Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P < 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10-12, R2 = 1.9%) and continuous (P = 6.4 × 10-9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22-5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10-4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.

Testing equivalence of two doses of intravenous iron to treat iron deficiency in pregnancy: A randomised controlled trial.

OBJECTIVE: To test the equivalence of two doses of intravenous iron (ferric carboxymaltose) in pregnancy. DESIGN: Parallel, two-arm equivalence randomised controlled trial with an equivalence margin of 5%. SETTING: Single centre in Australia. POPULATION: 278 pregnant women with iron deficiency. METHODS: Participants received either 500 mg (n = 152) or 1000 mg (n = 126) of intravenous ferric carboxymaltose in the second or third trimester. MAIN OUTCOME MEASURES: The proportion of participants requiring additional intravenous iron (500 mg) to achieve and maintain ferritin >30 microg/L (diagnostic threshold for iron deficiency) at 4 weeks post-infusion, and at 6 weeks, and 3-, 6- and 12-months postpartum. Secondary endpoints included repeat infusion rate, iron status, birth and safety outcomes. RESULTS: The two doses were not equivalent within a 5% margin at any time point. At 4 weeks post infusion, 26/73 (36%) participants required a repeat infusion in the 500-mg group compared with 5/67 (8%) in the 1000-mg group: difference in proportions, 0.283 (95% confidence interval [CI] 0.177-0.389). Overall, participants in the 500-mg arm received twice the repeat infusion rate (0.81 [SD = 0.824] versus 0.40 [SD = 0.69], rate ratio 2.05, 95% CI 1.45-2.91). CONCLUSIONS: Administration of 1000 mg ferric carboxymaltose in pregnancy maintains iron stores and reduces the need for repeat infusions. A 500- mg dose requires ongoing monitoring to ensure adequate iron stores are reached and sustained.

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach.

BACKGROUND: Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. AIMS: To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. METHOD: This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. RESULTS: The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. CONCLUSIONS: Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Association of polygenic score for major depression with response to lithium in patients with bipolar disorder.

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.

Supporting the vulnerable: developing a strategic community mental health response to the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic poses significant risks to the vulnerable patient population supported by community mental health (CMH) teams in South Australia. This paper describes a plan developed to understand and mitigate these risks. METHODS: Public health and psychiatric literature was reviewed and clinicians in CMH teams and infectious disease were consulted. Key risks posed by COVID-19 to CMH patients were identified and mitigation plans were prepared. RESULTS: A public health response plan for CMH teams was developed to support vulnerable individuals and respond to the COVID-19 pandemic. This plan will be reviewed regularly to respond to changes in public health recommendations, research findings and feedback from patients and clinicians. CONCLUSIONS: The strategic response plan developed to address risks to vulnerable patients from COVID-19 can assist other CMH services in managing the COVID-19 pandemic.

The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression.

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

Depressive symptom trajectories in late adolescence and early adulthood: A systematic review.

OBJECTIVE: In adolescents and young adults, depressive symptoms are highly prevalent and dynamic. For clinicians, it is difficult to determine whether a young person reporting depressive symptoms is at risk of developing ongoing mood difficulties or whether symptoms form part of a transient maturational process. Trajectory analyses of longitudinally assessed symptoms in large cohorts have the potential to untangle clinical heterogeneity by determining subgroups or classes of symptom course and their risk factors, by interrogating the impact of known or suspected risk factors on trajectory slope and intercept and by tracing the interrelation between depressive symptoms and other clinical outcomes over time. METHOD: We conducted a systematic review of trajectory studies conducted in cohorts including people aged between 15 and 25 years. RESULTS: We retrieved 47 relevant articles. These studies suggest that young people fall into common mood trajectory classes and that class membership and symptom course are mediated by biological and environmental risk factors. Furthermore, studies provide evidence that high and persistent depressive symptoms are associated with a range of concurrent health and behavioral outcomes. CONCLUSION: Findings could assist in the formulation of novel concepts of depressive disorders in young people and inform preventive strategies and predictive models for clinical practice.

The use of MALDI-MSI in the investigation of psychiatric and neurodegenerative disorders: A review.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a mass spectrometry technique used for the analysis of macromolecules on an intact tissue of interest, thereby allowing the assessment of molecular signatures in health and disease in the anatomical context. MALDI-MSI is increasingly used to investigate neurodegenerative and psychiatric disorders at the molecular level, including Alzheimer's disease (AD), Parkinson's disease (PD), and schizophrenia (SCZ). These illnesses are characterized by complex neuropathological processes, and conventional proteomic techniques investigating brain tissue homogenates have inherent limitations in determining the precise anatomical or cellular location of proteomic findings. In this article, we review MALDI-MSI studies on neurodegenerative and psychiatric disorders, and explore whether the technique could accelerate the translation of proteomic information into improved understanding and ultimately better therapeutic applications.

