Biomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality

Biosensors for psychiatric biomarkers in mental health monitoring

Psychiatric disorders are associated with serve disturbances in cognition, emotional control, and/or behavior regulation, yet few routine clinical tools are available for the real-time evaluation and early-stage diagnosis of mental health. Abnormal levels of relevant biomarkers may imply biological, neurological, and developmental dysfunctions of psychiatric patients. Exploring biosensors that can provide rapid, in-situ, and real-time monitoring of psychiatric biomarkers is therefore vital for prevention, diagnosis, treatment, and prognosis of mental disorders. Recently, psychiatric biosensors with high sensitivity, selectivity, and reproducibility have been widely developed, which are mainly based on electrochemical and optical sensing technologies. This review presented psychiatric disorders with high morbidity, disability, and mortality, followed by describing pathophysiology in a biomarker-implying manner. The latest biosensors developed for the detection of representative psychiatric biomarkers (e.g., cortisol, dopamine, and serotonin) were comprehensively summarized and compared in their sensitivities, sensing technologies, applicable biological platforms, and integrative readouts. These well-developed biosensors are promising for facilitating the clinical utility and commercialization of point-of-care diagnostics. It is anticipated that mental healthcare could be gradually improved in multiple perspectives, ranging from innovations in psychiatric biosensors in terms of biometric elements, transducing principles, and flexible readouts, to the construction of 'Big-Data' networks utilized for sharing intractable psychiatric indicators and cases.

Inflammatory and oxidative stress biomarkers in children and adolescents with bipolar disorder - A systematic review and meta-analysis

Evidence suggests a role for low-grade inflammation and oxidative stress in the pathophysiology of bipolar disorder. We conducted a systematic review and meta-analysis of peripheral markers of inflammation and oxidative stress in children and adolescents under 20 years of age with bipolar disorder. We searched PubMed, Embase and psycINFO and performed random effects meta-analysis calculating standardized mean differences (SMD) of marker levels between patients with bipolar disorder and healthy control individuals. Ten studies comprising a total of 418 patients with bipolar disorder and 3017 healthy control individuals were included. The levels of C-Reactive Protein were higher in patients with bipolar disorder compared with healthy individuals (SMD 0.53; 95 %CI: 0.33-0.74; I2 = 0 %). For other biomarkers there were no statistically significant differences between groups. Findings were limited by a low number of studies and participants and methodological issues in the included studies. More and larger studies using rigorous methodology are needed to establish the role of inflammation and oxidative stress in children and adolescents with bipolar disorder.

Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis

Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.

Quantum dots-based multiplexed immunosensors for accurate diagnosis of attention deficit hyperactivity disorder in childhood

Attention-deficit/hyperactivity disorder (ADHD) lacks objective diagnostic markers. In clinical settings, reliance on subjective judgments can often lead to missed or misdiagnoses. Some biomarkers have been reported to be associated with ADHD, but using one biomarker alone is not enough. To address this, we developed a fluorescent immunoassay platform based on quantum dots (QDs) to detect assay capable of detecting and quantifying multiple biomarkers simultaneously. Specifically, we were able to the simultaneously detect brain-derived neurotrophic factor, tumor necrosis factor-alpha, interleukin-6 and ferritin using different emission spectra QDs. The QD-based multiplexed immunoassay displayed a low detection of limit in the range of 0.021-0.068 pg/mL, and the assay showed satisfactory reproducibility and precision. We then quantified all four targets from ADHD patient's plasma samples, where it showed remarkable consistency with clinical test for ADHD diagnosis. This methodological comparison supports the diagnosis of ADHD using our assay.

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

A systematic review of the biomarkers associated with cognition and mood state in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a severe psychiatric disorder characterized by changes in mood that alternate between (hypo) mania or depression and mixed states, often associated with functional impairment and cognitive dysfunction. But little is known about biomarkers that contribute to the development and sustainment of cognitive deficits. The aim of this study was to review the association between neurocognition and biomarkers across different mood states.

METHOD

Search databases were Web of Science, Scopus and PubMed. A systematic review was carried out following the PRISMA guidelines. Risk of bias was assessed with the Newcastle-Ottawa Scale. Studies were selected that focused on the correlation between neuroimaging, physiological, genetic or peripheral biomarkers and cognition in at least two phases of BD: depression, (hypo)mania, euthymia or mixed. PROSPERO Registration No.: CRD42023410782.

RESULTS

A total of 1824 references were screened, identifying 1023 published articles, of which 336 were considered eligible. Only 16 provided information on the association between biomarkers and cognition in the different affective states of BD. The included studies found: (i) Differences in levels of total cholesterol and C reactive protein depending on mood state; (ii) There is no association found between cognition and peripheral biomarkers; (iii) Neuroimaging biomarkers highlighted hypoactivation of frontal areas as distinctive of acute state of BD; (iv) A deactivation failure has been reported in the ventromedial prefrontal cortex (vmPFC), potentially serving as a trait marker of BD.

CONCLUSION

Only a few recent articles have investigated biomarker-cognition associations in BD mood phases. Our findings underline that there appear to be central regions involved in BD that are observed in all mood states. However, there appear to be underlying mechanisms of cognitive dysfunction that may vary across different mood states in BD. This review highlights the importance of standardizing the data and the assessment of cognition, as well as the need for biomarkers to help prevent acute symptomatic phases of the disease, and the associated functional and cognitive impairment.

The LEADING Guideline: Reporting Standards for Expert Panel, Best-Estimate Diagnosis, and Longitudinal Expert All Data (LEAD) Studies

Accurate assessments of symptoms and diagnoses are essential for health research and clinical practice but face many challenges. The absence of a single error-free measure is currently addressed by assessment methods involving experts reviewing several sources of information to achieve a more accurate or best-estimate assessment. Three bodies of work spanning medicine, psychiatry, and psychology propose similar assessment methods: The Expert Panel, the Best-Estimate Diagnosis, and the Longitudinal Expert All Data (LEAD). However, the quality of such best-estimate assessments is typically very difficult to evaluate due to poor reporting of the assessment methods and when it is reported, the reporting quality varies substantially. Here we tackle this gap by developing reporting guidelines for such studies, using a four-stage approach: 1) drafting reporting standards accompanied by rationales and empirical evidence, which were further developed with a patient organization for depression, 2) incorporating expert feedback through a two-round Delphi procedure, 3) refining the guideline based on an expert consensus meeting, and 4) testing the guideline by i) having two researchers test it and ii) using it to examine the extent previously published articles report the standards. The last step also demonstrates the need for the guideline: 18 to 58% (Mean = 33%) of the standards were not reported across fifteen randomly selected studies. The LEADING guideline comprises 20 reporting standards related to four groups: The Longitudinal design; the Appropriate data; the Evaluation - experts, materials, and procedures; and the Validity group. We hope that the LEADING guideline will be useful in assisting researchers in planning, reporting, and evaluating research aiming to achieve best-estimate assessments.

