Innovative biomarkers in psychiatric disorders: a major clinical challenge in psychiatry

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.

Seventy Years of Antipsychotic Development: A Critical Review

Since the mid-1950s discovery of the first effective antipsychotic medications (APM), we have only been able to improve the tolerability but not the overall efficacy of currently available APMs, as reflected by effectiveness trials in Europe and the United States. This inability to develop more effective APMs is attributable to multiple factors, including failure to create and use assessment tools to assess core symptom domains in schizophrenia, move beyond the dopaminergic hypothesis and to develop "me too" drugs, imposing ill-defined research domain criteria, and lacking federal funding for clinical trials. The classification of APMs is also confusing, including second-generation, partial agonists, and multimodal APMs in the same class of APMs, despite significant differences in their mechanisms of action. Other factors stagnating drug development include inadequate sample sizes to address heterogeneity, lack of statistical measures correlating with clinical significance, using the atheoretical basis of psychiatric diagnoses, failure to control placebo response, and high cost of newer and perhaps more tolerable APMs. Furthermore, there has been a failure to develop early predictors of antipsychotic response and various tools to optimize an APM response. Finally, some mental health providers are also responsible for the suboptimal use of APMs, by using excessive maintenance doses, often with irrational polypharmacy, further compromising effectiveness and medication adherence. However, some bright spots in antipsychotic development include improved tolerability of APMs and long-acting injectables to address the high prevalence of medication nonadherence. This review critically reviews 70 years of antipsychotic development, the reasons behind the failure to develop more effective APMs, and suggestions for future direction.

Targeted sequencing approach: Comprehensive analysis of DNA methylation and gene expression across blood and brain regions in suicide victims

OBJECTIVES

Epigenetic mechanisms are involved in regulation of many pathologies, including suicidal behaviour. However, the factors through which epigenetics affect suicidal behaviour are not fully understood.

METHODS

We analysed DNA methylation of eight neuropsychiatric genes (NR3C1, SLC6A4, HTR1A, TPH2, SKA2, MAOA, GABRA1, and NRIP3) in brain regions (hippocampus, insula, amygdala, Brodmann area 46) and blood of 25 male suicide victims and 28 male control subjects, using bisulphite next-generation sequencing.

RESULTS

Comparing mean methylation values, notable changes were observed in NR3C1 (insula p-value = 0.05), HTR1A (insula p-value < 0.001, blood p-value = 0.001), SKA2 (insula p-value = 0.03, blood p-value = 0.016), MAOA (blood p-value < 0.001), GABRA1 (insula p-value = 0.05, blood p-value = 0.024) and NRIP3 (hippocampus p-value = 0.001, insula p-value = 0.002, amygdala p-value = 0.014). Comparing methylation pattern between blood and brain, similarity was observed between blood and insula for HTR1A. Gene expression analysis in hippocampus revealed changes in expression of NR3C1 (p-value = 0.049), SLC6A4 (p-value = 0.017) and HTR1A (p-value = 0.053).

CONCLUSIONS

Results provide an insight into the altered state of DNA methylation in suicidal behaviour. Epigenetic differences could therefore affect suicidal behaviour in both previously known and in novel neuropsychiatric candidate genes.

Sexual offenses and the brain

Sexual offenses are a great concern worldwide due to the devastating physical and psychological consequences the victims of such crimes often experience. It is an important task to investigate potential mechanisms that may underlie sexual offending and predispose an individual for such antisocial behavior. Advanced techniques in neuroscience are increasingly used to uncover biomarkers in psychiatric disorders and organic brain disease. As this type of research is flourishing, preliminary studies with the aim to explore the neural basis of sex offenders have started to appear. To this purpose, researchers began to study the brain's structural and functional changes and the neurocognitive profiles of sex offenders, in comparison to nonoffenders and nonsex offenders, or among different subtypes of sex offenders. Most of the research to date has focused on pedophilia, with some inconsistent findings, which hampers the translation of the results into the forensic and clinical context. Any attempt to increase convergent evidence may profit from the harmonization of data analysis and avoidance of methodological inconsistencies, which can account for the different results across studies. Today, uncovering the neural basis of sex offences has to become a priority, not only for clinical interventions, but might also be important knowledge for crime prevention.