Proteome and pathway effects of chronic haloperidol treatment in mouse hippocampus.

Proteomic exploration of the effects of psychotropic drugs on specific brain areas in rodents has the potential to uncover novel molecular networks and pathways affected by psychotropic medications, and may inform etiologic hypotheses on mental disorders. Haloperidol, a widely used first-generation antipsychotic, has been shown to produce structural and functional changes of the hippocampus, a brain region also implicated in the neuropathology of disorders such as schizophrenia and bipolar disorder. Seven adult male C57BL/6 mice were injected daily intraperitoneally with 0.5 mg/kg of haloperidol, for 28 days. A control group of six animals was injected with vehicle only (saline). Protein levels of postmortem hippocampus homogenate were determined using label-free LC/MS/MS. In the treatment group, 216 differentially expressed hippocampal proteins were identified as compared to controls. Ingenuity pathway analysis implicated oxidative phosphorylation and mitochondrial function as top canonical pathways, and local networks involved in tubulin-mediated cytoskeleton dynamics, clathrin-mediated endocytosis, and extracellular signal-regulated kinase and c-Jun N-terminal kinase signaling. The findings of this study could stimulate further research into the cellular mechanisms associated with haloperidol treatment and the pathophysiology of psychotic disorders, assisting treatment biomarker discovery. All MS data have been deposited in the ProteomeXchange with identifier PXD002250 (http://proteomecentral.proteomexchange.org/dataset/PXD002250).

Towards indicated prevention of psychosis: using probabilistic assessments of transition risk in psychosis prodrome.

The concept of indicated prevention has proliferated in psychiatry, and accumulating evidence suggests that it may indeed be possible to prevent or delay the onset of a first episode of psychosis though adequate interventions in individuals deemed at clinical high risk (CHR) for such an event. One challenge undermining these efforts is the relatively poor predictive accuracy of clinical assessments used in practice for CHR individuals, often leading to diagnostic and therapeutic uncertainty reflected in clinical guidelines promoting a 'watch and wait' approach to CHR patients. Using data from published studies, and employing predictive models based on the odds-ratio form of Bayes' rule, we simulated scenarios where clinical interview, neurocognitive testing, structural magnetic resonance imaging and electrophysiology are part of the initial assessment process of a CHR individual (extended diagnostic approach). Our findings indicate that for most at-risk patients, at least three of these assessments are necessary to arrive at a clinically meaningful differentiation into high- intermediate-, and low-risk groups. In particular, patients with equivocal results in the initial assessments require additional diagnostic testing to produce an accurate risk profile forming part of the comprehensive initial assessment. The findings may inform future research into reliable identification and personalized therapeutic targeting of CHR patients, to prevent transition to full-blown psychosis.

Long-term recreational exercise patterns in adolescents and young adults: Trajectory predictors and associations with health, mental-health, and educational outcomes.

Individual and societal factors influencing the formation of long-term recreational exercise habits during the transition from adolescence to young adulthood are not well explored. Using data from the Longitudinal Survey of Australian Youth (LSAY), a population-representative cohort study of Young People followed from age 15 to 25, we aimed to (1) model longitudinal recreational exercise trajectories from age 16 to 24, (2) examine predictors at age 15 of entering these trajectories, and (3) explore the association between the trajectories and health, mental health and educational achievement outcomes measured at the final study wave (age 25). Self-reported recreational exercise frequency data from 9353 LSAY participants were analysed using group-based trajectory modelling. We modelled the evolution of two patterns of recreational exercise behaviour: daily exercise, as per public health guidelines (Model 1); and at least once weekly exercise (Model 2). Model 1 trajectories were guideline-adherent exercisers (17.9% of the sample), never guideline exercisers (27.5%), guideline drop-outs (15.2%) and towards guideline (39.4%); Model 2 trajectories were weekly exercise (69.5% of the sample), decreasing (17.4%), increasing (4.8%), and infrequent (8.3%). For both models, at age 15, trajectory membership was predicted by gender, self-efficacy, time spent participating in sport, time spent watching TV, parental socioeconomic status, and academic literacy. At age 25, people in the guideline-adherent exerciser trajectory (model 1) reported better general health relative to other trajectories, Those in the weekly exerciser trajectory (model 2) had better general health and reduced rates of psychological distress, were happier with life and were more optimistic for the future relative to participants in less than weekly trajectory groups. Exercise-promoting interventions for Young People should specifically address the needs of females, people with low self-efficacy, reluctant exercisers, higher academic achievers, and those experiencing socioeconomic disadvantage.