Human olfactory neural progenitor cells reveal differences in IL-6, IL-8, thrombospondin-1, and MCP-1 in major depression disorder and borderline personality disorder

Background

Discovering biological markers is essential for understanding and treating mental disorders. Despite the limitations of current non-invasive methods, neural progenitor cells from the olfactory epithelium (hNPCs-OE) have been emphasized as potential biomarker sources. This study measured soluble factors in these cells in Major Depressive Disorder (MDD), Borderline Personality Disorder (BPD), and healthy controls (HC).

Methods

We assessed thirty-five participants divided into MDD (n=14), BPD (n=14), and HC (n=7). MDD was assessed using the Hamilton Depression Rating Scale. BPD was evaluated using the DSM-5 criteria and the Structured Clinical Interview for Personality Disorders. We isolated hNPCs-OE, collected intracellular proteins and conditioned medium, and quantified markers and soluble factors, including Interleukin-6, interleukin-8, and others. Analysis was conducted using one-way ANOVA or Kruskal-Wallis test and linear regression.

Results

We found that hNPCs-OE of MDD and BPD decreased Sox2 and laminin receptor-67 kDa levels. MASH-1 decreased in BPD, while tubulin beta-III decreased in MDD compared to controls and BPD. Also, we found significant differences in IL-6, IL-8, MCP-1, and thrombospondin-1 levels between controls and MDD, or BPD, but not between MDD and BPD.

Conclusions

Altered protein markers are evident in the nhNPCs-OE in MDD and BPD patients. These cells also secrete higher concentrations of inflammatory cytokines than HC cells. The results suggest the potential utility of hNPCs-OE as an in vitro model for researching biological protein markers in psychiatric disorders. However, more extensive validation studies are needed to confirm their effectiveness and specificity in neuropsychiatric disorders.

What are Mental Disorders? Exploring the Role of Culture in the Harmful Dysfunction Approach

A shared problem in psychology, psychiatry, and philosophy is how to define mental disorders. Various theories have been proposed, ranging from naturalism to social constructionism. In this article, I first briefly introduce the current landscape of such theories, before concentrating on one of the most influential approaches today: The harmful dysfunction theory developed by Jerome Wakefield. It claims that mental disorders are hybrid phenomena since they have a natural basis in dysfunctional mental mechanisms, but also a cultural component in the harm experienced by human beings. Although the theory is well thought through, I will raise a critical question: Is it possible to isolate mental mechanisms as naturally evolved from cultural factors? I will argue that it is not, but that the theory could still be helpful in an understanding of mental disorders, albeit on a new footing that does not operate with a natural and a cultural component as two separate factors. I argue that we need to develop a "naturecultural" approach to psychopathology that avoids mentalism, based on the fact that human beings are irreducibly persons.

Clinical and neuroimaging predictors of benzodiazepine response in catatonia: A machine learning approach

Catatonia is a well characterized psychomotor syndrome combining motor, behavioural and neurovegetative signs. Benzodiazepines are the first-choice treatment, effective in 70 % of cases. Currently, the factors associated with benzodiazepine resistance remain unknown. We aimed to develop machine learning models using clinical and neuroimaging data to predict benzodiazepine response in catatonic patients. This study examined a cohort of catatonic patients who underwent standardized clinical evaluation, 3 T brain MRI, and benzodiazepine trial. Based on clinical response, patients were classified as benzodiazepine responders or non-responders. Cortical thickness and regional brain volumes were measured. Two machine learning models (linear model and gradient boosting tree model) were developed to identify predictors of treatment response using clinical, demographic, and neuroimaging data. The cohort included 65 catatonic patients, comprising 30 benzodiazepine responders and 35 non-responders. Using clinical data alone, the linear model achieved 63% precision, 51% recall, a specificity of 61%, and 58% AUC, while the gradient boosting tree (GBT) model attained 46% precision, 60% recall, a specificity of 62% and 64% AUC. Incorporating neuroimaging data improved model performance, with the linear model achieving 66% precision, 57% recall, a specificity of 67%, and 70% AUC, and the GBT model attaining 50% precision, 50% recall, a specificity of 62% and 70% AUC. The integration of imaging data with demographic and clinical information significantly enhanced the predictive performance of the models. The duration of the catatonic syndrome, along with the presence of mitgehen (passive obedience) and immobility/stupor, and the volume of the right medial orbito-frontal cortex emerged as important factors in predicting non-response to benzodiazepines.

Interdependency of NINJ2 gene expression and polymorphism with susceptibility and response to interferon beta in patients with multiple sclerosis

OBJECTIVE

Multiple sclerosis (MS) is a multifactorial inflammatory and autoimmune condition that lead to chronic neurodegeneration and central nervous system (CNS) demyelination that mainly affects young adults. The incidence and prevalence rate of MS considerably vary in ethnicities and geographic regions and affecting women more than men. Interferon-β (IFN-β) is the first-line disease management for MS, while the majority of affected members does not respond to the IFN-β. Numerous recent studies shown a significant relationship between genetic variations and responsiveness to the IFN-β. Therefore, determining the genetic differences in the drug response could help determine precise treatment strategies.

METHODS

The genotyping of the rs7298096 polymorphism (SNP) and NINJ2 gene expression were assessed in 99 responders and 106 non-responder patients with IFN-β treated RRMS.

RESULTS

The distribution of rs7298096 SNP was significantly different in the responders and non-responder patients and the NINJ2 gene expression considerably increased in the non-responder patients compare to the responders. The NINJ2 gene expression level in the AA genotype of the non-responder group was higher than to the other genotypes of both groups.

