Lithium Response Research Articles

Patterns of Convergence and Divergence Between Bipolar Disorder Type I and Type II: Evidence From Integrative Genomic Analyses

Aim: Genome-wide association studies (GWAS) analyses have revealed genetic evidence of bipolar disorder (BD), but little is known about the genetic structure of BD subtypes. We aimed to investigate the genetic overlap and distinction of bipolar type I (BD I) & type II (BD II) by conducting integrative post-GWAS analyses. Methods: We utilized single nucleotide polymorphism (SNP)–level approaches to uncover correlated and distinct genetic loci. Transcriptome-wide association analyses (TWAS) were then approached to pinpoint functional genes expressed in specific brain tissues and blood. Next, we performed cross-phenotype analysis, including exploring the potential causal associations between two BD subtypes and lithium responses and comparing the difference in genetic structures among four different psychiatric traits. Results: SNP-level evidence revealed three genomic loci, SLC25A17, ZNF184, and RPL10AP3, shared by BD I and II, and one locus (MAD1L1) and significant gene sets involved in calcium channel activity, neural and synapsed signals that distinguished two subtypes. TWAS data implicated different genes affecting BD I and II through expression in specific brain regions (nucleus accumbens for BD I). Cross-phenotype analyses indicated that BD I and II share continuous genetic structures with schizophrenia and major depressive disorder, which help fill the gaps left by the dichotomy of mental disorders. Conclusion: These combined evidences illustrate genetic convergence and divergence between BD I and II and provide an underlying biological and trans-diagnostic insight into major psychiatric disorders.

Evaluation of the relationship between lithium treatment response and suicide attempt in bipolar disorder

IntroductionSuicide risk is 20-30 fold higher in bipolar disorder(BD) than general population. A positive family history of suicide, early-onset bipolar disorder, rapid cycling, and drug/alcohol addiction have been identified as risk factors for non-fatal suicidal behavior. Lithium is the only mood stabilizer known to have a suicide-reducing effect in patients with BD. Less than half of the bipolar patients respond lithium well. Even though mechanism of action on suicide behavior is not clearly known, it is thought that lithium significantly reduces “impulsive-aggressive” behavior via serotonergic system which might also be related with treatment response in BD.ObjectivesThe aim of this study is to evaluate the relationship between lithium response and history of suicide.MethodsThose who scored 7 points or more from the Alda total score were considered good responders. Patients were divided into those who responded well to lithium treatment and those who did not. History of suicide attemptbetween these two groups was compared.Results65.3% of the patients were female (n:49). The mean age of the patients was 36.82±13.35 years. 25 patients responded well to lithium treatment. Among the good responders, 32% of the patients and 25% of the non-responders had a history of suicide attempts. This difference was not statistically significant. (p=0.46 x²=0.13)ConclusionsThe insufficient number of data in the study was considered as a limitation of this study. In addition, there is a need for more studies as there are many factors that cause suicide attempts.DisclosureNo significant relationships.

Is Poor Lithium Response in Individuals with Bipolar Disorder Associated with Increased Degradation of Tryptophan along the Kynurenine Pathway? Results of an Exploratory Study.

Bipolar disorder is associated with an inflammation-triggered elevated catabolism of tryptophan to the kynurenine pathway, which impacts psychiatric symptoms and outcomes. The data indicate that lithium exerts anti-inflammatory effects by inhibiting indoleamine-2,3-dioxygenase (IDO)-1 activity. This exploratory study aimed to investigate the tryptophan catabolism in individuals with bipolar disorder (n = 48) compared to healthy controls (n = 48), and the associations with the response to mood stabilizers such as lithium, valproate, or lamotrigine rated with the Retrospective Assessment of the Lithium Response Phenotype Scale (or the Alda scale). The results demonstrate an association of a poorer response to lithium with higher levels of kynurenine, kynurenine/tryptophan ratio as a proxy for IDO-1 activity, as well as quinolinic acid, which, overall, indicates a pro-inflammatory state with a higher degradation of tryptophan towards the neurotoxic branch. The treatment response to valproate and lamotrigine was not associated with the levels of the tryptophan metabolites. These findings support the anti-inflammatory properties of lithium. Furthermore, since quinolinic acid has neurotoxic features via the glutamatergic pathway, they also strengthen the assumption that the clinical drug response might be associated with biochemical processes. The relationship between the lithium response and the measurements of the tryptophan to the kynurenine pathway is of clinical relevance and may potentially bring advantages towards a personalized medicine approach to bipolar disorder that allows for the selection of the most effective mood-stabilizing drug.

