Emotional Blunting in Depression in the PREDDICT Clinical Trial: Inflammation-Stratified Augmentation of Vortioxetine With Celecoxib.

BACKGROUND: Emotional symptoms are recognized as a key feature in individuals with major depressive disorder. Previously, emotional blunting has been described both as a side effect of antidepressant treatment and as a symptom of depression. Little is known about the change of emotional blunting during antidepressant treatment. METHODS: The PREDDICT trial is a randomized, placebo-controlled, 6-week trial on the augmentation of vortioxetine with the anti-inflammatory agent celecoxib or placebo. Presently we report on exploratory secondary outcomes of changes in emotional blunting in depression assessed with the Oxford Depression Questionnaire (ODQ) total score and subscores from baseline to 8-week, 3-month, and 6-month follow-up assessments. RESULTS: In the whole group, there was a significant improvement in the ODQ total score and all subscores after 8 weeks. After stratification of participants into the treatment groups, the ODQ total score as well as subscores related to emotional blunting as a symptom of depression (reduction in positive emotions, not caring) improved between baseline and all follow-up time points in both treatment groups. Changes in subscores considered as a side effect of antidepressants (general reduction in emotions, emotional detachment) were inconclusive in both treatment groups. Overall, the placebo-augmented group showed slightly better results in changes of emotional blunting scores than the celecoxib group as did those with elevated inflammation at screening, regardless of treatment group. CONCLUSIONS: This analysis suggests favorable effects of vortioxetine on emotional blunting in both short- and long-term course. The beneficial impact of vortioxetine on emotional blunting was weaker in celecoxib-augmented patients compared with placebo, possibly due to pharmacokinetic interactions. Clinical Trials Registration: Australian New Zealand Clinical Trials Registry (ANZCTR), ACTRN12617000527369p. Registered on 11 April 2017, http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617000527369p.

Inflammation-stratified augmentation of vortioxetine with celecoxib: Results from a double-blind, randomized, placebo-controlled trial in major depressive disorder.

Low-grade inflammation is considered as a pathophysiological mechanism in a subtype of patients with major depressive disorder (MDD). Anti-inflammatory drugs have shown efficacy in treating MDD. However, it remains unclear how to identify suitable patients for anti-inflammatory treatment of depression. This study investigates the predictive value of pre-treatment high-sensitivity C-Reactive Protein (hsCRP) stratification on the outcome of celecoxib augmentation of vortioxetine. The PREDDICT study was conducted as a randomized, double-blind, placebo-controlled 6-week trial on augmentation of vortioxetine with celecoxib between December 2017 and April 2020 at the University of Adelaide (Australia). The present analysis focusses on the question of whether the pre-treatment hsCRP measurement and stratification of patients to depression with inflammation (hsCRP >3 mg/L) or without inflammation (hsCRP ≤3 mg/L) has an impact on the outcome of anti-inflammatory treatment with celecoxib. A total of n = 119 mostly treatment-resistant MDD patients with moderate to severe symptomatology were recruited in the trial. There was no effect of treatment group (celecoxib or placebo), pre-treatment hsCRP strata (with/without inflammation), or interaction between the two terms on treatment outcome. The results of the current analysis do not support the hypothesis that pre-treatment hsCRP level is predictive for response to anti-inflammatory treatment with celecoxib in MDD patients. Further research is needed to identify appropriate biomarkers for the prediction of anti-inflammatory treatment outcome in depression. CLINICAL TRIALS REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR), ACTRN12617000527369p. Registered on 11 April 2017, http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617000527369p.

Genome-wide interaction study with major depression identifies novel variants associated with cognitive function.