Apolipoprotein E genotype, inflammatory biomarkers, and non-psychiatric multimorbidity contribute to the suicidal ideation phenotype in older age. The Salus in Apulia Study

BACKGROUND

Possible relationships between suicidal ideation and biopsychosocial predictors in older age are unclear. In the population-based Salus in Apulia Study, we investigated the relationships among biomarkers, socio-demographic, psychopathological, inflammatory and metabolic characteristics and suicidal ideation in 1252 older subjects.

METHODS

Suicidal ideation was evaluated with the brief version of the Columbia-Suicide Severity Rating Scale. Apolipoprotein E (APOE) genotype and inflammatory profile [interleukin (IL)-6, tumor necrosis factor (TNF)-α, C-reactive protein (CRP)] were evaluated. A machine learning algorithm, the Random Forest (RF), selected potential biopsychosocial factors associated to suicidal ideation.

RESULTS

Suicidal ideators accounted for 2.32 % of subjects, were female, smokers, and obese with multimorbidity. After adjusting for age, gender, education and social dysfunction, logistic regression analyses revealed that suicidal ideation was associated to late-life depression (LLD) (odds ratio:21.71,95 % confidence interval:9.22-51.14). In the full RF model, asthma was the most important contributor to suicidal ideation. In the final RF model, education, age, and mild cognitive impairment followed by gender and global cognition were considered the most important contributors. Among biomarkers, in the final RF model, IL-6 followed by TNF-α, APOE ε4 allele presence, CRP and high-density lipoprotein cholesterol contributed most to suicidal ideation.

LIMITATIONS

A relatively small number of older subjects with suicidal ideation (2.3 %); we did not distinguish between active and passive suicidal ideation.

CONCLUSIONS

Although LLD is a strong determinant of suicidal ideation, other non-psychiatric factors, i.e., serum inflammation biomarkers, APOE ε4 allele, and multimorbidity, should be taken into account when evaluating a suicidal ideation phenotype in older age.

Could epigenetics play a role in suicidal behavior in older age?

Tweetable abstract There is growing evidence of a role of environmental exposures in the pathogenesis and epigenetics of suicidal behavior in older age.

'Omics' of suicidal behaviour: A path to personalised psychiatry

Psychiatric disorders, including suicide, are complex disorders that are affected by many different risk factors. It has been estimated that genetic factors contribute up to 50% to suicide risk. As the candidate gene approach has not identified a gene or set of genes that can be defined as biomarkers for suicidal behaviour, much is expected from cutting edge technological approaches that can interrogate several hundred, or even millions, of biomarkers at a time. These include the '-omic' approaches, such as genomics, transcriptomics, epigenomics, proteomics and metabolomics. Indeed, these have revealed new candidate biomarkers associated with suicidal behaviour. The most interesting of these have been implicated in inflammation and immune responses, which have been revealed through different study approaches, from genome-wide single nucleotide studies and the micro-RNA transcriptome, to the proteome and metabolome. However, the massive amounts of data that are generated by the '-omic' technologies demand the use of powerful computational analysis, and also specifically trained personnel. In this regard, machine learning approaches are beginning to pave the way towards personalized psychiatry.