CONCLUSION

Our results showed that the NINJ2 gene expression level and rs7298096 genotype possibly affect the response to the IFN-β in patients with RRMS.

New Light on Prions: Putative Role of PrPc in Pathophysiology of Mood Disorders

Mood disorders are highly prevalent and heterogenous mental illnesses with devastating rates of mortality and treatment resistance. The molecular basis of those conditions involves complex interplay between genetic and environmental factors. Currently, there are no objective procedures for diagnosis, prognosis and personalization of patients' treatment. There is an urgent need to search for novel molecular targets for biomarkers in mood disorders. Cellular prion protein (PrPc) is infamous for its potential to convert its insoluble form, leading to neurodegeneration in Creutzfeldt-Jacob disease. Meanwhile, in its physiological state, PrPc presents neuroprotective features and regulates neurotransmission and synaptic plasticity. The aim of this study is to integrate the available knowledge about molecular mechanisms underlying the impact of PrPc on the pathophysiology of mood disorders. Our review indicates an important role of this protein in regulation of cognitive functions, emotions, sleep and biological rhythms, and its deficiency results in depressive-like behavior and cognitive impairment. PrPc plays a neuroprotective role against excitotoxicity, oxidative stress and inflammation, the main pathophysiological events in the course of mood disorders. Research indicates that PrPc may be a promising biomarker of cognitive decline. There is an urgent need of human studies to elucidate its potential utility in clinical practice.

Social pathways to care: how community-based network ties shape the health care response of individuals with mental health problems

PURPOSE

Mental health research has powerfully documented inequities related to characteristics, such as ethnicity and gender. Yet how and where disparities like unmet need occur have been more elusive. Drawing from a now modest body of research that deployed the Network Episode Model (NEM), we examine how individuals create patterns of response to mental health problems, influenced by the culture and resources embedded in their social networks.

METHODS

The Person-to-Person Health Interview Study (P2P; N ~ 2,700, 2018-2021) provides representative, community-based, NEM-tailored data. Both descriptive, latent class and multinomial regression analyses mark mental health care-seeking patterns, including individuals consulted and activities used, as well as the influence of the structure and cultural content of social networks.

RESULTS

Latent class analysis detected five pathways with good fit statistics. The Networked General Care Path (37.0%) and The Kin General Care Path (14.5%) differ only in whether friends are activated in using the general care sector. The Networked Multi-Sector Care Path (32.5%) and The Saturated Path (12.6%) involve family, friends, and both general and specialty care with only the latter expanding consultation to coworkers and clergy. The Null Path (3.3%), or no contacts, is not used as perceived problem severity increases. Network size and strength are associated with the more complex pathways that activate ties, respectively. Trust in doctors is associated with pathways that include specialty providers but not others at work or church. Race, age, and rural residence have specific pathway effects, while gender has no significant impact.

CONCLUSIONS

Social networks propel individuals with mental health problems into action. Tie strength and trust produce care responses that are fuller and more targeted. Considering the nature of homophily, results also suggest that majority status and college education are clearly implicated in networked pathways. Overall, findings support community-targeted rather than individually based efforts to increase service use.

Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls

Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain

This review offers an accessible primer to social neuroscientists interested in neural networks. It begins by providing an overview of key concepts in deep learning. It then discusses three ways neural networks can be useful to social neuroscientists: (i) building statistical models to predict behavior from brain activity; (ii) quantifying naturalistic stimuli and social interactions; and (iii) generating cognitive models of social brain function. These applications have the potential to enhance the clinical value of neuroimaging and improve the generalizability of social neuroscience research. We also discuss the significant practical challenges, theoretical limitations and ethical issues faced by deep learning. If the field can successfully navigate these hazards, we believe that artificial neural networks may prove indispensable for the next stage of the field's development: deep social neuroscience.

Proteomic insights into mental health status: plasma markers in young adults

Global emphasis on enhancing prevention and treatment strategies necessitates an increased understanding of the biological mechanisms of psychopathology. Plasma proteomics is a powerful tool that has been applied in the context of specific mental disorders for biomarker identification. The p-factor, also known as the "general psychopathology factor", is a concept in psychopathology suggesting that there is a common underlying factor that contributes to the development of various forms of mental disorders. It has been proposed that the p-factor can be used to understand the overall mental health status of an individual. Here, we aimed to discover plasma proteins associated with the p-factor in 775 young adults in the FinnTwin12 cohort. Using liquid chromatography-tandem mass spectrometry, 13 proteins with a significant connection with the p-factor were identified, 8 of which were linked to epidermal growth factor receptor (EGFR) signaling. This exploratory study provides new insight into biological alterations associated with mental health status in young adults.

Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance

Background

Research on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance.

Methods

We utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes.

Results

MR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance.

Conclusions

Identification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.

Advances and Challenges in Nanomaterial-Based Electrochemical Immunosensors for Small Cell Lung Cancer Biomarker Neuron-Specific Enolase

Early and rapid detection of neuron-specific enolase (NSE) is highly significant, as it is putative biomarker for small-cell lung cancer as well as COVID-19. Electrochemical techniques have attracted substantial attention for the early detection of cancer biomarkers due to the important properties of simplicity, high sensitivity, specificity, low cost, and point-of-care detection. This work reviews the clinically relevant labeled and label-free electrochemical immunosensors developed so far for the analysis of NSE. The prevailing role of nanostructured materials as electrode matrices is thoroughly discussed. Subsequently, the key performances of various immunoassays are critically evaluated in terms of limit of detection, linear ranges, and incubation time for clinical translation. Electrochemical techniques coupled with screen-printed electrodes developing market level commercialization of NSE sensors is also discussed. Finally, the review concludes with the current challenges associated with available methods and provides a future outlook toward commercialization opportunities for easy detection of NSE.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

Assessment of biomarker stability and assay performance parameters for medical diagnosis: a case study of diagnosis of major depressive disorder

Aim: Assessing the stability profiles and assay performance of 24 biomarker assays in 32 biomarker/body fluid combinations identified as relevant for prediction of major depressive disorder. Materials & methods: Combinations were tested for stability and assay performance with ELISA at different storage and freeze-thaw conditions in pooled samples of 40 patients. Results: Stability and assay performance issues were found in almost all cases except three biomarkers in urine and three in serum. Conclusion: This study shows that, to produce reliable measurement data, assessments of stability and assay performance are essential. In development, other quality assurance parameters might be implemented to increase the level of measurement reliability by increasing assay performance control.