P192. Can We Predict Lithium Response Status in Bipolar Disorder Using Total Brain Volumetric Parameters?

Patients with bipolar disorder (BD) demonstrate a differential response to prophylactic lithium therapy. Neuroimaging studies have found various volumetric alterations in BD, but whether these are related to lithium response is not clear. Further, chronic lithium therapy has been associated with an increment of gray matter.

A novel lithium phosphate production method by stripping of lithium from the lithium enolate in kerosene using orthophosphoric acid

A novel orthophosphoric acid stripping (OpaS) system has been developed for the stripping and crystallization of lithium from the synergistically produced organolithium compound consisting of lithium enolate in kerosene. In order to determine regression models and optimum conditions, a D-optimal experimental plan was constituted. Reduced quadratic, cubic and quartic models were selected for lithium and non‑lithium metal stripping responses. Accordingly, the linear terms of A and B (A: Organic/aqueous phase ratio [O/A], B: [H3PO4]) were determined as a major factor for all lithium responses while the only two-factor interaction (2FI) of AB was determined for all responses. In optimization, the non‑lithium responses were minimized while the lithium responses were maximized. The O/A was determined as 8.2 when the upper limit of 1.0 M was targeted for [H3PO4]. The optimum validated lithium stripping yield and mass fraction of lithium crystallized were predicted as 94.1% and 0.77, respectively. Based on the lithium strip McCabe Thiele diagram, two theoretical stages are required with a 1.0 M H3PO4 strip solution at an O/A ratio of 8.2. At the validated optimum equilibrium pH (pH[eq]) of 7.44, the Li3PO4 crystals of 92.2% purity of were obtained. As a result, the OpaS system has been proven to be a novel method for stripping and crystallization of lithium as Li3PO4 from the lithium enolate organic system without any requirement of thermal and extra chemical processes.

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

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. To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. 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. 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. 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.

Genetic and Epigenetic Markers of Lithium Response.

The mood stabilizer lithium represents a cornerstone in the long term treatment of bipolar disorder (BD), although with substantial interindividual variability in clinical response. This variability appears to be modulated by genetics, which has been significantly investigated in the last two decades with some promising findings. In addition, recently, the interest in the role of epigenetics has grown significantly, since the exploration of these mechanisms might allow the elucidation of the gene–environment interactions and explanation of missing heritability. In this article, we provide an overview of the most relevant findings regarding the pharmacogenomics and pharmacoepigenomics of lithium response in BD. We describe the most replicated findings among candidate gene studies, results from genome-wide association studies (GWAS) as well as post-GWAS approaches supporting an association between high genetic load for schizophrenia, major depressive disorder or attention deficit/hyperactivity disorder and poor lithium response. Next, we describe results from studies investigating epigenetic mechanisms, such as changes in methylation or noncoding RNA levels, which play a relevant role as regulators of gene expression. Finally, we discuss challenges related to the search for the molecular determinants of lithium response and potential future research directions to pave the path towards a biomarker guided approach in lithium treatment.

Methylomic Biomarkers of Lithium Response in Bipolar Disorder: A Proof of Transferability Study.

