Multi-omics data integration methods and their applications in psychiatric disorders.

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art.

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

Transcriptional biomarkers of response to pharmacological treatments in severe mental disorders: A systematic review.

Variation in the expression level and activity of genes involved in drug disposition and action in tissues of pharmacological importance have been increasingly investigated in patients treated with psychotropic drugs. Findings are promising, but reliable predictive biomarkers of response have yet to be identified. Here we conducted a PRISMA-compliant systematic search of PubMed, Scopus and PsycInfo up to 12 September 2020 for studies investigating RNA expression levels in cells or biofluids from patients with major depressive disorder, schizophrenia or bipolar disorder characterized for response to psychotropic drugs (antidepressants, antipsychotics or mood stabilizers) or adverse effects. Among 5497 retrieved studies, 123 (63 on antidepressants, 33 on antipsychotics and 27 on mood stabilizers) met inclusion criteria. Studies were either focused on mRNAs (n = 96), microRNAs (n = 19) or long non-coding RNAs (n = 1), with only a minority investigating both mRNAs and microRNAs levels (n = 7). The most replicated results include genes playing a role in inflammation (antidepressants), neurotransmission (antidepressants and antipsychotics) or mitochondrial function (mood stabilizers). Compared to those investigating response to antidepressants, studies focused on antipsychotics or mood stabilizers more often showed lower sample size and lacked replication. Strengths and limitations of available studies are presented and discussed in light of the specific designs, methodology and clinical characterization of included patients for transcriptomic compared to DNA-based studies. Finally, future directions of transcriptomics of psychopharmacological interventions in psychiatric disorders are discussed.

A meta-analysis of polygenic risk scores for mood disorders, neuroticism, and schizophrenia in antidepressant response.

About two-thirds of patients with major depressive disorder (MDD) fail to achieve symptom remission after the initial antidepressant treatment. Despite a role of genetic factors was proven, the specific underpinnings are not fully understood yet. Polygenic risk scores (PRSs), which summarise the additive effect of multiple risk variants across the genome, might provide insights into the underlying genetics. This study aims to investigate the possible association of PRSs for bipolar disorder, MDD, neuroticism, and schizophrenia (SCZ) with antidepressant non-response or non-remission in patients with MDD. PRSs were calculated at eight genome-wide P-thresholds based on publicly available summary statistics of the largest genome-wide association studies. Logistic regressions were performed between PRSs and non-response or non-remission in six European clinical samples, adjusting for age, sex, baseline symptom severity, recruitment sites, and population stratification. Results were meta-analysed across samples, including up to 3,637 individuals. Bonferroni correction was applied. In the meta-analysis, no result was significant after Bonferroni correction. The top result was found for MDD-PRS and non-remission (p = 0.004), with patients in the highest vs. lowest PRS quintile being more likely not to achieve remission (OR=1.5, 95% CI=1.11-1.98, p = 0.007). Nominal associations were also found between MDD-PRS and non-response (p = 0.013), as well as between SCZ-PRS and non-remission (p = 0.035). Although PRSs are still not able to predict non-response or non-remission, our results are in line with previous works; methodological improvements in PRSs calculation may improve their predictive performance and have a meaningful role in precision psychiatry.

A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials.

Criteria for treatment-resistant depression (TRD) and partially responsive depression (PRD) as subtypes of major depressive disorder (MDD) are not unequivocally defined. In the present document we used a Delphi-method-based consensus approach to define TRD and PRD and to serve as operational criteria for future clinical studies, especially if conducted for regulatory purposes. We reviewed the literature and brought together a group of international experts (including clinicians, academics, researchers, employees of pharmaceutical companies, regulatory bodies representatives, and one person with lived experience) to evaluate the state-of-the-art and main controversies regarding the current classification. We then provided recommendations on how to design clinical trials, and on how to guide research in unmet needs and knowledge gaps. This report will feed into one of the main objectives of the EUropean Patient-cEntric clinicAl tRial pLatforms, Innovative Medicines Initiative (EU-PEARL, IMI) MDD project, to design a protocol for platform trials of new medications for TRD/PRD.

Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression.

