Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data.

Bipolar disorder is a chronic and complex polygenic disease with high rates of comorbidity. However, the independent contribution of either diagnosis or genetic risk of bipolar disorder to the medical comorbidity profile of individuals with the disease remains unresolved. Here, we conducted a multi-step phenome-wide association study (PheWAS) of bipolar disorder using phenomes derived from the electronic health records of participants enrolled in the Mayo Clinic Biobank and the Mayo Clinic Bipolar Disorder Biobank. First, we explored the conditions associated with a diagnosis of bipolar disorder by conducting a phenotype-based PheWAS followed by LASSO-penalized regression to account for correlations within the phenome. Then, we explored the conditions associated with bipolar disorder polygenic risk score (BD-PRS) using a PRS-based PheWAS with a sequential exclusion approach to account for the possibility that diagnosis, instead of genetic risk, may drive such associations. 53,386 participants (58.7% women) with a mean age at analysis of 67.8 years (SD = 15.6) were included. A bipolar disorder diagnosis (n = 1479) was associated with higher rates of psychiatric conditions, injuries and poisonings, endocrine/metabolic and neurological conditions, viral hepatitis C, and asthma. BD-PRS was associated with psychiatric comorbidities but, in contrast, had no positive associations with general medical conditions. While our findings warrant confirmation with longitudinal-prospective studies, the limited associations between bipolar disorder genetics and medical conditions suggest that shared environmental effects or environmental consequences of diagnosis may have a greater impact on the general medical comorbidity profile of individuals with bipolar disorder than its genetic risk.

Antidepressants that increase mitochondrial energetics may elevate risk of treatment-emergent mania.

Preclinical evidence suggests that antidepressants (ADs) may differentially influence mitochondrial energetics. This study was conducted to investigate the relationship between mitochondrial function and illness vulnerability in bipolar disorder (BD), specifically risk of treatment-emergent mania (TEM). Participants with BD already clinically phenotyped as TEM+ (n = 176) or TEM- (n = 516) were further classified whether the TEM associated AD, based on preclinical studies, increased (Mito+, n = 600) or decreased (Mito-, n = 289) mitochondrial electron transport chain (ETC) activity. Comparison of TEM+ rates between Mito+ and Mito- ADs was performed using generalized estimating equations to account for participants exposed to multiple ADs while adjusting for sex, age at time of enrollment into the biobank and BD type (BD-I/schizoaffective vs. BD-II). A total of 692 subjects (62.7% female, 91.4% White, mean age 43.0 ± 14.0 years) including 176 cases (25.3%) of TEM+ and 516 cases (74.7%) of TEM- with previous exposure to Mito+ and/or Mito- antidepressants were identified. Adjusting for age, sex and BD subtype, TEM+ was more frequent with antidepressants that increased (24.7%), versus decreased (13.5%) mitochondrial energetics (OR = 2.21; p = 0.000009). Our preliminary retrospective data suggests there may be merit in reconceptualizing AD classification, not solely based on monoaminergic conventional drug mechanism of action, but additionally based on mitochondrial energetics. Future prospective clinical studies on specific antidepressants and mitochondrial activity are encouraged. Recognizing pharmacogenomic investigation of drug response may extend or overlap to genomics of disease risk, future studies should investigate potential interactions between mitochondrial mechanisms of disease risk and drug response.

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.

Patterns of pharmacotherapy for bipolar disorder: A GBC survey.

OBJECTIVES: To understand treatment practices for bipolar disorders (BD), this study leveraged the Global Bipolar Cohort collaborative network to investigate pharmacotherapeutic treatment patterns in multiple cohorts of well-characterized individuals with BD in North America, Europe, and Australia. METHODS: Data on pharmacotherapy, demographics, diagnostic subtypes, and comorbidities were provided from each participating cohort. Individual site and regional pooled proportional meta-analyses with generalized linear mixed methods were conducted to identify prescription patterns. RESULTS: This study included 10,351 individuals from North America (n = 3985), Europe (n = 3822), and Australia (n = 2544). Overall, participants were predominantly female (60%) with BD-I (60%; vs. BD-II = 33%). Cross-sectionally, mood-stabilizing anticonvulsants (44%), second-generation antipsychotics (42%), and antidepressants (38%) were the most prescribed medications. Lithium was prescribed in 29% of patients, primarily in the Australian (31%) and European (36%) cohorts. First-generation antipsychotics were prescribed in 24% of the European versus 1% in the North American cohort. Antidepressant prescription rates were higher in BD-II (47%) compared to BD-I (35%). Major limitations were significant differences among cohorts based on inclusion/exclusion criteria, data source, and time/year of enrollment into cohort. CONCLUSIONS: Mood-stabilizing anticonvulsants, second-generation antipsychotics, and antidepressants were the most prescribed medications suggesting prescription patterns that are not necessarily guideline concordant. Significant differences exist in the prescription practices across different geographic regions, especially the underutilization of lithium in the North American cohorts and the higher utilization of first-generation antipsychotics in the European cohorts. There is a need to conduct future longitudinal studies to further explore these differences and their impact on outcomes, and to inform and implement evidence-based guidelines to help improve treatment practices in BD.

