The Genetics of Response to and Side Effects of Lithium Treatment in Bipolar Disorder: Future Research Perspectives

Although the mood stabilizer lithium is a first-line treatment in bipolar disorder, a substantial number of patients do not benefit from it and experience side effects. No clinical tool is available for predicting lithium response or the occurrence of side effects in everyday clinical practice. Multiple genetic research efforts have been performed in this field because lithium response and side effects are considered to be multifactorial endophenotypes. Available results from linkage and segregation, candidate-gene, and genome-wide association studies indicate a role of genetic factors in determining response and side effects. For example, candidate-gene studies often report GSK3β, brain-derived neurotrophic factor, and SLC6A4 as being involved in lithium response, and the latest genome-wide association study found a genome-wide significant association of treatment response with a locus on chromosome 21 coding for two long non-coding RNAs. Although research results are promising, they are limited mainly by a lack of replicability and, despite the collaboration of consortia, insufficient sample sizes. The need for larger sample sizes and “multi-omics” approaches is apparent, and such approaches are crucial for choosing the best treatment options for patients with bipolar disorder. In this article, we delineate the mechanisms of action of lithium and summarize the results of genetic research on lithium response and side effects

Genomic and neuroimaging approaches to bipolar disorder

BACKGROUND: To date, besides genome-wide association studies, a variety of other genetic analyses (e.g. polygenic risk scores, whole-exome sequencing and whole-genome sequencing) have been conducted, and a large amount of data has been gathered for investigating the involvement of common, rare and very rare types of DNA sequence variants in bipolar disorder. Also, non-invasive neuroimaging methods can be used to quantify changes in brain structure and function in patients with bipolar disorder. AIMS: To provide a comprehensive assessment of genetic findings associated with bipolar disorder, based on the evaluation of different genomic approaches and neuroimaging studies. METHOD: We conducted a PubMed search of all relevant literatures from the beginning to the present, by querying related search strings. RESULTS: ANK3, CACNA1C, SYNE1, ODZ4 and TRANK1 are five genes that have been replicated as key gene candidates in bipolar disorder pathophysiology, through the investigated studies. The percentage of phenotypic variance explained by the identified variants is small (approximately 4.7%). Bipolar disorder polygenic risk scores are associated with other psychiatric phenotypes. The ENIGMA-BD studies show a replicable pattern of lower cortical thickness, altered white matter integrity and smaller subcortical volumes in bipolar disorder. CONCLUSIONS: The low amount of explained phenotypic variance highlights the need for further large-scale investigations, especially among non-European populations, to achieve a more complete understanding of the genetic architecture of bipolar disorder and the missing heritability. Combining neuroimaging data with genetic data in large-scale studies might help researchers acquire a better knowledge of the engaged brain regions in bipolar disorder

Association of early life stress and cognitive performance in patients with schizophrenia and healthy controls

As core symptoms of schizophrenia, cognitive deficits contribute substantially to poor outcomes. Early life stress (ELS) can negatively affect cognition in patients with schizophrenia and healthy controls, but the exact nature of the mediating factors is unclear. Therefore, we investigated how ELS, education, and symptom burden are related to cognitive performance. The sample comprised 215 patients with schizophrenia (age, 42.9 ± 12.0 years; 66.0 % male) and 197 healthy controls (age, 38.5 ± 16.4 years; 39.3 % male) from the PsyCourse Study. ELS was assessed with the Childhood Trauma Screener (CTS). We used analyses of covariance and correlation analyses to investigate the association of total ELS load and ELS subtypes with cognitive performance. ELS was reported by 52.1 % of patients and 24.9 % of controls. Independent of ELS, cognitive performance on neuropsychological tests was lower in patients than controls (p < 0.001). ELS load was more closely associated with neurocognitive deficits (cognitive composite score) in controls (r = −0.305, p < 0.001) than in patients (r = −0.163, p = 0.033). Moreover, the higher the ELS load, the more cognitive deficits were found in controls (r = −0.200, p = 0.006), while in patients, this correlation was not significant after adjusting for PANSS. ELS load was more strongly associated with cognitive deficits in healthy controls than in patients. In patients, disease-related positive and negative symptoms may mask the effects of ELS-related cognitive deficits. ELS subtypes were associated with impairments in various cognitive domains. Cognitive deficits appear to be mediated through higher symptom burden and lower educational level

Extracellular vesicle approach to major psychiatric disorders

Over the last few years, extracellular vesicles (EVs) have received increasing attention as potential non-invasive diagnostic and therapeutic biomarkers for various diseases. The interest in EVs is related to their structure and content, as well as to their changing cargo in response to different stimuli. One of the potential areas of use of EVs as biomarkers is the central nervous system (CNS), in particular the brain, because EVs can cross the blood-brain barrier, exist also in peripheral tissues and have a diverse cargo. Thus, they may represent liquid biopsies of the CNS that can reflect brain pathophysiology without the need for invasive surgical procedures. Overall, few studies to date have examined EVs in neuropsychiatric disorders, and the present evidence appears to lack reproducibility. This situation might be due to a variety of technical obstacles related to working with EVs, such as the use of different isolation strategies, which results in non-uniform vesicular and molecular outputs. Multi-omics approaches and improvements in the standardization of isolation procedures will allow highly pure EV fractions to be obtained in which the molecular cargo, particularly microRNAs and proteins, can be identified and accurately quantified. Eventually, these advances will enable researchers to decipher disease-relevant molecular signatures of the brain-derived EVs involved in synaptic plasticity, neuronal development, neuro-immune communication, and other related pathways. This narrative review summarizes the findings of studies on EVs in major psychiatric disorders, particularly in the field of biomarkers, and discusses the respective therapeutic potential of EVs

DataSheet1_Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.docx

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.</p

Medication adherence in a cross-diagnostic sample of patients from the affective-to-psychotic spectrum: results from the PsyCourse study

INTRODUCTION: According to the World Health Organization, medication adherence is defined as the extent to which a person's behavior corresponds with an agreed recommendation from a healthcare provider. Approximately 50% of patients do not take their medication as prescribed, and non-adherence can contribute to the progress of a disease. For patients suffering from mental diseases non-adherence plays an important role. Various factors have been proposed as contributing to non-adherence, however the literature remains heterogeneous dependent on the analyzed patient subgroups. This study comprehensively evaluates the association of sociodemographic, clinical, personality and quality of life related factors with medication adherence by analyzing data from the PsyCourse study. The PsyCourse study is a large and cross-diagnostic cohort of psychiatric patients from the affective-to-psychotic spectrum. METHODS: The study sample comprised 1,062 patients from the PsyCourse study with various psychiatric diagnoses (mean [SD] age, 42.82 [12.98] years; 47.4% female). Data were analyzed to identify specific factors associated with medication adherence, and adherence was measured by a self-rating questionnaire. Odds ratios (OR) were estimated by a logistic regression for binary outcomes. Missing data were imputed using multiple imputation. RESULTS: The following factors showed the strongest association with medication adherence: never having used illicit drugs (OR, 0.71), number of prescribed antipsychotics (OR, 1.40), the personality trait conscientiousness (OR, 1.26), and the environmental domain of quality of life (OR, 1.09). CONCLUSION: In a large and cross-diagnostic sample, we could show that a higher level of conscientiousness, a higher number of antipsychotic medication, a better quality of life within the environmental domain, and the absence of substance abuse contribute to a better medication adherence independent of the underlying disorder