EEG wakefulness regulation in transdiagnostic patients after a recent suicide attempt

OBJECTIVE

Decades of research have not yet produced statistically reliable predictors of preparatory behavior eventually leading to suicide attempts or deaths by suicide. As the nature of suicidal behavior is complex, it is best investigated in a transdiagnostic approach, while assessing objective markers, as proposed by the Research Domain Criteria (Cuthbert, 2013).

METHODS

A 15-min resting-state EEG was recorded in 45 healthy controls, and 49 transdiagnostic in-patients with a recent (<6 months) suicide attempt. Brain arousal regulation in eyes-closed condition was assessed with the Vigilance Algorithm Leipzig (VIGALL) (Sander et al., 2015).

RESULTS

A significant incline of median vigilance and vigilance slope was observed in patients within the first 3-min of the EEG recording. Additionally, a significant positive correlation of self-reported suicidal ideation with the vigilance slope over 15-min recording time, as well as a significant negative correlation with EEG vigilance stage A1 during the first 3-min was found.

CONCLUSIONS

Transdiagnostic patients with a recent suicide attempt show a distinct vigilance regulation pattern. Further studies including a control group consisting of patients without life-time suicide attempts are needed to increase the clinical utility of the findings.

SIGNIFICANCE

These findings might serve as potential objective markers of suicidal behavior.

Plasma biomarkers in adolescents with schizophrenia-spectrum disorder

AIM

Schizophrenia onset in the developmental age has a strong neurodevelopmental burden and is associated with a poorer prognosis. The approach to diagnosis is still based on symptomatic description without objective validation. In this study, we aimed to compare the peripheral blood levels of hypothesized biomarker proteins: brain-derived neurotrophic factor (BDNF), proBDNF, p75 neurotrophin receptor (p75NTR ) and S100B between early-onset schizophrenia-spectrum adolescents (n = 45) and healthy controls (n = 34).

METHODS

Clinical assessment of the participants encompassed symptomatic description with the use of structured interviews and executive function objective measurement. Plasma levels of BDNF protein were significantly lower in schizophrenia patients than in controls both at admission (p = .003) and 6-8 weeks follow-up (p = .007).

RESULTS

We observed significant correlations between BDNF, proBDNF and p75NTR levels and positive and negative symptoms scale (PANSS) scores, p75NTR and S100B levels and suicidal parameters, as well as a correlation of BDNF plasma level with the risky decision-making style in Iowa Gambling Task (IGT).

CONCLUSIONS

The results indicate a potential value of studied proteins as a biomarker in the diagnosis and monitoring of the disease's course.

Captive ERVWE1 triggers impairment of 5-HT neuronal plasticity in the first-episode schizophrenia by post-transcriptional activation of HTR1B in ALKBH5-m6A dependent epigenetic mechanisms

BACKGROUND

Abnormalities in the 5-HT system and synaptic plasticity are hallmark features of schizophrenia. Previous studies suggest that the human endogenous retrovirus W family envelope (ERVWE1) is an influential risk factor for schizophrenia and inversely correlates with 5-HT4 receptor in schizophrenia. To our knowledge, no data describes the effect of ERVWE1 on 5-HT neuronal plasticity. N6-methyladenosine (m6A) regulates gene expression and impacts synaptic plasticity. Our research aims to systematically investigate the effects of ERVWE1 on 5-HT neuronal plasticity through m6A modification in schizophrenia.

RESULTS

HTR1B, ALKBH5, and Arc exhibited higher levels in individuals with first-episode schizophrenia compared to the controls and showed a strong positive correlation with ERVWE1. Interestingly, HTR1B was also correlated with ALKBH5 and Arc. Further analyses confirmed that ALKBH5 may be an independent risk factor for schizophrenia. In vitro studies, we discovered that ERVWE1 enhanced HTR1B expression, thereby activating the ERK-ELK1-Arc pathway and reducing the complexity and spine density of 5-HT neurons. Furthermore, ERVWE1 reduced m6A levels through ALKBH5 demethylation. ERVWE1 induced HTR1B upregulation by improving its mRNA stability in ALKBH5-m6A-dependent epigenetic mechanisms. Importantly, ALKBH5 mediated the observed alterations in 5-HT neuronal plasticity induced by ERVWE1.

CONCLUSIONS

Overall, HTR1B, Arc, and ALKBH5 levels were increased in schizophrenia and positively associated with ERVWE1. Moreover, ALKBH5 was a novel risk gene for schizophrenia. ERVWE1 impaired 5-HT neuronal plasticity in ALKBH5-m6A dependent mechanism by the HTR1B-ERK-ELK1-Arc pathway, which may be an important contributor to aberrant synaptic plasticity in schizophrenia.

Association of granulocyte macrophage colony-stimulating factor and interleukin-17 levels with obsessive-compulsive disorder: a case-control study findings

Obsessive-compulsive disorder (OCD) is a mental condition that affects many people and is characterized by recurring obsessions and compulsions. It significantly impacts individuals' ability to function ordinarily daily, affecting people of all ages. This study aimed to investigate whether or not the cytokines granulocyte macrophage colony-stimulating factor (GM-CSF) and interleukin-17 (IL-17) are involved in the pathophysiology of OCD. A case-control study with 50 OCD patients and 38 healthy volunteers served as the controls for this investigation. The levels of GM-CSF and IL-17 in the serum of both groups were measured with enzyme-linked immunosorbent assay (ELISA) kits. In addition, the sociodemographic characteristics of the population under study were studied. Based on the findings of this study, OCD patients had significantly elevated levels of IL-17 than the controls, it appears that there may be a function for IL-17 in the pathophysiology of OCD. It was also discovered that the severity of OCD and IL-17 levels had a significant positive correlation. On the other hand, when comparing the levels of GM-CSF, there was no significant difference between the patients and the controls. This study provides evidence supporting the involvement of cytokine IL-17 in the pathophysiology of OCD. This study suggests IL-17 as a diagnostic biomarker for OCD and adds to our knowledge of the function that the immune system plays in this condition.

