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Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration

Dan j. stein.

1 South African Medical Research Council Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town South Africa

Steven J. Shoptaw

2 Division of Family Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles CA, USA

Daniel V. Vigo

3 Department of Psychiatry, University of British Columbia, Vancouver BC, Canada

4 Centre for Global Mental Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London UK

Pim Cuijpers

5 Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam The Netherlands

Jason Bantjes

6 Alcohol, Tobacco and Other Drug Research Unit, South African Medical Research Council, Cape Town South Africa

Norman Sartorius

7 Association for the Improvement of Mental Health Programmes, Geneva Switzerland

8 Department of Psychiatry, University of Campania “L. Vanvitelli”, Naples Italy

Psychiatry has always been characterized by a range of different models of and approaches to mental disorder, which have sometimes brought progress in clinical practice, but have often also been accompanied by critique from within and without the field. Psychiatric nosology has been a particular focus of debate in recent decades; successive editions of the DSM and ICD have strongly influenced both psychiatric practice and research, but have also led to assertions that psychiatry is in crisis, and to advocacy for entirely new paradigms for diagnosis and assessment. When thinking about etiology, many researchers currently refer to a biopsychosocial model, but this approach has received significant critique, being considered by some observers overly eclectic and vague. Despite the development of a range of evidence‐based pharmacotherapies and psychotherapies, current evidence points to both a treatment gap and a research‐practice gap in mental health. In this paper, after considering current clinical practice, we discuss some proposed novel perspectives that have recently achieved particular prominence and may significantly impact psychiatric practice and research in the future: clinical neuroscience and personalized pharmacotherapy; novel statistical approaches to psychiatric nosology, assessment and research; deinstitutionalization and community mental health care; the scale‐up of evidence‐based psychotherapy; digital phenotyping and digital therapies; and global mental health and task‐sharing approaches. We consider the extent to which proposed transitions from current practices to novel approaches reflect hype or hope. Our review indicates that each of the novel perspectives contributes important insights that allow hope for the future, but also that each provides only a partial view, and that any promise of a paradigm shift for the field is not well grounded. We conclude that there have been crucial advances in psychiatric diagnosis and treatment in recent decades; that, despite this important progress, there is considerable need for further improvements in assessment and intervention; and that such improvements will likely not be achieved by any specific paradigm shifts in psychiatric practice and research, but rather by incremental progress and iterative integration.

Psychiatry has over the course of its history been characterized by a range of different models of and approaches to mental disorder, each perhaps bringing forward some advances in science and in services, but at the same time also accompanied by considerable critique from within and without the field.

The shift away from psychoanalysis in the latter part of the 20th century was accompanied by key scientific and clinical advances, including the introduction of a wide range of evidence‐based pharmacotherapies and psychotherapies for the treatment of mental disorders. However, there has also been an extensive critique of pharmacological and cognitive‐behavioral interventions, whether focused on concerns about their “medical model” foundations, or emphasizing the need to build community psychiatry and to scale up these treatments globally 1 .

In the 21st century, global mental health has become an influential novel perspective on mental disorders and their treatment. This emergent discipline builds on advances in cross‐cultural psychiatry, psychiatric epidemiology, implementation science, and the human rights movement 2 . Global mental health has given impetus to a wide range of mental health research as well as to clinical strategies such as task‐shifting, with evidence that these are effective in diverse contexts and may be suitable for roll‐out at scale 3 . It is noteworthy, however, that global mental health has in turn been critiqued for inappropriate and imperial exportation of Western constructs to the global South 4 .

Psychiatric nosology has been a particular focus of both advances in and critique from the field. The 3rd edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM‐III) was paramount, providing an approach that attempted to eschew different models of etiology, focusing instead on reliable diagnostic constructs 5 . These constructs became widely used in epidemiological studies of mental illness, in psychiatric research on etiology and treatment, as well as in daily clinical practice throughout the world. The most recent editions of the DSM (DSM‐5) and of the International Classification of Diseases (ICD‐11) by the World Health Organization (WHO) have drawn on and given impetus to a considerable body of work in nosological science 6 , 7 .

Early on, psychoanalytic psychiatry criticized DSM diagnostic constructs for missing core psychic phenomena. With increasing concerns that these constructs have insufficient validity, neuroscientifically informed psychiatry has put forward approaches to assessing behavioral phenomena that emphasize laboratory models 8 . Despite the growing body of nosology science instantiated by the DSM‐5 and ICD‐11, many have argued for new paradigms of classification and assessment – e.g., the Research Domain Criteria (RDoC), the Hierarchical Taxonomy of Psychopathology (HiTOP) and other novel statistical approaches, and digital phenotyping.

Where do things stand currently with regard to psychiatry's models of and approaches to mental disorder? What are current clinical practices? What novel perspectives are being proposed, and what is the evidence base for them? To what extent will newly introduced models of clinical intervention, such as shared decision‐making or transdiagnostic psychotherapies, and novel approaches in psychiatric research, such as the use of “big data” in neurobiological research and treatment outcome prediction, have transformative impact for clinical practice in the foreseeable future?

In this paper we discuss proposed shifts to clinical neuroscience and personalized pharmacotherapy, innovative statistical approaches to psychiatric nosology and assessment, deinstitutionalization and community mental health care, the scale‐up of evidence‐based psychotherapy, digital phenotyping and digital therapies, and global mental health and task‐sharing approaches. We chose these novel perspectives because they have achieved particular prominence recently, and because many have argued that they will significantly impact psychiatric practice and research in the future.

We consider the extent to which proposed transitions from current practices to these novel perspectives reflect hype or hope, and whether they represent paradigm shifts or iterative progress in psychiatric research and practice. Although the contrast between hype and hope is itself likely oversimplistic, with many newly proposed models and approaches in psychiatry representing neither of these polar extremes, our point of departure is that false promises of paradigm shifts in health care may entail significant costs, while hope may justifiably be considered an important virtue for health professions 9 . We begin with a brief consideration of current models and approaches in psychiatric practice.

CURRENT MODELS AND APPROACHES IN PSYCHIATRY

Current practice in psychiatry varies in different parts of the world, but there are some important universalities. The duration and depth of training in psychiatry during the undergraduate and postgraduate years also differ across countries, but typically a general training in medicine and surgery is followed by specialized training in psychiatry, with exposure to both inpatient and outpatient settings. Globally, inpatient psychiatry focuses predominantly (but not exclusively) on severe mental disorders such as schizophrenia and bipolar disorder, while outpatient psychiatry focuses predominantly (but again not exclusively) on common mental disorders such as depression, anxiety disorders, and substance use disorders. In inpatient settings, psychiatrists are often leaders of a multidisciplinary team, with the extent and depth of this multidisciplinarity dependent on local resources. There are differences in sub‐specialization across the globe, but in many countries recognized sub‐specialties include child and adolescent psychiatry, geriatric psychiatry, and forensic psychiatry 10 .

A particularly important shift in the 20th century has been the process of deinstitutionalization, particularly in high‐income countries. Thus, there has been a decrease of bed numbers in specialized psychiatric hospitals, but an increase of these numbers in general medical hospitals, with variable strengthening of community services. It has been argued that, when it comes to mental health services, all countries are “developing”, since there is a relative underfunding of such services in relation to the burden of disease 1 .

Currently, the two major classification systems in psychiatry are the DSM‐5 and the ICD‐11. The DSM system is more commonly used by researchers, while the ICD is a legally mandated health data standard. The operational criteria and diagnostic guidelines included in the DSM‐III, the ICD‐10, and subsequent editions of the manuals have exerted considerable influence on modern psychiatry. They not only increase reliability of diagnosis, but also have clinical utility, since they provide clinicians with an approach to conceptualizing disorders and to communicating about them 11 , 12 . They have also played a key role in research, ranging from studies of the neurobiology of mental disorders, through to studies of interventions for particular conditions, and on to clinical and community epidemiological surveys.

However, there has also been considerable critique of the reliance of modern psychiatry on the DSM and the ICD. The notion that psychiatric diagnosis is itself “in crisis” has come both from within the field and from external critics. Two somewhat contradictory critiques have been that in daily practice the DSM and ICD criteria or guidelines are seldom applied formally by clinicians, and that over‐reliance on those criteria or guidelines leads to a checklist approach to assessment that ignores relevant symptoms and important contextual issues falling outside the focus of the nosologies. Additional key critiques have been that psychiatric diagnoses lack scientific validity, and that current nosologies are biased by influences such as that of the pharmaceutical industry 13 , 14 .

When thinking about etiology, many clinicians and researchers currently default to a biopsychosocial model acknowledging that a broad range of risk and protective factors are involved in the development and perpetuation of mental disorders. This model was introduced by G. Engel in an attempt to move from a reductionistic biomedical approach to include also psychological and social dimensions 15 . The model has important strengths insofar as it takes a systems‐based approach that considers a broad range of variables influencing disease onset and course, and attends to both the relevant biomedical disease and the patient's experience of illness 16 .

Nevertheless, the biopsychosocial approach has received significant critique. In particular, it has been argued that the biomedical model critiqued by Engel is a straw man, and that the biopsychosocial approach is overly eclectic and vague. By saying that all mental disorders have biological, psychological and social contributory factors, we are unable to be specific about any particular condition, and to target treatments accordingly 17 , 18 . While there are few data available on how rigorously psychiatrists consider the range of risk and protective factors in clinical work, a review of the research literature indicates ongoing work on multiple “difference‐makers”, distributed across a wide range of categories 19 .

Psychiatrists are trained to provide a range of both pharmacological and psychological interventions. However, data from psychiatric practice networks and from epidemiological surveys indicate that there has been a growing emphasis on pharmacotherapy interventions 20 , albeit with some exceptions 21 . Furthermore, the number of psychiatrists varies considerably from country to country, and from region to region within any particular country 22 . While primary care practitioners are also trained to deliver mental health treatments, and indeed provide the bulk of prescriptions for mental disorders in some regions, there is considerable evidence of underdiagnosis and undertreatment of such conditions in primary care settings.

Indeed, despite the development of a range of evidence‐based pharmacotherapies and psychotherapies in the last several decades, current data point to both a treatment gap and a research‐practice gap in mental health. The treatment gap refers to findings that, across the globe, many individuals with mental disorders do not have access to mental health care 23 . The research‐practice gap, also known as the “science‐practice” or “evidence‐practice gap”, refers to differences between treatments delivered in standard care and those supported by scientific evidence 24 . In particular, clinical practitioners have been criticized for employing an eclectic approach to choosing interventions, for not sufficiently adhering to evidence‐based clinical guidelines, and for not employing measurement‐based care.

The treatment gap and the research‐practice gap are of deep concern, given evidence of underdiagnosis and undertreatment, of misdiagnosis and inappropriate treatment, and of inadequate quality of treatment 25 , 26 . There are, however, some justifiable reasons for a gap between practice and research, including that the evidence base is relatively sparse for the management of treatment‐refractory and comorbid conditions, the relative lack of pragmatic “real‐world” research trials in psychiatry, and the possibly modest positive impact of guideline implementation on patient outcomes 27 , 28 . Indeed, several scholars have emphasized that including clinical experience and addressing patient values are key components of appropriate decision‐making 27 , 29 .

Considerably more research is needed to inform our knowledge of current psychiatric practice and its outcomes. Data from psychiatric practice networks have been useful in providing fine‐grained information in some settings, but much further work is warranted along these lines 30 . Data from randomized controlled trials indicate that psychiatric treatments are as effective as those in other areas of health care, but further evidence should be acquired using pragmatic designs in real‐world contexts 31 . Epidemiological data from across the globe suggest that individuals with mental disorders who received specialized, multi‐sector care are more likely than other patients to report being helped “a lot”, but there is an ongoing need for more accurate estimates of effective treatment coverage globally 32 .

In the interim, evidence of the treatment gap and the research‐practice gap in current mental health services has given impetus to the development of a number of novel diagnostic and treatment models and approaches, ranging from clinical neuroscience through to global mental health. Some of these models and approaches have achieved particular prominence in recent times, with proponents arguing that they will significantly impact psychiatric practice and research in the future. At times advocates for these perspectives and proposals have limited aims, while at other times they speak of paradigm shifts that will drastically alter or wholly reshape current clinical practices 33 , 34 , 35 , 36 . We next consider a number of these perspectives and proposals in turn.

CLINICAL NEUROSCIENCE AND PERSONALIZED PHARMACOTHERAPY

A key shift in 20th century psychiatry, at least in some parts of the world, was from psychoanalytic to biological psychiatry. The serendipitous discovery of a range of psychiatric medications in the mid‐20th century, and advances in molecular, genetic and neuroimaging methods, propelled this shift. More recently, terms such as clinical neuroscience, translational psychiatry, precision psychiatry, and personalized psychiatry have emerged, helping to articulate the conceptual foundations for a proposed psychiatric perspective aiming to replace or significantly augment current practice 37 , 38 , 39 .

The proposed paradigm of clinical neuroscience rests in part on a critique of current standard approaches. First, in terms of diagnosis, it has been argued that the DSM and ICD constructs are not sufficiently based on neuroscience 40 . Thus, for example, particular symptoms, which may involve quite specific neurobiological mechanisms, may be present across different diagnoses. Conversely, research findings demonstrate that there is considerable overlap of genetic architecture across different DSM and ICD mental disorders 41 . If current diagnostic constructs are not natural kinds, then arguably attempts to find specific biomarkers and develop targeted treatments for them are doomed to fail 42 , 43 .

The proposed new paradigm views psychiatry as a clinical neuroscience, which should rest on a firm foundation of neurobiological knowledge 44 . With advances in neurobiology, we will be better able to target relevant mechanisms and develop specific treatments for mental disorders. Neuroimaging and genomic research offer opportunities for personalizing psychiatric intervention: those with specific genetic variants may require tailoring of psychopharmacological intervention, while particular alterations in neural signatures may be used to choose a therapeutic modality or to alter parameters for neurostimulation.

The RDoC project, developed by the US National Institute of Mental Health (NIMH), has provided an influential conceptual framework for this proposed new paradigm 8 . Whereas the DSM‐III relied on the Research Diagnostic Criteria (RDC) in order to operationalize mental disorders, the RDoC project emphasizes domains of functioning that are underpinned by specific neurobiological mechanisms. Disruptions in these domains may lead to various symptoms and impairments. Domains of functioning are found across species, and their neurobiological substrates are sufficiently known to allow translational neuroscience, or productive movement from bench to bedside and back. Each domain of functioning can be assessed with specific laboratory paradigms.

The RDoC matrix initially included five domains of functioning and several “units of analysis” for assessing these domains (see Figure 1 ) 45 . Each domain in turn comprises a number of different “constructs” (or rows of the matrix): these were included on the basis of evidence that they entail a validated behavioral function, and that a neural circuit or system implements the function. Different “units of analysis” (or columns of the matrix) can be used to assess each construct: the center column refers to brain circuitry, with three columns to the left focusing on the genes, molecules and cells that comprise circuits, and three columns to the right focusing on circuit outputs (behavior, physiological responses, and verbal reports). A column to list paradigms is also included.

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The Research Domain Criteria matrix (from Cuthbert 45 )

The RDoC matrix is intended to include two further critical dimensions for integrating neuroscience and psychopathology, i.e. developmental trajectories and environmental effects 45 . Thus, from an RDoC perspective, many mental illnesses can be viewed as neurodevelopmental disorders, with maturation of the nervous system interacting with a range of external influences from the time of conception. Several key “pillars” of the RDoC framework, including its translational and dimensional focuses 8 , have been emphasized.

Anxiety, for example, can be studied in laboratory paradigms, and ranges from normal responses to threat through to pathological conditions. Indeed, a clinical neuroscience approach has contributed to the reconceptualization of several anxiety and related disorders 46 , 47 , 48 and to the introduction of novel therapeutic approaches for these conditions 49 . Further, work on stressors has usefully emphasized that environmental exposures become biologically embedded, with early adversity associated to alterations in both body and brain that occur irrespective of the DSM diagnostic category 50 , 51 .

The NIMH has linked the RDoC to funding applications, and this framework has given impetus to a range of clinical neuroscience research. Translational research will certainly advance our empirical knowledge of the neurobiology of behavior and of psychopathology. The RDoC has also prompted conceptual work related to the neurobiology of mental disorders, and the development of measures and methods. Indeed, to the extent that constructs in the RDoC matrix have validity as behavioral functions, and map onto specific biological systems such as brain circuits, the project summarizes key advances in the field, and provides useful guidance for ongoing research.

At the same time, it is relevant to note important limitations of the RDoC approach. First, the RDoC seems less an entirely new paradigm than a re‐articulation of existing ideas in biological psychiatry. Certainly, the importance of cross‐diagnostic neurobiological investigations of domains of functioning has long been emphasized 52 . Second, the neurobiology of any particular RDoC construct, such as social communication, may be enormously complex, so that alternative approaches to delineating the mechanisms involved in particular mental disorders may provide greater traction 53 . Third, methods used to measure domains in the RDoC framework may not be readily available to clinicians. The further one moves from academic centers to the practice of psychiatry in primary care settings around the globe, the less relevant an RDoC framework may be to daily clinical work.

Personalized and precision psychiatry are important aspirations of clinical neuroscience. The notion that psychiatric interventions need to be rigorously tailored to each individual patient makes good sense, given the substantial inter‐individual variability in the genome and exposome of those suffering from psychiatric disorders, as well as the considerable variation in response to current psychiatric interventions. With advances in genomic methods and findings, and the possibility that whole genome sequencing will become a standard clinical tool, with polygenic risk scores readily available, it is particularly relevant to consider the application of genomics to optimizing pharmacological and other treatments 54 .

The Clinical Pharmacogenetic Implementation Consortium (CPIC) has already provided a range of clinical guidelines for drugs used in psychiatry. For example, a CPIC guideline recommends that, given the association between the HLA‐B*15:02 variant and Stevens‐Johnson syndrome as well as toxic epidermal necrolysis after exposure to carbamazepine and oxcarbazepine, these drugs should be avoided in patients who are HLA‐B*15:02 positive and carbamazepine‐ or oxcarbazepine‐naïve 55 . The evidence base that pharmacogenomic testing improves outcomes is gradually beginning to accumulate, and recent guidelines have started to recommend a number of specific tests 56 .

From an RDoC perspective, particular domains of functioning involve specific neural circuits, which are in turn modulated by a range of molecular pathways. One notable recent development in these fields has been a focus on “big data”. Large collaborations in basic and clinical sciences have been established, which provide sufficient statistical power to advance the field in important ways.

Examples of such “big data” collaborations are the Enhancing Neuroimaging Meta‐analysis Consortium (ENIGMA) 57 , which includes tens of thousands of scans from across the world, and the Psychiatric Genetics Consortium (PGC) 58 , which includes hundreds of thousands of DNA samples from across the globe. The work of ENIGMA and PGC has been at the cutting edge of scientific research in psychiatry, and has provided crucial insights into mental disorders. Certain biological pathways, such as immune and metabolic systems, appear to play a role across different mental disorders, and genomic methods have contributed to delineating causal and modifiable mechanisms underlying these conditions 58 , 59 . At the same time, it must be acknowledged that to date few findings from this work have been successfully translated into daily clinical practice 36 , 54 , 60 .