Major Depressive Disorder (MDD) often is associated with significant cognitive dysfunction. We conducted a meta-analysis of genome-wide interaction of MDD and cognitive function using data from four large European cohorts in a total of 3510 MDD cases and 6057 controls. In addition, we conducted analyses using polygenic risk scores (PRS) based on data from the Psychiatric Genomics Consortium (PGC) on the traits of MDD, Bipolar disorder (BD), Schizophrenia (SCZ), and mood instability (MIN). Functional exploration contained gene expression analyses and Ingenuity Pathway Analysis (IPA®). We identified a set of significantly interacting single nucleotide polymorphisms (SNPs) between MDD and the genome-wide association study (GWAS) of cognitive domains of executive function, processing speed, and global cognition. Several of these SNPs are located in genes expressed in brain, with important roles such as neuronal development (REST), oligodendrocyte maturation (TNFRSF21), and myelination (ARFGEF1). IPA® identified a set of core genes from our dataset that mapped to a wide range of canonical pathways and biological functions (MPO, FOXO1, PDE3A, TSLP, NLRP9, ADAMTS5, ROBO1, REST). Furthermore, IPA® identified upstream regulator molecules and causal networks impacting on the expression of dataset genes, providing a genetic basis for further clinical exploration (vitamin D receptor, beta-estradiol, tadalafil). PRS of MIN and meta-PRS of MDD, MIN and SCZ were significantly associated with all cognitive domains. Our results suggest several genes involved in physiological processes for the development and maintenance of cognition in MDD, as well as potential novel therapeutic agents that could be explored in patients with MDD associated cognitive dysfunction.

A prospective registry analysis of psychosocial and metabolic health between women with and without metabolic syndrome after a complicated pregnancy.

PURPOSE: Pregnancy complications affect over one quarter of Australian pregnancies, and this group of mothers is vulnerable and more likely to experience adverse cardiometabolic health outcomes in the postpartum period. Metabolic syndrome is common in this population and may be associated with postpartum mental health issues. However, this relationship remains poorly understood. To compare the differences in psychosocial parameters and mental health outcomes between women with metabolic syndrome and women without metabolic syndrome 6 months after a complicated pregnancy. METHODS: This study is prospective registry analysis of women attending a postpartum healthy lifestyle clinic 6 months following a complicated pregnancy. Mental health measures included 9-item Patient Health Questionnaire (PHQ-9), 7-item Generalised Anxiety Disorder questionnaire (GAD-7), self-reported diagnosed history of depression, anxiety and/or other psychiatric condition, and current psychotropic medication use. RESULTS: Women with metabolic syndrome reported significantly more subjective mental health concerns, were more likely to have a history of depression and other psychiatric diagnoses and were more likely prescribed psychotropic medications. However, there were no significant differences in PHQ-9 and GAD-7 scores. CONCLUSION: Amongst new mothers who experienced complications of pregnancy, those with metabolic syndrome represent a particularly vulnerable group with regards to psychosocial disadvantage and mental health outcomes. These vulnerabilities may not be apparent when using common standardised cross-sectional mental health screening tools such as PHQ-9 and GAD-7.

Demonstration of automated non-adherence and service disengagement risk monitoring with active follow-up for severe mental illness.

AIMS: Medication cessation and service disengagement often precedes relapse in people with severe mental illnesses but currently specialist mental health services only become involved after a relapse. Early detection of non-adherence is needed to enable intervention to avert relapse. This paper aims to demonstrate how digitally automated non-adherence risk monitoring from Medicare data with active follow-up can work and perform in practice in a real-world mental health service setting. METHODS: AI2 software is an automated risk monitoring tool to detect non-adherence using Medicare data. It was implemented prospectively in a cohort of 354 registered patients of a community mental health clinic between July 2019 and February 2020. Patients flagged as at risk by the software were reviewed by two clinicians. We describe the risks automatically flagged for non-adherence and the clinical responses. We examine differences in clinical and demographic factors in patients flagged at increased risk of non-adherence. RESULTS: In total, 46.7% (142/304) were flagged by the software as at risk of non-adherence, and 22% (31/142) received an intervention following clinician review of their case notes. Patients flagged by the software were older in age and had more prior mental health treatment episodes. More alerts were associated with patients who had been transferred from the mental health service to the care of their general practitioners, and those with more alerts were more likely to receive a follow-up intervention. CONCLUSION: Digitally automated monitoring for non-adherence risk is feasible and can be integrated into clinical workflows in community psychiatric and primary care settings. The technology may assist clinicians and services to detect non-adherence behaviour early, thereby triggering interventions that have the potential to reduce rates of mental health deterioration and acute illness relapse.

