Sleep-wake variations of electrodermal activity in bipolar disorder.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Emotional intelligence: a comparison between patients after first episode mania and those suffering from chronic bipolar disorder type I.

BACKGROUND: Deficits in emotional intelligence (EI) were detected in patients with bipolar disorder (BD), but little is known about whether these deficits are already present in patients after presenting a first episode mania (FEM). We sought (i) to compare EI in patients after a FEM, chronic BD and healthy controls (HC); (ii) to examine the effect exerted on EI by socio-demographic, clinical and neurocognitive variables in FEM patients. METHODS: The Emotional Intelligence Quotient (EIQ) was calculated with the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Performance on MSCEIT was compared among the three groups using generalized linear models. In patients after a FEM, the influence of socio-demographic, clinical and neurocognitive variables on the EIQ was examined using a linear regression model. RESULTS: In total, 184 subjects were included (FEM n = 48, euthymic chronic BD type I n = 75, HC n = 61). BD patients performed significantly worse than HC on the EIQ [mean difference (MD) = 10.09, standard error (s.e.) = 3.14, p = 0.004] and on the understanding emotions branch (MD = 7.46, s.e. = 2.53, p = 0.010). FEM patients did not differ from HC and BD on other measures of MSCEIT. In patients after a FEM, EIQ was positively associated with female sex (ß = -0.293, p = 0.034) and verbal memory performance (ß = 0.374, p = 0.008). FEM patients performed worse than HC but better than BD on few neurocognitive domains. CONCLUSIONS: Patients after a FEM showed preserved EI, while patients in later stages of BD presented lower EIQ, suggesting that impairments in EI might result from the burden of disease and neurocognitive decline, associated with the chronicity of the illness.

Characterisation of age and polarity at onset in bipolar disorder.

BACKGROUND: Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools. AIMS: To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics. METHOD: Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts. RESULTS: Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (ß = -0.34 years, s.e. = 0.08), major depression (ß = -0.34 years, s.e. = 0.08), schizophrenia (ß = -0.39 years, s.e. = 0.08), and educational attainment (ß = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO. CONCLUSIONS: AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Genetic variants associated with response to lithium treatment in bipolar disorder: a genome-wide association study.

BACKGROUND: Lithium is a first-line treatment in bipolar disorder, but individual response is variable. Previous studies have suggested that lithium response is a heritable trait. However, no genetic markers of treatment response have been reproducibly identified. METHODS: Here, we report the results of a genome-wide association study of lithium response in 2563 patients collected by 22 participating sites from the International Consortium on Lithium Genetics (ConLiGen). Data from common single nucleotide polymorphisms (SNPs) were tested for association with categorical and continuous ratings of lithium response. Lithium response was measured using a well established scale (Alda scale). Genotyped SNPs were used to generate data at more than 6 million sites, using standard genomic imputation methods. Traits were regressed against genotype dosage. Results were combined across two batches by meta-analysis. FINDINGS: A single locus of four linked SNPs on chromosome 21 met genome-wide significance criteria for association with lithium response (rs79663003, p=1·37 × 10(-8); rs78015114, p=1·31 × 10(-8); rs74795342, p=3·31 × 10(-9); and rs75222709, p=3·50 × 10(-9)). In an independent, prospective study of 73 patients treated with lithium monotherapy for a period of up to 2 years, carriers of the response-associated alleles had a significantly lower rate of relapse than carriers of the alternate alleles (p=0·03268, hazard ratio 3·8, 95% CI 1·1-13·0). INTERPRETATION: The response-associated region contains two genes for long, non-coding RNAs (lncRNAs), AL157359.3 and AL157359.4. LncRNAs are increasingly appreciated as important regulators of gene expression, particularly in the CNS. Confirmed biomarkers of lithium response would constitute an important step forward in the clinical management of bipolar disorder. Further studies are needed to establish the biological context and potential clinical utility of these findings. FUNDING: Deutsche Forschungsgemeinschaft, National Institute of Mental Health Intramural Research Program.

Impact of childhood trauma on cognitive profile in bipolar disorder.