In summary, clinical neuroscience provides an important conceptual framework that may generate some useful clinical insights, and that may be particularly helpful in guiding clinical research. This framework has contributed to the reconceptualization of a number of mental disorders, and has on occasion contributed to the introduction of new therapies 61 . As clinical neuroscience generates new evidence, this may be incorporated in nosological systems in the future. There are already good arguments for including advances in this area in the curriculum of psychiatric training, and for updating clinicians on progress in the field 62 .

At the same time, there are currently few biomarkers with clinical utility in psychiatry, and methods such as functional neuroimaging and genome sequencing, which are key for future advances in frameworks such as the RDoC, are not readily available to or useful for practicing clinicians 63 . The vast majority of clinical neuroscience publications appear to have little link to clinical practice. At best, therefore, we can expect that ongoing advances in clinical neuroscience will contribute to clinical practice via iterative advances in our conceptualization of mental disorders, and via the ongoing introduction of new insights and new molecules that emerge from laboratory studies.

Indeed, the claim that any particular laboratory, neuroimaging or genetic finding will dramatically change clinical practice should raise a red flag. The neurobiology of behaviors and psychopathology is complex, reproducibility of findings is an ongoing important issue, and clinical neuroscience investigations only occasionally impact clinical practice 64 . Indeed, we should be careful not to be over‐optimistic about clinical neuroscience constituting a paradigm shift. Neurobiological research has not to date provided a rich pipeline of accurate biomarkers for mental disorders, nor speedily found new molecular entities that are efficacious for these conditions, and we cannot, for example, expect that the DSM and ICD will be replaced by the RDoC anytime soon.

NOVEL STATISTICAL APPROACHES TO PSYCHIATRIC NOSOLOGY, ASSESSMENT AND RESEARCH

Disease taxonomies are particularly complex, and may not be able to follow historical models of scientific taxonomies, which have defined all elements of a given set. An often‐used example of the latter taxonomies is the periodic table of elements. Another venerable example is Linnaeus’ Systema Naturae and the resulting nomenclature of biological species. The periodic table of elements has the simplicity of small numbers plus the hard and fast rules of chemistry, while the Systema Naturae , despite having to deal with an ever‐expanding number of entities, is arguably based on direct observation of beings. In contrast, a disease taxonomy deals with thousands of unruly entities (versus 118 elements), which cannot be directly observed, apprehended or dissected (as animals or plants can).

Despite these challenges, disease taxonomies have sought to provide a shared, evidence‐based, clinically meaningful, comprehensive classification that is informed by etiology and therapeutics. The notion that underneath the observable syndrome lies a causal entity, that we should investigate and treat, lies at the heart of the practice of medicine 65 . Such “disease entities” have specific characteristics that make them clear and distinct from others (i.e., presentation, etiology, response to intervention), are transparent to the clinician, and are well‐grounded in evidence.

Psychiatry has long faced the challenges of producing a causal nosology that is able to direct treatment 66 . Pinel developed the first comprehensive nosology for people with severe mental disorders, along with moral treatment, the first therapeutic framework of the scientific era 67 . Soon afterward, Kahlbaum, Kraepelin and Bleuler laid a firm groundwork for clinical psychiatry through close observation and systematic documentation of the natural history of severe mental illness. Arguably, Freud further advanced nosology and therapeutics by focusing on a different set of disorders (usually milder but much more prevalent), which he termed neuroses (to highlight their difference from psychoses ), and by developing the concept and practice of psychotherapy. These frameworks gave impetus to subsequent advances in our understanding of and interventions for mental disorders.

Perceptions of insufficiently rapid and robust advances in treatments have led to criticism of current nosology 68 . In particular, the DSM and ICD have been criticized for overly focusing on reliability at the expense of validity. In this view, schizophrenia and bipolar disorder may be genuine disease entities, but our syndromic definition lacks specificity, and there are likely different causal pathways that lead to clinically meaningful subtypes of these disorders. Major depressive disorder, on the other hand, is likely to be a hodgepodge of mood syndromes, some non‐dysfunctional (i.e., non‐disorders) or non‐specific (i.e., combining depressive with anxiety symptoms), including only a few true but potentially diverse disease entities (e.g., melancholia, psychotic depression). And when it comes to, say, personality disorders, the disease‐entity concept is even more distant, and the search for new approaches is seen as particularly key.

One such novel paradigm is the HiTOP. This proposes a hierarchical framework that, based on the observed covariation of dimensional traits, is able to identify latent super‐spectra and spectra (supra‐syndromes), syndromes (our current disorders), and lower‐level components 69 , 70 , 71 , 72 . In this conceptual framework, a dimension consists of a continuous space in which an element occurs in differences of degree, but not of kind, between the normal and the pathological.

The HiTOP relies on factor analysis and related techniques, which tap into the covariation of observable traits to identify an unobserved, common factor that, once included in the model, explains the covariation 73 . Costa and McCrae's studies leading to the identification of five personality domains were a prime example of this approach. There is a common underlying reason that explains a person's tendency to worry about many things, think that the future looks bleak, be bothered by intrusive thoughts, and be grouchy 74 . That unobserved factor was conceptualized as “neuroticism”, and fully explains the covariation of these traits in any given individual. A similar approach to the study of childhood psychopathology led to the binary characterization of an “internalizing” and an “externalizing” dimension to childhood disorders 75 .

The HiTOP paradigm seeks to leverage these well‐established lines of research to develop a data‐driven nosology that is free from the theory‐driven dead weight built into current approaches. The key conceptual departure relies on the premise that, since evidence points towards psychopathological dimensions existing on a continuum, disorders should be similarly conceptualized, and nosology should move away from a focus on categorical entities. Instead of insisting on questionable boundaries, this approach proposes dimensional thresholds, which are empirically determined and do not involve any difference “in kind”. By grouping co‐occurring symptoms within the same syndrome, and non‐co‐occurring symptoms separately, within‐disorder heterogeneity is reduced. And by assigning overlapping syndromes to the same unobserved spectra, excess comorbidity found when using current categories is explained.

The resulting dimensional classification, the proponents of HiTOP argue, is consistent with evidence on risk factors, biomarkers, course of illness, and treatment response 69 . Figure 2 shows a schema of the proposed new nosology. An intriguing element of this approach is what has been termed “p”, or general psychopathology factor (at the top of Figure 2 ). In addition to super‐spectra and spectra, factor analysis ultimately points towards the existence of a single latent trait that would explain all psychopathology, comparable to the well‐established “g” factor for general intelligence 76 , 77 .

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The Hierarchical Taxonomy of Psychopathology (HiTOP) model (from Krueger et al 69 )

If dimensional nosologies seek to overturn categorical ones, network analysis arguably aims to overturn both, insofar as it posits that the notion of an unobserved underlying construct is unwarranted, be it a categorical disease entity or a dimensional latent factor 78 . The network approach to psychopathology holds that mental disorders can be conceived as “problems in living”, and are best understood at the level of what is observable. Rather than by latent entities, disordered states are fully explained by the interaction between signs and symptoms (the “nodes” of the networks). These interactions are themselves the causal elements (i.e., a symptom causes another symptom, then another symptom, and so on), and a disorder is simply an alternative “stable state” of strongly connected symptom networks (as opposed to the “normal” steady state of health).

A conceptualization of disorders as “problems in living” does away with the medical notion of a disease as an underlying causal entity. In this view, deficiencies in our understanding of etiology are not necessarily due to diagnostic limitations or insufficiently accurate models for the unobserved but, on the contrary, may be due to our lack of attention to the surface, i.e. the symptoms themselves, which go about reinforcing each other while we are distracted by peeking behind imaginary curtains.

Unlike dimensional approaches, proponents of network analysis disavow any nosological hierarchy (super‐spectra, spectra, disorders, symptoms, etc.), and posit that there is only one level, that of symptoms, which can all cause and reinforce one another. Of note, network analysis posits that symptoms (or interacting nodes) can be activated by disturbances emerging from the “external field” (i.e., “external” to the symptom network, not necessarily to the body or person), such as the loss of a loved one (which may activate the symptom depressed mood, setting in motion the depressive network) or a brain abnormality (which may activate the symptom hallucination, setting in motion the psychotic network).

Whether an individual develops a new strongly connected network of symptoms in the face of a stressor depends on his/her “vulnerability”, which is based on the network's connectivity. Given a dataset with symptoms and/or signs for disorders, a network analysis can quantify all relevant nodes and interactions, including the frequency and co‐occurrence of symptoms, the strength and number of their associations, and the centrality of each symptom (i.e., the sum of the interactions with other nodes). Empirical work using network analysis potentially provides rigorous accounts of vulnerability to and evolution of mental disorders.

A number of other novel statistical approaches have also been put forward as potentially facilitating paradigm shifts in psychiatry. Psychiatry has long relied on linear models to explore associations and develop theories of risk and resilience for mental disorders. However, causal inference methods have now been developed in statistics, and provide new approaches to delineating causal relationships 79 . In genetics, Mendelian randomization provides an innovative method for addressing the causal relationships of different phenotypes, and has increasingly been employed in psychiatric research 80 . Neural networks and deep learning have played a key role in advancing artificial intelligence, and are increasingly being applied to the investigation of psychiatric disorders, including prediction of treatment outcomes 81 , 82 , 83 , 84 . While many view such techniques as allowing iterative advances, some are persuaded that they allow an entirely novel perspective and so constitute a paradigm shift in the field 85 .

Work on the HiTOP and network analysis has been important and useful in a number of respects. First, unbiased data‐driven approaches have an important role in strengthening the relevant science, whether of nosology, or of areas such as genetics. A focus on fear‐related anxiety disorders, for example, offers interesting avenues for research, both from a neuroscience and a therapeutic perspective, and network analysis has contributed insights into the presentation of some disorders 48 . Second, some dimensional constructs, including those of internalizing and externalizing disorders, have clinical utility. The “distress” subfactor reflects the notable overlap between depressive and anxious symptoms, and the association between symptoms from two different disorders (e.g., major depressive disorder and generalized anxiety disorder) may be stronger than associations “within” each disorder 86 . Third, the use of novel statistical methods to draw causal inferences has provided important insights into risk for and resilience to mental disorders 59 . For instance, network analysis offers a nuanced foundation for targeted treatment of the core symptoms of some mental disorders (e.g., reframing specific automatic thoughts through cognitive‐behavioral interventions).

At the same time, such approaches have important limitations. Notably, categorical and dimensional approaches are interchangeable: any dimension can be converted into a category, and any category can be converted into a dimension 87 . There is no reason to conceptualize mental disorders as exclusively dimensional. In physics, matter itself is sometimes better conceived in terms of waves (a dimensional concept) and other times in terms of particles (a categorical one). Similarly, in psychiatry, a pluralist approach that allows the employment of a range of different dichotomous and continuous constructs seems appropriate 88 , 89 .

Remarkably, the HiTOP employs DSM terminology at the disorder level. “Number‐driven” psychopathologies and their resulting nosologies may not necessarily lead to a shift in constructs grounded in long‐standing clinical practice and research. In the same vein, network analysis offers a useful model to understand the distribution of symptoms, identify therapeutic targets, and explain the effectiveness of symptomatic interventions. However, network analysis does not specify the particular levels of explanation that underlie a network structure; so, while it may be a useful organizing framework, it is unclear that it will provide novel insights into underlying etiological mechanisms.

Consider a set of patients presenting with the following symptoms, among others: headaches, vomiting and seizures. A factor analysis may point towards a latent factor explaining the covariation among them. Any clinician will know that, unless the cause is substance‐related, the first thing to rule out in these patients is a space‐occupying lesion in the brain, and that this unobserved element is only an intermediary that can itself be caused by multiple disease entities, most notably hemorrhage, infection and cancer. The fact that a latent factor may explain the covariation between anxious and depressive symptoms does not exclude that these symptoms are in fact caused by very different dysfunctions (upstream of the latent factor), and that other accompanying symptoms will hold the clue to the ultimate cause (just as high blood pressure, fever or weight loss would hold clues for a space‐occupying lesion syndrome).

Relatedly, consider the focus of the HiTOP on a general psychopathology factor “p”. This focus can be countered by a reductio ad absurdum argument suggesting that a latent factor “i” explains the covariation of any and all human illnesses. Given some datasets, we may find that the covariation of nausea, hemoptysis, jaundice and myocardial infarction is explained by a latent dimensional trait. We may choose to call this “sybaritism”, dimensionally distributed between one extreme (temperance) and another (debauchery). Readers who focus on values‐based medicine might well criticize the choice of words here, while those focused on evidence‐based medicine are unlikely to be persuaded that an approach that elides disease entities will advance studies of psychiatry, gastroenterology and cardiology 29 .

In a latent class analysis of depressive and anxious syndromes, Eaton et al 90 proposed an approach called “guided empiricism”, whereby they explicitly imposed a theory‐driven structure on various statistical models, compared them, and obtained the best empirical fit. Perhaps using such explicitly theory‐driven constraints is preferable to accepting hidden theoretical constructs. For example, rather than assuming that all the DSM depressive and some anxiety/stress related disorders are explained by a latent factor called “distress”, itself under a spectrum called “internalizing disorders”, a theory‐grounded structure can be imposed on the models to try to identify what is driving the overlap. Indeed, it should be emphasized that purportedly “number‐driven” nosologies all have built‐in qualitative components: from the questions in the scales used to measure traits, to the labels chosen for the latent factors, these classifications are theory‐laden.

In summary, the solution to nosologic challenges in psychiatry may not reside in the building of new nosologies or psychopathologies from scratch 91 , nor in the banishment of the “disease entity” concept, but rather in continuing the humble, laborious, iterative work of systematic clinical observation, painstaking research, and creative thinking, while purposefully comparing dimensional, categorical and hybrid models applied to the same datasets. The claim that a “quantitative” nosology is somehow “atheoretical” raises a red flag: where theory is seemingly absent, it is often hidden. Instead, we need thoughtful and explicit combinations of theories grounded on clinical practice and confirmatory quantitative evidence. Hypothesis formulation is a qualitative, creative, theory‐laden endeavour, while quantitative research helps us discard false theories and refine what we know (by proving hypotheses wrong or quantifying associations).

Similarly, etiological and treatment challenges in psychiatry are unlikely to be addressed merely by the employment of larger and larger datasets, using more and more sophisticated statistical methods. Certainly, big data consortia and sophisticated statistical analyses have yielded valuable insights into the nature of psychiatric disorders. However, it is important to recognize the limitations of any empirical dataset and any analytic method, as well as the value of a wide range of complementary research designs and statistical approaches – including the age‐old single‐case study, which may sometimes provide clinical insights that outweigh those from big data analyses 92 .

Indeed, the claim that a new statistical, bioinformatic or computational method will provide entirely novel insights that enable a paradigm shift in psychiatry should again raise a red flag. Furthermore, where solutions reside within a black box, there is ongoing uncertainty about the extent to which they will be able to provide clinically useful assistance 93 , 94 . Thoughtful and explicit combinations of existing and novel research designs and statistical methods should be employed, with the aim of achieving iterative and integrative progress in our diagnosis and treatment of psychiatric disorders.

DEINSTITUTIONALIZATION AND COMMUNITY MENTAL HEALTH CARE

The last 70 years have seen a seismic shift in models of mental health care delivery around the world. The first half of the 20th century was dominated by the growth of psychiatric hospitals, particularly in high‐income Western countries. By 1955, there were 558,239 severely mentally ill people living in psychiatric hospitals in the US, with a total population of 164 million at the time 95 . In the years that followed, there was a significant reduction in psychiatric hospital bed numbers in many high‐income countries, as part of a trend that came to be known as deinstitutionalization. In the UK, the US, Australia, New Zealand and countries in Western Europe, there was an 80‐90% reduction in psychiatric hospital beds between the mid‐1950s and the 1990s 96 .

Deinstitutionalization refers to the downscaling of large psychiatric institutions and the transition of patients into community‐based care. This is said to include three components: the discharge of people residing in psychiatric hospitals to care in the community, the diversion of new admissions to alternative facilities, and the development of new community‐based specialized services for those in need 97 . More recently, a focus in community‐based care has also been the development of models for integrating mental health into primary care, as well as of shared decision‐making and recovery approaches 98 . To the extent that these models propose new ways of addressing mental illness, as well as extensive scale‐up of community‐based services, many would argue that they constitute a crucial paradigm shift.

Deinstitutionalization was driven by three main forces. First, the introduction of new medications made it increasingly possible for people with severe and enduring mental disorders such as schizophrenia and bipolar disorder to live reasonably well in community settings. Second, the mushrooming of psychiatric hospitals had come with high costs, and deinstitutionalization was seen by many governments as a cost‐saving strategy. Third, the growth of the human rights movement in the 1950s and 1960s generated increasing public concern about practices in psychiatric institutions, including involuntary care. Films such as One Flew over the Cuckoo's Nest drew public attention to the conditions in those facilities and provided support to the idea that people living with mental disorders should have a choice over the nature and locus of their care. This trend was reinforced by research demonstrating that community‐based models of care, including for people with severe mental disorders, could be delivered effectively, in a manner that was more acceptable to service users, and in some cases less costly 97 .

However, in many regions of the world, these developments have not actually occurred. Particularly in many post‐colonial low‐income countries, for example in sub‐Saharan Africa and South Asia, large psychiatric hospitals have been left behind by departing administrations, and have remained the main locus of care. In these countries, there has been little substantial deinstitutionalization, and very limited scaling up of community‐based and primary care mental health services 22 . In low‐income countries, there were 0.02 psychiatric beds per 100,000 population in 2001, and this increased to 1.9 beds per 100,000 population in 2020.

The success of deinstitutionalization programmes in transitioning to community‐based care has been highly varied around the world. In some countries, such as Italy, legislation has mandated the establishment of community‐based services, and consequently these services have been widely implemented, although with substantial variation across the country 99 . In many other countries, funding did not follow people who were discharged from psychiatric hospitals into community settings. For example, in many parts of the US, deinstitutionalization has been associated with a burgeoning population of homeless mentally ill and mentally ill prisoners 95 .

In Central and Eastern Europe, even with recent reforms, studies have criticized the uneven pace of deinstitutionalization, the lack of investment in community‐based care, and the “reinstitutionalization” of many people with severe mental illness or intellectual disability 100 . In a tragic case in South Africa, deinstitutionalization of 2,000 people with severe mental illness or intellectual disability from the Life Esidimeni facility into unlicensed and unregulated community organizations led to the death of over 140 people, sparking a public outcry and a national enquiry by the Human Rights Commission 101 .