The importance of social activity to risk of major depression in older adults.

BACKGROUND: Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults. METHODS: We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis. RESULTS: Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]. CONCLUSION: Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people.

BACKGROUND: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation. METHODS: Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry. RESULTS: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity. CONCLUSIONS: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Antidepressant-Associated Treatment Emergent Mania: A Meta-Analysis to Guide Risk Modeling Pharmacogenomic Targets of Potential Clinical Value.

BACKGROUND: The purpose of this study was to review the association between the SLC6A4 5-HTTLPR polymorphism and antidepressant (AD)-associated treatment emergent mania (TEM) in bipolar disorder alongside starting a discussion on the merits of developing risk stratification models to guide when not to provide AD treatment for bipolar depression. METHODS: Studies that examined the association between clinical and genetic risk factors, specifically monoaminergic transporter genetic variation, and TEM were identified. A meta-analysis was performed using the odds ratio to estimate the effect size under the Der-Simonian and Laird model. RESULTS: Seven studies, referencing the SLC6A4 5-HTTLPR polymorphism and TEM (total N = 1578; TEM+ =594, TEM- = 984), of 142 identified articles were included. The time duration between the start of the AD to emergence of TEM ranged from 4 to 12 weeks. There was a nominally significant association between the s allele of the 5-HTTLPR polymorphism and TEM (odds ratio, 1.434; 95% confidence interval, 1.001-2.055; P = 0.0493; I2 = 52%). No studies have investigated norepinephrine or dopamine transporters. CONCLUSION: Although the serotonin transporter genetic variation is commercially available in pharmacogenomic decision support tools, greater efforts, more broadly, should focus on complete genome-wide approaches to determine genetic variants that may contribute to TEM. Moreover, these data are exemplary to the merits of developing risk stratification models, which include both clinical and biological risk factors, to guide when not to use ADs in bipolar disorder. Future studies will need to validate new risk models that best inform the development of personalized medicine best practices treating bipolar depression.

Real-World Clinical Practice Among Patients With Bipolar Disorder and Chronic Kidney Disease on Long-term Lithium Therapy.

PURPOSE: Long-term lithium therapy (LTLT) has been associated with chronic kidney disease (CKD). We investigated changes in clinical characteristics, pharmacotherapeutic treatments for medical/psychiatric disorders, and outcomes among patients with bipolar disorder (BD) and CKD on LTLT in a 2-year mirror-image study design. METHODS: Adult BD patients on LTLT for ≥1 year who enrolled in the Mayo Clinic Bipolar Disorder Biobank and developed CKD (stage 3) were included, and our study was approved by the Mayo Clinic Institutional Review Board. The primary outcome was the time to the first mood episode after CKD diagnosis among the lithium (Li) continuers and discontinuers. Cox proportional hazards models were used to estimate the time to the first mood episode. We tested for differences in other medication changes between the Li continuers and discontinuers group using Mantel-Haenszel χ2 tests (linear associations). RESULTS: Of 38 BD patients who developed CKD, 18 (47%) discontinued Li, and the remainder continued (n = 20). The median age of the cohort was 56 years (interquartile range [IQR], 48-67 years), 63.2% were female, and 97.4% were White. As compared with continuers, discontinuers had more psychotropic medication trials (6 [IQR, 4-6] vs 3 [IQR, 2-5], P = 0.02), a higher rate of 1 or more mood episodes (61% vs 10%, P = 0.002), and a higher risk of a mood episode after CKD diagnoses (Hazard Ratio, 8.38; 95% confidence interval, 1.85-38.0 [log-rank P = 0.001]]. CONCLUSIONS: Bipolar disorder patients on LTLT who discontinued Li had a higher risk for relapse and a shorter time to the first mood episode, suggesting a need for more thorough discussion before Li discontinuation after the CKD diagnosis.