Tuning Hydrophobicity of Paper Substrates for Effective Colorimetric detection of Glucose and Nucleic acids

This study investigated the colorimetric response of standard glucose, serum glucose, and nucleic acid assays on various paper surfaces with different wettability, including hydrophilic, hydrophobic, and nearly superhydrophobic surfaces. Water contact angles (WCA) formed by water droplets on each surface were measured using ImageJ software. The hydrophilic surface showed no contact angle, while the hydrophobic and nearly superhydrophobic surfaces exhibited contact angles of 115.667° and 133.933°, respectively. The colorimetric sensitivity of the standard glucose assay was analyzed on these surfaces, revealing enhanced sensitivity on the nearly superhydrophobic surface due to the high molecular crowding effect owing to its non-wetting behavior and eventually confined reaction product at the sample loading zone. The hydrophobic nature of the surface restricts the spreading and diffusion of the reaction product, leading to a controlled and localized concentration of the assay product leading to moderate colorimetric intensity. On the other hand, the hydrophilic surface showed the least enhancement in colorimetric sensitivity; this is attributed to the high wettability of the hydrophilic surface causing the reaction product to spread extensively, resulting in a larger area of dispersion and consequently a lower colorimetric intensity. The measured limit of detection (LOD) for nucleic acid on nearly superhydrophobic surfaces was found to be 16.15 ng/µL, which was almost four-fold lower than on hydrophilic surfaces (60.08 ng/µL). Additionally, the LODs of standard glucose and clinical serum samples were two-fold lower on nearly superhydrophobic surfaces compared to hydrophilic surfaces. Our findings clearly highlight the promising potential of utilizing superhydrophobic surfaces to significantly enhance colorimetric sensitivity in paper-based diagnostic applications. This innovative approach holds promise for advancing point-of-care diagnostics and improving disease detection in resource-limited settings.

Assessment of prognostic biomarkers in sudden sensorineural hearing loss: A systematic review and meta-analysis

Sudden sensorineural hearing loss (SSNHL) is defined as hearing loss of more than 30 dB in less than 72 h. SSNHL is a frequent complaint and an emergency in otolaryngology. Various biomarkers have been used to determine the prognosis of SSNHL. This systematic review and meta-analysis aims to evaluate the relationship between the different biomarkers and the prognosis of SSNHL. We searched English-language literature up to October 2022 in four databases, including PubMed, Google Scholar, Cochrane, and Science Direct. This search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. This study was reported in the International Prospective Register of Systematic Reviews (PROSPERO) database (ID = CRD42022369538). All studies examining the role of neutrophil to lymphocyte ratio (NLR) concluded that higher NLR is associated with a worse prognosis. The results of studies regarding the relationship between platelet to lymphocyte ratio (PLR) and tumor necrosis factor (TNF) are controversial. Other factors shown to be associated with SSNHL include Glycated hemoglobin (HbA1C), blood glucose, iron levels, serum endocan, salusin-beta, and bone turnover biomarkers. This meta-analysis showed that PLR, NLR, and neutrophils were significantly different between recovered and non-recovered patients. PLR, NLR, and neutrophil count are reliable tools to assess the prognosis of patients with SSNHL.

Apolipoprotein D as a Potential Biomarker in Neuropsychiatric Disorders

Neuropsychiatric disorders (NDs) are a diverse group of pathologies, including schizophrenia or bipolar disorders, that directly affect the mental and physical health of those who suffer from them, with an incidence that is increasing worldwide. Most NDs result from a complex interaction of multiple genes and environmental factors such as stress or traumatic events, including the recent Coronavirus Disease (COVID-19) pandemic. In addition to diverse clinical presentations, these diseases are heterogeneous in their pathogenesis, brain regions affected, and clinical symptoms, making diagnosis difficult. Therefore, finding new biomarkers is essential for the detection, prognosis, response prediction, and development of new treatments for NDs. Among the most promising candidates is the apolipoprotein D (Apo D), a component of lipoproteins implicated in lipid metabolism. Evidence suggests an increase in Apo D expression in association with aging and in the presence of neuropathological processes. As a part of the cellular neuroprotective defense machinery against oxidative stress and inflammation, changes in Apo D levels have been demonstrated in neuropsychiatric conditions like schizophrenia (SZ) or bipolar disorders (BPD), not only in some brain areas but in corporal fluids, i.e., blood or serum of patients. What is not clear is whether variation in Apo D quantity could be used as an indicator to detect NDs and their progression. This review aims to provide an updated view of the clinical potential of Apo D as a possible biomarker for NDs.

Genetic Landscape of Major Depressive Disorder: Assessment of Potential Diagnostic and Antidepressant Response Markers

BACKGROUND

The clinical heterogeneity in major depressive disorder (MDD), variable treatment response, and conflicting findings limit the ability of genomics toward the discovery of evidence-based diagnosis and treatment regimen. This study attempts to curate all genetic association findings to evaluate potential variants for clinical translation.

METHODS

We systematically reviewed all candidates and genome-wide association studies for both MDD susceptibility and antidepressant response, independently, using MEDLINE, particularly to identify replicated findings. These variants were evaluated for functional consequences using different in silico tools and further estimated their diagnostic predictability by calculating positive predictive values.

RESULTS

A total of 217 significantly associated studies comprising 1200 variants across 545 genes and 128 studies including 921 variants across 412 genes were included with MDD susceptibility and antidepressant response, respectively. Although the majority of associations were confirmed by a single study, we identified 31 and 18 replicated variants (in at least 2 studies) for MDD and antidepressant response. Functional annotation of these 31 variants predicted 20% coding variants as deleterious/damaging and 80.6% variants with regulatory effect. Similarly, the response-related 18 variants revealed 25% coding variant as damaging and 88.2% with substantial regulatory potential. Finally, we could calculate the diagnostic predictability of 19 and 5 variants whose positive predictive values ranges from 0.49 to 0.66 for MDD and 0.36 to 0.66 for response.

CONCLUSIONS

The replicated variants presented in our data are promising for disease diagnosis and improved response outcomes. Although these quantitative assessment measures are solely directive of available observational evidence, robust homogenous validation studies are required to strengthen these variants for molecular diagnostic application.

