Can an Integrated Science Approach to Precision Medicine Research Improve Lithium Treatment in Bipolar Disorders?

Clinical practice guidelines identify lithium as a first line treatment for mood stabilization and reduction of suicidality in bipolar disorders (BD); however, most individuals show sub-optimal response. Identifying biomarkers for lithium response could enable personalization of treatment and refine criteria for stratification of BD cases into treatment-relevant subgroups. Existing systematic reviews identify potential biomarkers of lithium response, but none directly address the conceptual issues that need to be addressed to enhance translation of research into precision prescribing of lithium. For example, although clinical syndrome subtyping of BD has not led to customized individual treatments, we emphasize the importance of assessing clinical response phenotypes in biomarker research. Also, we highlight the need to give greater consideration to the quality of prospective longitudinal monitoring of illness activity and the differentiation of non-response from partial or non-adherence with medication. It is unlikely that there is a single biomarker for lithium response or tolerability, so this review argues that more research should be directed toward the exploration of biosignatures. Importantly, we emphasize that an integrative science approach may improve the likelihood of discovering the optimal combination of clinical factors and multimodal biomarkers (e.g., blood omics, neuroimaging, and actigraphy derived-markers). This strategy could uncover a valid lithium response phenotype and facilitate development of a composite prediction algorithm. Lastly, this narrative review discusses how these strategies could improve eligibility criteria for lithium treatment in BD, and highlights barriers to translation to clinical practice including the often-overlooked issue of the cost-effectiveness of introducing biomarker tests in psychiatry

Association between the PPP3CC gene, coding for the calcineurin gamma catalytic subunit, and bipolar disorder

<p>Abstract</p> <p>Background</p> <p>Calcineurin is a neuron-enriched phosphatase that regulates synaptic plasticity and neuronal adaptation. Activation of calcineurin, overall, antagonizes the effects of the cyclic AMP activated protein/kinase A. Thus, kinase/phosphatase dynamic balance seems to be critical for transition to long-term cellular responses in neurons, and disruption of this equilibrium should induce behavioral impairments in animal models. Genetic animal models, as well as post-mortem studies in humans have implicated calcineurin dependent calcium and cyclic AMP regulated phosphorylation/dephosphorylation in both affective responses and psychosis. Recently, genetic association between schizophrenia and genetic variation of the human calcineurin A gamma subunit gene (PPP3CC) has been reported.</p> <p>Methods</p> <p>Based on the assumption of the common underlying genetic factor in schizophrenia and bipolar affective disorder (BPAD), we performed association analysis of CC33 and CCS3 polymorphisms of the PPP3CC gene reported to be associated with schizophrenia in a French sample of 115 BPAD patients and 97 healthy controls.</p> <p>Results</p> <p>Carrying 'CT' or 'TT' genotypes of the PPP3CC-CC33 polymorphism increased risk to develop BPAD comparing to carry 'CC' genotype (OR = 1.8 [1.01–3.0]; p = 0.05). For the PPP3CC-CCS3 polymorphism, 'AG' or 'GG' carriers have an increased risk to develop BPAD than 'AA' carriers (OR = 2.8 [1.5–5.2]). The CC33 and CCS3 polymorphisms were observed in significant linkage disequilibrium (D' = 0.91, r²= 0.72). Haplotype frequencies were significantly different in BPAD patients than in controls (p = 0.03), with a significant over-transmission of the 'TG' haplotype in BPAD patients (p = 0.001).</p> <p><b>Conclusion:</b></p> <p>We suggest that the PPP3CC gene might be a susceptibility gene for BPAD, in accordance with current neurobiological hypotheses that implicate dysregulation of signal-transduction pathways, such as those regulated by calcineurin, in the etiology of affective disorders.</p

Unipolar mania: identification and characterisation of cases in France and the United Kingdom

Background: Unipolar mania is a putative subtype of bipolar disorder (BD) in which individuals experience recurrent manic but not major depressive episodes. Few studies of unipolar mania have been conducted in developed countries and none in the UK. This study aimed to identify and characterise people with unipolar mania in the UK and France. Methods: People with unipolar mania were ascertained using a South London UK electronic case register and a French BD case series. Each unipolar mania group was compared to a matched group of people with BD who have experienced depressive episodes. Results: 17 people with unipolar mania were identified in South London and 13 in France. The frequency of unipolar mania as a percentage of the BD clinical population was 1.2% for the South London cohort and 3.3% for the French cohort. In both cohorts, people with unipolar mania experienced more manic episodes than people with BD, and in the French cohort were more likely to experience a psychotic illness onset and more psychiatric admissions. Treatment characteristics of people with unipolar mania were similar to people with BD except that the unipolar mania group was less likely to be treated with antidepressants. Limitations: The relatively small number of people with unipolar mania identified by this study limits its power to detect differences in clinical variables. Conclusions: People with unipolar mania can be identified in France and the UK, and they may experience a higher frequency of manic episodes but have similar treatment characteristics to people with BD

