Precision psychiatry in clinical practice

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Differences in facial emotion recognition between bipolar disorder and other clinical populations: A systematic review and meta-analysis

Facial emotion (or expression) recognition (FER) is a domain of affective cognition impaired across various psychiatric conditions, including bipolar disorder (BD). We conducted a systematic review and meta-analysis searching for eligible articles published from inception to April 26, 2023, in PubMed/MEDLINE, Scopus, EMBASE, and PsycINFO to examine whether and to what extent FER would differ between people with BD and those with other mental disorders. Thirty-three studies comparing 1506 BD patients with 1973 clinical controls were included in the present systematic review, and twenty-six of them were analyzed in random-effects meta-analyses exploring the discrepancies in discriminating or identifying emotional stimuli at a general and specific level. Individuals with BD were more accurate in identifying each type of emotion during a FER task compared to individuals diagnosed with schizophrenia (SCZ) (SMD = 0.27; p-value = 0.006), with specific differences in the perception of anger (SMD = 0.46; p-value = 1.19e-06), fear (SMD = 0.38; p-value = 8.2e-04), and sadness (SMD = 0.33; p-value = 0.026). In contrast, BD patients were less accurate than individuals with major depressive disorder (MDD) in identifying each type of emotion (SMD = -0.24; p-value = 0.014), but these differences were more specific for sad emotional stimuli (SMD = -0.31; p-value = 0.009). No significant differences were observed when BD was compared with children and adolescents diagnosed with attention-deficit/hyperactivity disorder. FER emerges as a potential integrative instrument for guiding diagnosis by enabling discrimination between BD and SCZ or MDD. Enhancing the standardization of adopted tasks could further enhance the accuracy of this tool, leveraging FER potential as a therapeutic target.

Improving mental health care in depression: A call for action

Depressive disorders have one of the highest disability-adjusted life years (DALYs) of all medical conditions, which led the European Psychiatric Association to propose a policy paper, pinpointing their unmet health care and research needs. The first part focuses on what can be currently done to improve the care of patients with depression, and then discuss future trends for research and healthcare. Through the narration of clinical cases, the different points are illustrated. The necessary political framework is formulated, to implement such changes to fundamentally improve psychiatric care. The group of European Psychiatrist Association (EPA) experts insist on the need for (1) increased awareness of mental illness in primary care settings, (2) the development of novel (biological) markers, (3) the rapid implementation of machine learning (supporting diagnostics, prognostics, and therapeutics), (4) the generalized use of electronic devices and apps into everyday treatment, (5) the development of the new generation of treatment options, such as plasticity-promoting agents, and (6) the importance of comprehensive recovery approach. At a political level, the group also proposed four priorities, the need to (1) increase the use of open science, (2) implement reasonable data protection laws, (3) establish ethical electronic health records, and (4) enable better healthcare research and saving resources.

Deep Clinical Phenotyping of Schizophrenia Spectrum Disorders Using Data-Driven Methods: Marching towards Precision Psychiatry

Heterogeneity is the main challenge in the traditional classification of mental disorders, including schizophrenia spectrum disorders (SSD). This can be partly attributed to the absence of objective diagnostic criteria and the multidimensional nature of symptoms and their associated factors. This article provides an overview of findings from the Genetic Risk and Outcome of Psychosis (GROUP) cohort study on the deep clinical phenotyping of schizophrenia spectrum disorders targeting positive and negative symptoms, cognitive impairments and psychosocial functioning. Three to four latent subtypes of positive and negative symptoms were identified in patients, siblings and controls, whereas four to six latent cognitive subtypes were identified. Five latent subtypes of psychosocial function-multidimensional social inclusion and premorbid adjustment-were also identified in patients. We discovered that the identified subtypes had mixed profiles and exhibited stable, deteriorating, relapsing and ameliorating longitudinal courses over time. Baseline positive and negative symptoms, premorbid adjustment, psychotic-like experiences, health-related quality of life and PRSSCZ were found to be the strong predictors of the identified subtypes. Our findings are comprehensive, novel and of clinical interest for precisely identifying high-risk population groups, patients with good or poor disease prognosis and the selection of optimal intervention, ultimately fostering precision psychiatry by tackling diagnostic and treatment selection challenges pertaining to heterogeneity.

