Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

Innovation at the Intersection: Emerging Translational Research in Neurology and Psychiatry

Translational research in neurological and psychiatric diseases is a rapidly advancing field that promises to redefine our approach to these complex conditions [...].

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

Changing trends in the global burden of mental disorders from 1990 to 2019 and predicted levels in 25 years

AIMS

The burden of mental disorders is increasing worldwide, thus, affecting society and healthcare systems. This study investigated the independent influences of age, period and cohort on the global prevalence of mental disorders from 1990 to 2019; compared them by sex; and predicted the future burden of mental disorders in the next 25 years.

METHODS

The age-specific and sex-specific incidence of mental disorders worldwide was analysed according to the general analysis strategy used in the Global Burden of Disease Study in 2019. The incidence and mortality trends of mental disorders from 1990 to 2019 were evaluated through joinpoint regression analysis. The influences of age, period and cohort on the incidence of mental disorders were evaluated with an age-period-cohort model.

RESULTS

From 1990 to 2019, the sex-specific age-standardized incidence and disability-adjusted life years (DALY) rate decreased slightly. Joinpoint regression analysis from 1990 to 2019 indicated four turning points in the male DALY rate and five turning points in the female DALY rate. In analysis of age effects, the relative risk (RR) of incidence and the DALY rate in mental disorders in men and women generally showed an inverted U-shaped pattern with increasing age. In analysis of period effects, the incidence of mental disorders increased gradually over time, and showed a sub-peak in 2004 (RR, 1.006 for males; 95% CI, 1.000-1.012; 1.002 for women, 0.997-1.008). Analysis of cohort effects showed that the incidence and DALY rate decreased in successive birth cohorts. The incidence of mental disorders is expected to decline slightly over the next 25 years, but the number of cases is expected to increase.

CONCLUSIONS

Although the age-standardized burden of mental disorders has declined in the past 30 years, the number of new cases and deaths of mental disorders worldwide has increased, and will continue to increase in the near future. Therefore, relevant policies should be used to promote the prevention and management of known risk factors and strengthen the understanding of risk profiles and incidence modes of mental disorders, to help guide future research on control and prevention strategies.

Inflammation and viral infection as disease modifiers in schizophrenia

Numerous studies have now implicated a role for inflammation in schizophrenia. However, many aspects surrounding this aspect of the disease are still controversial. This controversy has been driven by conflicting evidence on the role of both pro-and anti-inflammatory factors and by often contentious findings concerning cytokine and immune cell profiles in the central nervous system and periphery. Current evidence supports the point that interleukin-6 is elevated in CSF, but does not support activation of microglia, resident macrophage-like cells in the brain. Furthermore, the mechanisms involving transit of the peripheral immune system factors across the blood brain barrier to central parenchyma have still not been completely elucidated. This process appears to involve perivascular macrophages and accompanying dendritic cells retained in the parenchyma by the chemokine and cytokine composition of the surrounding milieu. In addition, a number of studies have shown that this can be modulated by infection with viruses such as herpes simplex virus type I which may disrupt antigen presentation in the perivascular space, with long-lasting consequences. In this review article, we discuss the role of inflammation and viral infection as potential disease modifiers in schizophrenia. The primary viral hit may occur in the fetus in utero, transforming the immune response regulatory T-cells or the virus may secondarily remain latent in immune cells or neurons and modify further immune responses in the developing individual. It is hoped that unraveling this pathway further and solidifying our understanding of the pathophysiological mechanisms involved will pave the way for future studies aimed at identification and implementation of new biomarkers and drug targets. This may facilitate the development of more effective personalized therapies for individuals suffering with schizophrenia.

The Inflammatory Signals Associated with Psychosis: Impact of Comorbid Drug Abuse

Psychosis and substance use disorders are two diagnostic categories whose association has been studied for decades. In addition, both psychosis spectrum disorders and drug abuse have recently been linked to multiple pro-inflammatory changes in the central nervous system. We have carried out a narrative review of the literature through a holistic approach. We used PubMed as our search engine. We included in the review all relevant studies looking at pro-inflammatory changes in psychotic disorders and substance use disorders. We found that there are multiple studies that relate various pro-inflammatory lipids and proteins with psychosis and substance use disorders, with an overlap between the two. The main findings involve inflammatory mediators such as cytokines, chemokines, endocannabinoids, eicosanoids, lysophospholipds and/or bacterial products. Many of these findings are present in different phases of psychosis and in substance use disorders such as cannabis, cocaine, methamphetamines, alcohol and nicotine. Psychosis and substance use disorders may have a common origin in an abnormal neurodevelopment caused, among other factors, by a neuroinflammatory process. A possible convergent pathway is that which interrelates the transcriptional factors NFκB and PPARγ. This may have future clinical implications.