Exploratory study of association between blood immune markers and cognitive symptom severity in major depressive disorder: Stratification by body mass index status.

BACKGROUND: A subset of patients with Major Depressive Disorder (MDD) have shown differences relative to healthy controls in blood inflammatory and immune markers. Meanwhile, MDD and comorbid obesity appear to present with distinct biological and symptom characteristics, categorised as "atypical" or "immunometabolic" depression, although the relevant underlying biological mechanisms are still uncertain. Therefore, this exploratory study aimed to better characterise the relationship between peripheral blood immune markers and symptoms of MDD, as well as the extent to which body mass index (BMI) may alter this relationship. METHODS: Linear regression analyses were performed between selected baseline characteristics including clinical scales and blood inflammatory markers in participants with MDD (n = 119) enrolled in the PREDDICT randomised controlled trial (RCT), using age, sex and BMI as covariates, and then stratified by BMI status. Specifically, the Montgomery-Åsberg Depression Rating Scale (MADRS) for symptom severity, Clinical Global Impression scale (CGI) for functional impairment, Oxford Depression Questionnaire (ODQ) for emotional blunting, and THINC integrated tool (THINC-it) for cognitive function were considered as clinical measures. RESULTS: There was a significant association between basophil count and THINC-it Codebreaker mean response time (associated with complex attention, perceptual motor, executive function, and learning and memory abilities) in overweight individuals and with THINC-it Trails total response time (associated with executive function ability) in moderately obese individuals, when controlling for age, sex, and years of education. No correlation was found between any tested blood markers and MADRS, CGI or ODQ clinical measures, regardless of BMI. DISCUSSION: Although the present study is exploratory, the results suggest that targeting of the immune system and of metabolic parameters might confer benefits, specifically in patients with high BMI and experiencing cognitive impairment associated with MDD. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR), ACTRN12617000527369p. Registered on 11 April 2017.

Impact of a history of maternal depression and anxiety on asthma control during pregnancy.

OBJECTIVE: To determine the impact of self-reported maternal depression/anxiety on asthma control during pregnancy. METHOD: Pregnant women with a doctor diagnosis of asthma (n = 189) were prospectively recruited at their antenatal booking visit, and the presence of maternal depression and anxiety was identified using self-report and routine questionnaire assessments. Data on exacerbations and asthma control were collected during gestation. Asthma control was assessed using the Juniper Asthma Control Questionnaire (ACQ) and women were classified as having recurrent uncontrolled asthma if their ACQ score was >1.5 during two or more consecutive study visits. Exacerbations were defined as events that led to increased treatment requirements, and doctor or hospital visits. RESULTS: There were 85 women with self-reported depression/anxiety and 104 women without self-reported depression/anxiety. The presence of depression/anxiety was associated with an increased likelihood (adjusted hazard ratio (HR) 1.67: 95% confidence interval (CI) 1.03-2.72) and incidence (adjusted incidence rate ratio (IRR) 1.71: 95% CI 1.13-2.58) of uncontrolled asthma during pregnancy, as well as an increased risk of recurrent uncontrolled asthma during 2 or more study visits (adjusted relative risk (RR) 1.98: 95% CI 1.00-3.91). No impact of depression/anxiety was observed with respect to the likelihood (adjusted HR 0.70: 95% CI 0.35-1.41) or incidence of exacerbations during pregnancy (adjusted IRR 0.66: 95% CI 0.35-1.26). CONCLUSIONS: This study provides evidence that the presence of maternal depression/anxiety is associated with an increased likelihood and incidence of uncontrolled asthma during pregnancy. Given the high prevalence of co-morbid depression/anxiety among asthmatics, further research investigating such associations is urgently required.

