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

Purpose: Pregnancy complications afect 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 diferences 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 signifcantly 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 signifcant diferences 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.Emily Aldridge, K. Oliver Schubert, Maleesa Pathirana, Susan Sierp, Shalem Y. Leemaqz, Claire T. Roberts, Gustaaf A. Dekker, and Margaret A. Arstal

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

Themed Issue: Precision Medicine and Personalised Healthcare in PsychiatryBACKGROUND: 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.Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, Jean- Michel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, Hsi- Chung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O, Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil Tekola- Ayele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark and Bernhard T. Baun

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Multimodal modeling for personalized psychiatry

Outcomes for people with mental illness are difficult to predict due to the syndromal nature of diagnosis and the complex relationships among clinical and biological predictors and outcomes. Due to the small amounts of variance explained by individual predictors, the application of multivariate modeling techniques is key to improving the accuracy of outcome prediction. The combination of data from multiple modes of clinical and biological assessment shows potential to increase the accuracy of such models. Superior performance may be achieved by combining clinical data with imaging, electrophysiology, and blood-based biomarkers. Multimodal multivariate modeling techniques are central to the development of personalized psychiatry, affording the potential for patient stratification and individual outcome prediction. This chapter outlines the range of multimodal data available, study design, and modeling techniques.Scott R. Clark, Micah Cearns, Klaus Oliver Schubert, Bernhard T. Baun

Mood trajectories as a basis for personalized psychiatry in young people

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. Hence, giving personalized treatment recommendations is a challenge in this group; current clinical practice guidelines promote a “watch and wait” approach, where generic and low-level treatments are offered first, while more intensive therapies, such as medication, are reserved for cases who do not benefit from first-line interventions. Trajectory analyses of longitudinally assessed symptoms in large cohorts have the potential to untangle clinical heterogeneity by determining subgroups or classes of symptom courses and their risk factors. Further, they explore the impact of known or suspected risk factors on a trajectory slope and intercept, and can trace the interrelation between depressive symptoms and other clinical outcomes over time. 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. Studies also provide evidence that high and persistent depressive symptoms are associated with a range of concurrent health- and behavioral outcomes. These findings could assist in informing personalized and preventive strategies for clinical practice.Klaus Oliver Schubert, Scott R. Clark, Linh K. Van, Jane L. Collinson, Bernhard T. Baun

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.Sumana Thomson, Trung Doan, Dennis Liu, Klaus Oliver Schubert, Julian Toh, Mark A Boyd, Cherrie Galletl

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.Liliana G.Ciobanu, Perminder S.Sachdev ... Bernhard Baune ... Sarah Cohen-Woods ... Klaus Schubert .... Catherine Toben ... et al

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

Published online: 16 March 2020Lithium 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 (ConLiGen) 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 BDAzmeraw T. Amare, Klaus Oliver Schubert ... Scott R. Clark ... Micah Cearns ... et al. Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium ... Tracy M. Air ... Bernhard T. Baun

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

Corrected by: Correction: Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients. Translational Psychiatry volume 12, Article number: 278 (2022). Funding information missing from the article. The original article has been corrected.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 metaanalysis 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.Klaus Oliver Schubert ... Azmeraw T. Amare ... Micah Cearns ... Scott R. Clark ... et al. and Bernhard T. Baun

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

OnlinePublLithium 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.Azmeraw Amare ... Scott Clark ... Klaus Schubert ... Simon Hartmann ... Muktar Ahmed ... et al