Importantly, deinstitutionalization has been associated with “revolving door” patterns of care, in which people are discharged from hospital after admission for an acute episode, but do not have adequate care and support in the community, and therefore relapse and need to be readmitted. Indeed, readmission rates have been an important indicator for service managers to monitor in the post‐deinstitutionalization era, and the focus of several intervention studies 102 .

The WHO has advocated for the development of community‐based services for mental disorders for many decades. In the early 2000s, it produced a set of guidelines for countries to develop national mental health policies, plans and services 103 . This included the now widely cited “optimal mix of services” to guide countries on how to balance hospital‐ and community‐based care. This model proposed a pyramid structure, in which specialist psychiatric inpatient care represents only a small proportion of services at the apex of the pyramid, and is supported by psychiatric services in general hospitals, specialist community outreach, primary care services, and self‐care at the base of the pyramid. Others have developed similar “balanced care” models 104 .

The 21st century has also seen the development of models for integrating mental health into primary care, such as collaborative care models 105 . These latter initially focused on managing people with comorbid depression and other chronic diseases. Subsequently this work has been expanded to include other mental disorders, through models in which a mental health specialist provides support to non‐specialist health care providers, who are the main point of contact for people needing care. The WHO has endorsed this approach, particularly through its flagship mhGAP programme, which provides clinical guidelines for the delivery of mental health care through non‐specialist health care platforms in primary care and general hospital settings 106 . The mhGAP Intervention Guide has now been implemented in over 100 countries.

In parallel, the latter part of the 20th century and early 21st century have seen the rapid development of shared decision‐making and recovery approaches to mental health care. Shared decision‐making involves clinicians and people with mental disorders working together to make decisions, particularly about care needs, in a collaborative, mutually respectful manner 98 . This approach is consistent with an emphasis on human rights, as well as on the importance of patients’ lived experience, explanatory models and specific values, and clearly deserves support 107 , 108 . Recovery models have challenged traditional roles of “patients” to reframe recovery as a way of living a satisfying, hopeful life that makes a contribution even within the limitations of illness 109 . The recovery movement has been highly influential, is now incorporated into mental health policies, and has shaped the design of mental health systems in several countries 109 .

Yet, despite the strong scientific and ethical principles supporting community‐based care, collaborative care and moves towards shared decision‐making and recovery approaches, there remain major challenges, and the proposed paradigm shift remains to a large extent aspirational. While community care models have been developed, tested and shown to be effective in landmark studies, there are few cases of countries systematically investing in these models at scale, in a manner that substantially influences the mental health of populations. In addition, although there are apparent advantages to approaches such as shared decision‐making, a wide range of barriers across individual, organizational and system levels have been reported 110 , and implementation remains limited in mental health care 98 .

Indeed, it has been noticed that the agreement about the concept of shared decision‐making among stakeholders is only superficial 98 . After all, clinicians may not support this approach if it leads to patients being more empowered, but less adherent to treatment recommendations. This example raises broader questions about community‐based care models: is the failure to systematically scale up these models just due to a lack of political will and related scarcity of resources, or are there fundamental concerns with the model? Our view is that both of these may be true.

There is certainly a lack of political will and investment. Despite the courageous campaigning by people with lived experience for their rights to make decisions about their care, together with the robust evidence of improved outcomes associated with community‐based collaborative care models, governments often remain indifferent 1 . In 2020, 70% of total government expenditure on mental health in middle‐income countries was allocated to mental hospitals, compared to 35% in high‐income countries 22 . These differences need to be viewed in the context of massive global inequities in governments’ commitments to mental health more broadly. While high‐income countries spend US$ 52.7 per capita on mental health, low‐income countries spend US$ 0.08 per capita 22 .

On the other hand, it may also be the case that community‐based care does not go far enough in addressing the social determinants of mental health. While many community‐based care models focus on individuals with a mental disorder and their immediate family, very few address the fundamental structural drivers of mental illness in populations, such as inequality, poverty, food insecurity, violence, and hazardous living conditions 111 , 112 . Successful community‐based mental health services arguably require the existence of viable communities.

The strategy of deinstitutionalization was founded on the premise that communities can provide a safe, supportive environment in which people with severe mental illness can thrive. In countries marked by high levels of poverty, inequality, civil conflict and domestic violence, this is certainly not the case. Advocating for community‐based care requires addressing the fundamental social injustices which precipitate and sustain mental illness in populations.

Furthermore, community‐based service planners may have not gone far enough in considering demand‐side drivers of mental health care. For example, in many low‐ and middle‐income countries, traditional and faith‐based healers continue to be major providers of care for people with severe mental disorders, due to the scarcity of mainstream mental health professionals, and shared beliefs about the causes and treatments of such conditions.

The effectiveness and cost‐effectiveness of collaborative shared care models with traditional and faith‐based healers has been documented in Ghana and Nigeria 113 . Similarly, the possibility of addressing demand‐side barriers by implementing a community informant detection tool, based on local idioms of distress and vignettes to identify people with various mental health conditions, has been demonstrated in Nepal 114 . These innovations from low‐ and middle‐income countries provide potential lessons for high‐income countries in developing collaborative care models that are aligned with the belief systems of mental health care users and address demand‐side barriers to care.

In summary, despite the development of community‐based services, collaborative care, shared decision‐making and recovery models, a paradigm shift towards the implementation of well‐functioning and effective community mental health care around the globe has not occurred. A red flag should be raised when plans for community‐based services are under‐resourced (for example, not providing sufficient human resources to do the work), or are over‐optimistic about implementation (for example, overlooking important barriers to shared decision‐making) 115 .

Nevertheless, community‐based models have many strengths, and should be incorporated into attempts to iteratively improve clinical practices and society responses to mental disorder. Indeed, it has been argued that the shift to community‐based services has not been a sudden change, but rather the culmination of a slow, gradual, evolutionary development, which has old historical roots and will hopefully continue over time 116 . Efforts to strengthen community‐based approaches around the world are needed to consolidate and extend the advances that have been achieved.

Taken together, the slow transition from institutional to community‐based mental health care is partly attributable to the failure of governments in low‐, middle‐ and high‐income countries to adequately invest in such care – to mandate the funding to follow people with mental disorders into their communities and provide them with the support and choices they need to live productive meaningful lives – and strategies are needed to persuade them to do so. But, perhaps to an equally important degree, there are shortcomings in models of community care, with unrealistic expectations of a dramatic paradigm shift.

CBT AND THE SCALE‐UP OF EVIDENCE‐BASED PSYCHOTHERAPY

Since its development in the 1970s, cognitive behavioral therapy (CBT) has been at the core of an important shift in clinical practice towards the use of evidence‐based psychotherapies. Hundreds of randomized controlled trials have examined the effects of CBT for a wide range of mental disorders, including depression, anxiety disorders, substance use disorders, bipolar disorder, psychotic disorders, somatoform disorders, eating disorders, personality disorders, and also other conditions, such as anger and aggression, chronic pain, and fatigue 117 . CBT has also been tested across age groups and specific target groups, such as women with perinatal conditions and people with general medical disorders 117 .

Several other types of psychotherapy have also been rigorously investigated, and even psychotherapies that had not traditionally been explored using randomized controlled trials, such as psychoanalytically oriented therapies and experiential therapies, have now also been tested using such methods 118 , 119 , 120 . Nevertheless, CBT is by far the best examined type of psychotherapy and therefore dominates the transition of the field towards the use of evidence‐based psychotherapies 121 .

CBT is highly consistent with a neurobiological model of mental disorders, insofar as it focuses on symptom reduction, improvement in functioning, and remission of the disorder. Furthermore, the literature on the neurobiological bases of behavioral and cognitive interventions has become increasingly sophisticated 122 , 123 , and a more recent literature on process‐based CBT aligns well with the focus of RDoC on transdiagnostic mechanisms 124 . CBT can therefore be readily combined with neurobiologically oriented approaches, especially pharmacotherapy.

However, despite the strength of the evidence and its compatibility with other evidence‐based interventions, CBT has not been integrated into mental health systems globally. In many countries, it is still often seen as a reductionist approach that does not tackle the real underlying problems. Psychoanalytic approaches remain dominant, for example, in France and in Latin America 125 .

In low‐ and middle‐income countries, psychotherapies in general are often not available for people suffering from mental disorders, due to lack of resources and trained clinicians. Even in high‐income countries such as the US, the uptake of psychotherapies has declined since the 1990s 20 , while the use of antidepressant medication has increased considerably 126 , despite the fact that most patients prefer psychotherapy over pharmacotherapy 127 .

In most treatment guidelines, CBT is recommended as a first‐line treatment for several mental disorders. However, the actual implementation of such guidelines in routine care has been consistently shown to be suboptimal 128 , 129 , 130 . In addition, when CBT is employed, it is unclear whether therapists actually use it as detailed in standardized treatment protocols, or whether they combine it with other approaches.

The Increasing Access to Psychological Therapies (IAPT) program in the UK represents the most ambitious attempt to address the barriers faced by evidence‐based psychotherapy, with scaling up of CBT across an entire country. The main goal of the program was to massively increase accessibility to evidence‐based psychotherapies for individuals suffering from common mental disorders, such as depression and anxiety disorders.

An important argument for massively scaling up evidence‐based therapies was economic. Depression and anxiety disorders often start during the working age, and therefore the economic costs associated with them are large, due to production losses and costs of welfare benefits. If these conditions are treated timeously, costs of treatment are balanced by increased productivity and reduced welfare costs 131 . A global return on investment analysis confirmed this assumption cross‐nationally, indicating that every invested US dollar would result in a benefit of 2.3 to 3 dollars when only economic costs are considered, and 3.3 to 5.7 dollars when the value of health returns is included 132 . Hence, the hope was that IAPT would pay for itself.

The IAPT model has a number of key features 133 . First, patients can be referred by a general practitioner or another health professional, but can also be self‐referred. People with depression, generalized anxiety disorder, mixed anxiety/depression, social anxiety disorder, post‐traumatic stress disorder (PTSD), panic disorder, agoraphobia, obsessive‐compulsive disorder, and health anxiety receive a person‐centered assessment that identifies the key problems, and an agreed‐upon course of treatment is defined 131 .

Second, IAPT works according to a stepped‐care model. Patients are first treated with an evidence‐based low‐intensity intervention, typically a self‐help intervention based on CBT. Only if this is not appropriate or patients do not recover, they receive a high‐intensity psychological treatment. Low‐intensity therapies are delivered by “psychological well‐being practitioners” who are trained to deliver guided self‐help interventions, either digitally, by telephone, or face to face. High‐intensity therapies are delivered by therapists who are fully trained in CBT or other evidence‐based interventions.

Third, the therapies offered by IAPT are those recommended by the UK National Institute for Health and Care Excellence (NICE). When the NICE recommends different therapies for a mental disorder, patients are offered a choice of which therapy they prefer. This means that IAPT does not only deliver CBT, although a recurring criticism has been that the program is overly focused on that type of psychotherapy.

Fourth, outcome data are routinely collected in IAPT. Patients are asked to fill in various validated questionnaires before each session, so that clinicians can review the outcomes and use them in treatment planning.

Between April 1, 2019 and March 31, 2020, 1.69 million patients were referred to IAPT, of whom 1.17 million started treatment, with 606 thousand completing treatment, and 51% of them reporting recovery. The proportion of those recovered, however, is substantially lower (26%) when it is calculated based on those who started treatment (assuming that dropouts did not recover), and it has been argued that IAPT outcomes have been reported in an overly positive way 134 , 135 .

An important issue is that the outcomes vary considerably across IAPT services. In 2015/2016, the lowest recovery rate was 21% and the highest was 63%. There is some evidence that recovery rates are higher with an increasing number of sessions and more patients stepping up to more intensive therapy 136 . Other variables that are associated with better outcomes include shorter waiting times, lower number of missed appointments, and a greater proportion of patients who go on with treatment after assessment 137 .

A recent systematic review and meta‐analysis of the IAPT program identified 60 open studies, of which 47 could be used to pool pre‐post outcome data 138 . Large pre‐post treatment effect sizes were found for depression (d=0.87, 95% CI: 0.78‐0.96) and anxiety (d=0.88, 95% CI: 0.79‐0.97), and a moderate effect for functional impairment (d=0.55, 95% CI: 0.48‐0.61).

The IAPT program arguably represents the state‐of‐the‐art for implementation of evidence‐based psychotherapy in routine clinical care. Indeed, it has served as a model for the development of similar programs in other countries 138 , including Australia 139 , Canada 140 , Norway 141 , and Japan 142 . More broadly, IAPT indicates recognition of the importance of mental health and of the allocation of sufficient resources to treatment of mental disorders, as well as acknowledgement of the importance of psychotherapies and their role in addressing mental disorders.

There are other large scale implementation programs of CBT, especially in digital mental health care. For example, MoodGYM 143 , an online CBT program for depression, had acquired over 850,000 users by 2015. Psychological task‐sharing interventions developed by the WHO, especially Problem Management Plus, have been tested in several randomized trials and are now being implemented in low‐ and middle‐income countries on a broad scale 144 , 145 . However, the IAPT program is still the largest systematic implementation program of psychotherapies across the world.

Given the ambitiousness of IAPT, with extensive and rigorous roll‐out across an entire country, it seems reasonable to raise the key question of whether this program has had real‐world impacts, including a reduction in the disease burden of mental disorders. A first issue, however, is that comparison of IAPT with other treatment services would require a community intervention trial in which people are randomized to either IAPT or “regular” mental health care. Such a trial has not been conducted and probably never will be. Thus, although it is possible to claim on the basis of outcome data from routine care that other services are as effective as IAPT 146 , or that IAPT services may not provide interventions that match the level of complexity of the problems of patients 147 , it is difficult to validate such claims.

A second issue is whether any mental health treatments, including IAPT, are truly capable of reducing the disease burden of mental disorders. A key modeling study has estimated that current treatments only reduce about 13% of the disease burden of mental disorders at a population level 148 . In optimal conditions, in which all those with a mental disorder receive an evidence‐based treatment, this percentage can be increased to 40%. So, even under optimal conditions of 100% uptake and 100% evidence‐based treatments, reduction of disease burden is not expected to be more than 40%. This is true for IAPT as well as other programs disseminated on a broad scale.

The limited ability of current treatments to reduce the disease burden of mental disorders raises the so‐called “treatment‐prevalence paradox” 149 . This refers to the fact that clinical treatment rates have increased in the past decades, while population prevalence rates of mental disorders have not decreased. Increased availability of treatments could shorten episodes, prevent relapses, and reduce recurrences, in turn leading to lower point prevalence estimates of depression, but this has not transpired. Most meta‐analyses indicate stable prevalence rates or even small increases in prevalence, despite increased uptake of services 150 and the demonstrated efficacy of psychiatric treatments 31 .

There are several possible explanations for this “treatment‐prevalence paradox” 149 . First, it is possible that prevalence rates of depression have dropped, but that at the same time incidence has increased due to societal changes. Second, it is possible that prevalence rates have dropped, but that emotional distress has been more often diagnosed as a depressive disorder over the past decades, thereby masking the drop. Third, it is possible that prevalence rates have not dropped, because treatments may not be as effective as the field would like 151 . Indeed, treatment effects may be overestimated in trials due to publication bias, selective outcome reporting, use of inappropriate control groups, or the allegiance effect. Moreover, treatments may not benefit chronic depressive patients, or treatments may have iatrogenic effects that block natural recovery and prolong depressive episodes 152 .

Taken together, the development of evidence‐based psychotherapies has been a remarkable step forward for psychiatry, and the scale‐up of such effective psychotherapies in IAPT and other large‐scale implementation programs has contributed to consolidating this advancement. That said, the several criticisms of IAPT suggest that it is by no means a panacea. Instead, the implementation of evidence‐based psychotherapies is arguably best conceptualized as representing incremental progress. The impact of evidence‐based treatments on the disease burden of mental disorders currently appears to be modest; and the time horizons for introduction of interventions that are notably more successful is unclear.

DIGITAL PHENOTYPING AND DIGITAL THERAPIES

Rapid technological advances and the expansion of the Internet have spurred the development and widespread use of a host of digital devices with the potential to transform psychiatric research and practice 153 . Indeed, the fourth industrial revolution and the nudge towards telepsychiatry by the COVID‐19 pandemic have already revealed that digital technologies provide novel opportunities to improve psychiatric diagnosis, expand the delivery of mental health care, and collect large quantities of data for psychiatric research 154 , 155 .

There are many examples of how these advances have enabled digital solutions in psychiatry 156 , 157 . To name a few, virtual reality can facilitate exposure therapy for phobias and PTSD 158 , chatbots can deliver remote CBT anonymously day‐and‐night 159 , computer analysis of closed circuit television (CCTV) images can identify suicide attempts in progress at suicide hot‐spots 160 , voice and facial recognition software may enhance psychiatric diagnosis 161 , 162 , wearable devices may enable real‐time monitoring and evaluation of patients 163 , analyses of human‐computer interaction may detect manic and depressive episodes in real‐time 164 , and suicide risk may be assessed by analysis of social media posts 165 .

Furthermore, the widespread use of digital medical records, the collection of vast quantities of data from individuals via smart devices, the ability to link multiple databases, and the use of machine learning algorithms have redefined the use of big data in psychiatry with the promise of overcoming the failures of conventional statistical methods and small samples to capture the underlying heterogeneity of psychiatric phenotypes 81 , 82 , 83 . The ability to access, store and manipulate data, together with the use of machine learning algorithms, promises to advance the practice of individualized medicine in psychiatry by allowing matching of patients with the most appropriate therapies 81 , 82 , 83 .

Smartphone use is now ubiquitous even in remote and resource‐constrained environments across the globe 166 , making these devices a powerful medium to improve access to psychiatric care 167 . Smartphones are already being used to deliver interventions for common mental disorders 168 , 169 , 170 , 171 , and more than 10,000 mental health apps are available in the commercial marketplace 172 . There is considerable potential to turn smartphones into cost‐effective and cost‐efficient treatment portals by literally placing mental health interventions in the hands of the 6,378 billion people who own these devices (i.e., 87% of the world's population), many of whom do not currently have access to mental health care.

As communication devices, smartphones can be used to facilitate peer support, deliver personalized messages, provide access to psychoeducational resources, and facilitate timely referrals to appropriate in‐person clinical care 153 . The communication capabilities of smartphones have enabled the expansion of telepsychiatry via high‐quality low‐cost voice and video calls 173 , with evidence indicating that the use of video conferencing is not inferior to in‐person psychiatric consultations 174 .

Because smartphones are equipped with a range of sensors and the ability to store and upload data, they can be easily used to collect real‐time active data (i.e., data which the user deliberately and actively provides in response to prompts). Active data collected via smartphones are already being used in psychiatry for ecological momentary assessments, cognitive assessments, diagnosis, symptom monitoring, and relapse prevention 175 , 176 . Beyond these clinical applications, smartphones are also powerful tools for data collection in psychiatric research 177 , 178 .