Panic disorder (PD) has a lifetime prevalence of 2-4% and heritability estimates of 40%. The contributory genetic variants remain largely unknown, with few and inconsistent loci having been reported. The present report describes the largest genome-wide association study (GWAS) of PD to date comprising genome-wide genotype data of 2248 clinically well-characterized PD patients and 7992 ethnically matched controls. The samples originated from four European countries (Denmark, Estonia, Germany, and Sweden). Standard GWAS quality control procedures were conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project reference panel. A meta-analysis was then performed using the Ricopili pipeline. No genome-wide significant locus was identified. Leave-one-out analyses generated highly significant polygenic risk scores (PRS) (explained variance of up to 2.6%). Linkage disequilibrium (LD) score regression analysis of the GWAS data showed that the estimated heritability for PD was 28.0-34.2%. After correction for multiple testing, a significant genetic correlation was found between PD and major depressive disorder, depressive symptoms, and neuroticism. A total of 255 single-nucleotide polymorphisms (SNPs) with p < 1 × 10-4 were followed up in an independent sample of 2408 PD patients and 228,470 controls from Denmark, Iceland and the Netherlands. In the combined analysis, SNP rs144783209 showed the strongest association with PD (pcomb = 3.10 × 10-7). Sign tests revealed a significant enrichment of SNPs with a discovery p-value of <0.0001 in the combined follow up cohort (p = 0.048). The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD.

Precision psychiatry in clinical practice.

The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing guidelines, giving clinicians the possibility to use this additional tool to guide prescription and improve therapeutic outcomes. This marked an important step towards precision psychiatry, which aim is to integrate biological and environmental information to personalise treatments. Only genetic variants in cytochrome enzymes are endorsed by prescribing guidelines, but in the future polygenic predictors of treatment outcomes may be translated into the clinic. The integration of genetics with other relevant information (e.g., concomitant diseases and treatments, drug plasma levels) could be managed in a standardised way through ad hoc software. The overcoming of the current obstacles (e.g., staff training, genotyping and informatics facilities) can lead to a broad implementation of precision psychiatry and represent a revolution for psychiatric care.Key pointsPrecision psychiatry aims to integrate biological and environmental information to personalise treatments and complement clinical judgementPharmacogenetic biomarkers in cytochrome genes were included in prescribing guidelines and represented an important step towards precision psychiatryTherapeutic drug monitoring is an important and cost-effective tool which should be integrated with genetic testing and clinical evaluation in order to optimise pharmacotherapyOther individual factors relevant to pharmacotherapy response (e.g., individual's symptom profile, concomitant diseases) can be integrated with genetic information through artificial intelligence to provide treatment recommendationsThe creation of pharmacogenetic services within healthcare systems is a challenging and multi-step process, education of health professionals, promotion by institutions and regulatory bodies, economic and ethical barriers are the main issues.

Correction to: Imaging and Genetic Approaches to Inform Biomarkers for Anxiety Disorders, Obsessive-Compulsive Disorders, and PSTD.

This chapter was inadvertently published with Fig. 1 which do not belong to this chapter and hence Fig. 1 is deleted from this chapter later.

Imaging and Genetic Approaches to Inform Biomarkers for Anxiety Disorders, Obsessive-Compulsive Disorders, and PSTD.

Anxiety disorders are the most common mental health problem in the world and also claim the highest health care cost among various neuropsychiatric disorders. Anxiety disorders have a chronic and recurrent course and cause significantly negative impacts on patients' social, personal, and occupational functioning as well as quality of life. Despite their high prevalence rates, anxiety disorders have often been under-diagnosed or misdiagnosed, and consequently under-treated. Even with the correct diagnosis, anxiety disorders are known to be difficult to treat successfully. In order to implement better strategies in diagnosis, prognosis, treatment decision, and early prevention for anxiety disorders, tremendous efforts have been put into studies using genetic and neuroimaging techniques to advance our understandings of the underlying biological mechanisms. In addition to anxiety disorders including panic disorder, generalised anxiety disorder (GAD), specific phobias, social anxiety disorders (SAD), due to overlapping symptom dimensions, obsessive-compulsive disorder (OCD), and post-traumatic stress disorder (PTSD) (which were removed from the anxiety disorder category in DSM-5 to become separate categories) are also included for review of relevant genetic and neuroimaging findings. Although the number of genetic or neuroimaging studies focusing on anxiety disorders is relatively small compare to other psychiatric disorders such as psychotic disorders or mood disorders, various structural abnormalities in the grey or white matter, functional alterations of activity during resting-state or task conditions, molecular changes of neurotransmitter receptors or transporters, and genetic associations have all been reported. With continuing effort, further genetic and neuroimaging research may potentially lead to clinically useful biomarkers for the prevention, diagnosis, and management of these disorders.