Diabetes mellitus type 2 as an underlying, comorbid or consequent state of mental disorders

Somatic disturbances that occur in parallel with psychiatric diseases are a major challenge in clinical practice. Various factors contribute to the development of mental and somatic disorders. Type 2 diabetes mellitus (T2DM) is a significant health burden worldwide, and the prevalence of diabetes in adults is increasing. The comorbidity of diabetes and mental disorders is very common. By sharing a bidirectional link, both T2DM and mental disorders influence each other in various manners, but the exact mechanisms underlying this link are not yet elucidated. The potential mechanisms of both mental disorders and T2DM are related to immune and inflammatory system dysfunction, oxidative stress, endothelial dysfunction, and metabolic disturbances. Moreover, diabetes is also a risk factor for cognitive dysfunction that can range from subtle diabetes-associated cognitive decline to pre-dementia and dementia. A complex re-lationship between the gut and the brain also represents a new therapeutic approach since gut-brain signalling pathways regulate food intake and hepatic glucose production. The aim of this minireview is to summarize and present the latest data on mutual pathogenic pathways in these disorders, emphasizing their complexity and interweaving. We also focused on the cognitive performances and changes in neurodegenerative disorders. The importance of implementing integrated approaches in treating both of these states is highlighted, along with the need for individual therapeutic strategies.

A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression

Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.

Genetics of nonpharmacological treatments of depression

Nonpharmacological antidepressant treatments are effective and well tolerated in selected patients. However, response is heterogeneous and validated biomarkers would be precious to aid treatment choice. We searched Pubmed, Scopus, and Google Scholar until May 2022 for original articles evaluating the association of genetic variables with the efficacy of nonpharmacological treatments for major depressive episodes. Most studies analyzed small sample sizes using the candidate gene approach, leading to poorly replicated findings that need to be interpreted cautiously. The few available methylome-wide and genome-wide association studies (GWASs) considered only electroconvulsive therapy (ECT) and cognitive-behavioral therapy in small samples, providing interesting findings by using polygenic risk scores. A deeper knowledge of the genetic factors implicated in treatment response may lead to a better understanding of the neurobiological mechanisms of nonpharmacological therapies for depression, and depression itself. Future GWAS are going to expand their sample size, thanks to consortia such as the gen-ECT-ic consortium.

Characterisation of medication side effects in patients with mostly resistant depression in a real-world setting

OBJECTIVES

This study aimed to identify factors associated with side effects of psychotropic drugs in a real-world setting enriched with treatment-resistant depression (TRD) patients.

METHODS

A total of 1410 depressed patients were treated in a naturalistic setting. Side effects were measured with the Udvalg for Kliniske Undersogelser Side Effect Rating Scale (UKU); the total score and UKU subscales were considered. Clinical-demographic variables were tested for association with side effects in univariate and then multivariate analyses.

RESULTS

Total, psychic and neurological side effects were associated with depressive symptom severity, while autonomic side effects were higher in those with somatic comorbidities and other side effects were lower in patients receiving trazodone. In multivariate analyses, depressive symptom severity was associated with psychic and total side effects, while generalised anxiety disorder (GAD) with neurological side effects and somatic comorbidities remained associated with autonomic side effects. Trazodone was associated with lower side effects and with augmentation treatments. Augmentation therapies showed opposite effects depending on response status, i.e. increased or decreased the risk of side effects in responders and non-responders/resistant patients, respectively.

CONCLUSIONS

Psychic side effects may be difficult to distinguish from depressive symptoms and factors associated with different types of side effects are heterogeneous and likely interacting.

Subjective assessments of research domain criteria constructs in addiction and compulsive disorders: a scoping review protocol

INTRODUCTION

Obsessive-compulsive and related disorders (OCRDs) and disorders due to addictive behaviours (DABs) are prevalent conditions that share behavioural and neurobiological characteristics. The Research Domain Criteria lists a series of constructs whose dysfunctions may be present in both groups of disorders. The present study will describe the research protocol of a scoping review of the literature on self-report scales and questionnaires that tap dysfunctional constructs that underlie OCRDs and DABs.