Digital devices, including smartphones and wearables, can also collect and store a host of passive data (that is, data generated as a by‐product of using the device for everyday tasks, without the active participation of the user) with near zero marginal costs. These passive data have been likened to fingerprints or digital footprints. They provide objective continuous longitudinal measures of individuals’ moment‐to‐moment behavior in their natural environments and could be used to develop precise and temporally dynamic markers of psychiatric illness, a practice known as digital phenotyping 155 , 179 .

If digital phenotyping delivers on its promises, it will enable continuous inexpensive surveillance of mental disorders in large populations, early identification of at‐risk individuals who can then be nudged to access psychiatric treatment, and early identification of treatment failure to prompt timely individualized treatment decisions 180 . These potential applications are important, given the dearth of accurate real‐time psychiatric surveillance systems in many parts of the world, individuals’ reluctance to seek treatment at the early stages of psychiatric illness, and the high rates of treatment failure which necessitate timely adjustments to management.

Identifying digital markers for mental disorders is, however, not without potential pitfalls, that will need to be mapped and navigated before digital phenotyping can realize its full potential. There are still unanswered questions about the sensitivity, reliability and validity of smartphone sensors for health monitoring and diagnosis 181 . Furthermore, there appears to be a bias in measurement of everyday activities from smartphone sensors, because of variations in how people use their devices 182 . It still remains to be seen if actuarial models developed from population level digital footprints are clinically useful at the level of individual patients, as well as how digital phenotyping can be meaningfully integrated into routine clinical practice, and how patients will respond to and accept passive monitoring of their day‐to‐day activities 180 , 183 .

Digital solutions are not without shortcomings, and a digital intervention is not necessarily better than no intervention 184 , 185 , 186 . Reviews of the quality and efficacy of mental health apps indicate that there is often little evidence to support the effectiveness of direct‐to‐consumer apps 184 , 185 , 186 . Even when mental health apps seem to be useful, data indicate that many of them suffer from high rates of attrition and are not used long enough or consistently enough to be effective 187 .

Concerns about data privacy and security are a significant obstacle to expanding the use of digital technologies in psychiatric practice and research 188 , 189 . Psychiatry is often concerned with deeply personal, sensitive, and potentially embarrassing information, that requires secure data storage and stringent privacy safeguards. The risks associated with collecting and storing digital mental health information need to be clearly articulated in terms that patients understand, so that they can provide informed consent. Privacy policies in digital solutions such as smartphone apps are unfortunately often written in inaccessible language and “legalese”, making them incomprehensible to many users 189 , and there is as yet insufficient regulation of mental health apps and no minimum safety standards 188 .

While digital technology use has increased across the globe, there are ongoing inequalities in the access to these technologies within and between countries 166 . The rapid digitalization of psychiatry may unintentionally exacerbate health inequalities if digital mental health solutions cannot be shared 190 . Psychiatry will need to grapple with thorny questions about how to share digital technologies with those most in need of access to mental health care, and how to develop digital solutions for culturally diverse resource‐constrained environments. High data costs, unstable Internet connections, and bandwidth limitations can create logistical constraints on the utilization of digital mental health solutions in low‐income countries 191 .

The development of digital mental health solutions has typically been driven by the information technology industry and commercial interests 172 . On the other hand, the demand for mental health apps has been largely driven by consumers through social media, personal searches, and word of mouth, rather than professional recommendations 192 . Commercialization of health care and the repositioning of patients as customers has certainly created some efficiencies in health care delivery 193 . However, the profit motive is not always aligned with good patient care, as illustrated by the recent opioid crisis 194 .

Ensuring that clinicians are part of the process of digitalization of psychiatry will entail training them to understand, use and develop digital technologies; establishing ethical guidelines for the use of these technologies; ensuring independent evaluation of the effectiveness of digital interventions by researchers who have no commercial interest in the products; and protecting patient safety by ensuring that the claims made about the benefits of digital solutions are supported by robust evidence.

Emerging evidence suggests that screen time may be associated with mental health problems, although most of the work in this area focuses on children and adolescents 195 , 196 , 197 . While research is mostly cross‐sectional, there are a small number of longitudinal studies showing that screen time has small to very small effects on subsequent depressive symptoms, and that these associations depend on device type and use 198 , 199 . If screen time is bad for mental health, would it be wise to promote the use of digital mental health interventions that entail more time online or in front of a screen? This is not an easy question to answer, and the answer is likely not a simple yes or no.

The challenge is to think about how digitizing psychiatry can be balanced with a careful understanding of the potential for digital devices to harm mental health. Few interventions in psychiatry are without potential side effects, and it would be naïve to think that digital ones are different. As with any psychiatric treatment, the prescription of digital interventions needs to be accompanied with consideration of the contraindications, advice about how to use the intervention to its maximum benefit, and warnings about potential side effects and how to manage them. To enable this we require data, which we do not yet have, about the contraindications and side effects of digital interventions 188 .

We already have evidence to show that digital technologies can be at least as effective as traditional practices in making a psychiatric diagnosis, identifying appropriate individualized interventions, and teaching psychological skills such as mindfulness and attentional training 180 , 200 , 201 . Yet, most clinicians would likely agree that psychiatric practice is fundamentally relational and that most mental illnesses have an interpersonal dimension. The increasing use of technology in psychiatry will change the relationship between physician and patient in ways that we probably do not yet understand and cannot anticipate.

How technology is utilized in psychiatry will be a function of how central we think relationships are in diagnosis and treatment, and whether or not we see digital technologies as primarily a tool to enhance the therapeutic relationship, or simply a conduit to deliver content or collect and process information 202 . Theories will need to be developed to conceptualize and understand the digital therapeutic relationship, while we hold in mind the potential to harness technology to deepen the relationship between clinicians and patients. Indeed, evidence suggests that digital interventions are most effective when they have at least some person‐to‐person interaction 179 , 200 .

Digital technologies may change the way psychiatry is practiced, but to date much of the research in this area has been experimental, with proof‐of‐concept and clinical trials in highly controlled settings using very small samples 172 . The translational potential of these technologies has not yet been realized, and we still have some way to go to bring digital advances in mental health “from code to clinic” 172 . There are relatively few examples of digital technologies other than teleconferencing being used routinely in everyday real‐world psychiatric practice, and there is an urgent need for pragmatic trials and translational research to understand the barriers to adoption and implementation of new technologies 203 . The attitudes of clinicians and patients towards digital solutions in psychiatry and their perceptions of the effectiveness and safety of these devices are important determinants of how widely new technologies will be adopted.

Taken together, the science is still too young to let us know the extent to which the introduction of digital technologies will truly constitute a paradigm shift in psychiatric diagnosis and treatment, and whether these technologies will deliver on their promise to reduce the burden of disease caused by mental disorders. The available evidence gives cause for optimism and suggests that these technologies could assist in iteratively progressing the science and practice of psychiatry. However, there are many red flags when it comes to digital psychiatry, including overpromising with regards to efficacy and overlooking the human relationship. In order for iterative progress to happen, we will need continuous critical reflection, with an ongoing emphasis on equitable access, appropriate regulation, and quality assurance 204 .

GLOBAL MENTAL HEALTH AND TASK‐SHARING

The concept of global health emerged in the aftermath of World War II, when cross‐national organizations were needed to coordinate health efforts, particularly against infectious diseases 205 . The WHO was established in 1948, and became a key advocate for global health, exemplifying the key pillars of this approach, including the recognition that health is a public good requiring support from all sectors of the governments, that health involves a continuum ranging from wellness to illness, and that the determinants of health are biological, sociocultural and environmental 206 . Global health saw the protection of human rights as a central concern of all action concerning health, and expected that action to improve health includes the formulation of working policies addressing upstream social determinants of health, and a strengthening of health services 207 .

With growing recognition of the burden of non‐communicable diseases, including mental, neurological and substance use disorders, global mental health became an important focus. B. Chisholm, a psychiatrist who was the first WHO Director General, introduced the mantra “No health without mental health” 208 . An early 4x4 model of non‐communicable diseases emphasized the comorbidity of cardiovascular diseases, diabetes, cancer and respiratory diseases with tobacco use, unhealthy diet, physical inactivity and harmful alcohol use as risk factors for these conditions. A later 5x5 approach has emphasized that these non‐communicable diseases are commonly comorbid with mental disorders, and that childhood adversity is an important common risk factor 209 .

Over the past several decades, global mental health has become a significant discipline, with specific departments established at several leading universities, textbooks and journals devoted to the subject, and significant support for research obtained from funders 210 . In addition to a focus on mental health as a public good and human right, on mental health as entailing a continuum and a life course approach, on the importance of social determinants of mental health, and on the need of strengthening mental health services, work in global mental health has emphasized the efficacy of task‐shifting interventions, the importance of addressing stigma, and the value of including service users’ perspectives in research and planning 1 , 2 .

Early work by the WHO, and subsequent work by others in global mental health, has led to important contributions. A first key contribution has been the recognition of the burden of mental disorders, and advocacy that this burden needs to be urgently and appropriately addressed. There are far too few mental health clinicians in low‐ and middle‐income countries, where the vast majority of the world's population resides 22 .

A second key contribution has been a focus on addressing mental health in primary care. In the 1970s, the WHO conducted a multinational collaborative study demonstrating the feasibility and effectiveness of offering community‐based mental health care, delivered by primary health care workers, in developing countries 211 . A few years later, in 1978, the Primary Health Care Conference in Alma Ata, composed of representatives of almost all countries in the world, included the promotion of mental health into the list of essential components of primary health care.

Nevertheless, global health in general and global mental health in particular have faced many challenges. Early hopes were that globalization would entail a border‐free world with easy communication, trade, and mutual support. However, globalization has also arguably allowed unidirectional unloading of products of the North to the less industrially developed South, and a simultaneous migration of many individuals, including health professionals, from the global South to the North. Colonial practices, including large psychiatric hospitals, have remained in existence in many low‐income countries. Rapid urbanization and breakdown of traditional communities, which provided some support to vulnerable individuals, have further complicated the provision of health care. The introduction of digital technologies – which has been considered as a potential equalizer – also runs the risk of creating a new divide, the digital divide.

In terms of the clinical practice of psychiatry, while the numbers of psychiatrists and other mental health care workers has significantly increased across the globe, their inequitable distribution has not significantly improved 22 . There are still many countries with only a few psychiatrists, and the brain drain – the movement of fully trained psychiatrists from the global South to the North – continues 212 . Training programs which can be used for primary health care providers in mental health have been produced by the WHO and other agencies, and the situation has improved in some countries, but the numbers of those left with no adequate care remain high. Primary care practitioners are not always willing to accept responsibility for the treatment of mental disorders, and many well‐trained psychiatrists have continued to work in private health care services that reach only a minority of those who need help.

Earlier sections of this paper considered some of the concerns about current psychiatry nosology raised by neurobiologically‐focused and “number‐driven” researchers. But even from a public health perspective, application of key aspects of the chapter on mental disorders of the ICD rises problems 213 . First, most practicing clinicians feel that in daily work the number of diagnostic categories proposed for use should follow the number of options for therapeutic interventions, and so ICD approaches may be too complex. Second, reporting about inpatient mental health services to national authorities in most instances follows the guidelines provided by hospitals, which do not allow for the collection of sufficiently detailed or validated data. The interpretation of findings may be made even more difficult by the fact that in federal countries the rules of reporting to the central authority differ from area to area.

Global mental health has been crucially important in putting forward a number of innovative models and approaches. At the same time, critics might suggest that the strategies of global mental health are not so much an entirely new paradigm but instead a re‐packaging of long‐standing ideas in the field, and that each of these strategies has important limitations which deserve emphasis.

First, global mental health has focused on the notion of “task‐shifting”. This involves the use of non‐specialized health care workers, who are trained and supervised by mental health specialists. Systematic reviews have concluded that there is now considerable evidence for the efficacy of this approach 3 , 214 . Nevertheless, this strategy is not a panacea. There are limits to what can be done by untrained personnel. The treatment of more complex conditions, such as treatment‐refractory mental disorders, requires well‐trained clinicians. Moreover, significant supervision and monitoring may be required, and this entails human and financial resources. There is now interest in how to assess therapist competence in task‐shifting trials 215 , 216 . Finally, there is a difference between demonstration projects conducted by academic researchers and real‐life scale‐up projects undertaken by governments. Pharmacotherapy outcomes are worse in real‐world pragmatic trials than in academic‐centre explanatory trials, and we might expect that the same will hold true in the case of task‐shifting research.

A second important strategy of global mental health has been to build the investment case for mental health, demonstrating the return on investment for countries scaling up community‐based care. As noted earlier, this gave key impetus to the implementation of psychotherapies in the UK. However, a number of challenges remain. Many economic returns accrue to sectors outside ministries of health, which traditionally hold mental health budgets. Economic returns on scaled‐up mental health care are likely to accrue through improved labour market participation, reduced homelessness, and savings to correctional services and police services, and not necessarily to the health sector. Moreover, such savings might only be realized at some time in the future, creating what has been termed pernicious “diagonal accounting” 217 . Finally, it must be conceded that not all investment in mental health – for example, care for those with severe neurodevelopmental disorders – will yield significant economic returns.

A third key strategy of global mental health has been to focus on building stronger, better coordinated advocacy, with partnerships between people with lived experience and clinicians to campaign for better and more resources for mental health care. It has been argued that ongoing dialogue between the various stakeholders involved in community‐based care is essential to reach common ground on service development priorities. This should also include maximizing opportunities for leadership from people with lived experience, to address demand‐side barriers to community‐based mental health care. Nevertheless, there are key barriers to advocacy work, including low mental health literacy of policy‐makers, and a gap in frameworks linking research to policy 218 .

A fourth key strategy of global mental health has been to focus on stigma reduction strategies. Certainly, reducing stigma and discrimination against people living with mental illness is vital if we are to promote care in the community. Furthermore, there is a growing evidence base for the positive impact of stigma reduction campaigns for mental health, such as the World Psychiatric Association's “Open the Doors” program. At the same time, there are important challenges to acknowledge. Much more needs to be done to both improve the effectiveness of these interventions and extend stigma reduction programmes to a range of different countries 219 . Stigma reduction strategies should not deny the dysfunction that accompanies severe mental disorders (services for such conditions remain sorely needed), and they need to also highlight that individuals suffering from psychiatric disorders have “responsibility without blame” 220 . Finally, it is notable that, in some contexts, providing neurobiologically focused information increases rather than decreases stigma 221 .

A fifth key strategy of global mental health is to address social determinants of mental disorders. Governments need to address fundamental social injustice such as rampant inequality, high unemployment, civil conflict and violence, particularly gender‐based violence, that drive mental disorders in populations 222 . That said, the evidence base for population‐level interventions to address the social determinants of mental health is rather sparse and of low quality 223 . Ironically, global mental health has been accused of ignoring key contextual data 224 , and of perpetuating some of the sociopolitical inequities it critiques 225 . Less contentiously, while some clinicians may well contribute to efforts focused on social determinants, the majority will focus on providing direct clinical care. Public mental health skills are needed to supplement, rather than replace, standard clinical training.

Taken together, it is clear that the concepts and methods of global mental health have many strengths, have contributed to important advances, and should be incorporated into further attempts to incrementally improve health policies as well as clinical practice. As always, discourse about a paradigm shift and over‐optimism about the extent of envisaged change raise red flags. Indeed, the key strategies of global mental health that may facilitate ongoing incremental progress may themselves require iterative attention: we need to continue to be innovative about task‐sharing, to gradually strengthen the investment case, to steadily develop better advocacy strategies, to further reduce stigma about mental disorders and increase mental health literacy, and to better address social determinants of these conditions.

Kuhn's notion of scientific paradigms has been extraordinarily influential 226 . He argued that most of science is “normal”: scientists have a particular conceptual framework, with various exemplars that are key for the field, which allows them to address a range of relatively minor “puzzles” 227 . However, from time to time, there is a paradigm shift, with an entirely new conceptual framework and new exemplars coming to fore and causing a “crisis”, and so entailing a major revolution in the field. Thus, for example, at one point phlogiston was thought to explain combustion, but this paradigm was replaced by one that emphasized the importance of oxygen, providing an entirely new perspective. Notably, from a “critical” perspective, scientific paradigms are incommensurable; those who adopt different paradigms are really talking past one another, and the shift from one paradigm to another happens not because of scientific advancement, but rather due to a sociopolitical shift in the field 228 , 229 .

From this perspective, psychiatry has been characterized by a history of continual paradigm shifts, with the field lurching over time from one set of models to another, with no substantive scientific advances in our knowledge, but rather merely a responsiveness to the prevailing sociopolitical winds of the day 229 . Thus, as noted earlier, psychiatry has seen movements from psychodynamic approaches to neuroscientific ones, and from institutional care to community‐based care. While a good deal of the critique of psychiatry has come from external fields, there is a significant contribution from within the discipline, with proponents of new paradigms at times being very critical of current practices. The idea that psychiatry is in crisis seems to be prevalent and persistent in both the professional literature and in social media 230 , 231 , 232 , 233 , 234 .

We would argue strongly against this view of psychiatry. This is not to disagree that there have been important shifts in the field over its history: there certainly have been. Nor is it to disagree with the valid points that sociopolitical and sociocultural factors are key to such issues as determining budgets for mental health services, and in influencing the experience and expression of mental disorders 235 . Nor is to deny or downplay the many crucial challenges that continue to face psychiatry as a profession, and psychiatrists as practitioners 236 , 237 . And perhaps most importantly, it is not to ignore or to minimize the enormity of the treatment and the research‐practice gaps discussed in detail earlier in this paper. Clearly, considerably more needs to be done to improve mental health care services, and to effectively address the burden of disease due to mental disorder.

However, we wish to emphasize that there has been a gradual accretion of knowledge about mental disorders, and that our understanding of their causes and our ability to manage them has significantly increased over time. We also wish to argue that the different proposals for the field discussed in this paper are not necessarily incommensurable paradigms, but rather are important perspectives that can productively be drawn on and integrated into contemporary practice 238 . The integration of clinical neuroscience and global mental health, for example, may facilitate advances in precision public mental health 239 . Space precludes a detailed consideration of a range of other innovative perspectives that may also contribute to the incremental and integrative advance of psychiatric practice, including collaborative care 240 , preventive psychiatry 241 , evolutionary psychiatry 242 , positive psychiatry 243 , intergenerational psychiatry 244 , and welfarist psychiatry 245 .

Perhaps most importantly, we would wish to problematize the notion that psychiatry is in perennial and perpetual crisis. Tools provided by “critical” authors, who emphasize the sociopolitical aspects of science and medicine, may be in fact be useful in investigating why psychiatry is so often viewed in this way, and why a view of psychiatry as steadily accreting knowledge and improving clinical practices is less often put forward than seems reasonable, even from within the field. Are there specific interests that stand to gain from negative views of the psychiatric profession? What are the benefits to particular authors of being overly critical of existing practices and of promising entirely novel or disruptive solutions? What can be done to encourage those without and within the field to emphasize that scientific progress is often iterative and incremental, with gradual consolidation of knowledge, with inclusion and integration of a range of different models and approaches?