OBJECTIVES: Bipolar Disorder (BD) is associated with cognitive impairment even during remission periods. Nonetheless, this impairment seems to adjust to different profiles of severity. Our aim was to examine the potential impact of childhood trauma (CT) on cognitive performance and, more specifically, on neurocognitive profile membership. METHODS: Using a data-driven strategy, 113 euthymic bipolar patients were grouped according to their cognitive performance using a hierarchical clustering technique. Patients from the three resulting clusters, the so-called "low", "average", and "high performance" groups, were then compared in terms of main sociodemographic, clinical and functioning variables, including CT measures. One-way ANOVA, a chi-square test and partial correlations were used for this purpose, as appropriate. A multinomial logistic regression model was used to determine which variables contributed to neurocognitive clustering membership. RESULTS: Patients from the three neurocognitive clusters differed in terms of sociodemographic, clinical, functioning and CT variables. Scores on the Childhood Trauma Questionnaire (CTQ), especially on the physical negligence subscale, were also associated with a poor cognitive performance. The multinomial regression model indicated that CTQ total scores and the estimated intelligence quotient (IQ) significantly contributed to differentiation among the three neurocognitive groups. CONCLUSIONS: Our results confirmed that CT significantly impacts on cognitive performance during adulthood in BD. The data obtained suggest that a history of CT could act as a liability marker for cognitive impairment. A higher estimated IQ may act as a protective factor against cognitive decline in this group of patients.

Exploring Genetic Variability at PI, GSK3, HPA, and Glutamatergic Pathways in Lithium Response: Association With IMPA2, INPP1, and GSK3B Genes.

Lithium is considered the first-line treatment in bipolar disorder, although response could range from an excellent response to a complete lack of response. Response to lithium is a complex phenotype in which different factors, part of them genetics, are involved. In this sense, the aim of this study was to investigate the potential association of genetic variability at genes related to phosphoinositide, glycogen synthetase kinase-3 (GSK3), hypothalamic-pituitary-adrenal, and glutamatergic pathways with lithium response. A sample of 131 bipolar patients (99 type I, 32 type II) were grouped and compared according to their level of response: excellent responders (ER), partial responders (PR), and nonresponders (NR). Genotype and allele distributions of the rs669838 (IMPA2), rs909270 (INNP1), rs11921360 (GSK3B), and rs28522620 (GRIK2) polymorphisms significantly differed between ER, PR, and NR. When we compared the ER versus PR+NR, the logistic regression showed significant association for rs669838-C (IMPA2; P = 0.021), rs909270-G (INPP1; P = 0.009), and rs11921360-A (GSK3B; P = 0.004) with lithium nonresponse. Haplotype analysis showed significant association for the haplotypes rs3791809-rs4853694-rs909270 (INPP1) and rs1732170-rs11921360-rs334558 (GSK3B) and lithium response. Our study is in line with previous studies reporting association between genetic variability at these genes and lithium response, pointing to an effect of IMPA2, INPP1, and GSK3B genes to lithium response in bipolar disorder patients. Further studies with larger samples are warranted to assess the strength of the reported associations.

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. OBJECTIVE: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. METHODS: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. RESULTS: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. CONCLUSIONS: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Lithium response in bipolar disorder: Epigenome-wide DNA methylation signatures and epigenetic aging.

Lithium (Li) is the first-line treatment for bipolar disorder (BD) even though only 30 % of BD patients are considered excellent responders. The mechanisms by which Li exerts its action are not clearly understood, but it has been suggested that specific epigenetic mechanisms, such as methylation processes, may play a role. In this regard, DNA methylation patterns can be used to estimate epigenetic age (EpiAge), which is accelerated in BD patients and reversed by Li treatment. Our first aim was to compare the DNA methylation profile in peripheral blood between BD patients categorized as excellent responders to Li (Ex-Rp) and non-responders (N-Rp). Secondly, EpiAge was estimated to detect differential age acceleration between the two groups. A total of 130 differentially methylated positions (DMPs) and 16 differentially methylated regions (DMRs) between Ex-Rp (n = 26) and N-Rp (n = 37) were identified (FDR adjusted p-value < 0.05). We found 122 genes mapping the DMPs and DMRs, nine of which (HOXB6, HOXB3, HOXB-AS3, TENM2, CACNA1B, ANK3, EEF2K, CYP1A1, and SORCS2) had previously been linked to Li response. We found genes related to the GSK3ß pathway to be highly represented. Using FUMA, we found enrichment in Gene Ontology Cell Component for the synapse. Gene network analysis highlighted functions related to the cell cycle, nervous system development and function, and gene expression. No significant differences in age acceleration were found between Ex-Rp and N-Rp for any of the epigenetic clocks analysed. Our findings indicate that a specific methylation pattern could determine the response to Li in BD patients. We also found that a significant portion of the differentially methylated genes are closely associated with the GSK3ß pathway, reinforcing the role of this system in Li response. Future longitudinal studies with larger samples will help to elucidate the epigenetic mechanisms underlying Li response.

Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results.

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study.