Genomic regulatory sequences in the pathogenesis of bipolar disorder

The lifetime prevalence of bipolar disorder is estimated to be about 2%. Epigenetics defines regulatory mechanisms that determine relatively stable patterns of gene expression by controlling all key steps, from DNA to messenger RNA to protein. This Mini Review highlights recent discoveries of modified epigenetic control resulting from genetic variants associated with bipolar disorder in genome-wide association studies. The revealed epigenetic abnormalities implicate gene transcription and post-transcriptional regulation. In the light of these discoveries, the Mini Review focuses on the genes PACS1, MCHR1, DCLK3, HAPLN4, LMAN2L, TMEM258, GNL3, LRRC57, CACNA1C, CACNA1D, and NOVA2 and their potential biological role in the pathogenesis of bipolar disorder. Molecular mechanisms under control of these genes do not translate into a unified picture and substantially more research is needed to fill the gaps in knowledge and to solve current limitations in prognosis and treatment of bipolar disorder. In conclusion, the genetic and functional studies confirm the complex nature of bipolar disorder and indicate future research directions to explore possible targeted treatment options, eventually working toward a personalized approach.

Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.

Human-Induced Pluripotent Stem Cell Technology: Toward the Future of Personalized Psychiatry

The polygenic and multifactorial nature of many psychiatric disorders has hampered implementation of the personalized medicine approach in clinical practice. However, induced pluripotent stem cell (iPSC) technology has emerged as an innovative tool for patient-specific disease modeling to expand the pathophysiology knowledge and treatment perspectives in the last decade. Current technologies enable adult human somatic cell reprogramming into iPSCs to generate neural cells and direct neural cell conversion to model organisms that exhibit phenotypes close to human diseases, thereby effectively representing relevant aspects of neuropsychiatric disorders. In this regard, iPSCs reflect patient pathophysiology and pharmacological responsiveness, particularly when cultured under conditions that emulate spatial tissue organization in brain organoids. Recently, the application of iPSCs has been frequently associated with gene editing that targets the disease-causing gene to deepen the illness pathophysiology and to conduct drug screening. Moreover, gene editing has provided a unique opportunity to repair the putative causative genetic lesions in patient-derived cells. Here, we review the use of iPSC technology to model and potentially treat neuropsychiatric disorders by illustrating the key studies on a series of mental disorders, including schizophrenia, major depressive disorder, bipolar disorder, and autism spectrum disorder. Future perspectives will involve the development of organ-on-a-chip platforms that control the microenvironmental conditions so as to reflect individual pathophysiological by adjusting physiochemical parameters according to personal health data. This strategy could open new ways by which to build a disease model that considers individual variability and tailors personalized treatments.

Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development

BACKGROUND

In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions.

OBJECTIVE

We aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder.

METHODS

We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation.

RESULTS

The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61).

CONCLUSIONS

This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.

Long-Term Real-World Effectiveness of Aripiprazole Once-Monthly. Treatment Persistence and Its Correlates in the Italian and Spanish Clinical Practice: A Pooled Analysis

BACKGROUND

One of the most significant risk factors for relapse and hospitalization in schizophrenia is non-adherence to antipsychotic medications, very common in patients with schizophrenia. The aim of this analysis was to evaluate the treatment persistence to aripiprazole once-monthly (AOM) and the factors affecting it in the pooled population of two similar studies performed previously in two different European countries.

METHODS

Pooled analysis of two non-interventional, retrospective, patient record-based studies: DOMINO and PROSIGO. Both analyzed treatment persistence after starting AOM treatment in the real-world setting. The primary variable was persistence with AOM treatment during the first 6 months after treatment initiation. A multivariate Cox regression model was used to evaluate the influence of several baseline characteristics on the persistence.

RESULTS

The study population comprised 352 patients included in the two studies, DOMINO (n = 261) and PROSIGO (n = 91). The overall persistence with AOM treatment at the end of the 6-month observation period was 82.4%. The multivariate analysis showed that patients with "secondary school" level of education present a 67.4% lower risk of discontinuation within 6 months after AOM initiation when compared with "no/compulsory education patients" (p = 0.024). In addition, patients with an occupation present a 62.7% lower risk of discontinuation when compared with unemployed patients (p = 0.023). Regarding clinical history, patients with a Clinical Global Impression-Severity scale (CGI-S) score ≤3 present a 78.1% lower risk of discontinuation when compared with patients with a CGI-S score ≥6 (p = 0.044), while patients with a time since schizophrenia diagnosis ≤8.4 years present a 52.9% lower risk of discontinuation when compared with the rest of patients (p = 0.039).

CONCLUSION

The AOM persistence rate observed in this study was 82.4%, which was higher than that reported in clinical trials, aligned with other real-life studies and higher than reported for other long-acting injectable antipsychotics. The persistence rate was high in complex patients, although patients with higher level of education, active occupation, lower initial CGI-S score and shorter time since the diagnosis of schizophrenia appear to be more likely to remain persistent with AOM during the 6 months after initiation.

Precision Psychiatry: Machine learning as a tool to find new pharmacological targets

There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

OBJECTIVE

The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

METHODS

Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

RESULTS

The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

CONCLUSION

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.