We have noted in this paper a number of red flags, which seem indicative of overly optimistic promises of a paradigm shift in psychiatry practice and research, and that may inadvertently even support an antipsychiatry position that discourages patients from seeking sorely needed professional care, or policy‐makers from funding desperately needed mental health care services. A few of these red flags deserve particular emphasis here.

First, given the complexity of mental disorders, and the need to avoid both a brainless and a mindless psychiatry 246 , various forms of reductionism serve as red flags, whether these involve neuro‐reductionism (e.g., mental disorders are merely brain disorders) or culturalism (e.g., mental disorders merely reflect social inequalities). As a field, we should promote the breadth and depth of psychiatric concepts and findings, emphasizing that psychiatry builds bridges across biological, psychological and social domains, and that – despite the complexity of mental disorders – this has allowed important insights into their phenomenology and etiology, and has facilitated the development of a broad range of different evidence‐based treatment modalities and types of intervention. The complexity of mental disorders may, however, mean that there are few “silver bullets” in psychiatry: any individual mental health intervention may have only modest effect sizes, and reduction of disease burden due to mental disorders is a massive goal, likely requiring a broad range of interventions 247 .

Second, economic over‐optimism may be a red flag: bringing new drugs to market requires significant financial investment, deinstitutionalization is not an inexpensive option, and it is a challenge to demonstrate that large‐scale implementation programs such as IAPT save money. While a range of different metaphors may be useful in describing psychiatric work, and in encouraging policy‐makers to fund mental health services, we need perhaps to be particularly careful of seeing patients as merely consumers, and psychiatry as simply providing a return on investment. Similarly, while a collaborative relationship between professional clinicians and patient partners may be useful in encouraging shared decision‐making, this metaphor of psychiatric work and mental health services may miss some aspects of the clinical encounter. The metaphor of clinicians providing care is a crucial one, and we need to call for more such care, even if at times it is somewhat expensive 115 .

Third, calls for a radical transformation of psychiatry's research agenda are a red flag. Hubris may result in downplaying what has already been achieved over decades, or in overly focusing on one or other favoured perspective. A more humble position that emphasizes how difficult is to know what approaches and models will lead to the largest advances, that encourages a broad range of promising work, that insists on principles of reproducible science including the common metrics agenda, and that acknowledges the key role of serendipity, is appropriate 64 , 248 , 249 . Analogously, calls for a radical transformation or narrowing of the training curriculum also constitute a red flag: psychiatry trainees need exposure to a broad range of concepts and methods, including neuroscience, statistics, evidence‐based psychotherapy, digital psychiatry, and public mental health. The field needs well‐rounded graduates who are able to access and employ the full range of concepts and findings from our rich discipline.

How can we facilitate an ongoing focus on incremental advances in clinical practice, with integration of a range of different perspectives and findings? It may be useful to approach the issues discussed in this paper with a particular knowledge of how science works, and with a particular attitude towards progress.

From the perspective of knowledge, it seems useful to emphasize that concepts of scientific crisis and paradigm shifts often serve as rhetorical devices, that in sciences ranging from physics to psychiatry multiple approaches and models are potentially useful, and that in psychiatry there is a particular need for pluralistic and pragmatic approaches that integrate a range of different concepts, methods and findings 229 , 250 . From the perspective of attitude, we would emphasize the value of staying hopeful, avoiding hype, and committing to the important work of closing the treatment gap as well as the research‐practice gap.

Thus, in terms used earlier in this paper, the solution to challenges in psychiatric diagnosis and treatment is unlikely to lie in entirely novel paradigms, but rather in the humble, laborious, iterative work of systematic clinical observation, painstaking research, and creative thinking. In the case of psychiatric assessment, for example, we have elsewhere argued for the need for more work on post‐diagnostic assessments and measures that are consistent with measurement‐based care and that promote personalized psychiatry 251 , 252 , 253 . In the case of psychiatric treatment, addressing the treatment and the research‐practice gaps will require more attention to expanding innovative delivery models that will reach more people in need 254 , systematic adoption and roll‐out of integrated evidence‐based interventions 255 , and an iterative discovery‐confirmation process to assess and improve efficacy 256 .

In conclusion, this review of a range of proposed approaches to and models of diagnosis and treatment of mental disorders suggests caution in concluding that we are facing a crisis in psychiatry which necessitates a disruptive transitioning from traditional to new practices. We argue instead that an approach which emphasizes paradigm shifts should be replaced by one that focuses on the importance and value of incremental and integrative advances. In particular, we caution against an advocacy for paradigm shifts that inadvertently represents a disguised manifestation of antipsychiatry, and we instead suggest the need for a position that emphasizes both the accomplishments and limitations of psychiatric diagnosis and treatment, and that is cautiously optimistic about their future.

FORUM – PSYCHIATRIC PRACTICE AND RESEARCH: THE VALUE OF INCREMENTAL AND INTEGRATIVE ADVANCES

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Depression, schizophrenia and bipolar disorder linked with ancient viral DNA in our genome – new research

Research Fellow, King's College London

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Rodrigo Duarte received funding from the National Institutes of Health, USA.

Douglas Nixon receives funding from the National Institutes of Health, USA.

Timothy Powell receives funding from the National Institute for Health and Care Research, the National Institutes of Health, and the Medical Research Council.

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Around 8% of human DNA is made up of genetic sequences acquired from ancient viruses . These sequences, known as human endogenous retroviruses (or Hervs), date back hundreds of thousands to millions of years – with some even predating the emergence of Homo sapiens .

Our latest research suggests that some ancient viral DNA sequences in the human genome play a role in susceptibility to psychiatric disorders such as schizophrenia, bipolar disorder and major depressive disorder.

Hervs represent the remnants of these infections with ancient retroviruses. Retroviruses are viruses that insert a copy of their genetic material into the DNA of the cells they infect. Retroviruses probably infected us on multiple occasions during our evolutionary past. When these infections occurred in sperm or egg cells that generated offspring, the genetic material from these retroviruses was passed on to subsequent generations , becoming a permanent part of our lineage.

Initially, scientists considered Hervs to be “junk DNA” – parts of our genome with no discernible function. But as our understanding of the human genome has advanced, it’s become evident that this so-called junk DNA is responsible for more functions than originally hypothesised.

First, researchers found that Hervs can regulate the expression of other human genes. A genetic feature is said to be “expressed” if its DNA segment is used to produce RNA (ribonucleic acid) molecules. These RNA molecules can then serve as intermediaries leading to the production of specific proteins , or help to regulate other parts of the genome .

Initial research suggested that Hervs regulate the expression of neighbouring genes with important biological functions. One example of this is a Herv that regulates the expression of a gene involved in modifying connections between brain cells.

Hervs have also been found to produce RNAs and even proteins in blood and brain samples . These molecules have the potential to exert a wide range of functions, as they can travel across cellular compartments to execute different roles.

Scientists have also found evidence suggesting certain human genes are derived from Hervs. This indicates there were instances during evolution where Hervs were co-opted for specialised biological functions. For example, the human genes syncytins 1 and 2, which are derived from Hervs, play pivotal roles in placental development .

HERVs in psychiatric disorders

Considering the abundance of Hervs in the genome and their potentially numerous functions, we wanted to better understand whether genetic susceptibility to certain psychiatric disorders was associated with differences in Herv expression.

In our study , we profiled Herv expression in nearly 800 autopsy brain samples. This helped us identify DNA variations that influenced Herv expression in the brain.

We then cross-referenced this information with findings from large genetic studies which had compared genetic differences between tens of thousands of people – both with and without mental health conditions. These studies identified variations in DNA associated with different psychiatric conditions.

We found that that the expression of four Hervs was linked with genetic susceptibility to major psychiatric disorders. The expression of two of these Hervs was associated with schizophrenia, one Herv with both schizophrenia and bipolar disorder, and one with depression. These results suggest that Hervs may be playing a more important role in the brain than initially thought.

Read more: Discovering how genetic 'dark matter' plays a role in mental illness is just the tip of the iceberg for human health

There are many genes involved in psychiatric disorders – and Hervs are only a part of this puzzle. Although the precise impact of these Hervs on brain cells and on a person’s susceptibility to certain psychiatric disorders requires further research, our study is the first to show that genetic susceptibility for a psychiatric disorder also acts through these ancient viral DNA sequences.

It’s still too early to determine the practical applications of our findings – and whether they might be used to develop new treatments. But we’re optimistic about this line of research. By linking Herv expression in the brain with psychiatric disorders, our research recognises the importance of these mysterious sequences in the human genome, which have been ignored for years.

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Finding new and better treatments for psychiatric disorders

Steven M. Paul 1 &
William Z. Potter ORCID: orcid.org/0000-0003-2529-6568 2

Neuropsychopharmacology volume 49 , pages 3–9 ( 2024 ) Cite this article

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Neuroscience

In contrast to most fields of medicine, progress to discover and develop new and improved psychiatric drugs has been slow and disappointing. The vast majority of currently prescribed drugs to treat schizophrenia, mood and anxiety disorders are arguably no more effective than the first generation of psychiatric drugs introduced well over 50 years ago. With only a few exceptions current psychiatric drugs work via the same fundamental mechanisms of action as first-generation agents. Here we describe the reasons for this slow progress and outline a number of areas of research that involve a greater reliance on experimental therapeutics utilizing recent advances in neuroscience to better understand disease biology. We exemplify the potential impact of these areas of research focus with several recent examples of novel agents that have emerged and which support our optimism that newer, more effective and better tolerated agents, are on the horizon. Together with existing drugs these newer agents and novel mechanisms could offer markedly improved functional outcomes for the millions of people still disabled by psychiatric disorders.

New and emerging approaches to treat psychiatric disorders

Schizophrenia: from neurochemistry to circuits, symptoms and treatments

The neurobiology of treatment-resistant schizophrenia: paths to antipsychotic resistance and a roadmap for future research

Historical perspective.

It remains the case that the majority of pharmacologic treatments for the major psychiatric disorders were initially discovered through serendipity and astute empiricism on the part of clinicians. Following the introduction of first-generation antipsychotics and antidepressants developed this way in the 1950s and 1960s, the significant advances in molecular pharmacology in the 1970s and 1980s provided the “mechanistic” foundation for subsequent identification of the many psychiatric drugs on the market today, that nonetheless work via one or more of the same molecular mechanisms of action (MOA) of the first-generation compounds. The major advances since the introduction of these first-generation agents for both schizophrenia and mood disorders have been primarily in improved tolerability and safety but not in enhanced efficacy. Side effect profiles have benefited from developing compounds that retained elements needed for efficacy while eliminating pharmacologic activities that produce some of the more common side effects. Unfortunately, despite decades of work, the current drugs for treating psychiatric disorders, and their overall efficacy, are arguably no better, especially at improving functional outcomes, than the very first agents introduced well over 50 years ago!

As we will discuss, this disappointing and rather sobering reality has recently begun to change for the better with the emergence of several novel (mechanistically speaking) potential new drugs and approaches for treating schizophrenia and various mood disorders. Here we discuss some of the historical challenges and new opportunities for discovering novel and improved psychiatric drugs.

The problem

The problem simply stated is that there is too little known about the etiology and pathophysiology of the major psychiatric disorders. Our current, rather limited knowledge of disease biology has not yet meaningfully informed our attempts to find better therapeutics. It was optimistically believed that the great progress in molecular pharmacology (i.e. the application of molecular biology to pharmacology) resulting in the identification of a large number of neurotransmitter receptor families, ion channels, and enzymes as potential drug targets, combined with the modern tools of industrialized drug development, e.g. synthesis of large chemical libraries amenable to high throughput screening, combined with subsequent optimization of leads through modern medicinal chemistry, followed by toxicology and metabolic studies and an understanding of basic pharmacokinetics, would lead to a range of new and more effective medications for those who did not respond well to the original antipsychotics, antidepressants or anxiolytics. There was also the early success story in Parkinson’s disease where the well characterized loss of dopamine neurons and dopamine in the substantia nigra and basal ganglia gleaned from postmortem studies led directly to the use of L-dopa and other dopamine receptor agonists to effectively control the motor symptoms of the disease [ 1 ]. Almost two decades of research starting in the early 1970’s focused on the possibility that similar biochemically-defined subgroups of patients with various psychiatric disorders could be identified by measuring monoamine and other neurotransmitters and(or) their metabolites in brain, CSF, blood, or urine. Despite many positive reports from rather small studies supporting this possibility, none of these findings proved sufficiently robust or reproducible to inform the development of new treatments.

Another source of initial optimism was the revolutionary progress being made in genetics and genomics, including the successful sequencing of the human genome and the identification and cataloguing of a very large number of coding and non-coding DNA sequence variants for conducting genome-wide association studies (GWAS) and other genetic studies. Many of us believed that either causative genes or major risk alleles for conditions with high heritability such as schizophrenia and bipolar disorder would be rapidly identified and allow for a specific pathophysiological process to be targeted. Potential drug targets emerging from these genomic approaches could, in theory, follow a linear path from identification to validation and testing with the drug discovery tools that have become available to both academia and industry over the last decade. Unfortunately, such a linear path is not possible with highly polygenic disorders where many individual genes have been shown to contribute very small effects to the disease phenotype or to the risk of developing a DSM-diagnosed syndromal disorder, in contrast to disorders where only one or at most a few causal genes lead to readily identified biochemical and physiological abnormalities that can be corrected pharmacologically. And even if/when there were a demonstrated genetic etiology to a psychiatric disorder such as schizophrenia or bipolar disorder, a linear path to developing effective treatments may prove elusive [ 2 ]. For example, while the genetics of schizophrenia are unquestionably complex and highly polygenic [ 3 ] there are recent reports of ultra-rare mutations that confer substantial risk of developing schizophrenia and several of these represent “loss of function“ mutations in genes encoding specific glutamate receptor subunits for both N-methyl-D-aspartate (NMDA) and α amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors that are of plausible etiologic relevance to schizophrenia and also highly druggable [ 4 ]. However, exactly how these genes contribute to the risk of developing schizophrenia and how drugs acting at these receptors might be used to effectively “treat“ the disease is far from certain and maybe less straightforward than anticipated. For example, if schizophrenia is a neurodevelopmental disorder as has been postulated [ 5 ], would these drugs need to be administered at a critical stage of neurodevelopment and would such drugs still be effective years later when symptoms have emerged? It’s also unclear which of the core symptoms of schizophrenia these genetic mutations actually contribute to. To date, drugs designed to enhance NMDAR function have proved of little value in treating any of the core symptoms of schizophrenia. Would they have been more effective if used earlier in its pathogenesis? Unfortunately, the hope that genetic discoveries would rapidly yield compelling targets for drugs with novel MOAs to correct underlying etiology/pathophysiology has in psychiatry been largely unfulfilled. Other powerful tools have also emerged but have yet to prove instrumental in finding new drugs. These include neuroimaging methods to quantify target engagement by drugs in living subjects for example using positron emission tomography (PET) tracers i.e. to quantify receptor occupancy or using electroencephalography (pharmaco EEG) and functional magnetic resonance imaging (fMRI) to measure the effects of drugs on brain activity and neural circuits associated with a given psychiatric disorder (see below) [ 6 ].

Another relevant drug discovery discipline of significant scientific and intellectual pursuit over now several decades has been in what we describe as “behavioral pharmacology“, an extension of behavioral neuroscience, where drugs are systematically studied to impact various observed and readily quantifiable behaviors in nonhuman species, mostly (but not limited to) rodents (i.e. rats and mice). Although these studies have provided very useful information and are almost always employed in any drug discovery effort in psychiatry, their practical value for the discovery of novel psychiatric drugs has, with just a few exceptions, been limited.

Moreover, these behavioral studies have also resulted in what many have mistakenly described as “animal models” of a given disorder, purportedly validated by drugs that have been shown to be effective in various psychiatric disorders and thus where novel mechanisms and agents can then be studied, with the hope that the behavioral findings with these new drugs will ultimately prove translatable to human psychiatric disorders. Such animal models usually involve a behavioral “stressor” or administration of a drug which mimics, in some respect, a particular psychiatric symptom. These models have been developed for most of the psychiatric syndromes, including anxiety, mood, and psychotic disorders [ 7 ]. While certain aspects of complex psychiatric syndromes may be “modeled” in such a manner in animals, there are clear limitations. The obvious anthropomorphic limitation of these models of course is that they require a “leap of faith“ as to whether an observed behavior in a rodent can truly reflect any complex emotional disorder in a human being, and especially those characterized by emotions/feelings and behaviors that cannot be readily assessed in non-human species. More recently, genetically modified mice have been used to both identify and confirm the exact mechanisms of drug action and to incorporate genetic mutations believed or shown to contribute to disease etiology/pathophysiology. For example, by genetically eliminating the gene encoding a specific receptor target one can be fairly certain that the behavioral consequences of a given drug, if also eliminated, were in fact due to that receptor. While theoretically powerful, especially when coupled with the advances in molecular pharmacology described above, there have been to date only a few examples where novel psychiatric drugs have been discovered in this way (see below). Moreover since these animal models have been mostly “validated“ with drugs that predominately work via the same MOA they are really mostly suited for discovering “me too“ iterations of existing agents.

By contrast, genetically modified mice expressing disease-causing mutations or risk alleles have been used extensively in drug discovery for virtually all other therapeutic areas. In psychiatry, as discussed above, there have been virtually no examples of simple Mendelian inheritance and thus no single major genes have been discovered or exploited for developing a useful animal model of a common psychiatric disorder. Still, rare mutations causing neurodevelopmental disorders manifesting psychiatric symptoms (e.g. 22q deletion syndrome) for example have been used to study drugs that may prove useful for the more common and highly polygenic psychiatric disorders, but again with very limited if any success to date. The latter is in stark contrast to the more heritable neurological disorders where very useful and translatable animal models have been developed (e.g. for Huntington’s disease, amyotrophic lateral sclerosis [ALS], frontotemporal dementia [FTD] and Alzheimer’s Disease) (see below).

To our knowledge, few if any of the scores of molecular targets “validated” using these behavioral and genetic approaches and which have led to compounds reaching clinical development have been successfully developed for an actual psychiatric disorder. To make matters worse, in most cases there is limited data to actually confirm that the novel drug being studied actually resulted in the desired pharmacological action in the brain. In many cases it is not known whether a negative clinical study with such compounds provides evidence against the hypothesis being tested or simply reflects the fact that for reasons of poor dose selection or accessibility to the brain, the MOA in question was never evoked. Additionally, both increases and variability in placebo response rates have resulted in hugely expensive ultimately uninterpretable trials that did not meet their primary endpoints for studies of antidepressants in mood disorders and even in the case of antipsychotics for treating schizophrenia [ 8 ]. Moreover, in most studies the placebo response is 60-80% of the response observed with active drug! Not surprisingly, the failure to establish the clinical utility of literally dozens of compounds brought forward on the basis of such approaches has dampened enthusiasm by large pharmaceutical companies to make the substantial R&D investments required to discover and develop novel treatments for psychiatric disorders [ 9 ], despite the great medical and commercial success of the first and second-generation psychiatric drugs.