The use of clinical and biological characteristics to predict outcome following First Episode Psychosis.

OBJECTIVE: Psychotic illnesses such as schizophrenia and other non-affective psychoses are heterogeneous in disease course and functional outcomes. We review evidence from investigations in clinical psychiatry, neuroimaging, neurocognition, and blood biomarker research suggesting that distinct bio-psycho-social patterns exist at the onset and during the early phase of a First Episode Psychosis (FEP), which can describe the risk of individual illness progression and functional trajectories. METHOD: A selective literature review was performed on articles drawn from Medline searches for relevant key words. A simulation model was constructed from data derived from two recent publications, selected as examples of studies that investigated multivariate predictors of long-term outcome following FEP. RESULTS: We illustrate how illness trajectories following FEP could be described based on multimodal sociodemographic, clinical, psychological, and neurobiological information. A clinical modeling simulation shows thatrisk trajectories for achieving long-term favorable or unfavorable outcomes can differ significantly depending on baseline characteristics in combination with MRI and functional measurements within 6 months of disease onset. CONCLUSIONS: Multimodal trajectory modeling may be useful to describe longitudinal outcomes following FEP. Richlongitudinal data on predictors and outcomes, and better integration of multimodal (sociodemographic, clinical, psychological, biological) data, are required to operationalize this approach. This technique may improve our understanding of course of illness and help to provide a more personalized approach to the assessment and treatment of people presenting with FEP.

Pharmacogenomic scores in psychiatry: systematic review of current evidence.

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

A Tale of Three Spectra: Basic Symptoms in Clinical-High-Risk of Psychosis Vary Across Autism Spectrum Disorder, Schizotypal Personality Disorder, and Borderline Personality Disorder.

Background and Hypothesis: The clinical-high-risk (CHR) approach was developed to prevent psychosis through the detection of psychosis risk. CHR services are transdiagnostic in nature, therefore the appropriate management of comorbidity is a central part of care. Differential diagnosis is particularly challenging across 3 common comorbidities, schizotypal personality disorder (SPD), autism spectrum disorder (ASD), and borderline personality disorder (BPD). Phenomenological research indicates a disturbance of "basic self" may differentiate between these commonly comorbid disorders and can be captured by Huber's basic symptoms (BS) concept. We investigated whether BS vary across these disorders and may inform differential diagnosis in young person's meeting CHR criteria. Study Design: A total of 685 participants meeting CHR criteria from the NAPLS-3 cohort completed the COGDIS items of the schizophrenia proneness instrument, a measure of BS, as well as the structured interview for DSM-5 (SCID-5). A logistic regression model was used to investigate the variation of COGDIS across SPD, ASD, and BPD, while controlling for age and SIPs positive severity. Study Results: Meeting COGDIS criteria was positively associated with SPD (OR = 1.72, CI = [1.31-2.28], P = .001) but not ASD nor BPD. Conclusions: Our results indicate that "basic self-disturbance" as indicated by COGDIS differs across SPD, ASD, and BPD. COGDIS may be useful to inform the management of comorbidities in CHR services, by providing insight into subtle subjective experiences that may benefit from disorder-specific interventions.

Validating cognitive screening in young people with first-episode psychosis: The CogScreen protocol.