METHODS AND ANALYSIS

This protocol outlines a scoping review on self-report tools and questionnaires that assess OCRDs and DABs-related constructs. The scoping review will select sources in MEDLINE, EMBASE, PsychINFO and Web of Science databases. Inclusion and exclusion criteria will be designed according to the Population, Concept, Context, Types of source framework. Two reviewers will screen independently titles, abstracts and full texts to determine the eligibility of articles. A methodological framework including six stages steps ((1) identifying a research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; (5) collating, summarising and reporting the result) will be used, and the findings will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. Information extracted will be collated, and quantitative results will be presented using descriptive statistics such as percentages, tables, charts and flow diagrams as appropriate.

ETHICS AND DISSEMINATION

Ethical approval for conducting this scoping review is not required, as this study will involve secondary analysis of existing literature. The researchers will disseminate the study results via conference presentations and publication in a peer-reviewed journal.

SCOPING REVIEW PROTOCOL REGISTRATION

DOI 10.17605/OSF.IO/UJ7G5.

Real-World Implementation of Precision Psychiatry: A Systematic Review of Barriers and Facilitators

Background: Despite significant research progress surrounding precision medicine in psychiatry, there has been little tangible impact upon real-world clinical care. Objective: To identify barriers and facilitators affecting the real-world implementation of precision psychiatry. Method: A PRISMA-compliant systematic literature search of primary research studies, conducted in the Web of Science, Cochrane Central Register of Controlled Trials, PsycINFO and OpenGrey databases. We included a qualitative data synthesis structured according to the ‘Consolidated Framework for Implementation Research’ (CFIR) key constructs. Results: Of 93,886 records screened, 28 studies were suitable for inclusion. The included studies reported 38 barriers and facilitators attributed to the CFIR constructs. Commonly reported barriers included: potential psychological harm to the service user (n = 11), cost and time investments (n = 9), potential economic and occupational harm to the service user (n = 8), poor accuracy and utility of the model (n = 8), and poor perceived competence in precision medicine amongst staff (n = 7). The most highly reported facilitator was the availability of adequate competence and skills training for staff (n = 7). Conclusions: Psychiatry faces widespread challenges in the implementation of precision medicine methods. Innovative solutions are required at the level of the individual and the wider system to fulfil the translational gap and impact real-world care.

Investigation of Psychoactive Medications: Challenges and a Practical and Scalable New Path

In the last two decades the validity of clinical trials in psychiatry has been subject to discussion. The most accepted clinical study method in the medical area, randomized controlled trial (RCT), faces significant problems when applied to the psychiatric world. One of the causes for this scenario is the strict participant inclusion and exclusion criteria that may not represent the real world. The inconsistency of the different endpoint parameters that are used in the field is another cause. We think that psychiatric RCTs' challenges, together with the underlying complexity of psychiatry, lead to a problematic clinical practice. Today, psychoactive drugs are routinely tested not in an official clinical trial setting. Off-label psychoactive drugs are commonly prescribed, and other substances, such as herbal remedies, are also regularly consumed. Learning from those real-life experiments can teach us useful lessons. Real-world data (RWD) includes information about heterogeneous patient populations, and it can be measured with standardized parameters. Collecting RWD can also address the need for systematically documenting and sharing case reports' outcomes. We suggest using digital tools to capture objective and continuous behavioral data from patients passively. New conclusions will be constantly drawn, possibly allowing more personalized treatment outcomes. The relevant next-generation decision support tools are already available.