Personality traits and escitalopram treatment outcome in major depression.

BACKGROUND: Selective serotonin re-uptake inhibitors (SSRI) have proven to be effective in treatment of depression. Still, treatment efficacy varies significantly from patient to patient and about 40% of patients do not respond to initial treatment. Personality traits have been considered one source of variability in treatment outcome. AIM: Current study aimed at identifying specific personality traits that could be predictive of treatment response and/or the dynamics of symptom change in depressive patients. METHOD: In a sample of 132 outpatients with major depressive disorder (MDD) treated with an SSRI-group antidepressant escitalopram, the Swedish universities Scales of Personality (SSP) were used in order to find predictive personality traits. For the assessment of the severity of depressive symptoms and the improvement rates, the Hamilton Depression Scale (HAM-D) and Montgomery-Åsberg Depression Rating Scale (MADRS) were used. RESULTS: Escitalopram-treated MDD patients with higher social desirability achieved more rapid decrease in symptom severity. None of the studied traits predicted the end result of the treatment. CONCLUSION: The findings suggest that specific personality traits may predict the trajectory of symptom change rather than the overall improvement rate.

Biological Markers of Generalized Anxiety Disorder.

(Reprinted with permission from Dialogues in Clinical Neuroscience, 2017; 19:147-157).

Biological markers of generalized anxiety disorder.

Generalized anxiety disorder (GAD) is a prevalent and highly disabling mental health condition; however, there is still much to learn with regard to pertinent biomarkers, as well as diagnosis, made more difficult by the marked and common overlap of GAD with affective and anxiety disorders. Recently, intensive research efforts have focused on GAD, applying neuroimaging, genetic, and blood-based approaches toward discovery of pathogenetic and treatment-related biomarkers. In this paper, we review the large amount of available data, and we focus in particular on evidence from neuroimaging, genetic, and neurochemical measurements in GAD in order to better understand potential biomarkers involved in its etiology and treatment. Overall, the majority of these studies have produced results that are solitary findings, sometimes inconsistent and not clearly replicable. For these reasons, they have not yet been translated into clinical practice. Therefore, further research efforts are needed to distinguish GAD from other mental disorders and to provide new biological insights into its pathogenesis and treatment.

El trastorno de ansiedad generalizada (TAG) es una condición de salud mental prevalente y muy discapacitante; sin embargo, aún hay mucho que aprender en relación con biomarcadores específicos, como también respecto del diagnóstico, lo que se dificulta más por la sobreposición marcada y frecuente del TAG con los trastornos afectivos y de ansiedad. Recientemente, el TAG ha sido objeto de grandes esfuerzos de investigación, mediante la aplicación de neuroimágenes, estudios genéticos y exámenes sanguíneos enfocados en el descubrimiento de biomarcadores relacionados con la patogenética y el tratamiento. Este artículo revisa la gran cantidad de información disponible y se enfoca especialmente en la evidencia que proviene de los resultados de las neuroimágenes, la genética y las mediciones neuroquímicas en el TAG, con el objetivo de tener una mejor comprensión de los potenciales biomarcadores involucrados en su etiología y terapéutica. En general, la mayoría de estos estudios ha entregado resultados que constituyen hallazgos aislados, algunas veces inconsistentes y no claramente replicables. Por estas razones, estos resultados aún no se han traducido en la práctica clínica. Por consiguiente, se necesitan más esfuerzos de investigación para distinguir el TAG de otros trastornos mentales y contar con nuevos hallazgos biológicos para su patogenia y tratamiento.

L'anxiété généralisée (AG) est un trouble de santé mentale prévalent et très invalidant. Il reste cependant beaucoup à apprendre sur des biomarqueurs pertinents ainsi que sur le diagnostic, rendu plus difficile par le chevauchement important et courant de l'AG avec les troubles affectifs et anxieux. Récemment, l'AG a fait l'objet d'efforts intenses de recherche, appliquant la neuro-imagerie, la génétique et les analyses sanguines à la découverte de biomarqueurs pathogènes et liés au traitement. Dans cet article, nous analysons l'important volume de données disponibles et nous nous concentrons en particulier sur des données de neuro-imagerie, de génétique et des mesures neurochimiques dans l'AG, afin de mieux comprendre les biomarqueurs potentiels impliqués dans son étiologie et son traitement. Globalement, la majorité de ces études sort des résultats isolés, parfois contradictoires et non clairement reproductibles. C'est pourquoi ils n'ont toujours pas été trans-posés en pratique clinique. Il faut donc d'autres efforts de recherche pour différentier l'AG des autres troubles mentaux et permettre de nouvelles découvertes biologiques dans sa pathogenèse et son traitement.

