Biomarkers in psychosis: an approach to early identification and individualized treatment

On the proportion of patients who experience a prodrome prior to psychosis onset: A systematic review and meta-analysis

BACKGROUND

Preventing or delaying the onset of psychosis requires identification of those at risk for developing psychosis. For predictive purposes, the prodrome - a constellation of symptoms which may occur before the onset of psychosis - has been increasingly recognized as having utility. However, it is unclear what proportion of patients experience a prodrome or how this varies based on the multiple definitions used.

METHODS

We conducted a systematic review and meta-analysis of studies of patients with psychosis with the objective of determining the proportion of patients who experienced a prodrome prior to psychosis onset. Inclusion criteria included a consistent prodrome definition and reporting the proportion of patients who experienced a prodrome. We excluded studies of only patients with a prodrome or solely substance-induced psychosis, qualitative studies without prevalence data, conference abstracts, and case reports/case series. We searched Ovid MEDLINE, Embase (Ovid), APA PsycInfo (Ovid), Web of Science Core Collection (Clarivate), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, APA PsycBooks (Ovid), ProQuest Dissertation & Thesis, on March 3, 2021. Studies were assessed for quality using the Critical Appraisal Checklist for Prevalence Studies. Narrative synthesis and proportion meta-analysis were used to estimate prodrome prevalence. I2 and predictive interval were used to assess heterogeneity. Subgroup analyses were used to probe sources of heterogeneity. (PROSPERO ID: CRD42021239797).

RESULTS

Seventy-one articles were included, representing 13,774 patients. Studies varied significantly in terms of methodology and prodrome definition used. The random effects proportion meta-analysis estimate for prodrome prevalence was 78.3% (95% CI = 72.8-83.2); heterogeneity was high (I2 97.98% [95% CI = 97.71-98.22]); and the prediction interval was wide (95% PI = 0.411-0.936). There were no meaningful differences in prevalence between grouped prodrome definitions, and subgroup analyses failed to reveal a consistent source of heterogeneity.

CONCLUSIONS

This is the first meta-analysis on the prevalence of a prodrome prior to the onset of first episode psychosis. The majority of patients (78.3%) were found to have experienced a prodrome prior to psychosis onset. However, findings are highly heterogenous across study and no definitive source of heterogeneity was found despite extensive subgroup analyses. As most studies were retrospective in nature, recall bias likely affects these results. While the large majority of patients with psychosis experience a prodrome in some form, it is unclear if the remainder of patients experience no prodrome, or if ascertainment methods employed in the studies were not sensitive to their experiences. Given widespread investment in indicated prevention of psychosis through prospective identification and intervention during the prodrome, a resolution of this question as well as a consensus definition of the prodrome is much needed in order to effectively direct and organize services, and may be accomplished through novel, densely sampled and phenotyped prospective cohort studies that aim for representative sampling across multiple settings.

Illness Phase as a Key Assessment and Intervention Window for Psychosis

The phenotype of schizophrenia, regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness stage, including temporal variation in neurobiology, can be targeted to develop phase-specific clinical assessment, biomarkers, and interventions. We review an earlier model whereby an initial glutamate signaling deficit progresses through different phases of allostatic adaptation, moving from potentially reversible functional abnormalities associated with early psychosis and working memory dysfunction, and ending with difficult-to-reverse structural changes after chronic illness. We integrate this model with evidence of dopaminergic abnormalities, including cortical D1 dysfunction, which develop during adolescence. We discuss how this model and a focus on a potential critical window of intervention in the early stages of schizophrenia impact the approach to research design and clinical care. This impact includes stage-specific considerations for symptom assessment as well as genetic, cognitive, and neurophysiological biomarkers. We examine how phase-specific biomarkers of illness phase and brain development can be incorporated into current strategies for large-scale research and clinical programs implementing coordinated specialty care. We highlight working memory and D1 dysfunction as early treatment targets that can substantially affect functional outcome.

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

PURPOSE OF REVIEW

This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment.

RECENT FINDINGS

Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.

Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.