Response to lithium (Li) is highly variable in bipolar disorders (BD) and no clinical or biological predictors of long-term response have been validated to date. Using a genome-wide methylomic approach (SeqCapEpi), we previously identified seven differentially methylated regions (DMRs) that discriminated good from non-responders (prophylactic response phenotype defined using the “Alda” scale). This study is a proof of transferability from bench to bedside of this epigenetic signature. For this purpose, we used Methylation Specific High-Resolution Melting (MS-HRM), a PCR based method that can be implemented in any medical laboratory at low cost and with minimal equipment. In 23 individuals with BD, MS-HRM measures of three out of seven DMRs were technically feasible and consistencies between SeqCapEpi and MS-HRM-measures were moderate to high. In an extended sample of individuals with BD (n = 70), the three MS-HRM-measured DMRs mainly predicted nonresponse, with AUC between 0.70–0.80 according to different definitions of the phenotype (Alda- or machine-learning-based definitions). Classification tree analyses further suggested that the MS-HRM-measured DMRs correctly classified up to 84% of individuals as good or non-responders. This study suggested that epigenetic biomarkers, identified in a retrospective sample, accurately discriminate non-responders from responders to Li and may be transferrable to routine practice.

Abstract- Poster

Background:Lithium continues to be one of first-line prophylactic agents for bipolar disorder (BD) but only about one-third of patients are excellent responders. Studies have described various clinical correlates of treatment response but Indian literature is limited.Aim:to assess the relationship of clinical variables to lithium response in subjects with BD on long-term prophylaxisMethodology:This was a cross-sectional, observational study of 40 subjects, aged 18-60 years, with DSM-5 BD type I, currently in remission. Subjects were included if they were on regular and long-term lithium for atleast one year or more. All subjects were screened using MINI 7.0 to exclude co morbidities and standard scales (YMRS≤4, HAM-D<8) to establish euthymia. Subjects were assessed using clinical proforma, NIMH Life Chart Method, Alda Lithium Response Scale (LRS) and IGSLI scale for typical/atypical features.Results:Mean age for sample was 38.28 ±10.41 years (72.5% males). Mean illness duration was 13.84±9.23 years, with average onset at 24.40±8.21 years. Mean duration on lithium was 5.49±4.11 years (1-18 years) The LRS total (A-B) score showed a significant positive correlation to IGSLI typical features score (p=0.003), time since remission (p=0.034) and a significant negative correlation to IGSLI atypical features score (p=0.019) and age of onset of BD (p=0.037). There was a trend towards poorer lithium response with higher BMI (p=0.052)Conclusion:The clinical course characterized by an early age of onset and more atypical features (via. more manic than depressive episodes, presence of residual symptoms, mood incongruent psychotic features) appear to respond poorer to lithium.

Abstract- Poster

Background:Lithium continues to be one of first-line prophylactic agents for bipolar disorder (BD) but only about one-third of patients are excellent responders. Studies have described various clinical correlates of treatment response but Indian literature is limited.Aim:to assess the relationship of clinical variables to lithium response in subjects with BD on long-term prophylaxisMethodology:This was a cross-sectional, observational study of 40 subjects, aged 18-60 years, with DSM-5 BD type I, currently in remission. Subjects were included if they were on regular and long-term lithium for atleast one year or more. All subjects were screened using MINI 7.0 to exclude co morbidities and standard scales (YMRS≤4, HAM-D<8) to establish euthymia. Subjects were assessed using clinical proforma, NIMH Life Chart Method, Alda Lithium Response Scale (LRS) and IGSLI scale for typical/atypical features.Results:Mean age for sample was 38.28 ±10.41 years (72.5% males). Mean illness duration was 13.84±9.23 years, with average onset at 24.40±8.21 years. Mean duration on lithium was 5.49±4.11 years (1-18 years) The LRS total (A-B) score showed a significant positive correlation to IGSLI typical features score (p=0.003), time since remission (p=0.034) and a significant negative correlation to IGSLI atypical features score (p=0.019) and age of onset of BD (p=0.037). There was a trend towards poorer lithium response with higher BMI (p=0.052)Conclusion:The clinical course characterized by an early age of onset and more atypical features (via. more manic than depressive episodes, presence of residual symptoms, mood incongruent psychotic features) appear to respond poorer to lithium.

Abelson helper integration site 1 haplotypes and peripheral blood expression associates with lithium response in bipolar patients

Abelson helper integration site 1 haplotypes and peripheral blood expression associates with lithium response in bipolar patients

Correction of depression-associated circadian rhythm abnormalities is associated with lithium response in bipolar disorder.