Association between personality traits and Escitalopram treatment efficacy in panic disorder.

BACKGROUND: There is strong evidence to suggest that personality factors may interact with the development and clinical expression of panic disorder (PD). A greater understanding of these relationships may have important implications for clinical practice and implications for searching reliable predictors of treatment outcome. AIMS: The study aimed to examine the effect of escitalopram treatment on personality traits in PD patients, and to identify whether the treatment outcome could be predicted by any personality trait. METHOD: A study sample consisting of 110 outpatients with PD treated with 10-20 mg/day of escitalopram for 12 weeks. The personality traits were evaluated before and after 12 weeks of medication by using the Swedish universities Scales of Personality (SSP). RESULTS: Although almost all personality traits on the SSP measurement were improved after 12 weeks of medication in comparison with the baseline scores, none of these changes reached a statistically significant level. Only higher impulsivity at baseline SSP predicted non-remission to 12-weeks treatment with escitalopram; however, this association did not withstand the Bonferroni correction in multiple comparisons. LIMITATIONS: All patients were treated in a naturalistic way using an open-label drug, so placebo responses cannot be excluded. The sample size can still be considered not large enough to reveal statistically significant findings. CONCLUSIONS: Maladaptive personality disposition in patients with PD seems to have a trait character and shows little trend toward normalization after 12-weeks treatment with the antidepressant, while the association between impulsivity and treatment response needs further investigation.

The course of cognitive functioning after first-episode of psychosis: A six month follow-up study.

Our aim with the present study was to evaluate rank-order and mean-level cognitive functioning stability among first-episode psychosis (FEP) patients, measured using the Cambridge Neuropsychological Test Automated Battery (CANTAB), over a six month period. We also aimed to examine longitudinal measurement invariance and identify factors-such as age, gender, educational level, treatment and psychopathological change scores-potentially linked to cognitive change among patients. In addition, correlations between objectively measured and subjectively evaluated cognitive functioning were estimated. Neuropsychological assessments were administered to 85 patients after the initial stabilisation of their psychosis; 82 of the patients were retested. Subjectively perceived cognitive functioning was measured using a subscale derived from the Estonian version of the Subjective Well-Being Under Neuroleptic Scale (SWN-K-E). On average, executive functioning and processing speed improved significantly, while memory test scores decreased significantly, over time. Very high rank-order stability (r=0.80 to 0.94, p<0.001) was observed with all measured ability scores. Confirmatory factor analysis revealed the loadings of a single (broad ability) factor model were equal across both measurement occasions, but the lack of intercept invariance suggested that mean-level comparisons are more appropriately carried out at a subtest level. On average psychopathology scores and antipsychotics doses declined over time, with the latter also significantly correlating with better executive functioning. Gender was a significant moderator of some domains of cognitive performance, and decline tended to be somewhat more pronounced for women. The results also indicated the lack of any relationship between objective and subjective measurements of cognitive functioning.

Biological markers for anxiety disorders, OCD and PTSD: A consensus statement. Part II: Neurochemistry, neurophysiology and neurocognition.

OBJECTIVE: Biomarkers are defined as anatomical, biochemical or physiological traits that are specific to certain disorders or syndromes. The objective of this paper is to summarise the current knowledge of biomarkers for anxiety disorders, obsessive-compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). METHODS: Findings in biomarker research were reviewed by a task force of international experts in the field, consisting of members of the World Federation of Societies for Biological Psychiatry Task Force on Biological Markers and of the European College of Neuropsychopharmacology Anxiety Disorders Research Network. RESULTS: The present article (Part II) summarises findings on potential biomarkers in neurochemistry (neurotransmitters such as serotonin, norepinephrine, dopamine or GABA, neuropeptides such as cholecystokinin, neurokinins, atrial natriuretic peptide, or oxytocin, the HPA axis, neurotrophic factors such as NGF and BDNF, immunology and CO2 hypersensitivity), neurophysiology (EEG, heart rate variability) and neurocognition. The accompanying paper (Part I) focuses on neuroimaging and genetics. CONCLUSIONS: Although at present, none of the putative biomarkers is sufficient and specific as a diagnostic tool, an abundance of high quality research has accumulated that should improve our understanding of the neurobiological causes of anxiety disorders, OCD and PTSD.