AIM: Cognitive impairments are a core feature of first-episode psychosis (FEP) and one of the strongest predictors of long-term psychosocial functioning. Cognition should be assessed and treated as part of routine clinical care for FEP. Cognitive screening offers the opportunity to rapidly identify and triage those in most need of cognitive support. However, there are currently no validated screening measures for young people with FEP. CogScreen is a hybrid effectiveness-implementation study which aims to evaluate the classification accuracy (relative to a neuropsychological assessment as a reference standard), test-retest reliability and acceptability of two cognitive screening tools in young people with FEP. METHODS: Participants will be 350 young people (aged 12-25) attending primary and specialist FEP treatment centres in three large metropolitan cities (Adelaide, Sydney, and Melbourne) in Australia. All participants will complete a cross-sectional assessment over two sessions including two cognitive screening tools (Screen for Cognitive Impairment in Psychiatry and Montreal Cognitive Assessment), a comprehensive neuropsychological assessment battery, psychiatric and neurodevelopmental assessments, and other supplementary clinical measures. To determine the test-retest reliability of the cognitive screening tools, a subset of 120 participants will repeat the screening measures two weeks later. RESULTS: The protocol, rationale, and hypotheses for CogScreen are presented. CONCLUSIONS: CogScreen will provide empirical evidence for the validity and reliability of two cognitive screening tools when compared to a comprehensive neuropsychological assessment. The screening measures may later be incorporated into clinical practice to assist with rapid identification and treatment of cognitive deficits commonly experienced by young people with FEP.

Towards a Neurophenomenological Understanding of Self-Disorder in Schizophrenia Spectrum Disorders: A Systematic Review and Synthesis of Anatomical, Physiological, and Neurocognitive Findings.

The concept of anomalous self-experience, also termed Self-Disorder, has attracted both clinical and research interest, as empirical studies suggest such experiences specifically aggregate in and are a core feature of schizophrenia spectrum disorders. A comprehensive neurophenomenological understanding of Self-Disorder may improve diagnostic and therapeutic practice. This systematic review aims to evaluate anatomical, physiological, and neurocognitive correlates of Self-Disorder (SD), considered a core feature of Schizophrenia Spectrum Disorders (SSDs), towards developing a neurophenomenological understanding. A search of the PubMed database retrieved 285 articles, which were evaluated for inclusion using PRISMA guidelines. Non-experimental studies, studies with no validated measure of Self-Disorder, or those with no physiological variable were excluded. In total, 21 articles were included in the review. Findings may be interpreted in the context of triple-network theory and support a core dysfunction of signal integration within two anatomical components of the Salience Network (SN), the anterior insula and dorsal anterior cingulate cortex, which may mediate connectivity across both the Default Mode Network (DMN) and Fronto-Parietal Network (FPN). We propose a theoretical Triple-Network Model of Self-Disorder characterized by increased connectivity between the Salience Network (SN) and the DMN, increased connectivity between the SN and FPN, decreased connectivity between the DMN and FPN, and increased connectivity within both the DMN and FPN. We go on to describe translational opportunities for clinical practice and provide suggestions for future research.

General intelligence and executive functioning are overlapping but separable at genetic and molecular pathway levels: An analytical review of existing GWAS findings.

Understanding the genomic architecture and molecular mechanisms of cognitive functioning in healthy individuals is critical for developing tailored interventions to enhance cognitive functioning, as well as for identifying targets for treating impaired cognition. There has been substantial progress in uncovering the genetic composition of the general cognitive ability (g). However, there is an ongoing debate whether executive functioning (EF)-another key predictor of cognitive health and performance, is separable from general g. To provide an analytical review on existing findings on genetic influences on the relationship between g and EF, we re-analysed a subset of genome-wide association studies (GWAS) from the GWAS catalogue that used measures of g and EF as outcomes in non-clinical populations. We identified two sets of single nucleotide polymorphisms (SNPs) associated with g (1,372 SNPs across 12 studies), and EF (300 SNPs across 5 studies) at p<5x10-6. A comparative analysis of GWAS-identified g and EF SNPs in high linkage disequilibrium (LD), followed by pathway enrichment analyses suggest that g and EF are overlapping but separable at genetic variant and molecular pathway levels, however more evidence is required to characterize the genetic overlap/distinction between the two constructs. While not without limitations, these findings may have implications for navigating further research towards translatable genetic findings for cognitive remediation, enhancement, and augmentation.

No evidence for clinical efficacy of adjunctive celecoxib with vortioxetine in the treatment of depression: A 6-week double-blind placebo controlled randomized trial.