Pro/Antioxidant State as a Potential Biomarker of Schizophrenia

To allow better diagnosis and management of psychiatric illnesses, the use of easily accessible biomarkers are proposed. Therefore, recognition of some diseases by a set of related pathogenesis biomarkers is a promising approach. The study aims to assess the usefulness of examining oxidative stress (OS) in schizophrenia as a potential biomarker of illness using the commonly used data mining decision tree method. The study group was comprised of 147 participants: 98 patients with schizophrenia (SZ group), and the control group (n = 49; HC). The patients with schizophrenia were divided into two groups: first-episode schizophrenia (n = 49; FS) and chronic schizophrenia (n = 49; CS). The assessment included the following biomarkers in sera of patients: catalase (CAT), glutathione peroxidase (GPx), superoxide dismutase-1 (SOD-1), glutathione reductase (GR), reduced glutathione (GSH), total antioxidant capacity (TAC), ferric reducing ability of plasma (FRAP), advanced glycation end products (AGEs), advanced oxidation protein products (AOPP), dityrosine (DITYR), kynurenine (KYN), N-formylkynurenine (NFK), tryptophan (TRY), total oxidant status (TOS), nitric oxide (NO) and total protein. Maximum accuracy (89.36%) for distinguishing SZ from HC was attained with TOS and GPx (cut-off points: 392.70 and 15.33). For differentiating between FS and CS, the most promising were KYN, AOPP, TAC and NO (100%; cut-off points: 721.20, 0.55, 64.76 and 2.59). To distinguish FS from HC, maximum accuracy was found for GSH and TOS (100%; cut-off points: 859.96 and 0.31), and in order to distinguish CS from HC, the most promising were GSH and TOS (100%; cut-off points: 0.26 and 343.28). Using redox biomarkers would be the most promising approach for discriminating patients with schizophrenia from healthy individuals and, in the future, could be used as an add-on marker to diagnose and/or respond to treatment.

Machine learning as the new approach in understanding biomarkers of suicidal behavior

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore 'omic' studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

Vitamin D in the development and progression of alzheimer's disease: implications for clinical management

INTRODUCTION

Although the pathophysiological bases of Alzheimer's disease (AD) remain incompletely understood and disease-modifying therapies are not available, intervention on modifiable risk factors is warranted. Research on nutrition and dietary components is challenging and controversies still persist about the role of micro- and macronutrients and health outcomes in dementia. Importantly, results of preclinical investigations have shown that vitamin D triggers different neural pathways that may be protective against these neurodegenerative mechanisms, including the deposition of amyloid plaques, inflammatory processes, neurofibrillary degeneration, glutamatergic excitotoxicity, excessive intraneuronal calcium influx, and oxidative stress, although its relationship with AD still needs to be fully understood.

AREAS COVERED

The authors analyzed the recent evidence about the effects of vitamin D insufficiency on AD and the role of supplementation.

EXPERT OPINION

Both insufficient (25-49.9 ng/ml) and deficient levels (<25 ng/ml) of vitamin D may contribute to an increased susceptibility to AD. However, further well-designed prospective studies are needed for a better understanding of the involvement of low vitamin D concentrations in the AD natural history. Randomized clinical trials will also be necessary to address the issue of causality and determine whether vitamin D supplementation may be effective for the prevention or treatment of AD.

A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease

The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the "progressive" MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the "stable" MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan-Meier survival analyses were applied to explore each variable's relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were "unfavorable" (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.

Pharmacogenomic Biomarkers and Their Applications in Psychiatry

Realizing the promise of precision medicine in psychiatry is a laudable and beneficial endeavor, since it should markedly reduce morbidity and mortality and, in effect, alleviate the economic and social burden of psychiatric disorders. This review aims to summarize important issues on pharmacogenomics in psychiatry that have laid the foundation towards personalized pharmacotherapy and, in a broader sense, precision medicine. We present major pharmacogenomic biomarkers and their applications in a variety of psychiatric disorders, such as depression, attention-deficit/hyperactivity disorder (ADHD), narcolepsy, schizophrenia, and bipolar disorder. In addition, we extend the scope into epilepsy, since antiepileptic drugs are widely used to treat psychiatric disorders, although epilepsy is conventionally considered to be a neurological disorder.