The relationship between resilience and mental health of undergraduate students: A scoping review

OBJECTIVE

The aim of this scoping review was to examine what is known about the relationship between the resilience and mental health of undergraduate students enrolled in university or college programs, globally.

METHODS

Five electronic databases were searched, yielding a total of 1,498 articles that were screened independently by two researchers. Thirteen articles were eligible for inclusion.

RESULTS

The mental health of undergraduate students in the studies reviewed ranged from low to moderate. Undergraduate students also reported high, moderate, and low levels of resilience. Further, resilience was positively correlated with mental well-being.

CONCLUSION

Findings revealed that the mental health of undergraduate students was poor. Given the established relationship between students' mental health and resilience, evidence-based approaches aimed at strengthening students' resilience, such as providing opportunities for social support, are warranted in order to improve students' mental health. Additional research to rigorously assess this relationship in representative student populations is needed.

World Psychiatric Association-Asian Journal of Psychiatry Commission on Psychiatric Education in the 21st century

Psychiatric practice faces many challenges in the first quarter of 21st century. Society has transformed, as have training requirements and patient expectations, underlining an urgent need to look at educational programmes. Meanwhile, awareness has grown around psychiatric disorders and there are evolving workforce trends, with more women going to medical school and specialising in psychiatry. Trainee psychiatrists carry different expectations for work-life balance and are increasingly becoming conscious of their own mental health. A tendency to see health as a commodity and the litigious nature of society has elicited additional pressures for healthcare professionals. Cartesian mind-body dualism has created further complexity and this can often be frustrating for patients and care-partners alike. In many cultures across Asia and beyond, patients can present with physical symptoms to express underlying psychological distress with increasing physical investigations. Simultaneously, in various countries, a shift from asylums to community-based interventions and then home treatments have changed psychiatric care in remarkable ways. These changes have added to pressures faced by mental healthcare professionals. However, trainees and other mental healthcare professionals continue to receive similar training as they did a generation ago. The tensions and differences in ideology/orientation between different branches of psychiatry have made responses to patient needs challenging. Recognising that it is difficult to predict the future, this World Psychiatric Association-Asian Journal of Psychiatry Commission makes recommendations that could help institutions and individuals enhance psychiatric education. This Commission draws from existing resources and recent developments to propose a training framework for future psychiatrists.

Biomarkers in psychiatric disorders

Psychiatric disorders represent a significant socioeconomic and healthcare burden worldwide. Of these, schizophrenia, bipolar disorder, major depressive disorder and anxiety are among the most prevalent. Unfortunately, diagnosis remains problematic and largely complicated by the lack of disease specific biomarkers. Accordingly, much research has focused on elucidating these conditions to more fully understand underlying pathophysiology and potentially identify biomarkers, especially those of early stage disease. In this chapter, we review current status of this endeavor as well as the potential development of novel biomarkers for clinical applications and future research study.

Rodent models for mood disorders - understanding molecular changes by investigating social behavior

Mood disorders, including depressive and bipolar disorders, are the group of psychiatric disorders with the highest prevalence and disease burden. However, their pathophysiology remains poorly understood. Animal models are an extremely useful tool for the investigation of molecular mechanisms underlying these disorders. For psychiatric symptom assessment in animals, a meaningful behavioral phenotype is needed. Social behaviors constitute naturally occurring complex behaviors in rodents and can therefore serve as such a phenotype, contributing to insights into disorder related molecular changes. In this narrative review, we give a fundamental overview of social behaviors in laboratory rodents, as well as their underlying neuronal mechanisms and their assessment. Relevant behavioral and molecular changes in models for mood disorders are presented and an outlook on promising future directions is given.

Pioneering Changes in Psychiatry: Biomarkers, Psychedelics, and AI

This viewpoint discusses integrating biomarkers, psychedelics, and AI into psychiatry for enhanced diagnostics, prognosis, and treatment. It highlights the potential of psychedelics in therapy, AI's role in predicting treatment response, and the challenges that must be addressed. The aim is to encourage research for more precise, personalized psychiatric care.

Is Another kind of Biologization Possible? On Biology and the psy Sciences

The relationship between biology and the psy disciplines (psychology, psychiatry, and psychotherapy) is a complex one. Many scholars have criticized how these disciplines have been biologized in the 20th century, especially since the emergence of psychopharmacology, neuroscience, and genetic research. However, biology is not just a laboratory-based science of chemical compounds, scanners, and DNA sequencing, but also a field science based on observations of organisms in their milieus. In this paper, we draw a contrast between laboratory-based biology with a focus on brains and genes, and an ecology-based biology with a focus on lives and niches. Our argument is philosophical in nature - building partly on Wittgenstein as a "philosopher of life" - to the effect that the psy sciences need not just less biologization of the former kind, but also more biologization of the latter kind to avoid a prevalent mentalism. Not least when it comes to an understanding of psychological distress, which can favorably be viewed situationally and coupled to human lives in ecological niches.

Understanding anxiety symptoms as aberrant defensive responding along the threat imminence continuum

Threat-anticipatory defensive responses have evolved to promote survival in a dynamic world. While inherently adaptive, aberrant expression of defensive responses to potential threat could manifest as pathological anxiety, which is prevalent, impairing, and associated with adverse outcomes. Extensive translational neuroscience research indicates that normative defensive responses are organized by threat imminence, such that distinct response patterns are observed in each phase of threat encounter and orchestrated by partially conserved neural circuitry. Anxiety symptoms, such as excessive and pervasive worry, physiological arousal, and avoidance behavior, may reflect aberrant expression of otherwise normative defensive responses, and therefore follow the same imminence-based organization. Here, empirical evidence linking aberrant expression of specific, imminence-dependent defensive responding to distinct anxiety symptoms is reviewed, and plausible contributing neural circuitry is highlighted. Drawing from translational and clinical research, the proposed framework informs our understanding of pathological anxiety by grounding anxiety symptoms in conserved psychobiological mechanisms. Potential implications for research and treatment are discussed.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

The relationship between immune fitness and saliva biomarkers of systemic inflammation

Purpose

It is vital that Immune fitness, i.e., how well the immune system functions and reacts to challenges, can be reliably be examined. The current study aimed to compare immune fitness with assessments of saliva biomarkers of systemic inflammation.