With respect to the economic incentives necessary for the substantial R&D investments required to discover new medicines, we can attest that these still exist for psychiatric drugs, especially if new and improved versions can be developed. In fact, the limited “effect sizes“ (poor overall efficacy) and troubling side effect profiles and tolerability of many, if not most, of the currently marketed psychiatric drugs make this a highly desirable business proposition, i.e. if it was not for the high attrition/failure rates described above. Thus, in our opinion, it is the comparatively low probability of technical success [ 9 ] coupled with, and in good part due to, our limited knowledge of disease biology that makes psychiatric drug development so risky and thus unappealing to the larger pharmaceutical companies, many of whom ironically were historically quite active and successful in the field of developing psychiatric drugs. This contrasts with many other therapeutic areas that currently compete for R&D investments, such as oncology, diabetes, cardiovascular and inflammatory diseases where we have a much better understanding of disease biology and many compelling drug targets. Today, with only a few exceptions, psychiatric drug discovery is now the purview of only a handful of smaller more risk-taking biotech companies and a few academic drug discovery programs. Hopefully, this picture will change as more compelling and less risky approaches to psychiatric drug R&D emerge.

The solution

Given the substantial challenges outlined above, and notwithstanding the huge unmet medical need given the limitations of current psychiatric drugs, as well as the lessons learned from the last nearly three decades of rather dismal R&D productivity in psychiatry, we believe there are a number of opportunities to accelerate psychiatric drug discovery and development. We will outline and exemplify several of these opportunities below:

Focus on disease biology

As emphasized above, our very limited understanding of either the etiology or pathophysiology of the major psychiatric disorders makes discovering highly effective drugs very challenging. Academic research on basic disease biology using all of the powerful tools available today (mentioned above) as well as the new tools on the horizon, some of which may emerge from the BRAIN initiative [ 10 ], must be adequately prioritized and funded. While progress has been slow in psychiatry, there are good examples of how a better understanding of disease biology (etiology and/or pathophysiology) can lead to successful drug discovery and development. Alzheimer’s disease (AD) is the most common form of dementia and is currently a uniformly fatal illness. Like many common and complex medical disorders there is now an impressive body of work that has unequivocally identified important genetic risk factors (e.g. apoE4 and TREM2) and even causal genes (e.g. APP, PS1) for subgroups of patients [ 11 ]. Importantly, large aligned investments delivered a variety of highly validated/qualified noninvasive biomarkers (e.g. PET/MRI neuroimaging, CSF and blood proteomics) [ 6 ] of the well described neuropathological hallmarks of the disease (amyloid plaques, neurofibrillary tangles, neuroinflammation and neurodegeneration) [ 12 ]. With these biomarkers to diagnose the disease at its earliest stages and follow progression from a preclinical or asymptomatic stage through a prodromal phase (MCI) to later stages, we can rationally discover and develop disease-modifying treatments for AD. The first disease-modifying drugs are just now being approved by the FDA and while far from perfect we submit that there are now at least a dozen drug targets for AD hotly being pursued by the biopharmaceutical industry that in our view will likely result in effective secondary and eventually primary disease prevention therapeutics [ 13 ]. This represents one of the best examples, including the kind of investments required, of how a better understanding of disease biology can enable drug discovery for a common and complex neuropsychiatric disorder. There are many other examples of inherited neurodegenerative disorders with simple Mendelian inheritance and where the identification of causal genes has or will undoubtedly lead to potential disease-modifying treatments. However, we fully acknowledge that such disorders are not typical of the more complex psychiatric syndromes and will still require novel and technically challenging approaches to correct (e.g. gene therapy). Nonetheless, as mentioned above, mutations causing even rare genetic neurodevelopmental disorders that have manifest psychiatric symptoms (e.g. psychosis) might provide valuable clues as to potential new drug targets for the more common disorders [ 14 ].

Another example of successful rational drug discovery in psychiatry is the recent discovery and development of brexanolone for treating postpartum depression (PPD) Brexanolone is a proprietary formulation of allopregnanolone, the major metabolite of progesterone and shown years ago to be a very potent positive allosteric modulator (PAM) of GABA A receptors [ 15 ]. The increase in serum and brain progesterone and allopregnanolone levels that occurs during pregnancy and the sudden drop in allopregnanolone levels following childbirth have long been hypothesized to trigger PPD. An elegant series of preclinical experiments by Maguire and Mody [ 16 , 17 ] provided support for this hypothesis by showing that the expression of GABA A receptors (notably α and δ subunits) in critical brain regions is markedly reduced during pregnancy. Following parturition these receptors have to readjust to the normal very low levels of allopregnanolone observed in the absence of the pregnancy. Additional mouse experiments revealed a role for extra-synaptic GABA A receptor δ subunits involved in maintaining excitatory: inhibitory (E/I) balance in the brain in maternal mice [ 17 ]. When the E/I balance is altered by genetically removing these δ subunits, abnormal maternal behaviors (poor nesting and nursing behavior and even infanticide) are observed following pregnancy and importantly these behaviors can be effectively treated with neurosteroids like allopregnanolone [ 16 ]. These findings led to a series of clinical trials in women with PPD where infusion of brexanolone over 60 hours followed by gradual tapering was shown to rapidly improve mood and anxiety symptoms in approximately 70% of women with PPD [ 18 ]. In many PPD patients the antidepressant effects of brexanolone were quite rapid and dramatic. And currently, given the issues attending prolonged IV administration, an oral synthetic neuroactive steroid zuranolone is being developed for both PPD and even MDD, with encouraging early clinical data [ 19 ]. In this example, clinical and preclinical evidence pointed to a targetable pathophysiology. We mention this because such a fundamentally empirical approach combined with the use of biomarkers to identify homogenous subgroups may prove a more successful way to uncover targetable pathologies than currently applied genetic strategies. The latter may hopefully one day constitute an additional source of targetable pathologies and importantly in potentially predicting drug response.

Focus on sub-syndromal therapeutic targets

A focus on symptom domains rather than overall syndromal improvement in complex psychiatric diseases constitutes a major opportunity with recent adoption by both academia and industry. The most widely and arguably most misunderstood articulation of this general approach is captured in the Research Domain Criteria (RDoC) framework developed with NIMH funding [ 20 ]. Cognitive impairment associated with schizophrenia (CIAS) can be viewed as falling into this category: the domain of cognition in schizophrenia. While it is true that industry has funded many unsuccessful studies in CIAS we do not see this as an argument against focusing on this important and disabling symptom domain, since these earlier studies were undertaken in the absence of any strong evidence in humans of a quantitative brain effect or MOA that might reasonably be expected to lead to cognitive improvement. There have been and likely will be additional studies in schizophrenia and other psychiatric disorders which can be recast in the RDoC framework. As an example at least one industry sponsored study is pursuing “apathy” associated with Alzheimer’s disease [ 21 ]. It is anticipated that as the field matures and our ability to correlate objective brain measures (biomarkers of brain function) with clinical symptom domains, the probability of success of matching a specific molecular mechanism with a clinically-relevant therapeutic domain will increase.

If a reasonable case can be made that a drug effect on brain chemistry or function raises the probability of a beneficial therapeutic effect, then an expensive clinical study can be justified. One of the best examples of this to data in the field of psychiatry is provided by studies on a kappa opioid receptor (KOR) antagonist originally developed for depression by Eli Lilly. NIMH funded a study showing that this KOR antagonist can alter neural circuits involved in response to a reward task selected as the measure best suited to assess the domain of “hedonic response” [ 22 ]. Following positive findings using this biomarker, Johnson and Johnson, carried out a Phase 2 study with the KOR antagonist that had earlier been discontinued for treatment of depression. Positive results with atipracant as the compound is now called led to an current Phase 3 study that may lead to the first approved KOR antagonist for depression [ 23 ]. Nonetheless, by only advancing agents to Phase 2 trials which have been established to affect a brain process which can reasonably be linked to an important symptom domain, the success rates of such clinical trials will undoubtedly improve.

Focus on experimental therapeutics (in homo veritas)

Despite the sophisticated science and tools the field of psychiatry now has at its disposal but considering all of the challenges highlighted above, the value of expeditiously obtaining actual clinical data in well characterized patient populations cannot be overstated. It is our view that there needs to be considerably more investments made to study mechanistically novel compounds in disorders where core symptoms can be reliably assessed along with the use of quantitative biomarkers. We refer to this broadly as “experimental therapeutics.” We would add that while seemingly heretical in psychiatry, even small uncontrolled studies can be informative. In fact, brexanolone was initially studied in a small open label study in women with post-partum depression (PPD) and the very encouraging results led quickly to more definitive placebo-controlled studies to confirm its efficacy and safety profile. In such open label studies, we feel that Type 2 errors (false negative results) are less likely to occur then Type 1 errors (false positive results) and therefore time and resources can be saved by conducting even small open label proof of concept (PoC) studies. If a given compound fails to show any activity in even a small open label study, it is unlikely to work in a larger placebo-controlled study. Moreover, the clinical observations gleaned from studies carried out in patients with psychiatric syndromes can also lead to additional more fruitful hypotheses The KOR antagonist anhedonia example cited above is a good case in point.

Another example of how astute clinical observations can lead to mechanistically novel approaches to treating psychiatric disorders is the case of xanomeline. Xanomeline is a muscarinic acetylcholine receptor agonist discovered in the early 1990’s and initially developed by Eli Lilly to improve cognition in patients with Alzheimer’s disease. In a large phase 2 study in AD patients Lilly investigators serendipitously observed that disruptive behavioral and psychotic symptoms (seen in approximately 30% of the patients) improved on xanomeline vs placebo [ 24 ]. While xanomeline modestly improved cognition, its effect in reducing psychotic symptoms was quite dramatic. It should be remembered that this antipsychotic activity was not shown by a pre-specified primary or secondary endpoint and thus would never pass rigorous statistical scrutiny. The results were nonetheless striking enough, like many serendipitous findings in psychiatry, to invest in a small, placebo-controlled study in schizophrenic patients with positive results [ 25 ] setting in motion the development of a completely novel drug (and MOA) to treat schizophrenia. The peripheral cholinergic AEs of xanomeline, especially the GI side effects, argued against further development of xanomeline. But considerable work in both academia and industry to better understand the exact MOA underlying xanomeline’s antipsychotic properties [ 26 ] provided the basis for developing a completely novel antipsychotic drug. These studies catalyzed parallel approaches to both improve the tolerability of xanomeline by co-administering it with a peripherally-restricted muscarinic receptor antagonist (trospium) as well as attempts to find more subtype-selective muscarinic receptor agonists devoid of the GI and other side effects of xanomeline. As a result, several recent placebo-controlled pivotal trials of xanomeline-trospium (KarXT) have confirmed the rather robust antipsychotic effects of xanomeline in patients with schizophrenia [ 27 ] and another recently completed phase 1 trial of emraclidine, a M4-selective muscarinic receptor allosteric agonist, suggests that it too may also have antipsychotic properties [ 28 ]. Importantly, these findings may eventually lead to one of the first non-D2 dopamine receptor blocking antipsychotic agents (among other compounds in Phase 3 studies) and devoid of the troubling side effects of the current standards of care such as weight gain, sedation, EPS, hyperprolactinemia and a risk of developing tardive dyskinesia [ 29 ]. Two other examples of this serendipitous approach to drug discovery in psychiatry immediately come to mind. The first is the observation that the dissociative anesthetic ketamine, later shown to be an NMDA receptor channel blocker, produced mood-elevating effects in some patients with schizophrenia as well as the more general finding of exacerbation of psychotic symptoms [ 30 ]. The mood-elevating properties of ketamine were unanticipated and subsequently led to ketamine’s use as an antidepressant in MDD/TRD and now in multiple other psychiatric disorders. This initial serendipitous finding has been exploited to come up with various ketamine-like drugs and in fact the S-isomer of ketamine (esketamine) has recently been approved by the FDA to treat treatment-resistant depression. The second example is related to the broader class of psychedelic drugs, including psilocybin and LSD. The antidepressant properties of these psychedelic drugs were initially and accidentally observed in essentially open label studies designed to relieve anxiety and depression in terminal cancer patients [ 31 , 32 ]. These findings have led to multiple trials of both psilocybin and LSD in MDD and a large body of work on the underlying MOA of these drugs, with the hope of possibly finding non-psychedelic versions for more routine use [ 32 ]. While the issues surrounding the science and clinical utility of psychedelic drugs to treat various psychiatric disorders is well beyond the scope of this review their re-emergence for therapeutic purposes provides another example of the value of open label observational and subsequent placebo controlled clinical trials of novel experimental agents.

Taken together, with the examples of brexanalone and xanomeline, the experience with ketamine and psychedelic agents further underscores the value of studying compounds in humans and in the importance of astute clinical observations of symptom domains outside of a traditional primary outcome measure. While perhaps a less elegant approach to drug discovery than ideal (vide supra) we believe the field needs to embrace more experimental therapeutics and ideally by also incorporating the biomarker and sub-syndromal assessments described above. And there has been progress in terms of signal detection in psychiatric and other syndromal CNS trials captured in an ongoing series of position papers generated and listed by the International Society for CNS Clinical Trials and Methodology [ 33 ] which are already impacting clinical study design in ways that increase interpretability. Studying novel compounds in humans with psychiatric disorders already has and will, for all the above reasons, likely still lead to unanticipated findings and either directly or indirectly to mechanistically unique agents. Finally, we submit that establishing NIMH funded clinical research networks which should include clinical research training as has been done by the NCI will accelerate our ability to find and objectively characterize novel agents.

Focus on biologically homogenous subgroups

There is now widespread acceptance that for common and complex psychiatric syndromes investigational drugs are unlikely to work well in all patients and identifying subgroups of responders and nonresponders will likely to be necessary to find better agents. Such stratification can be provided by a range of biomarkers based on measures of brain function. The most mature example of the utility of such biomarkers as a basis for patient selection for clinical trials is the aforementioned utilization of PET, serum, and cerebrospinal fluid biomarkers for measuring amyloid and tau pathology in Alzheimer’s disease which are now used routinely to diagnose patients and to monitor disease progression in clinical trials [ 12 ]. Projects to advance stratification biomarkers for a range of neurodegenerative disorders are underway through the Neuroscience Steering Committee of the Foundation of the National Institutes of Health Biomarkers Consortium [ 34 ]. The potential of similarly useful biomarkers in schizophrenia is the focus of the Schizophrenia Spectrum Biomarkers Consortium [ 35 ] which, based on recent genetic findings, seeks to test the hypothesis of abnormal complement function in schizophrenia as well as the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) [ 36 ], an advocacy catalyzed public:private consortia effort aimed at characterizing multimodal biomarkers and clinical assessment strategies to better predict the trajectory of individuals at clinical high risk (CHR) for psychosis to enable studies of novel agents for early intervention in schizophrenia.

Another source of potential functional biomarkers may emerge from investments in differentiating patient-derived inducible pluripotent cells (iPSCs) into various types of neurons and even small “brain-like” organoids [ 37 ]. Many technical issues remain but multiple efforts are underway to see if these iPSC-derived neurons from individuals with defined psychiatric disorders can be used as in vitro test systems that allow one to predict whether or not that individual might respond to a specific drug such as lithium [ 38 ].

Functional biomarkers derived from EEG or fMRI measures are also showing promise for psychiatric disorders even in the absence of a known pathophysiological process. For instance, the B-SNIP consortium has found that differential EEG responses underpin three different biotypes of individuals who all present with psychotic symptoms and meet schizophrenia, schizoaffective or bipolar diagnoses [ 39 ]. A study is underway to see if such EEG informed subtyping predicts preferential response to clozapine [ 40 ] Similarly, analyses of EEG data generated by a large study of SSRI response in depression (the NIMH funded EMBARC) study using sophisticated machine learning and artificial intelligence methods revealed patterns that differentially predict either drug or placebo response [ 41 ]. And a consortium effort is focused on validating EEG methods as drug development tools [ 42 ].These and related AI approaches which utilize both EEG and clinical and task performance data form the basis of an approach taken by at least one company, Alto Neuroscience [ 43 ], to identify “responder” subgroups of patients to a variety of agents which have failed to separate from placebo in more traditional studies. Utilizing fMRI to measure brain activity and neural circuits in humans to bridge from optogenetic studies of circuit function in rodents has resulted in another set of predictors of antidepressant response [ 44 ]. Although it is too early to know which of these biomarkers or combination of biomarkers, if any, will prove most useful with regard to subgrouping of patients for clinical studies, early data suggests that one or more will prove successful. And, as noted above, a biomarker that proves useful in a clinical study may also point the way to a pathological process that can be targeted for novel drug development.

Focus on combination treatments

It is also likely, given the polygenic and complex nature of psychiatric syndromes that drugs displaying considerable polypharmacology or combinations of drugs with distinct MOAs may work best for many, if not most, psychiatric disorders. The example of clozapine for treatment-resistant schizophrenia stands out as a serendipitous illustration of this possibility. It was only because there was a clinical impression of superiority in terms of efficacy to other antipsychotics that clozapine with all of its attendant side effects and risks was “rediscovered” and developed into the important therapeutic agent that it remains. At therapeutic doses, clozapine has been shown to affect not only various dopamine receptors but also several subtypes of serotonergic and noradrenergic receptors along with a myriad of other potentially relevant activities [ 45 ]. And, in another example of possible benefits of engaging more than one mechanism, earlier depressed inpatient studies showed superior efficacy of dual action TCAs over SSRIs [ 46 ].

The strategy of testing novel agents in combination with an existing standard treatment in patients who have shown at best a partial response is now being widely pursued. Here both clinical science and business opportunities align. This approach incorporates both the possibility that an added drug would treat a specific domain as in the CIAS example mentioned above as well as the possibility that overall global efficacy. As we learn more about potential additive and synergistic effects of combining more than one MOA in various preclinical models and systems and apply the tools of quantitative systems pharmacology [ 47 ] testable hypotheses on the benefits of combined treatments are emerging. As part of this hypothesis generation, biomarker-identified subgroups of patients may well help identify where a particular combination might be most effective. As noted above, biomarker development is promising in this regard and therefore considerably more than just “shot gun” empiricism in deciding what combinations to explore and in what patients. It should be noted that drug combination strategies are now the norm for treating most cancers, something we believe will become common place in psychiatry as mechanistically unique psychiatric drugs emerge. We would advocate considerably larger investments in biomarker discovery and qualification by NIMH/NIH, which has already recognized some of these opportunities and expanded investments in public private partnerships [ 48 , 49 ], as the private sector is unlikely to make such investments at the scale required.