Given the role of low-grade inflammation in the pathophysiology of major depressive disorder (MDD), anti-inflammatory strategies may improve treatment outcomes in some patients. However, it is controversial whether they can be used as adjunctive treatments and whether pre-treatment levels of inflammation can predict treatment outcomes. This study was conducted to measure the efficacy of anti-inflammatory augmentation of antidepressant treatment in MDD patients; and to investigate whether treatment response was dependent on baseline inflammation levels. This parallel-group randomised, double-blind, placebo-controlled trial was conducted at the University of Adelaide (Australia). Participants with MDD were randomised to receive vortioxetine with celecoxib or vortioxetine with placebo for six weeks, and baseline blood high sensitivity C reactive protein levels were measured. Primary outcome was change in depressive symptoms (Montgomery-Åsberg Depression Rating Scale) and secondary outcomes included change in cognition (THINC-integrated tool - Codebreaker task) and functioning (Functioning Assessment Short Test) over 6 weeks. There was no evidence of superior efficacy of celecoxib augmentation over placebo on depressive symptom severity, response and remission rates, cognition and psychosocial functioning. There was also no evidence that pre-treatment inflammation levels modified the effect of celecoxib augmentation versus placebo. This observed lack of efficacy of celecoxib add-on does not support the use of celecoxib augmentation of antidepressants in the treatment of MDD in a cohort that mostly comprises treatment-resistant individuals. Additionally, C-reactive protein may not be suitable to predict treatment selection and response in MDD. The study was registered on the Australian New Zealand Clinical Trials Registry: ACTRN12617000527369 (www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617000527369p).

Autoantibody profiles associated with clinical features in psychotic disorders.

Autoimmune processes are suspected to play a role in the pathophysiology of psychotic disorders. Better understanding of the associations between auto-immunoglobulin G (IgG) repertoires and clinical features of mental illness could yield novel models of the pathophysiology of psychosis, and markers for biological patient stratification. We undertook cross-sectional detection and quantification of auto-IgGs in peripheral blood plasma of 461 people (39% females) with established psychotic disorder diagnoses. Broad screening of 24 individuals was carried out on group level in eight clinically defined groups using planar protein microarrays containing 42,100 human antigens representing 18,914 proteins. Autoantibodies indicated by broad screening and in the previous literature were measured using a 380-plex bead-based array for autoantibody profiling of all 461 individuals. Associations between autoantibody profiles and dichotomized clinical characteristics were assessed using a stepwise selection procedure. Broad screening and follow-up targeted analyses revealed highly individual autoantibody profiles. Females, and people with family histories of obesity or of psychiatric disorders other than schizophrenia had the highest overall autoantibody counts. People who had experienced subjective thought disorder and/or were treated with clozapine (trend) had the lowest overall counts. Furthermore, six autoantibodies were associated with specific psychopathology symptoms: anti-AP3B2 (persecutory delusions), anti-TDO2 (hallucinations), anti-CRYGN (initial insomnia); anti-APMAP (poor appetite), anti-OLFM1 (above-median cognitive function), and anti-WHAMMP3 (anhedonia and dysphoria). Future studies should clarify whether there are causal biological relationships, and whether autoantibodies could be used as clinical markers to inform diagnostic patient stratification and choice of treatment.

Transcriptome-based polygenic score links depression-related corticolimbic gene expression changes to sex-specific brain morphology and depression risk.