ALGOS: the development of a randomized controlled trial testing a case management algorithm designed to reduce suicide risk among suicide attempters

<p>Abstract</p> <p>Background</p> <p>Suicide attempts (SA) constitute a serious clinical problem. People who attempt suicide are at high risk of further repetition. However, no interventions have been shown to be effective in reducing repetition in this group of patients.</p> <p>Methods/Design</p> <p>Multicentre randomized controlled trial.</p> <p>We examine the effectiveness of «ALGOS algorithm»: an intervention based in a decisional tree of contact type which aims at reducing the incidence of repeated suicide attempt during 6 months. This algorithm of case management comprises the two strategies of intervention that showed a significant reduction in the number of SA repeaters: systematic telephone contact (ineffective in first-attempters) and «Crisis card» (effective only in first-attempters). Participants who are lost from contact and those refusing healthcare, can then benefit from «short letters» or «postcards».</p> <p>Discussion</p> <p>ALGOS algorithm is easily reproducible and inexpensive intervention that will supply the guidelines for assessment and management of a population sometimes in difficulties with healthcare compliance. Furthermore, it will target some of these subgroups of patients by providing specific interventions for optimizing the benefits of case management strategy.</p> <p>Trial Registration</p> <p>The study was registered with the ClinicalTrials.gov Registry; number: NCT01123174.</p

Corrigendum to "Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action [European Neuropsychopharmacology (2020) 36, 121-136]"

The authors regret that there was an error in the author list, this should appear as above. The authors apologise for any inconvenience caused

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

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemination and public involvement with the research programs. We believe the time is right for a step change in our approach to understanding, treating and even preventing BD effectively

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

Bipolar afective 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 stratifcation are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identifed 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 infammatory/autoimmune processes, our fndings strongly suggest that HLA-mediated low infammatory background may contribute to the efcient response to Li in BD patients, while an infammatory status overriding Li anti-infammatory properties would favor a weak response

Variations in seasonal solar insolation are associated with a history of suicide attempts in bipolar I disorder

Background: Bipolar disorder is associated with circadian disruption and a high risk of suicidal behavior. In a previous exploratory study of patients with bipolar I disorder, we found that a history of suicide attempts was associated with diferences between winter and summer levels of solar insolation. The purpose of this study was to confrm this fnding using international data from 42% more collection sites and 25% more countries. Methods: Data analyzed were from 71 prior and new collection sites in 40 countries at a wide range of latitudes. The analysis included 4876 patients with bipolar I disorder, 45% more data than previously analyzed. Of the patients, 1496 (30.7%) had a history of suicide attempt. Solar insolation data, the amount of the sun's electromagnetic energy striking the surface of the earth, was obtained for each onset location (479 locations in 64 countries). Results: This analysis confrmed the results of the exploratory study with the same best model and slightly better statistical signifcance. There was a signifcant inverse association between a history of suicide attempts and the ratio of mean winter insolation to mean summer insolation (mean winter insolation/mean summer insolation). This ratio is largest near the equator which has little change in solar insolation over the year, and smallest near the poles where the winter insolation is very small compared to the summer insolation. Other variables in the model associated with an increased risk of suicide attempts were a history of alcohol or substance abuse, female gender, and younger birth cohort. The winter/summer insolation ratio was also replaced with the ratio of minimum mean monthly insolation to the maximum mean monthly insolation to accommodate insolation patterns in the tropics, and nearly identical results were found. All estimated coefcients were signifcant at p<0.01. Conclusion: A large change in solar insolation, both between winter and summer and between the minimum and maximum monthly values, may increase the risk of suicide attempts in bipolar I disorder. With frequent circadian rhythm dysfunction and suicidal behavior in bipolar disorder, greater understanding of the optimal roles of daylight and electric lighting in circadian entrainment is needed. Keywords: Bipolar disorder, Suicide, Sunlight, Solar insolation, Psychiatry, Circadian, Seasonal variatio

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