Hope for black swan events

We have focused our attention on more or less traditional small molecule drug discovery approaches but of course the brain is an electrochemical organ and altering brain chemistry via small molecule drugs is but one way to potentially treat serious psychiatric disorders. We should remain opportunistic and open to the possibility that better unanticipated approaches may emerge. A similar “black swan“ event has occurred in oncology just over the past decade where very targeted approaches to treating cancer based on well accepted somatic driver mutations (e.g. gain of function receptor tyrosine kinase mutations) were dominant but in many cases have now been superseded by the advent of immuno-oncology (IO) which has been shown to be very effective for certain cancers where boosting immune cell function can dramatically inhibit tumor growth and increase survival [ 50 ]. These IO drugs have revolutionized the treatment of certain cancers (e.g. melanoma). Are there similar potential “black swan events“ awaiting psychiatric therapeutics? We will mention only two possibilities here. The first is the potential of developing large molecule therapeutics, such as antibodies, for treating psychiatric disorders. New antibody technology, utilizing for example receptor-mediated trancytosis, has recently been developed which allows larger proteins like antibodies normally unable to readily cross the blood:brain barrier to any appreciable extent, to more efficiently access the brain [ 51 ]. This technology is being adopted for neurodegenerative disorders primarily but could open up the field of antibody therapeutics to psychiatry as well. Finally, advances in “precision“ deep brain stimulation (DBS) while too invasive for routine use for most psychiatric disorders [ 52 ] suggest that the development of noninvasive brain stimulation techniques (eg deep TMS-like technology) could potentially revolutionize psychiatric therapeutics and preliminary data in mood disorders are encouraging [ 53 ].

While progress in discovering and successfully developing truly novel (new and improved) psychiatric drugs has been slow and disappointing, in large part due to our rather limited understanding of the etiology and pathophysiology of the major psychiatric syndromes, we remain optimistic and believe there is now “light at the end of the tunnel.” Several novel, mechanistically speaking, drugs for depression and schizophrenia are on the horizon and there is renewed interest on the part of both large and small biopharmaceutical companies to invest the necessary resources, intellectual and financial, required. While the field of psychiatry works to better define the etiology and pathophysiology of the major psychiatric disorders there is still much to be done in the absence of such information and we have highlighted several areas of fruitful pursuit in this review. Given the complexity of these brain disorders we must reduce their unquestionable heterogeneity in order to discover precision medicines that will be more effective, not just for reducing core symptoms, but in improving functional outcomes for the many people who are still disabled with current treatments and especially for those who will unfortunately otherwise succumb to their illness.

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SMP, MD is an employee of Karuna Therapeutics, Boston, MA and a faculty member at Washington University School of Medicine, St. Louis. He is also a stockholder at Karuna Therapeutics, Sage Therapeutics, Voyager Therapeutics, and Alnylam Pharmaceuticals. WZP, MD PhD is an Independent Consultant. He is also a stockholder at Merck & Co., an Advisory Board member and Consultant at Neurocrine Biosciences, Karuna Therapeutics, Theravance Biopharma, Praxis Bioresearch, DeuteRx, Empyrean Neuroscience, and Eliem Therapeutics. He is also a Co-PI on NIA Small Business Grant to Praxis Bioresearch.

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Paul, S.M., Potter, W.Z. Finding new and better treatments for psychiatric disorders. Neuropsychopharmacol. 49 , 3–9 (2024). https://doi.org/10.1038/s41386-023-01690-5

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Published: 27 May 2024

Association between gut microbiota and anxiety disorders: a bidirectional two-sample mendelian randomization study

Jianbing Li 1 ,
Changhe Fan 1 ,
Jiaqi Wang 2 ,
Bulang Tang 2 ,
Jiafan Cao 2 ,
Xianzhe Hu 2 ,
Xuan Zhao 2 &
Caiqin Feng 1

BMC Psychiatry volume 24 , Article number: 398 ( 2024 ) Cite this article

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There are many articles reporting that the component of intestinal microbiota implies a link to anxiety disorders (AD), and the brain-gut axis is also a hot topic in current research. However, the specific relevance between gut microbiota and AD is uncertain. We aimed to investigate causal relationship between gut microbiota and AD by using bidirectional Mendelian randomization (MR).

Genetic instrumental variable (IV) for the gut microbiota were obtained from a genome-wide association study (GWAS) involving 18,340 participants. Summary data for AD were derived from the GWAS and included 158,565 cases and 300,995 controls. We applied the inverse variance weighted (IVW) method as the main analysis. Cochran’s Q values was computed to evaluate the heterogeneity among IVs. Sensitivity analyses including intercept of MR-Egger method and MR-PRESSO analysis were used to test the horizontal pleiotropy.

We discovered 9 potential connections between bacterial traits on genus level and AD. Utilizing the IVW method, we identified 5 bacterial genera that exhibited a direct correlation with the risk of AD: genus Eubacteriumbrachygroup , genus Coprococcus3 , genus Enterorhabdus , genus Oxalobacter , genus Ruminiclostridium6 . Additionally, we found 4 bacterial genera that exhibited a negative association with AD: genus Blautia , genus Butyricicoccus , genus Erysipelotrichaceae-UCG003 and genus Parasutterella . The associations were confirmed by the sensitivity analyses.

Our study found a causal relation between parts of the gut microbiota and AD. Further randomized controlled trials are crucial to elucidate the positive effects of probiotics on AD and their particular protection systems.

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Introduction

Anxiety disorders (AD), being the prevailing mental disorders, have a substantial impact on individuals and society alike [ 1 ]. The core features of AD contain indiscriminate anxiety and fear or elusion of persistent and debilitating threats, resulting in substantial medical costs and a burdensome morbidity burden [ 1 , 2 ]. As one of the most popular mental illnesses among young individuals, AD are also the earliest-onset mental disorders [ 3 ]. Amidst the COVID-19 pandemic, there has been a significant surge in the occurrence of AD among children, adolescents, and young adults globally [ 4 ]. First-line treatments for AD include medication and psychotherapy [ 5 ]. However, medication treatments carry certain side effects and risks, such as dependence, cognitive impairment, and an increased risk of heart disease [ 6 ]. The majority of individuals suffering from AD lack access to efficacious treatment options, leaving them vulnerable to relapse [ 7 , 8 ].

Many studies have shown that the occurrence of AD is related to changes in intestinal flora [ 9 , 10 ]. In social anxiety disorder (SAD), there was an increase in the relative abundance of Anaeromassillibacillus and Gordonibacter genera, whereas healthy controls exhibited an enrichment of Parasuterella [ 11 ]. Another article found a reduction in Eubacterium rectale and Fecalibacterium , as well as an increase in Escherichia , Shigella , Fusobacterium , and Ruminococcus in patients with generalized anxiety disorder (GAD) [ 12 ]. In addition, there are numerous documents demonstrating an association between the gut microbiota and mental illness, and the modulation of the gut microbiota on the gut-brain axis has garnered significant attention, such as an elevation of Enterobacteriaceae and Desulfovibrio , and a reduction of Faecalibacterium in patients with AD [ 10 , 13 , 14 , 15 , 16 , 17 ]. In the aforementioned section, it was observed that the evidence exhibits complexities and disparities, as well as some contradictory results, potentially stemming from various confounding factors among different studies.

The previous studies examining the connection between gut microbiota and AD have predominantly relied on cross-sectional designs, which limits the ability to establish a causal relationship between these associations. Therefore, unraveling the causal mechanisms behind gut microbiota-derived AD not only enhances our understanding of their pathogenesis but also provides valuable guidance for implementing microbiota-directed interventions in clinical settings to address AD. Previous Mendelian randomization (MR) studies have primarily focused on investigating the causal relationship between oral microbiota abundance and AD, or between gut microbiota and other psychiatric disorders. A systematic MR study specifically examining the causal relationship between gut microbiota and AD is still lacking in the current literature. In light of this, it is imperative to unravel the causal link between the gut microbiota and AD.

MR is a statistical approach that infers a causal relationship with exposure to a result. It leverages genetic variations linked to the exposure as a proxy for the exposure itself, enabling the assessment of the association between the exposure and the outcome [ 18 ]. Due to the highly effective findings of large-scale genome-wide association study (GWAS) at the gut microbiota and disease level, MR analysis has been abroad used in many scenarios, such as between the oral microbiome and AD, relations between genetically determined metabolites and anxiety symptoms [ 19 , 20 ]. However, there are no specific studies on the causal relationship between gut microbiota and AD. In this research, we applied a bidirectional two-sample MR method to investigate causal relationship between the gut microbiota and AD.

Materials and methods

The assumptions and study design of mr.

MR is a methodology employed to assess causal associations between variables. In order to ensure the validity of MR analysis, 3 fundamental assumptions must be met: (i) the instrumental variable (IV) exhibits a strong link to the exposure factor, (ii) the IV remains unaffected by potential confounding factors., and (iii) the IV influences the result factor solely via the exposure factor [ 21 ]. By applying strict selection criteria, appropriate SNPs were selected as IV for conducting MR analysis on two independent samples. The main aim was to examine the causal relationship between gut microbiota and AD. Furthermore, this study adhered to the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) framework [ 22 ] (Fig. 1 ).

A flowchart illustrating the MR analysis process for the association between gut microbiota and AD

Data sources

The data on gut microbiota GWAS used in this study were obtained from an overall meta-analysis conducted by the MiBioGen consortium. The meta-analysis comprised a total of 18,340 individuals from 24 different groups. The alliance combines human whole-genome genotyping with fecal 16 S rRNA sequencing data to perform thorough research and analysis. The large-scale, multi-ethnic genome-wide meta-analysis provided valuable insights into the genetic influences on the gut microbiome composition [ 23 ]. The GWAS data on the gut microbiome can be integrated into MR studies to explore the causal relationship between genetic variations in the gut microbiome and phenotypic traits, providing valuable insights into the role of the microbiome in human health and disease.

As for the data on genetic variants linked to AD, they were sourced from the Medical Research Council Integrative Epidemiology Unit (MRC-IEU) consortium. The cases were defined as individuals who had sought medical attention for symptoms of nervousness, anxiety, or depression. The study population consisted of individuals of European descent, comprising both males and females, and the data were sourced from the year 2018. The dataset included a total of 158,565 cases and 300,995 controls. The diagnosis was based on self-report questionnaires. Detailed information regarding the data origins for this MR study can be found in Table 1 [ 24 , 25 ].

Selection of IV

The GWAS data of exposure contained a total of 5 taxonomic levels for 211 bacterial groups. The genus level is the smallest and most specific classification level. To accurately identify each pathogenic bacterial group, we focused our analysis only on the genus level, specifically examining 131 bacterial classifications. After excluding 12 unknown groups, a total of 119 bacterial genera were included in the study.

To fulfill the demands of MR studies, our initial step involved the SNPs that exhibited an intense association with the exposure factors. However, when employing a stringent threshold of ( P < 5 × 10 − 8 ), we obtained a limited number of IVs. Consequently, we adjusted the threshold to ( P < 1 × 10 − 5 ) to ensure the inclusion of more IVs, thereby enabling robust and reliable results. For the selection of IVs associated with AD in the reverse MR analysis, a heightened level of stringency was implemented by applying a P -value threshold of P < 5 × 10 − 8 .

We utilized the F-statistic to further evaluate the instrument strength. The F-statistic was determined using the formula: F = β 2 / SE 2 . This statistic provided an assessment of the overall instrument strength [ 26 ] (Fig. 2 ). An F-statistic exceeding 10 was considered indicative of an intense conjunction between the IV and the exposure. Besides P -value threshold, the F statistic in our analysis would provide additional information on the instrument strength beyond P -value.

Assumptions in MR studies: a brief overview

Statistical analysis

The primary methodology employed in MR analysis is the inverse variance weighting (IVW) method. This approach utilizes a meta-analysis technique to combine the Wald estimates connected to individual single nucleotide polymorphisms (SNPs), providing comprehensive estimate of the collective impact of gut microbiota on AD. A crucial assumption in MR is the absence of horizontal pleiotropy, where the IV has a direct impact on the outcome variable solely through the exposure factor, without any influence from through alternative pathways. When this assumption is satisfied, the IVW method can provide estimates that are consistent and estimates [ 27 ]. In cases where a causal relationship ( P < 0.05) is established by the IVW method, two alternative approaches, namely MR-Egger and the weighted median approach, are utilized to supplement an enrich the IVW results. The MR-Egger method relaxes the assumption of a zero intercept, and it can estimate causal effects, even pleiotropy was presented in IVs. The intercept in the MR-Egger method can indicate the extent of horizontal pleiotropy [ 27 ]. These additional methods provide valuable insights and strengthen the overall analysis by considering potential biases and alternative causal pathways.

The weighted median method can return unbiased causal estimate when only 50% of SNPs are valid [ 28 ]. In this study, we employed a significance threshold of P < 0.05 to determine statistical significance, and the assessment of causality was expressed through odds ratios (OR) and 95% confidence intervals (CI). In instances where causal relationships were established, unidentified taxa were excluded, and additional sensitivity analyses were performed to guarantee the stability of the consequences. The false discovery rate (FDR) is utilized to control for multiple testing and reduce the likelihood of false positive findings. All of the aforementioned analyses were performed utilizing the TwoSampleMR package (version 0.5.7) in R (version 4.3.0), providing a robust and standardized approach to MR analysis.

According to the criteria for IV selection, a total of 1,531 SNPs were identified and selected as IV associated with gut microbiota. The F-statistics for these IVs all exceed 10, suggesting that the estimated coefficients are improbable to be influenced by the bias caused by weak instruments. Supplementary Tables 1 and 2 provides detailed information about the selected IVs. None of the SNPs were involved in more than one of the association results in Fig. 3 .

The scatter plots depict the causal relationship between gut microbiota and AD

The majority of gut microbiota showed no significant correlation with AD. However, using the IVW method, we identified 9 bacterial features that were significantly associated with the risk of AD on genus level (Supplementary Table 3 ). We used 3 methods, IVW, weighted median and MR-Egger, and defined P < 0.05 for IVW method screening as a positive result.

Among them, 4 bacterial genera are negatively correlated with AD, indicating that a higher genetically predicted a lower risk of for AD (Fig. 4 and Supplementary Table 4 ). They are: genus Blautia (OR = 0.9838, 95% CI, 0.9725–0.9952, P = 0.0056), genus Butyricicoccus (OR = 0.9859, 95% CI, 0.9739–0.9981, P = 0.0233), genus ErysipelotrichaceaeUCG003 (OR = 0.9914, 95% CI, 0.9833–0.9995, P = 0.0381) and genus Parasutterella (OR = 0.9911, 95% CI, 0.9823–0.9999, P = 0.0478). Supplementary Table 4 shows the completed data. In sensitivity analysis, MR-Egger, weighted median demonstrated consistent results, except for genus ErysipelotrichaceaeUCG003 , where the MR-Egger trend was in the contrary direction compared to IVW and weighted median.

The forest plot illustrates the connections between 9 bacterial genus traits and the likelihood of developing AD

Another 5 bacterial genera showed a positive correlation with AD, genus Eubacteriumbrachygroup (OR = 1.0068, 95% CI, 1.0010–1.0127, P = 0.0225), genus Coprococcus3 (OR = 1.0164, 95% CI, 1.0046–1.0285, P = 0.0065), genus Enterorhabdus (OR = 1.0117, 95% CI, 1.0027–1.0208, P = 0.0108), genus Oxalobacter (OR = 1.0067, 95% CI, 1.0009–1.0125, P = 0.0231) and genus Ruminiclostridium6 (OR = 1.0129, 95% CI, 1.0048–1.0212, P = 0.0019) (Fig. 4 and Supplementary Table 4 ). In the MR-Egger method, the trends of genus Eubacteriumbrachygroup are different from those of the IVW and WM methods.

In horizontal pleiotropy analysis, we used the MR-Egger method and found P -value of the MR-intercept were all greater than 0.05. In addition, further MR PRESSO analysis was conducted, ruling out the existence of horizontal pleiotropy ( P > 0.05) (Supplementary Tables 5 and 6 ). To assess the heterogeneity of gut microbiome IVs, we employed Cochran’s Q test statistics, which revealed no heterogeneity among the gut microbiome IVs ( P > 0.05) (Supplementary Table 7 ).

Reverse MR analyses were conducted to examine the links between the 9 bacterial genera and AD. No significant statistical relationship was observed using the IVW method: genus Eubacteriumbrachygroup (OR = 1.4058, 95% CI, 0.4060–4.8674, P = 0.5909), genus Blautia (OR = 0.9453, 95% CI, 0.5572–1.6038, P = 0.8348), genus Butyricicoccus (OR = 0.9834, 95% CI, 0.5704–1.6952, P = 0.9518), genus Coprococcus3 (OR = 0.8886, 95% CI, 0.5040–1.5667, P = 0.6831), genus Enterorhabdus (OR = 1.0383, 95% CI, 0.4168–2.5868, P = 0.9356), genus ErysipelotrichaceaeUCG003 (OR = 0.6593, 95% CI, 0.3556–1.2221, P = 0.1858), genus Oxalobacter (OR = 1.2849, 95% CI, 0.4021–4.1051, P = 0.6724), genus Parasutterella (OR = 0.7245, 95% CI, 0.3713–1.4136, P = 0.3447), genus Ruminiclostridium6 (OR = 0.7095, 95% CI, 0.3825–1.3162, P = 0.2764) (Supplementary Tables 8 and 9 ).

In the context of this study, we used two-sample MR studies to discover the link between AD and gut microbiota. Among the 9 bacterial genus we found, 4 bacteria were negatively correlated with AD and may have a positive effect on AD, and the other 5 bacteria were positively correlated with the occurrence of AD and may promote the development of AD.

Blautia stercoris MRx0006 has been shown to alleviate social dysfunctions, monotonous behaviors, and anxiety-like behaviors relevant to autism disorders in a mouse model. MRx0006 administration at the microbial level, as observed by Paromita Sen et al., resulted in a reduction in the abundance of Alistipes putredinis, which likely underlie the observed increase in expressions of oxytocin, arginine vasopressin, and their receptors, ultimately leading to improved behavioral outcomes [ 29 ]. Butyricicoccus was also inversely associated with AD in a cross-sectional study, which is consistent with our findings [ 12 ]. Approximately 70% of individuals with autism spectrum disorder (ASD) exhibit comorbid symptoms of anxiety, and the findings from a published article confirming the decreased relative abundance of ErysipelotrichaceaeUCG003 in ASD patients further support our research results indicating a negative correlation between ErysipelotrichaceaeUCG003 and AD [ 30 ]. In a study examining SAD, the control group exhibited higher levels of the positive bacteria Parasutterella compared to the anxiety group. The term “psychobiotics” has been coined to refer to these microbes that are associated with improved mood [ 11 ]. However, in a study by Yi Zhang et al., a psychological stress model was established in C57BL/6J mice, followed by fecal microbiota transplantation using samples from stressed (S) and non-stressed (NS) mice. The results showed an increased abundance of Parasutterella in S mice and mechanistic analysis suggested its potential involvement in negative regulation of metabolism. Despite this controversial finding, our study utilized MR to reveal a negative association between Parasutterella and anxiety disorders. However, further experimental investigations are required to elucidate the underlying molecular mechanisms [ 31 ].