Bipolar disorder (BD) is characterized by episodes of depression and mania and disrupted circadian rhythms. Lithium is an effective therapy for BD, but only 30%-40% of patients are fully responsive. Preclinical models show that lithium alters circadian rhythms. However, it is unknown if the circadian rhythm effects of lithium are essential to its therapeutic properties. In secondary analyses of a multi-center, prospective, trial of lithium for BD, we examined the relationship between circadian rhythms and therapeutic response to lithium. Using standardized instruments, we measured morningness, diurnal changes in mood, sleep, and energy (circadian rhythm disturbances) in a cross-sectional study of 386 BD subjects with varying lithium exposure histories. Next, we tracked symptoms of depression and mania prospectively over 12weeks in a subset of 88 BD patients initiating treatment with lithium. Total, circadian, and affective mood symptoms were scored separately and analyzed. Subjects with no prior lithium exposure had the most circadian disruption, while patients stable on lithium monotherapy had the least. Patients who were stable on lithium with another drug or unstable on lithium showed intermediate levels of disruption. Treatment with lithium for 12weeks yielded significant reductions in total and affective depression symptoms. Lithium responders (Li-Rs) showed improvement in circadian symptoms of depression, but non-responders did not. There was no difference between Li-Rs and nonresponders in affective, circadian, or total symptoms of mania. Exposure to lithium is associated with reduced circadian disruption. Lithium response at 12weeks was selectively associated with the reduction of circadian depressive symptoms. We conclude that stabilization of circadian rhythms may be an important feature of lithium's therapeutic effects. NCT0127253.

P.0898 Biomarkers of lithium response in neural progenitors of the olfactory neuroepithelium derived from patients with bipolar disorder

P.0898 Biomarkers of lithium response in neural progenitors of the olfactory neuroepithelium derived from patients with bipolar disorder

P.0112 Evaluation of circadian rhythm parameters and NR1D1 single nucleotide polymorphism relationship to predict lithium response in bipolar disorder

P.0112 Evaluation of circadian rhythm parameters and NR1D1 single nucleotide polymorphism relationship to predict lithium response in bipolar disorder

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.

Association of Attention-Deficit/Hyperactivity Disorder and Depression Polygenic Scores with Lithium Response: A Consortium for Lithium Genetics Study

Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients (N = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosum and in a model including all three PRSs and other covariates, and the PRS of ADHD (β = −0.14; 95% confidence interval [CI]: −0.24 to −0.03; p value = 0.010) and MDD (β = −0.16; 95% CI: −0.27 to −0.04; p value = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34–1.93; p value = 2e−7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD.

A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes.

Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic markers. We operationalized Li response phenotypes using the Retrospective Assessment of Response to Lithium Scale (i.e., the Alda scale) in a sample of 164 cases with bipolar disorder (BD). Three phenotypes were defined using the established approaches, whilst two phenotypes were generated by machine learning algorithms. We examined whether these five different Li response phenotypes showed different levels of statistically significant associations with polymorphisms of three candidate circadian genes (RORA, TIMELESS and PPARGC1A), which were selected for this study because they were plausibly linked with the response to Li. The three original and two revised Alda ratings showed low levels of discordance (misclassification rates: 8–12%). However, the significance of associations with circadian genes differed when examining previously recommended categorical and continuous phenotypes versus machine-learning derived phenotypes. Findings using machine learning approaches identified more putative signals of the Li response. Established approaches to Li response phenotyping are easy to use but may lead to a significant loss of data (excluding partial responders) due to recent attempts to improve the reliability of the original rating system. While machine learning approaches require additional modeling to generate Li response phenotypes, they may offer a more nuanced approach, which, in turn, would enhance the probability of identifying significant signals in genetic studies.

Insight into susceptibility genes associated with bipolar disorder: a systematic review.