Shared brain transcriptomic signature in TDP-43 type A FTLD patients with or without GRN mutations.

Frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) is a complex heterogeneous neurodegenerative disorder for which mechanisms are poorly understood. To explore transcriptional changes underlying FTLD-TDP, we performed RNA-sequencing on 66 genetically unexplained FTLD-TDP patients, 24 FTLD-TDP patients with GRN mutations and 24 control participants. Using principal component analysis, hierarchical clustering, differential expression and coexpression network analyses, we showed that GRN mutation carriers and FTLD-TDP-A patients without a known mutation shared a common transcriptional signature that is independent of GRN loss-of-function. After combining both groups, differential expression as compared to the control group and coexpression analyses revealed alteration of processes related to immune response, synaptic transmission, RNA metabolism, angiogenesis and vesicle-mediated transport. Deconvolution of the data highlighted strong cellular alterations that were similar in FTLD-TDP-A and GRN mutation carriers with NSF as a potentially important player in both groups. We propose several potentially druggable pathways such as the GABAergic, GDNF and sphingolipid pathways. Our findings underline new disease mechanisms and strongly suggest that affected pathways in GRN mutation carriers extend beyond GRN and contribute to genetically unexplained forms of FTLD-TDP-A.

Polygenic prediction of bipolar disorder in a Latin American sample.

To date, bipolar disorder (BD) genetic studies and polygenic risk scores (PRSs) for BD are based primarily on populations of European descent (EUR) and lack representation from other ancestries including Latin American (LAT). Here, we describe a new LAT cohort from the Mayo Clinic Bipolar Biobank (MCBB), a multisite collaboration with recruitment sites in the United States (EUR; 1,443 cases and 777 controls) and Mexico and Chile (LAT; 211 cases and 161 controls) and use the sample to explore the performance of a BD-PRS in a LAT population. Using results from the largest genome-wide association study of BD in EUR individuals, PRSice2 and LDpred2 were used to compute BD-PRSs in the LAT and EUR samples from the MCBB. PRSs explained up to 1.4% (PRSice) and 4% (LDpred2) of the phenotypic variance on the liability scale in the LAT sample compared to 3.8% (PRSice2) and 3.4% (LDpred2) in the EUR samples. Future larger studies should further explore the differential performance of different PRS approaches across ancestries. International multisite studies, such as this one, have the potential to address diversity-related limitations of prior genomic studies and ultimately contribute to the reduction of health disparities.

Testing and estimation of X-chromosome SNP effects: Impact of model assumptions.

Interest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate the impact of X chromosome model assumptions on effect estimates. We investigated the coefficient biases of SNP and sex effects with commonly used models for X chromosome SNPs, including models with and without assumptions of X chromosome inactivation (XCI), and with and without SNP-sex interaction terms. Sex and SNP coefficient biases were observed when assumptions made about XCI and sex differences in SNP effect in the analysis model were inconsistent with the data-generating model. However, including a SNP-sex interaction term often eliminated these biases. To illustrate these findings, estimates under different genetic model assumptions are compared and interpreted in a real data example. Models to analyze X chromosome SNPs make assumptions beyond those made in autosomal variant analysis. Assumptions made about X chromosome SNP effects should be stated clearly when reporting and interpreting X chromosome associations. Fitting models with SNP × Sex interaction terms can avoid reliance on assumptions, eliminating coefficient bias even in the absence of sex differences in SNP effect.

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.

Clinical Phenotype of Tardive Dyskinesia in Bipolar Disorder.