Five bacterial genera positively linked to anxiety may indicate that they exacerbate anxiety, but they were less reported. In a study in which consuming prebiotics altered the microbiota of healthy adults, the prebiotics reduced Eubacteriumbrachygroup but did not significantly change biomarkers of stress or mental health symptoms [ 32 ]. In previous studies on AD cases, it has been found that individuals with AD have lower levels of Coprococcus [ 33 ]. However, in our study, we observed an increasing trend in Coprococcus3 , despite belonging to the same genus. This suggests that even within the same genus, the impact of different genus may vary. In contrast to our findings, Enterorhabdus exhibited a declining pattern in a mouse model of anxiety and depression induced by social defeat [ 34 ]. This observation highlights the influence of various factors on alterations in gut microbiota, which may diverge across different species.

Nevertheless, it is crucial to acknowledge that our study has certain limitations. First, the results of this analysis are limited to European populations and may not be generalizable to other populations. Secondly, we observed that the adjusted P -values remained relatively large after multiple test adjustment. The reduced statistical power resulting from the limited sample size may also constrain our ability to detect significant associations between variables. Finally, proving the direct impact of sample types on the outcomes is challenging. However, the selection of sample types is often constrained by the availability of suitable genetic instruments and relevant data sources. The dataset we utilized does not provide specific information on the dietary habits of the individuals or their other medical conditions. Therefore, further examination and validation are needed in the future.

In summary, utilizing large-scale GWAS analysis, MR studies have disclosed a causal relationship between gut microbiota and AD. Among these, 4 bacterial genera exhibited a negative correlation, while 5 bacteria genera showed a positive correlation with AD. However, further exploration of the mechanisms linking gut microbiota to AD requires the establishment of larger GWAS databases. Several gut bacteria have been identified to reduce the occurrence of anxiety, offering promising prospects for the treatment and precaution of AD. Subsequent research should prioritize the exploration of the underlying mechanisms and the development of targeted interventions based on these findings.

Data availability

The raw data analyzed during the current study were available in public databases including IEU database(ukb-b-6991) and MiBioGen database(https://mibiogen.gcc.rug.nl). The code and data related to this study are available from the corresponding author upon reasonable request.

Abbreviations

Anxiety disorders
Mendelian randomization

Instrumental variable(s)

Genome-wide association study

Medical Research Council Integrative Epidemiology Unit

Inverse variance weighting

Social anxiety disorder

Generalized anxiety disorder

Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization

Single nucleotide polymorphism(s)

Odds ratios

Confidence intervals

Autism spectrum disorder

Major depressive disorder

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Acknowledgements

We express our gratitude to the hospital action teams, staff, and participants from the participating hospitals for their valuable support in data collection. Additionally, we extend our appreciation to our collaborators for their assistance throughout the process.

Program of Guangzhou Science and Technology Program Project (No. 202102010115) and Guangdong Yiyang Healthcare Charity Foundation (No. JZ2022001-3).

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CQF designed the research framework. JBL is responsible for data and analysis methods determination as well as manuscript writing. CHF assisted in conducting the literature review. JQW was responsible for manuscript writing. BLT and JFC performed the data statistical analysis. XZH and XZ were responsible for critical revisions.

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Li, J., Fan, C., Wang, J. et al. Association between gut microbiota and anxiety disorders: a bidirectional two-sample mendelian randomization study. BMC Psychiatry 24 , 398 (2024). https://doi.org/10.1186/s12888-024-05824-x

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Suicide continues to be a major public health problem in the United States, and suicide rates have risen by 35% since 2000 ( 1 , 2 ). Suicide attempts are the single most important risk factor for suicide and the risk factor most likely to precipitate contact with the health care system ( 3 – 5 ). It is estimated that 1 in 25 individuals who present to the hospital for self-harm will die by suicide within 5 years ( 6 ). Since suicide prevention relies on identifying individuals at high risk for suicide, suicide attempts offer critical opportunities to intervene to prevent future suicide ( 7 – 9 ).

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Deep brain stimulation, by implanted electrodes that deliver electrical pulses to the brain, is often used to treat Parkinson’s disease and other neurological disorders. However, the electrodes used for this treatment can eventually corrode and accumulate scar tissue, requiring them to be removed.

MIT researchers have now developed an alternative approach that uses ultrasound instead of electricity to perform deep brain stimulation, delivered by a fiber about the thickness of a human hair. In a study of mice, they showed that this stimulation can trigger neurons to release dopamine, in a part of the brain that is often targeted in patients with Parkinson’s disease.

“By using ultrasonography, we can create a new way of stimulating neurons to fire in the deep brain,” says Canan Dagdeviren, an associate professor in the MIT Media Lab and the senior author of the new study. “This device is thinner than a hair fiber, so there will be negligible tissue damage, and it is easy for us to navigate this device in the deep brain.”

In addition to offering a potentially safer way to deliver deep brain stimulation, this approach could also become a valuable tool for researchers seeking to learn more about how the brain works.

MIT graduate student Jason Hou and MIT postdoc Md Osman Goni Nayeem are the lead authors of the paper, along with collaborators from MIT’s McGovern Institute for Brain Research, Boston University, and Caltech. The study appears today in Nature Communications .

Deep in the brain

Dagdeviren’s lab has previously developed wearable ultrasound devices that can be used to deliver drugs through the skin or perform diagnostic imaging on various organs . However, ultrasound cannot penetrate deeply into the brain from a device attached to the head or skull.

“If we want to go into the deep brain, then it cannot be just wearable or attachable anymore. It has to be implantable,” Dagdeviren says. “We carefully customize the device so that it will be minimally invasive and avoid major blood vessels in the deep brain.”

Deep brain stimulation with electrical impulses is FDA-approved to treat symptoms of Parkinson’s disease. This approach uses millimeter-thick electrodes to activate dopamine-producing cells in a brain region called the substantia nigra. However, once implanted in the brain, the devices eventually begin to corrode, and scar tissue that builds up surrounding the implant can interfere with the electrical impulses.

The MIT team set out to see if they could overcome some of those drawbacks by replacing electrical stimulation with ultrasound. Most neurons have ion channels that are responsive to mechanical stimulation, such as the vibrations from sound waves, so ultrasound can be used to elicit activity in those cells. However, existing technologies for delivering ultrasound to the brain through the skull can’t reach deep into the brain with high precision because the skull itself can interfere with the ultrasound waves and cause off-target stimulation.

“To precisely modulate neurons, we must go deeper, leading us to design a new kind of ultrasound-based implant that produces localized ultrasound fields,” Nayeem says. To safely reach those deep brain regions, the researchers designed a hair-thin fiber made from a flexible polymer. The tip of the fiber contains a drum-like ultrasound transducer with a vibrating membrane. When this membrane, which encapsulates a thin piezoelectric film, is driven by a small electrical voltage, it generates ultrasonic waves that can be detected by nearby cells.

“It’s tissue-safe, there’s no exposed electrode surface, and it’s very low-power, which bodes well for translation to patient use,” Hou says.

In tests in mice, the researchers showed that this ultrasound device, which they call ImPULS (Implantable Piezoelectric Ultrasound Stimulator), can provoke activity in neurons of the hippocampus. Then, they implanted the fibers into the dopamine-producing substantia nigra and showed that they could stimulate neurons in the dorsal striatum to produce dopamine.

“Brain stimulation has been one of the most effective, yet least understood, methods used to restore health to the brain. ImPULS gives us the ability to stimulate brain cells with exquisite spatial-temporal resolution and in a manner that doesn’t produce the kind of damage or inflammation as other methods. Seeing its effectiveness in areas like the hippocampus opened an entirely new way for us to deliver precise stimulation to targeted circuits in the brain,” says Steve Ramirez, an assistant professor of psychological and brain sciences at Boston University, and a faculty member at B.U.’s Center for Systems Neuroscience, who is also an author of the study.

A customizable device

All of the components of the device are biocompatible, including the piezoelectric layer, which is made of a novel ceramic called potassium sodium niobate, or KNN. The current version of the implant is powered by an external power source, but the researchers envision that future versions could be powered a small implantable battery and electronics unit.

The researchers developed a microfabrication process that enables them to easily alter the length and thickness of the fiber, as well as the frequency of the sound waves produced by the piezoelectric transducer. This could allow the devices to be customized for different brain regions.

“We cannot say that the device will give the same effect on every region in the brain, but we can easily and very confidently say that the technology is scalable, and not only for mice. We can also make it bigger for eventual use in humans,” Dagdeviren says.

The researchers now plan to investigate how ultrasound stimulation might affect different regions of the brain, and if the devices can remain functional when implanted for year-long timescales. They are also interested in the possibility of incorporating a microfluidic channel, which could allow the device to deliver drugs as well as ultrasound.

In addition to holding promise as a potential therapeutic for Parkinson’s or other diseases, this type of ultrasound device could also be a valuable tool to help researchers learn more about the brain, the researchers say.

“Our goal to provide this as a research tool for the neuroscience community, because we believe that we don’t have enough effective tools to understand the brain,” Dagdeviren says. “As device engineers, we are trying to provide new tools so that we can learn more about different regions of the brain.”

The research was funded by the MIT Media Lab Consortium and the Brain and Behavior Foundation Research (BBRF) NARSAD Young Investigator Award.

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D'Souza Named Inaugural Vikram Sodhi ’92 Professor of Psychiatry

A new endowed professorship at Yale School of Medicine seeks to advance the important research of Deepak Cyril D’Souza, MD and expand upon the university’s leadership in medical sciences.

Established by Vikram Sodhi ’92, managing partner of Sun Valley Investments and founder of the Sodhi Foundation, the inaugural professorship is the first at the Yale School of Medicine to be provided by or named after a person from India.

The endowed position will provide critical resources and support for D’Souza to pursue cutting-edge research in the therapeutic applications of psychedelics. Expanding upon his foundational and groundbreaking work exploring how chemicals like psilocybin and ketamine interact with the brain, further research may uncover new potential treatments for persistent neuropsychiatric conditions like post traumatic stress disorder (PTSD) and depression.

Sodhi called his years at Yale “a transformational experience” and highlights that this gift aspires to transform our collective understanding of this nascent and compelling body of research. His time as a student, he continued, “was a launchpad for my life and career and provided the opportunity to be immersed in a global community that embodied the university’s mission of improving the world today and for future generations.”

He said D’Souza’s scholarship exemplifies Yale’s mission and pioneering role in the field.

“Dr. D’Souza has made significant contributions to exploring the therapeutic potential of different drugs for several neuropsychiatric conditions,” Sodhi said. “These efforts will hopefully drive the field forward in pursuit of solutions that will benefit society as a whole and change lives for the better. Amidst a complex regulatory environment, this endowed professorship provides the resources to catalyze new knowledge in the area of psychedelic and other therapies for conditions like PTSD and depression, which impact millions of people across the globe.”

The World Health Organization estimates that more than 3 percent of the global population experiences PTSD in a given year. The National Comorbidity Survey Replication estimated that 6.8 percent of Americans will have PTSD at some point in their life.

While more study is necessary, current research suggests that roughly a third of those with PTSD do not respond to traditional treatments, emphasizing the urgent need for investment in deeper understanding and discovery of new and innovative therapies.

“Neuropsychiatric disorders account for a significant cause of global disease burden,” D’Souza said. “While many treatments for these conditions exist, there remains a great need to develop treatments that work faster, better, last longer and are safe.

“In the past decade there is growing interest in the therapeutic potential of drugs that are commonly referred to as serotonergic psychedelics. Early research suggests that unlike most existing treatments for neuropsychiatric disorders, psychedelics may produce rapid and long-lasting reductions in depression, after just one or two doses,” he said. “While early research suggests that these drugs hold promise, there is a need for much more research to answer fundamental questions about them before they can be used clinically.”

D’Souza holds several leadership positions in the Yale Department of Psychiatry and at Yale School of Medicine.

He is director of the Yale Center for the Science of Cannabis and Cannabinoids, a research center established in 2023 where scientists are studying the acute and chronic effects of cannabis and cannabinoids on neurodevelopment and mental health.

He directs the Schizophrenia Neuropharmacology Research Group at Yale, the Neurobiological Studies Unit at the VA Connecticut Healthcare System, and the VA-Connecticut Mental Health Center Schizophrenia Research Clinic.

D’Souza said he is “deeply honored” to receive the endowed professorship.

The generous support from Sodhi, he said, “will allow me to devote more time and effort toward studying psychedelics and other drugs. On a more personal note, that this endowed chair position is the first in the school of medicine to be supported and named after someone from India, my country of origin, makes it even more special.”

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Deepak Cyril Dsouza, MBBS, MD Vikram Sodhi ’94 Professor of Psychiatry; Chair, Research and Development Committee; Director Schizophrenia Neuropharmacology Research Group at Yale (SNRGY); Director, Neurobiological Studies Unit, VACHS; Director, VA-CMHC Schizophrenia Research Clinic; Director, Yale Center for the Science of Cannabis and Cannabinoids

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Psychiatry and the Dangerous Patient

More from our inbox:, ‘disgraceful’ grilling of dr. anthony fauci by republicans, a shortage of immigration lawyers, presidential rankings.

A portrait of Matthew Tuleja, who stands by a wall indoors with his arms crossed in a blue button-down shirt and a blue paisley tie. He is looking off to the side.

To the Editor:

Re “ Psychiatrists Confront Use of Force ” (Science Times, May 21):

I read this article with interest and with sympathy for Matthew Tuleja’s distressing experience; it is a story I have heard many times before. The article mentions that staff members are assaulted, and on inpatient units, patients are also sometimes assaulted by other patients. It is hard in emergency departments where the staff doesn’t know the patients, and who can be talked down and who is truly dangerous.

The article presents the distress from the patient’s (Mr. Tuleja’s) point of view; it doesn’t provide extensive reporting on how scary it must be for a former therapist to hear that an angry football player and ex-patient with homicidal thoughts is coming to confront them. Or what it’s like to be a security guard when that patient charges at an opening in an effort to escape.

At the same time, it is obvious that we need better staffing on inpatient units, and not immediately default to injecting medications and restraining patients. So many people could be de-escalated, but it takes time, patience, staffing and some willingness to tolerate risk. People shouldn’t have PTSD from medical care but they do; this type of treatment weighs on people for years and years.

Having talked to many people about this, and having thought about it for years, I don’t think there is a great answer to this problem. I’m always left with the idea that we should try harder to make inpatient psychiatric care a less stigmatized, less miserable experience for patients, so that going into the hospital isn’t something people dread, resist and say they’d rather die or go to jail before they’d go back.

Dinah Miller Baltimore The writer is a psychiatrist and co-author of the book “Committed: The Battle Over Involuntary Psychiatric Care.”

It has long been known that the incidence of seclusion and restraint in psychiatry varies greatly depending on whether such interventions are considered a necessary part of a patient’s treatment or evidence of its failure.

The one time I was slapped by a patient during my residency was in response to insisting that this man talk to me, or else he would be hospitalized. That sure was not a form of acceptable treatment, not unlike what happened to Matthew Tuleja, simply because he refused to accept the drug that was offered to him.

Since the mid-19th century, way before the advent of psychotropic drugs, it has been repeatedly demonstrated that a determined search for alternatives to coercion can almost completely eliminate such traumatic interventions, but psychiatry in the U.S. seems far from achieving consensus on this matter.

Peter Stastny New York The writer is a psychiatrist.

Re “ Fauci Calls Claims He Hid Leak of Covid From Lab ‘Preposterous’ ” (news article, June 4):

Dr. Anthony Fauci, whose knowledge and experience are exemplary, was grilled and maligned by far-right Republicans in his appearance at a House committee hearing on his alleged incompetence and deception when he was a leader of the nation’s effort to control the pandemic.

The abuse and disrespect directed at Dr. Fauci by some members of the pathetically uninformed and often nasty Republican majority on the House Select Subcommittee on the Coronavirus Pandemic was disgraceful, as they pursued outrageous allegations that he helped cover up the origin of the virus.

Dr. Fauci, it will be recalled, contended with obstacles posed by anti-science and anti-vaccine politics and an incompetent president whose denial of the extent of the crisis accounted for missed opportunities for the early containment of the virus.

Though hindsight suggests that aspects of this crisis probably could have been managed differently, Dr. Fauci, who fears for his personal safety, has clearly been made a scapegoat by vindictive Republicans, a role this loyal public servant certainly does not deserve.

Roger Hirschberg South Burlington, Vt.

Dr. Anthony Fauci is not responsible for China’s failure to regulate its coronavirus laboratory and field work or its wet markets, and he neither caused nor exacerbated the pandemic. Rather, politicians and partisan media use him to distract voters from our multiple systemic and leadership failures that led to the highest Covid-19 death rate of any developed nation.

If the United States had the per capita Covid-19 death rate of France or Germany, about 400,000 of the nearly 1.2 million Americans who died from the infection would be alive.

We need to acknowledge this enormous failure and address its causes with at least the attention that we give to the origins of the virus.

Michael Farzan Brookline, Mass.

Re “ Lawyer Shortage Is Yet Another Challenge for Asylum Seekers ” (news article, May 21):

The chronic shortage of immigration lawyers for asylum seekers is one of many barriers to justice that asylum seekers face in the U.S. immigration system. For 40 years, the National Immigrant Justice Center has worked to expand access to counsel by partnering with law firms across the country to represent asylum seekers and other immigrants applying for legal status.

Today our expert in-house legal staff recruits, trains and supports a network of more than 2,000 volunteer attorneys. However, their ability to respond is impeded by the federal government’s failure to invest in a fair and functional court system, as well as increasingly punitive policies that make the legal system nearly unnavigable — even for trained lawyers.

The administration and Congress must recommit to restoring justice to the immigration system, improve pathways to legal protection, and divert billions of dollars currently being spent to incarcerate and turn away asylum seekers to instead invest in processes that uphold due process and the right to asylum.

Chad R. Doobay Mary Meg McCarthy Chicago The writers are, respectively, the chair and the executive director of the National Immigrant Justice Center.

Re “ What Trump Looks Like to Historians ,” by Thomas B. Edsall (Opinion guest essay, nytimes.com, May 22):

After reading the article about presidential rankings, I feel compelled to try to reverse a grave injustice that has irked me for years: Isn’t it a bit strange that William Henry Harrison and James Garfield are consistently ranked near the bottom by historians, and for what reason?

Harrison died 31 days into his term — most likely from enteric fever linked to the White House water supply, which was downstream from public sewage. He didn’t have the time to do any serious damage, other than giving the longest inaugural speech in American history. But does that merit being placed lower in the polls than, for example, George W. Bush, who was responsible for the disastrous war in Iraq?

James Garfield died of a gunshot wound only six months into his presidency.

I think historians are unfair to condemn these two presidents. They really weren’t given much of a chance to succeed. Their lives were taken away from them. Isn’t that enough?

David Pawel Concord, Calif.

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