The Discovery of Clinically Applicable Biomarkers for Bipolar Disorder: A Review of Candidate and Proteomic Approaches

Bipolar disorder (BD) is a severe psychiatric condition which affects innumerable people across the globe. The etiopathogenesis of BD is multi-faceted with genetic, environmental and psychosocial factors playing a role. Hitherto, the diagnosis and management of BD are purely on empirical grounds as we lack confirmed biomarkers for this condition. In this regard, hypothesis-driven investigations have been unable to identify clinically applicable biomarkers, steering the field towards newer technologies. Innovative, state-of-the-art techniques like multiplex immunoassays and mass spectrometry can potentially investigate the entire proteome. By detecting up or down regulated proteins, novel biomarkers are identified and new postulates about the etiopathogenesis of BD are specified. Hence, biological pathways are uncovered which are involved in the initiation and advancement of the disease and new therapeutic targets are identified. In this manuscript, the extant literature is thoroughly reviewed and the latest findings on candidate BD biomarkers are provided, followed by an overview of the proteomic approaches. It was found that due to the heterogeneous nature of BD no single biomarker is feasible, instead a panel of tests is more likely to be useful. With the application of latest technologies, it is expected that validated biomarkers will be discovered which will be useful as diagnostic tools and help in the delivery of individually tailored therapies to the patients.

The relationship between epigenetics and microbiota in neuropsychiatric diseases

Microbiota might be considered as a pool for environmental epigenetic factors. Evidence is accumulating that environmental exposures - including microbes, diet, drugs - play a role in the pathogenesis of many neuropsychiatric disorders. Underlying mechanisms are complex, involving the sensitive interplay of genetics with epigenetics, neuroinflammation and the innate immune system. Modifications of microbiota affect neurogenesis and the maturation of microglia, influencing social behavior, stress-related responses and fear learning mechanisms. The excitatory neurons in the medial prefrontal cortex appear to play a key role. The mechanisms through which antibiotics administration may modulate microbiota and, therefore, behavior and neuropsychiatric disorders, may be influenced by several variables such as pre-existing gastrointestinal inflammation, the baseline microbiota composition, diet and stress perception. Probiotics, individualized diet, antibiotics and fecal transplantation could positively modulate the effects of epigenetic factors on neuropsychiatric disorders.

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

BACKGROUND

Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions.

OBJECTIVE

We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders.

METHODS

We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity.

RESULTS

Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health.

CONCLUSIONS

Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

The Challenge of Antidepressant Therapeutics in Alzheimer's Disease

The link between depression and Alzheimer's disease (AD) is controversial, because it is not clear if depression is an independent risk factor for the disease or a prodromal symptom in the older population. Cerebral amyloid-β (Aβ) peptide deposition is associated with both cognitive symptoms and neuropsychiatric symptoms (NPS), which may be a biological mechanism of compensation. Despite the widespread use of antidepressant therapeutics (30-50% of patients with AD/dementia are on antidepressants), there is mixed evidence regarding the benefits from their use in AD depression. Monoaminergic antidepressant drugs have shown only modest or no clinical benefits. Therefore, it is important to understand the reason of this drug-resistance and the relationship between antidepressant drugs and the Aβ peptide. The goal of the present review is to highlight the etiology of depression in patients affected by AD in comparison to depressive disorders without AD, and to speculate on more appropriate and alternative therapeutics.

Metabolomics in Psychiatric Disorders: What We Learn from Animal Models

Biomarkers are a recent research target within biological factors of psychiatric disorders. There is growing evidence for deriving biomarkers within psychiatric disorders in serum or urine samples in humans, however, few studies have investigated this differentiation in brain or cerebral fluid samples in psychiatric disorders. As brain samples from humans are only available at autopsy, animal models are commonly applied to determine the pathogenesis of psychiatric diseases and to test treatment strategies. The aim of this review is to summarize studies on biomarkers in animal models for psychiatric disorders. For depression, anxiety and addiction disorders studies, biomarkers in animal brains are available. Furthermore, several studies have investigated psychiatric medication, e.g., antipsychotics, antidepressants, or mood stabilizers, in animals. The most notable changes in biomarkers in depressed animal models were related to the glutamate-γ-aminobutyric acid-glutamine-cycle. In anxiety models, alterations in amino acid and energy metabolism (i.e., mitochondrial regulation) were observed. Addicted animals showed several biomarkers according to the induced drugs. In summary, animal models provide some direct insights into the cellular metabolites that are produced during psychiatric processes. In addition, the influence on biomarkers due to short- or long-term medication is a noticeable finding. Further studies should combine representative animal models and human studies on cerebral fluid to improve insight into mental disorders and advance the development of novel treatment strategies.