Alpha7 nicotinic-N-methyl-D-aspartate hypothesis in the treatment of schizophrenia and beyond

Development of novel treatments for positive, cognitive, and negative symptoms continue to be a high-priority area of schizophrenia research and a major unmet clinical need. Given that all randomized controlled trials (RCTs) conducted to date failed with one add-on medication/mechanism of action, future RCTs with the same approach are not warranted. Even if the field develops a medication for cognition, others are still needed to treat negative and positive symptoms. Therefore, fixing one domain does not completely solve the problem. Also, targeting the cholinergic system, glutamatergic system, and cholinergic plus alpha7 nicotinic and N-methyl-D-aspartate (NMDA) receptors failed independently. Hence, targeting other less important pathophysiological mechanisms/targets is unlikely to be successful. Meta-analyses of RCTs targeting major pathophysiological mechanisms have found some efficacy signal in schizophrenia; thus, combination treatments with different mechanisms of action may enhance the efficacy signal. The objective of this article is to highlight the importance of conducting RCTs with novel combination treatments in schizophrenia to develop antischizophrenia treatments. Positive RCTs with novel combination treatments that target the alpha7 nicotinic and NMDA receptors simultaneously may lead to a disease-modifying therapeutic armamentarium in schizophrenia. Novel combination treatments that concurrently improve the three domains of psychopathology and several prognostic and theranostic biomarkers may facilitate therapeutic discovery in schizophrenia.

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

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

Autoimmunity and immune system dysregulation in schizophrenia: IgGs from sera of patients hydrolyze myelin basic protein

Several different theories of schizophrenia (SCZ) were discussed; the causes of this disease are not yet clear. Using ELISA, it was shown that titers of autoantibodies against myelin basic protein (MBP) in SCZ patients are ~1.8-fold higher than in healthy individuals but 5.0-fold lower than in patients with multiple sclerosis. Several rigid criteria were checked to show that the MBP-hydrolyzing activity is an intrinsic property of SCZ IgGs. Approximately 82% electrophoretically homogeneous SCZ IgGs purified using several affinity sorbents including Sepharose with immobilized MBP hydrolyze specifically only MBP but not many other tested proteins. The average relative activity of IgGs from patients with negative symptoms was 2.5-fold higher than that of patients with positive symptoms of SCZ, and it increases with the duration of this pathology. It was shown that abzymes are the earliest statistically significant markers of many autoimmune pathologies. Our findings surmise that the immune systems of individual SCZ patients can generate a variety of anti-MBP abzymes with different catalytic properties, which can attack MBP of the myelin-proteolipid shell of axons. Therefore, autoimmune processes together with other mechanisms can play an important role in SCZ pathogenesis. MBP-hydrolyzing antibodies were previously detected in the blood of 80% to 90% of patients with systemic lupus erythematosus (SLE) and multiple sclerosis (MS). In addition, some similar neuropsychiatric indicators of disease common to SLE, MS, and SCZ were described in the literature. Thus, the destruction of the myelin sheath and the production of MBP-hydrolyzing antibodies can be a common phenomenon for some different diseases.

GAD1 Gene Expression in Blood of Patients with First-Episode Psychosis

γ-Aminobutyric acid (GABA), the primary inhibitory neurotransmitter, has often been studied in relation to its role in the pathophysiology of schizophrenia. GABA is synthesized from glutamate by glutamic acid decarboxylase (GAD), derived from two genes, GAD1 and GAD2. GAD1 is expressed as both GAD67 and GAD25 mRNA transcripts with the former reported to have a lower expression level in schizophrenia compared to healthy controls and latter was reported to be predominantly expressed fetally, suggesting a role in developmental process. In this study, GAD67 and GAD25 mRNA levels were measured by quantitative PCR (qPCR) in peripheral blood of subjects with first-episode psychosis (FEP) and from healthy controls. We observed low GAD25 and GAD67 gene expression levels in human peripheral blood. There was no difference in GAD25 and GAD67 gene expression level, and GAD25/GAD67 ratio between patients with FEP and healthy controls. PANSS negative symptoms were associated with levels of GAD25 mRNA transcripts in patients with FEP. While the current study provides information on GAD25 and GAD67 mRNA transcript levels in whole blood of FEP patients, further correlation and validation work between brain regions, cerebrospinal fluid and peripheral blood expression profiling are required to provide a better understanding of GAD25 and GAD67.