PURPOSE: Recognizing the negative impact that antipsychotic-induced movement disorders have on the quality of life and treatment outcomes in bipolar disorder (BD), this study aimed to assess clinical correlates and antipsychotic use patterns of tardive dyskinesia (TD+) in BD. MATERIALS AND METHODS: Participants with and without TD were included. Clinical variables were compared using t-test and χ2 test. Antipsychotic use patterns in TD+, including number of trials, mean doses, and estimated cumulative exposure, were assessed in a case-only analysis. RESULTS: The prevalence rate of TD was 5.1%. In comparison to the TD- group (n = 1074), TD+ participants (n = 58) were older, more likely to be female and have type I bipolar illness. There were 60.3% of the TD+ group that continued using antipsychotics at study entry and had a mean cumulative exposure to antipsychotics of 18.2 ± 15.6 years. Average dose, in haloperidol equivalents, was 5.9 ± 3.5 mg and 77.7% of the trials were second-generation antipsychotics. CONCLUSIONS: This study confirms previously identified TD risk factors, such as age, sex, and bipolar subtype in a large BD cohort. Limitations included a cross-sectional design and the lack of tardive illness severity assessment. As atypical antipsychotics continue to be primary mood stabilization treatment, attempting to harmonize large data sets to identify additional biomarkers of tardive risk will optimize individualized care for patients with BD.

TCF7L2 lncRNA: a link between bipolar disorder and body mass index through glucocorticoid signaling.

Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI), which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in the BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells that highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas that are involved in metabolism. In astrocytes, knockdown of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes. We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knockdown with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.

Genotype-dependent epigenetic regulation of DLGAP2 in alcohol use and dependence.

Alcohol misuse is a major public health problem originating from genetic and environmental risk factors. Alterations in the brain epigenome may orchestrate changes in gene expression that lead to alcohol misuse and dependence. Through epigenome-wide association analysis of DNA methylation from human brain tissues, we identified a differentially methylated region, DMR-DLGAP2, associated with alcohol dependence. Methylation within DMR-DLGAP2 was found to be genotype-dependent, allele-specific and associated with reward processing in brain. Methylation at the DMR-DLGAP2 regulated expression of DLGAP2 in vitro, and Dlgap2-deficient mice showed reduced alcohol consumption compared with wild-type controls. These results suggest that DLGAP2 may be an interface for genetic and epigenetic factors controlling alcohol use and dependence.

ERICH3: vesicular association and antidepressant treatment response.

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

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.

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

INTRODUCTION: A key mechanism of lithium is the inhibition of glycogen synthase kinase-3ß (GSK3ß) 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). METHODS: Thirty-four subjects with BD who had been taking lithium for ≥2 years 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 GSK3ß 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 (10 µg) and change in protein expression was determined using Western blot. RESULTS: 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 pGSK3ß. In contrast, functional PBMC responses to 5 min of insulin stimulation demonstrated robust increases in pGSK3ß (87.05 ± 43.41%) and pmTOR (105.7 ± 66.48%) in the lithium responsive group only. This contrasted observed decreases in pGSK3ß (34.08 ± 16.12%) and pmTOR (37.84 ± 14.39%) 5 mins post-insulin in non-responders. CONCLUSIONS: Dynamic increases in pmTOR and pGSK3ß 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.

Predictors of functional impairment in bipolar disorder: Results from 13 cohorts from seven countries by the global bipolar cohort collaborative.

OBJECTIVES: Persistent functional impairment is common in bipolar disorder (BD) and is influenced by a number of demographic, clinical, and cognitive features. The goal of this project was to estimate and compare the influence of key factors on community function in multiple cohorts of well-characterized samples of individuals with BD. METHODS: Thirteen cohorts from 7 countries included n = 5882 individuals with BD across multiple sites. The statistical approach consisted of a systematic uniform application of analyses across sites. Each site performed a logistic regression analysis with empirically derived "higher versus lower function" as the dependent variable and selected clinical and demographic variables as predictors. RESULTS: We found high rates of functional impairment, ranging from 41 to 75%. Lower community functioning was associated with depressive symptoms in 10 of 12 of the cohorts that included this variable in the analysis. Lower levels of education, a greater number of prior mood episodes, the presence of a comorbid substance use disorder, and a greater total number of psychotropic medications were also associated with low functioning. CONCLUSIONS: The bipolar clinical research community is poised to work together to characterize the multi-dimensional contributors to impairment and address the barriers that impede patients' complete recovery. We must also identify the core features which enable many to thrive and live successfully with BD. A large-scale, worldwide, prospective longitudinal study focused squarely on BD and its heterogeneous presentations will serve as a platform for discovery and promote major advances toward optimizing outcomes for every individual with this illness.

A principal component approach to improve association testing with polygenic risk scores.

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS-PCA approach with a permutation test and an a priori selected p-value threshold. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.