Methods

N = 108 healthy young adults (18-30-year-old students of Utrecht University, the Netherlands) participated in the study. A saliva sample was collected for biomarker assessment (Interleukin (IL)-1β, IL-6, IL-8, IL-10, immunoglobulin A (IgA), and tumor necrosis factor-alpha (TNF-α), and c-reactive protein (CRP). Additionally, a survey was completed to assess immune fitness, mood, mental resilience, and quality of life. The correlations between the biomarker assessments, immune fitness and mood were determined.

Results

No significant correlations between immune fitness and biomarkers of systemic inflammation were found. Significant sex differences in correlations with immune fitness were demonstrated for loneliness (significant only in men) and fatigue (significant only in women). For both sexes, immune fitness correlated significantly with anxiety, mental resilience, and quality of life.

Conclusion

No significant correlations were found between immune fitness and saliva biomarkers of systemic inflammation. Immune fitness correlated significantly with anxiety, mental resilience, and quality of life. Sex differences were demonstrated in the relation of immune fitness with loneliness and fatigue. Future research should further investigate factors that may influence the relationship between immune fitness, mood, and biomarkers of systemic inflammation, including underlying psychological mechanisms of possible sex differences.

Breath volatile organic compounds (VOCs) as biomarkers for the diagnosis of pathological conditions: A review

Normal and abnormal/pathological status of physiological processes in the human organism can be characterized through Volatile Organic Compounds (VOCs) emitted in breath. Recently, a wide range of volatile analytes has risen as biomarkers. These compounds have been addressed in the scientific and medical communities as an extremely valuable metabolic window. Once collected and analysed, VOCs can represent a tool for a rapid, accurate, non-invasive, and painless diagnosis of several diseases and health conditions. These biomarkers are released by exhaled breath, urine, faeces, skin, and several other ways, at trace concentration levels, usually in the ppbv (μg/L) range. For this reason, the analytical techniques applied for detecting and clinically exploiting the VOCs are extremely important. The present work reviews the most promising results in the field of breath biomarkers and the most common methods of detection of VOCs. A total of 16 pathologies and the respective database of compounds are addressed. An updated version of the VOCs biomarkers database can be consulted at: https://neomeditec.com/VOCdatabase/.

Variation of Circulating Brain-Derived Neurotrophic Factor (BDNF) in Depression: Relationships with Inflammatory Indices, Metabolic Status and Patients' Clinical Features

This study seeks to offer a contribution to the method of subtyping major depressed patients by exploring the possible relationships between circulating brain-derived neurotrophic factor (BDNF), different peripheral inflammatory/metabolic markers in the blood and clinical characteristics. Thirty-nine patients, thoroughly diagnosed according to the DSM-5 criteria, underwent a comprehensive set of evaluations encompassing structured interviews, rating scales and a panel of blood tests. Correlation and comparison analyses were carried out by means of non-parametric statistical tests. Concurrently, a principal component analysis was performed to explain biochemical variance. The findings of our research unveiled that leukocyte counts, their ratios and other inflammatory parameters are positively correlated with depression scores. Moreover, we found variations within the BDNF pools of depressed patients. Specifically, higher levels of platelet-poor plasma BDNF (PPP-BDNF) were correlated with augmented inflammatory markers in patients showing specific episode characteristics, whereas reduced platelet BDNF (PLT-BDNF) provided a better indication of the changes that were linked to a diagnosis of long-term depression. Our findings suggest that PPP-BDNF and PLT-BDNF might differentiate depression conditions. They also imply usefulness in appraising peripheral biomarker profiles in patients for a deeper characterization of major depressive episodes. At the same time, it is plausible that they might constitute novel avenues for developing more tailored therapeutic strategies for patients with MDs.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Epigenetics in psychiatry: Beyond DNA methylation

The global burden of psychopathologies appears to be underestimated, since the global psychiatric disorder burden is exceeding other medical burdens. To be able to address this problem more effectively, we need to better understand the etiology of psychiatric disorders. One of the hallmarks of psychiatric disorders appears to be epigenetic dysregulation. While some epigenetic modifications (such as DNA methylation) are well known and studied, the roles of others have been investigated much less. DNA hydroxymethylation is a rarely studied epigenetic modification, which as well as being an intermediate stage in the DNA demethylation cycle is also an independent steady cell state involved in neurodevelopment and plasticity. In contrast to DNA methylation, DNA hydroxymethylation appears to be related to an increase in gene expression and subsequent protein expression. Although no particular gene or genetic locus can be at this point linked to changes in DNA hydroxymethylation in psychiatric disorders, the epigenetic marks present good potential for biomarker identification because the epigenetic landscape is a result of the interplay between genes and environment, which both influence the development of psychiatric disorders, and because hydoxymethylation changes are particularly enriched in the brain and in synapse-related genes.

Event-related potential (ERP) markers of 22q11.2 deletion syndrome and associated psychosis

22q11.2 deletion syndrome (22q11.2DS) is a multisystemic disorder characterized by a wide range of clinical features, ranging from life-threatening to less severe conditions. One-third of individuals with the deletion live with mild to moderate intellectual disability; approximately 60% meet criteria for at least one psychiatric condition.22q11.2DS has become an important model for several medical, developmental, and psychiatric disorders. We have been particularly interested in understanding the risk for psychosis in this population: Approximately 30% of the individuals with the deletion go on to develop schizophrenia. The characterization of cognitive and neural differences between those individuals who develop schizophrenia and those who do not, despite being at genetic risk, holds important promise in what pertains to the clarification of paths to disease and to the development of tools for early identification and intervention.Here, we review our previous event-related potential (ERP) findings as potential markers for 22q11.2DS and the associated risk for psychosis, while discussing others' work. We focus on auditory processing (auditory-evoked potentials, auditory adaptation, and auditory sensory memory), visual processing (visual-evoked potentials and visual adaptation), and inhibition and error monitoring.The findings discussed suggest basic mechanistic and disease process effects on neural processing in 22q11.2DS that are present in both early sensory and later cognitive processing, with possible implications for phenotype. In early sensory processes, both during auditory and visual processing, two mechanisms that impact neural responses in opposite ways seem to coexist-one related to the deletion, which increases brain responses; another linked to psychosis, decreasing neural activity. Later, higher-order cognitive processes may be equally relevant as markers for psychosis. More specifically, we argue that components related to error monitoring may hold particular promise in the study of risk for schizophrenia in the general population.