Short Communication: Interaction of Nerve Growth Factor (NGF) and Vascular Endothelial Growth Factor (VEGF) in Healthy Individuals

NGF and VEGF are known to be involved in different psychiatric diseases. In order to verify hints from basic research that both neurotrophines interact with each other, serum levels of NGF and VEGF were measured in a cohort of 33 healthy individuals and correlated. NGF level was 126.30 pg/mL (±155.43), and VEGF level was 57.28 pg/mL (±44.48). Both factors were significantly correlated, confirming their interaction and legitimising the usage of their respective ratio (0.8 (±0.42)) as a less varying additional marker in prospective studies.

The Role of Biomarkers in Psychiatry

Psychiatric illnesses are cognitive and behavioral disorders of the brain. At present, psychiatric diagnosis is based on DSM-5 criteria. Even if endophenotype specificity for psychiatric disorders is discussed, it is difficult to study and identify psychiatric biomarkers to support diagnosis, prognosis, or clinical response to treatment. This chapter investigates the innovative biomarkers of psychiatric diseases for diagnosis and personalized treatment, in particular post-genomic data and proteomic analyses.

Epigenetic meta-analysis across three civilian cohorts identifies NRG1 and HGS as blood-based biomarkers for post-traumatic stress disorder

AIM

Trauma exposure is a necessary, but not deterministic, contributor to post-traumatic stress disorder (PTSD). Epigenetic factors may distinguish between trauma-exposed individuals with versus without PTSD.

MATERIALS & METHODS

We conducted a meta-analysis of PTSD epigenome-wide association studies in trauma-exposed cohorts drawn from civilian contexts. Whole blood-derived DNA methylation levels were analyzed in 545 study participants, drawn from the three civilian cohorts participating in the PTSD working group of the Psychiatric Genomics Consortium.

RESULTS

Two CpG sites significantly associated with current PTSD in NRG1 (cg23637605) and in HGS (cg19577098).

CONCLUSION

PTSD is associated with differential methylation, measured in blood, within HGS and NRG1 across three civilian cohorts.

Pharmacotherapy for the treatment of depression in patients with alzheimer's disease: a treatment-resistant depressive disorder

INTRODUCTION

Pharmacotherapy for the treatment of depressive disorders in Alzheimer's Disease (AD) represents a clinical challenge. pharmacological options are often attempted after a period of watchful waiting (8-12 weeks). monoaminergic antidepressant drugs have shown only modest or null clinical benefits, maybe because the etiology of depressive symptoms in ad patients is fundamentally different from that of nondemented subjects.

AREAS COVERED

The following article looks at the selective serotonin reuptake inhibitor sertraline, which is one of the most frequently studied antidepressant medications in randomized controlled trials (RCTs). It also discusses many other pharmacological approaches that have proven to be inadequate (antipsychotics, acetylcholinesterase inhibitors, anticonvulsants, hormone replacement therapy) and new drug classes (mainly affecting glutamate transmission) that are being studied for treating depression in AD. It also gives discussion to the phase II RCT on the alternative drug S47445 and the potential effect on cognition of the multimodal antidepressant vortioxetine in older depressed patients. Finally, it discusses the N-methyl-D-aspartate antagonist ketamine.

EXPERT OPINION

The present RCT methodologies are too disparate to draw firm conclusions. Future studies are required to identify effective and multimodal pharmacological treatments that efficiently treat depression in AD. Genotyping may boost antidepressant treatment success.