Genetics in psychiatry: Methods, clinical applications and future perspectives

Psychiatric disorders and related traits have a demonstrated genetic component, with heritability estimated by twin studies generally between 80% and 40%. Their pathogenesis is complex and multi-determined: environmental factors interact with a polygenic architecture, making difficult the development of models able to stratify patients or predict mental health outcomes. Despite this difficult challenge, relevant progress has been made in the field of psychiatric genetics in recent years. This review aims to present the main current methods in psychiatric genetics, their output, limitations, clinical applications, and possible future developments. Genome-wide association studies (GWASs) performed in increasingly large samples have led to the identification of replicated genetic loci associated with the risk of major psychiatric disorders, including schizophrenia and mood disorders. Statistical and biological approaches have been developed to improve our understanding of the etiopathogenetic mechanisms behind genome-wide significant associations, as well as for estimating the cumulative effect of risk variants at the individual level and the genetic overlap between different disorders, as pleiotropy is the rule rather than the exception. Clinical applications are available in the pharmacogenetics field. The main issues that remain to be addressed include improving ethnic diversity in genetic studies and the optimization of statistical power through methodological improvements, such as the definition of dimensional phenotypes with specific biological correlates and the integration of different types of omics data.

Evaluating study designs and treatment outcomes of antidepressant pharmacogenetic clinical trials - Challenges and future perspectives. A critical review

Several data indicate that the success of pharmacological treatment in major depressive disorder (MDD) is still unsatisfactory. The reasons for the low response and remission rates are multiple and depend on environmental and biological factors intrinsic to the disease and drug treatments. Pharmacogenetic (PG) tests have the potential to increase efficacy predicting outcome and to reduce antidepressant discontinuation due to side effects. Several studies investigated the utility of PG tests for antidepressants in MDD with interesting but contrasting results. To date most of them are observational studies with no comparator group, and few are randomized controlled trials (RCTs). The aim of this review is to provide an evaluation of the state of art on clinical methodologic features of RCTs with PG tests for antidepressant drugs in MDD, offering suggestions and favoring new insights that could be useful in the implementation of future trials. Several limitations concerning study design, generalization of results, duration of trials, patients group studied, and cost-effectiveness ratio were found, and a number of barriers have been noted in the adoption of PG tests into clinical practice. Despite some preliminary positive results, there is the need for larger and longer-term RCT studies, with the goal to capture the real impact of PG tests, also with stratified analysis concerning MDD features in terms of severity and antidepressant treatment failures in different ethnicity cohorts.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person's own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

Patients' and psychiatrists' stance on the current state of pharmacological depression treatment in Serbia and prospects of introduction of personalized pharmacotherapy and its potential effects

The use of antidepressants has been steadily increasing. Even though the amount of evidence on the usefulness of personalized drug dosing in depression treatment is growing, there is still resistance and skepticism among physicians and regulators regarding the implementation of CYP450 genotyping and therapeutic drug monitoring in psychiatric clinical practice. The aim of this study was to quantify the opinions of psychiatrists and patients from three large psychiatric clinics in Belgrade, Serbia, and to examine what requirements need to be met to make changes in clinical guidelines or recommendations. All participants completed an anonymous questionnaire that was developed at the Faculty of Pharmacy, University of Belgrade. Fourteen practicing psychiatrists and 30 patients currently treated for depression completed the questionnaire. Distributions of opinion scores were compared between the psychiatrists and patients upon the visual inspection of the violin plots. Our results show that psychiatrists predominantly have a positive opinion on personalized dosing in psychiatry and that patients are most likely to comply with new approaches in depression pharmacotherapy. However, due to the long time needed for regulatory change, it is very unlikely that personalized dosing would be rapidly implemented in clinical practice, even if adequate evidence was to emerge.

Resolving discordant CYP2D6 genotyping results in Thai subjects: platform limitations and novel haplotypes

Aim: Several CYP2D6 Luminex xTAG genotype calls were identified as inconsistent or suspicious among Thai subjects and further characterized to identify the root causes. Material & methods: Forty-eight subjects were followed-up with long-range-PCR, quantitative copy number assays and/or Sanger sequencing. Results: Most of the Luminex-duplication calls were either negative or had hybrid structures involving CYP2D6*36 in various configurations. Ten samples were inaccurately called as CYP2D6*2, *29 or *35 alleles. Sequencing revealed three novel haplotypes, CYP2D6*142, *143 and *144 of which two are nonfunctional. Conclusion: The Luminex platform produced a relatively high number of false genotype calls for Thai subjects. Our findings underscore the need for the systematic characterization of the CYP2D6 locus in diverse populations and rigorous platform validation.