Use of Quantile Treatment Effects Analysis to Describe Antidepressant Response in Randomized Clinical Trials Submitted to the US Food and Drug Administration: A Secondary Analysis of Pooled Trial Data

Importance

Major depressive disorder (MDD) is a leading cause of global distress and disability. Earlier studies have indicated that antidepressant therapy confers a modest reduction in depressive symptoms on average, but the distribution of this reduction requires more research.

Objective

To estimate the distribution of antidepressant response by depression severity.

Design, Setting, and Participants

In this secondary analysis of pooled trial data, quantile treatment effect (QTE) analysis was conducted from the US Food and Drug Administration (FDA) database of antidepressant monotherapy for patients with MDD, encompassing 232 positive and negative trials submitted to the FDA between 1979 and 2016. Analysis was restricted to participants with severe MDD (17-item Hamilton Rating Scale for Depression [HAMD-17] score ≥20). Data analysis was conducted from August 16, 2022, to April 16, 2023.

Intervention

Antidepressant monotherapy compared with placebo.

Main Outcomes and Measures

The distribution of percentage depression response was compared between the pooled treatment arm and pooled placebo arm. Percentage depression response was defined as 1 minus the ratio of final depression severity to baseline depression severity, expressed as a percentage. Depression severity was reported in HAMD-17-equivalent units.

Results

A total of 57 313 participants with severe depression were included in the analysis. There was no significant imbalance in baseline depression severity between the pooled treatment arm and pooled placebo arm, with a mean HAMD-17 difference of 0.037 points (P = .11 by Wilcoxon rank sum test). An interaction term test for rank similarity did not reject the rank similarity governing percentage depression response (P > .99). The entire distribution of depression response was more favorable in the pooled treatment arm than in the pooled placebo arm. The maximum separation between treatment and placebo occurred at the 55th quantile and corresponded to an absolute improvement in depression due to active drug of 13.5% (95% CI, 12.4%-14.4%). The separation between treatment and placebo diminished near the tails of the distribution.

Conclusions and Relevance

In this QTE analysis of pooled clinical trial data from the FDA, antidepressants were found to confer a small reduction in depression severity that was broadly distributed across participants with severe depression. Alternatively, if the assumptions behind the QTE analysis are not met, then the data are also compatible with antidepressants eliciting more complete response in a smaller subset of participants than is suggested by this QTE analysis.

Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases

A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.

G protein-coupled receptors in neurodegenerative diseases and psychiatric disorders

Neuropsychiatric disorders are multifactorial disorders with diverse aetiological factors. Identifying treatment targets is challenging because the diseases are resulting from heterogeneous biological, genetic, and environmental factors. Nevertheless, the increasing understanding of G protein-coupled receptor (GPCR) opens a new possibility in drug discovery. Harnessing our knowledge of molecular mechanisms and structural information of GPCRs will be advantageous for developing effective drugs. This review provides an overview of the role of GPCRs in various neurodegenerative and psychiatric diseases. Besides, we highlight the emerging opportunities of novel GPCR targets and address recent progress in GPCR drug development.

Time for Brain Medicine

Unprecedented knowledge of the brain is inevitably contributing to the convergence of neurology and psychiatry. However, clinical training continues to follow a divergent approach established in the 19th century. An etiological approach will continue to shift more psychiatric patients to the care of neurologists who are untrained in psychiatric management. At the same time, this new era of diagnostic biomarkers and neuroscience-based precision treatments requires skills not readily available to those trained in psychiatry. The challenges in training the next generation of doctors include establishing competence involving aspects of the whole brain, fostering the subspecialized expertise needed to remain current, and developing programs that are feasible in duration and practical in implementation. A new 4-year residency training program proposed in this article could replace existing residency programs. The program includes 2 years of common and urgent training in various aspects of neurology and psychiatry followed by 2 years of elective subspecialty tracks. The concept is similar to internal medicine residencies and fellowships. No changes to existing departmental structures are necessary. In concert with the emerging biological approach to the brain, "brain medicine" is proposed as a new name to denote this practice in the simplest terms: a focus on all aspects of the brain.

Plasma biomarker panel for major depressive disorder by quantitative proteomics using ensemble learning algorithm: A preliminary study

Major depressive disorder (MDD) is a major international public health issue; thus, investigating its underlying mechanisms and identifying suitable biomarkers to enable its early detection are imperative. Using data-independent acquisition-mass spectrometry-based proteomics, the plasma of 44 patients with MDD and 25 healthy controls was studied to detect differentially expressed proteins. Bioinformatics analyses, such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, Protein-Protein Interaction network, and weighted gene co-expression network analysis were employed. Moreover, an ensemble learning technique was used to build a prediction model. A panel of two biomarkers, L-selectin and an isoform of the Ras oncogene family was identified. With an area under the receiver operating characteristic curve of 0.925 and 0.901 for the training and test sets, respectively, the panel was able to distinguish MDD from the controls. Our investigation revealed numerous potential biomarkers and a diagnostic panel based on several algorithms, which may contribute to the future development of a plasma-based diagnostic approach and better understanding of the molecular mechanisms of MDD.

Risperidone response in patients with schizophrenia drives DNA methylation changes in immune and neuronal systems

Background: The choice of efficient antipsychotic therapy for schizophrenia relies on a time-consuming trial-and-error approach, whereas the social and economic burdens of the disease call for faster alternatives. Material & methods: In a search for predictive biomarkers of antipsychotic response, blood methylomes of 28 patients were analyzed before and 4 weeks into risperidone therapy. Results: Several CpGs exhibiting response-specific temporal dynamics were identified in otherwise temporally stable methylomes and noticeable global response-related differences were observed between good and bad responders. These were associated with genes involved in immunity, neurotransmission and neuronal development. Polymorphisms in many of these genes were previously linked with schizophrenia etiology and antipsychotic response. Conclusion: Antipsychotic response seems to be shaped by both stable and medication-induced methylation differences.