Whole-genome expression analysis reveals genes associated with treatment response to escitalopram in major depression.

The reasons for variability in treatment response in major depressive disorder (MDD) are not fully understood, but there is accumulating evidence suggesting that therapeutic outcomes of antidepressants can be influenced by genetic factors. In the present study we applied the microarray Illumina platform for whole genome expression profiling in depressive patients treated with escitalopram medication in order to identify genes underlying response to antidepressant treatment. The initial study sample consisted of 135 outpatients with major depressive disorder (mean age 31.1±11.6 years, 68% females) treated with escitalopram 10-20mg/day for 12 weeks, from which 87 patients (55 females) were included in gene expression analyzing. The gene expression profiles were measured on peripheral blood cells at baseline, at week 4 and at the end of treatment (week 12) using BeadChips Illumina. The fold change was used to demonstrate rate of changes in average gene expressions between studied groups. Statistical analyses were performed using the false discovery rate (FDR). The most interesting gene, which showed the predictive effect on treatment outcome by delineating low dose responders and treatment-resistant patients at the beginning of medication, was NLGN2, belonging to a family of neuronal cell surface proteins and involving in synapse formation. In addition, the several gene clusters, related to immune response, signal transduction and neurotrophin pathway, have distinguished responders from non-responders at the week 4 of treatment. After 4 weeks of escitalopram treatment (10mg/day), the YWHAZ gene has showed the highest transcriptional change in responders as compared with non-responders. Finally, at the end of the treatment we noticed that at least three genes (NR2C2, ZNF641, FKBP1A) have been strongly associated with resistance to escitalopram. Thus the results of this study support that exploration of peripheral gene expression is a useful tool in the further identification of novel genetic biomarkers for antidepressant treatment response.

Biological markers for anxiety disorders, OCD and PTSD - a consensus statement. Part I: Neuroimaging and genetics.

OBJECTIVES: Biomarkers are defined as anatomical, biochemical or physiological traits that are specific to certain disorders or syndromes. The objective of this paper is to summarise the current knowledge of biomarkers for anxiety disorders, obsessive-compulsive disorder (OCD) and post-traumatic stress disorder (PTSD). METHODS: Findings in biomarker research were reviewed by a task force of international experts in the field, consisting of members of the World Federation of Societies for Biological Psychiatry Task Force on Biological Markers and of the European College of Neuropsychopharmacology Anxiety Disorders Research Network. RESULTS: The present article (Part I) summarises findings on potential biomarkers in neuroimaging studies, including structural brain morphology, functional magnetic resonance imaging and techniques for measuring metabolic changes, including positron emission tomography and others. Furthermore, this review reports on the clinical and molecular genetic findings of family, twin, linkage, association and genome-wide association studies. Part II of the review focuses on neurochemistry, neurophysiology and neurocognition. CONCLUSIONS: Although at present, none of the putative biomarkers is sufficient and specific as a diagnostic tool, an abundance of high-quality research has accumulated that will improve our understanding of the neurobiological causes of anxiety disorders, OCD and PTSD.

Effect of short-term escitalopram treatment on neural activation during emotional processing.

Recent functional magnetic resonance (fMRI) imaging studies have revealed that subchronic medication with escitalopram leads to significant reduction in both amygdala and medial frontal gyrus reactivity during processing of emotional faces, suggesting that escitalopram may have a distinguishable modulatory effect on neural activation as compared with other serotonin-selective antidepressants. In this fMRI study we aimed to explore whether short-term medication with escitalopram in healthy volunteers is associated with reduced neural response to emotional processing, and whether this effect is predicted by drug plasma concentration. The neural response to fearful and happy faces was measured before and on day 7 of treatment with escitalopram (10mg) in 15 healthy volunteers and compared with those in a control unmedicated group (n=14). Significantly reduced activation to fearful, but not to happy facial expressions was observed in the bilateral amygdala, cingulate and right medial frontal gyrus following escitalopram medication. This effect was not correlated with plasma drug concentration. In accordance with previous data, we showed that escitalopram exerts its rapid direct effect on emotional processing via attenuation of neural activation in pathways involving medial frontal gyrus and amygdala, an effect that seems to be distinguishable from that of other SSRIs.