Studies in post-mortem human brain tissue have associated major depressive disorder (MDD) with cortical transcriptomic changes, whose potential in vivo impact remains unexplored. To address this translational gap, we recently developed a transcriptome-based polygenic risk score (T-PRS) based on common functional variants capturing 'depression-like' shifts in cortical gene expression. Here, we used a non-clinical sample of young adults (n = 482, Duke Neurogenetics Study: 53% women; aged 19.8 ± 1.2 years) to map T-PRS onto brain morphology measures, including Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index, as well as broad MDD risk, indexed by self-reported family history of depression. We conducted side-by-side comparisons with a PRS independently derived from a Psychiatric Genomics Consortium (PGC) MDD GWAS (PGC-PRS), and sought to link T-PRS with diagnosis and symptom severity directly in PGC-MDD participants (n = 29,340, 59% women; 12,923 MDD cases, 16,417 controls). T-PRS was associated with smaller amygdala volume in women (t = -3.478, p = 0.001) and lower prefrontal gyrification across sexes. In men, T-PRS was associated with hypergyrification in temporal and occipital regions. Prefrontal hypogyrification mediated a male-specific indirect link between T-PRS and familial depression (b = 0.005, p = 0.029). PGC-PRS was similarly associated with lower amygdala volume and cortical gyrification; however, both effects were male-specific and hypogyrification emerged in distinct parietal and temporo-occipital regions, unassociated with familial depression. In PGC-MDD, T-PRS did not predict diagnosis (OR = 1.007, 95% CI = [0.997-1.018]) but correlated with symptom severity in men (rho = 0.175, p = 7.957 × 10-4) in one cohort (N = 762, 48% men). Depression-like shifts in cortical gene expression have sex-specific effects on brain morphology and may contribute to broad depression vulnerability in men.

Downregulated transferrin receptor in the blood predicts recurrent MDD in the elderly cohort: A fuzzy forests approach.

BACKGROUND: At present, no predictive markers for Major Depressive Disorder (MDD) exist. The search for such markers has been challenging due to clinical and molecular heterogeneity of MDD, the lack of statistical power in studies and suboptimal statistical tools applied to multidimensional data. Machine learning is a powerful approach to mitigate some of these limitations. METHODS: We aimed to identify the predictive markers of recurrent MDD in the elderly using peripheral whole blood from the Sydney Memory and Aging Study (SMAS) (N = 521, aged over 65) and adopting machine learning methodology on transcriptome data. Fuzzy Forests is a Random Forests-based classification algorithm that takes advantage of the co-expression network structure between genes; it allows to alleviate the problem of p >> n via reducing the dimensionality of transcriptomic feature space. RESULTS: By adopting Fuzzy Forests on transcriptome data, we found that the downregulated TFRC (transferrin receptor) can predict recurrent MDD with an accuracy of 63%. LIMITATIONS: Although we corrected our data for several important confounders, we were not able to account for the comorbidities and medication taken, which may be numerous in the elderly and might have affected the levels of gene transcription. CONCLUSIONS: We found that downregulated TFRC is predictive of recurrent MDD, which is consistent with the previous literature, indicating the role of the innate immune system in depression. This study is the first to successfully apply Fuzzy Forests methodology on psychiatric condition, opening, therefore, a methodological avenue that can lead to clinically useful predictive markers of complex traits.

Cognitive and Functional Assessment of Psychosis Stratification Study (CoFAPSS): Rationale, Design, and Characteristics.

Prediction of treatment response and illness trajectory in psychotic disorders including schizophrenia, bipolar affective disorder, schizoaffective disorder, and psychotic depression is difficult due to heterogeneity in presentation and outcome. Consequently, patients may receive prolonged ineffective treatments leading to functional decline, illness chronicity, and iatrogenic physical illness. One approach to addressing these problems is to stratify patients based on historical, clinical, and biological signatures. Such an approach has the potential to improve categorization resulting in better understanding of underlying mechanisms and earlier evidence-based treatment with reduced side effect burden. To investigate these multimodal signatures we developed the Cognitive and Functional Assessment of Psychosis Stratification Study (CoFAPSS) employing a prospective study design and a healthy control group comparison. The main aim of this study is to investigate cognitive, and biological "genomics" markers of psychotic illnesses that can be integrated with clinical data to improve prediction of risk and define functional trajectories. We also aim to identify biological "genomic" signatures underpinning variation in treatment response and adverse medical outcomes. The study commenced in June 2016, including patients with primary diagnosis of psychotic disorders including schizophrenia, bipolar affective disorder, schizoaffective disorder, and psychotic depression according to DSM-5 criteria. The assessment covers a wide range of participant history (life stressors, trauma, and family history), cognitive dimensions (social perception, memory and learning, attention, executive function, and general cognition), measures to assess psychosocial function and quality of life, psychotic symptom severity, clinical course of illness, and parameters for adverse medical outcome. Blood is collected for comprehensive genomic discovery analyses of biological (genomic, transcriptomic, proteomic, and cell-biologic) markers. The CoFAPSS is a novel approach that integrates clinical, cognitive and biological "genomic" markers to clarify clinico-pathological basis of risk, functional trajectories, disease stratification, treatment response, and adverse medical outcome. The CoFAPSS team welcomes collaborations with both national and international investigators.