Bipolar disorder (BD) is a severe disorder, and it is associated with an increased risk of mortality. About 25% of patients with BD have attempted and 11% have died by suicide. All these characteristics suggest that the disorders within the bipolar spectrum are a crucial public health problem. With the development of molecular genetics in recent decades, it was possible to more easily detect risk genes associated with this disorder. This study aimed at summarizing the findings of systematic reviews and meta-analyses on the topic and assessing the quality of the available evidence. PubMed/Medline and Web of Science were searched to identify systematic reviews and meta-analyses published during 2013-2019. Standard methodology was applied to synthesize and assess the retrieved literature. This systematic review identifies a number of potential risk genes associated with bipolar disorder whose mechanism of action has yet to be confirmed. They are divided into several groups: 1) a list of the most significant susceptibility genetic factors associated with BD; 2) the implication of the ZNF804A gene in BD; 3) the role of genes involved in calcium signaling in BD; 4) DNA methylation in BD; 5) BD and risk suicide genes; 6) susceptibility genes for early-onset BD; 7) candidate genes common to both BD and schizophrenia; 8) genes involved in cognitive status in BD cases; 9) genes involved in structural alteration in BD brain tissue; 10) genes involved in lithium response in BD. Future research should concentrate on molecular mechanisms by which genetic variants play a major role in BD. Supplemental research is needed to replicate the applicable results.

Dynamic insulin-stimulated mTOR/GSK3 signaling in peripheral immune cells: Preliminary evidence for an association with lithium response in bipolar disorder.

A key mechanism of lithium is the inhibition of glycogen synthase kinase-3β (GSK3β) and activation of mammalian target of rapamycin (mTOR), two contributors to insulin signaling. We explored the relationship between these markers and clinical response to lithium in bipolar disorder (BD). Thirty-four subjects with BD who had been taking lithium for ≥2years and had a maintenance lithium Alda score defined as either high (≥7; n=20) or low (≤2; n=14) were included in the study. Baseline protein expression of GSK3β and mTOR (total and phosphorylated (p)) was obtained from a buffy coat. Peripheral blood mononuclear cells (PBMCs) from a subset of each group (n=11) were stimulated with insulin (10µg) and change in protein expression was determined using Western blot. In buffy coat samples, significantly higher levels of pmTOR were present in subjects with an Alda score ≤2 (lithium non-responsive), relative to those with scores ≥7 (lithium-responsive). No differences were observed for pGSK3β. In contrast, functional PBMC responses to 5min of insulin stimulation demonstrated robust increases in pGSK3β (87.05±43.41%) and pmTOR (105.7±66.48%) in the lithium responsive group only. This contrasted observed decreases in pGSK3β (34.08±16.12%) and pmTOR (37.84±14.39%) 5mins post-insulin in non-responders. Dynamic increases in pmTOR and pGSK3β post-insulin stimulation may reflect an immunometabolic state that facilitates lithium response. Further prospective analyses are needed to replicate and extend these preliminary findings and further investigate the role of insulin signaling in lithium response in BD.

Clinical predictors of non‐response to lithium treatment in the Pharmacogenomics of Bipolar Disorder (PGBD) study

Lithium is regarded as a first-line treatment for bipolar disorder (BD), but partial response and non-response commonly occurs. There exists a need to identify lithium non-responders prior to initiating treatment. The Pharmacogenomics of Bipolar Disorder (PGBD) Study was designed to identify predictors of lithium response. The PGBD Study was an eleven site prospective trial of lithium treatment in bipolar I disorder. Subjects were stabilized on lithium monotherapy over 4 months and gradually discontinued from all other psychotropic medications. After ensuring a sustained clinical remission (defined by a score of ≤3 on the CGI for 4 weeks) had been achieved, subjects were followed for up to 2years to monitor clinical response. Cox proportional hazard models were used to examine the relationship between clinical measures and time until failure to remit or relapse. A total of 345 individuals were enrolled into the study and included in the analysis. Of these, 101 subjects failed to remit or relapsed, 88 achieved remission and continued to study completion, and 156 were terminated from the study for other reasons. Significant clinical predictors of treatment failure (p<0.05) included baseline anxiety symptoms, functional impairments, negative life events and lifetime clinical features such as a history of migraine, suicidal ideation/attempts, and mixed episodes, as well as a chronic course of illness. In this PGBD Study of lithium response, several clinical features were found to be associated with failure to respond to lithium. Future validation is needed to confirm these clinical predictors of treatment failure and their use clinically to distinguish who will do well on lithium before starting pharmacotherapy.