] [Accepted: 10/19/2022] [Indexed: 12/23/2022]
Abstract
Depression is common, costly, debilitating, and associated with increased risk of suicide. It is one of the leading global public health problems. Although existing available pharmacological treatments can be effective, their onset of action can take up to 6 weeks, side-effects are common, and recovery can require treatment with multiple different agents. Although psychosocial interventions might also be recommended, more effective treatments than those currently available are needed for people with moderate or severe depression. In the past 10 years, treatment trials have developed and tested many new targeted interventions. In this Review, we assess novel and emerging biological treatments for major depressive disorder, evaluate their putative brain and body mechanisms, and highlight how close each might be to clinical use.
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Affiliation(s)
- Steven Marwaha
- Institute for Mental Health, University of Birmingham, Birmingham, UK; Birmingham and Solihull Mental Health NHS Trust, Birmingham, UK
| | - Edward Palmer
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Trisha Suppes
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Emily Cons
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Allan H Young
- Centre for Affective Disorders, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK; Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Edgbaston, UK.
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10
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Griffiths SL, Upthegrove R, Corsi-Zuelli F, Deakin B. Rethinking Immunity and Cognition in Clinical High Risk for Psychosis. Curr Top Behav Neurosci 2023; 63:475-497. [PMID: 36409457 DOI: 10.1007/7854_2022_399] [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: 06/16/2023]
Abstract
It is well known that schizophrenia is associated with cognitive impairment, reduced cortical grey matter and increased circulating concentrations of inflammatory cytokines. However, the relationship between these findings is not clear. We outline the influential neuroinflammatory hypotheses that raised cytokines provoke a damaging immune response in microglia that results in reduced grey matter and associated cognitive performance. We investigated whether such an interaction might be detectable in the prodromal period as illness emerges from the Clinical High Risk for Psychosis (CHR-P). Meta-analyses suggest that compared with controls, impaired cognition and reduced grey matter are already present by the prodrome and that greater decrements are present in those who later develop symptoms. In contrast, the few cytokine studies report no abnormalities in CHR-P except that interleukin-6 (IL-6) levels were raised versus controls and to a greater extent in the future patients, in one study. We noted that cognitive impairment and less cortical grey matter are more severe in schizophrenia than in affective disorders, but that increased cytokine levels are similarly prevalent across disorders. We found no studies correlating cytokine levels with cognitive impairment in CHR-P but such correlations seem unlikely given the minimal relationship reported in a recent meta-analysis of the many cytokine-cognition studies in established illness. From this and other evidence, we conclude that neuroinflammation is not a core feature of schizophrenia nor a substrate for reduced grey matter volume or cognitive function. We draw attention instead to the emerging evidence that brain-resident immune cells and signalling molecules such as Tregs and IL-6, which are influenced by schizophrenia risk genes, regulate and are necessary for the development and function of neuron-glia interaction. We suggest that cognitive impairment in schizophrenia can be seen as a convergence of genetic and immune-neurodevelopmental dysregulation whereas raised cytokines are a consequence of impaired Tregs control of systemic inflammation.
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Affiliation(s)
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Fabiana Corsi-Zuelli
- Institute for Mental Health, University of Birmingham, Birmingham, UK
- Division of Psychiatry, Department of Neuroscience and Behaviour, Ribeirao Preto Medical School, University of São Paulo, São Paulo, Brazil
| | - Bill Deakin
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, School of Biological Sciences, University of Manchester, Manchester, UK.
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