Targeted proteomic analysis of cognitive dysfunction in remitted major depressive disorder: Opportunities of multi-omics approaches towards predictive, preventive, and personalized psychiatry.

In order to accelerate the understanding of pathophysiological mechanisms and clinical biomarker discovery and in psychiatry, approaches that integrate multiple -omics platforms are needed. We introduce a workflow that investigates a narrowly defined psychiatric phenotype, makes use of the potent and cost-effective discovery technology of gene expression microarrays, applies Weighted Gene Co-Expression Network Analysis (WGCNA) to better capture complex and polygenic traits, and finally explores gene expression findings on the proteomic level using targeted mass-spectrometry (MS) technologies. To illustrate the effectiveness of the workflow, we present a proteomic analysis of peripheral blood plasma from patient's remitted major depressive disorder (MDD) who experience ongoing cognitive deficits. We show that co-expression patterns previous detected on the transcript level could be replicated for plasma proteins, as could the module eigengene correlation with cognitive performance. Further, we demonstrate that functional analysis of multi-omics data has the potential to point to cellular mechanisms and candidate biomarkers for cognitive dysfunction in MDD, implicating cell cycle regulation by cyclin D3 (CCND3), regulation of protein processing in the endoplasmatic reticulum by Thioredoxin domain-containing protein 5 (TXND5), and modulation of inflammatory cytokines by Tripartite Motif Containing 26 (TRI26). SIGNIFICANCE: This paper discusses how data from multiple -omics platforms can be integrated to accelerate biomarker discovery in psychiatry. Using the phenotype of cognitive impairment in remitted major depressive disorder (MDD) as an example, we show that the application of a systems biology approach - weighted gene co-expression network analysis (WGCNA) - in the discovery phase, and targeted proteomic follow-up of results, provides a structured avenue towards uncovering novel candidate markers and pathways for personalized clinical psychiatry.

A gene co-expression module implicating the mitochondrial electron transport chain is associated with long-term response to lithium treatment in bipolar affective disorder.

Lithium is the first-line treatment for bipolar affective disorder (BPAD) but two-thirds of patients respond only partially or not at all. The reasons for this high variability in lithium response are not well understood. Transcriptome-wide profiling, which tests the interface between genes and the environment, represents a viable means of exploring the molecular mechanisms underlying lithium response variability. Thus, in the present study we performed co-expression network analyses of whole-blood-derived RNA-seq data from n = 50 lithium-treated BPAD patients. Lithium response was assessed using the well-validated ALDA scale, which we used to define both a continuous and a dichotomous measure. We identified a nominally significant correlation between a co-expression module comprising 46 genes and lithium response represented as a continuous (i.e., scale ranging 0-10) phenotype (cor = -0.299, p = 0.035). Forty-three of these 46 genes had reduced mRNA expression levels in better lithium responders relative to poorer responders, and the central regulators of this module were all mitochondrially-encoded (MT-ND1, MT-ATP6, MT-CYB). Accordingly, enrichment analyses indicated that genes involved in mitochondrial functioning were heavily over-represented in this module, specifically highlighting the electron transport chain (ETC) and oxidative phosphorylation (OXPHOS) as affected processes. Disrupted ETC and OXPHOS activity have previously been implicated in the pathophysiology of BPAD. Our data adds to previous evidence suggesting that a normalisation of these processes could be central to lithium's mode of action, and could underlie a favourable therapeutic response.