Evolving Adult ADHD Care: Preparatory Evaluation of a Prototype Digital Service Model Innovation for ADHD Care.

BACKGROUND: Given the prevalence of ADHD and the gaps in ADHD care in Australia, this study investigates the critical barriers and driving forces for innovation. It does so by conducting a preparatory evaluation of an ADHD prototype digital service innovation designed to help streamline ADHD care and empower individual self-management. METHODS: Semi-structured interviews with ADHD care consumers/participants and practitioners explored their experiences and provided feedback on a mobile self-monitoring app and related service innovations. Interview transcripts were double coded to explore thematic barriers and the enablers for better ADHD care. RESULTS: Fifteen interviews (9 consumers, 6 practitioners) revealed barriers to better ADHD care for consumers (ignorance and prejudice, trust, impatience) and for practitioners (complexity, sustainability). Enablers for consumers included validation/empowerment, privacy, and security frameworks, tailoring, and access. Practitioners highlighted the value of transparency, privacy and security frameworks, streamlined content, connected care between services, and the tailoring of broader metrics. CONCLUSIONS: A consumer-centred approach to digital health service innovation, featuring streamlined, private, and secure solutions with enhanced mobile tools proves instrumental in bridging gaps in ADHD care in Australia. These innovations should help to address the gaps in ADHD care in Australia. These innovations should encompass integrated care, targeted treatment outcome data, and additional lifestyle support, whilst recognising the tensions between customised functionalities and streamlined displays.

Association of Polygenic Score and the involvement of Cholinergic and Glutamatergic Pathways with Lithium Treatment Response in Patients with Bipolar Disorder.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N=2,367) and replicated in the combined PsyCourse (N=89) and BipoLife (N=102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������.

Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.

HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders.

Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10-3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.

Elevated clozapine levels associated with infection: A systematic review.

Clozapine is the most effective anti-psychotic medication for treatment refractory schizophrenia. A growing number of case reports have linked infection to high clozapine levels and associated adverse outcomes. We present a systematic review of published cases to clarify the relationship between infection and elevated clozapine levels. The case reports were located through PubMed and Embase. In addition, 8 new cases from two Australian states were included. Demographics, psychiatric diagnoses and medical morbidities, medications, clinical symptoms, clozapine levels, inflammatory markers and final clinical outcome were extracted. 40 cases were identified in 23 publications that demonstrated elevated clozapine levels associated with infection. Infections were commonly respiratory in origin. Adverse events, typically sedation, were associated with raised clozapine levels during infection. In many cases the signs of infection such as fever and white blood cell count were reduced. Severe adverse effects were uncommon, with one case each of seizure, myocarditis and neutropenia. The relationship between infection, clozapine levels and adverse events is complex and multi-factorial. Monitoring of clozapine levels is essential during hospitalisation for infection and consideration should be given to gradual dose reduction to minimise dose related side effects.

Association of the Polygenic Scores for Personality Traits and Response to Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder.

Studies reported a strong genetic correlation between the Big Five personality traits and major depressive disorder (MDD). Moreover, personality traits are thought to be associated with response to antidepressants treatment that might partly be mediated by genetic factors. In this study, we examined whether polygenic scores (PGSs) derived from the Big Five personality traits predict treatment response and remission in patients with MDD who were prescribed selective serotonin reuptake inhibitors (SSRIs). In addition, we performed meta-analyses of genome-wide association studies (GWASs) on these traits to identify genetic variants underpinning the cross-trait polygenic association. The PGS analysis was performed using data from two cohorts: the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS, n = 529) and the International SSRI Pharmacogenomics Consortium (ISPC, n = 865). The cross-trait GWAS meta-analyses were conducted by combining GWAS summary statistics on SSRIs treatment outcome and on the personality traits. The results showed that the PGS for openness and neuroticism were associated with SSRIs treatment outcomes at p < 0.05 across PT thresholds in both cohorts. A significant association was also found between the PGS for conscientiousness and SSRIs treatment response in the PGRN-AMPS sample. In the cross-trait GWAS meta-analyses, we identified eight loci associated with (a) SSRIs response and conscientiousness near YEATS4 gene and (b) SSRI remission and neuroticism eight loci near PRAG1, MSRA, XKR6, ELAVL2, PLXNC1, PLEKHM1, and BRUNOL4 genes. An assessment of a polygenic load for personality traits may assist in conjunction with clinical data to predict whether MDD patients might respond favorably to SSRIs.