BACKGROUND: Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. AIMS: The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. METHOD: We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. RESULTS: Recruitment is ongoing. CONCLUSIONS: This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study.

BACKGROUND: Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. OBJECTIVE: Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data. METHODS: We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses. RESULTS: Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with "increased motor activity" (NMI>0.55), "insomnia" (NMI=0.6), and "motor inhibition" (NMI=0.75). EDA was associated with "aggressive behavior" (NMI=1.0) and "psychic anxiety" (NMI=0.52). CONCLUSIONS: Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes.

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 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=2,367) 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<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������.

Psychometric properties of the Spanish version of the Body Weight, Image and Self-Esteem Evaluation Questionnaire in patients with severe mental disorders.

OBJECTIVE: Clinicians need brief and valid instruments to monitor the psychosocial impact of weight gain in persons with psychiatric disorders. We examined the psychometric properties of the Spanish version of the Body Weight, Image and Self-Esteem Evaluation (B-WISE) questionnaire in patients with severe mental disorders. METHOD: The data come from a naturalistic, cross-sectional, validation study conducted at 6 centres in Spain. A total of 211 outpatients with severe mental disorders, 118 with schizophrenia and 93 with bipolar disorder, were evaluated using the B-WISE, the Visual Analogue Scale for Weight and Body Image, and the Clinical Global Impression-Severity (CGI-S). The body mass index was also obtained. RESULTS: The principal component analysis confirms 3 components explaining 50.93% of the variance. The Cronbach α values for B-WISE scales ranged between .55 and .73. Significant Pearson correlations were found between B-WISE total score and CGI-S (r = -0.25; P < .001) and Visual Analogue Scale for Weight and Body Image (r = 0.47; P < .001). The B-WISE discriminates among patients with mild, moderate, and severe mental disorders according to CGI-S scores (F = 6.52; P < .005). Body mass index categorization significantly influenced total B-WISE scores (F = 3.586, P < .050). The B-WISE score corresponding to the 5th and 10th percentiles was 22. CONCLUSIONS: We were able to demonstrate that the Spanish version of the B-WISE is a valid instrument for assessing psychosocial impact of weight gain in patients with severe mental disorders in daily clinical practice.

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 (ß = -0.14; 95% confidence interval [CI]: -0.24 to -0.03; p value = 0.010) and MDD (ß = -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.

Completed suicide in bipolar disorder patients: A cohort study after first hospitalization.

BACKGROUND: Bipolar disorder (BD) is a mental health condition that has one of the greatest risk of completed suicide (CS). Hospitalization in affective disorders is associated with increased illness severity and suicide risk, so the study of suicide after the first hospitalization is of special interest. METHOD: We studied a retrospective cohort consisting on all BD type I (BD-I) and II (BD-II) (according to DSM-IV criteria) admitted for the first time in their lives to the psychiatry unit of a general hospital between 1996 and 2016 from an area in Catalonia (Spain). All patients were also followed-up in a community center of mental health as outpatients until the end of 2017. Multiple variables were prospectively collected during the first hospital admission and were compared between patients who CS and those who did not. RESULTS: 14 of 313 (4.5%) bipolar patients included CS during the 11-year follow-up, and 93% used a violent method. In the univariate analysis we found that Bipolar II Disorder, treatment with antidepressants and/or with lamotrigine were associated with higher risk of CS, however, treatment with valproate and/or with antipsychotics were associated with lower risk of CS . After logistic regression multivariant analysis, only immediately previous violent suicide attempt and first-degree family history of CS remain significant risk factors of CS. A limitation is the relatively small sample from a local hospital and followed locally. CONCLUSION: Followed during an average of 11 years after the first hospital admission, Bipolar patients completed suicide at a rate 58 times higher than the general population and almost always performed through a violent method. Violent attempted suicide before admission and first- degree family history of CS, are clear and potent predictors of completed suicide.

Spanish validation of the Barcelona TEMPS-A questionnaire in patients with bipolar disorder and general population.

BACKGROUND: The Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego (TEMPS-A) is a self-administered questionnaire intended to assess five affective temperaments: depressive, cyclothymic, hyperthymic, irritable and anxious. Our objective was to examine the psychometric properties of the TEMPS-A using a sample comprised by patients with bipolar disorder (BD) and healthy controls (HC) and to determine cut-off scores for each temperament. METHODS: Five hundred and ninety-eight individuals (327 BD and 271 HC) completed the TEMPS-A. Cronbach's alpha was used to examine internal consistency reliability. Test-retest reliability and association between different temperamental scales were assessed using Spearman correlation. To confirm factor structure a confirmatory factor analysis (CFA) was carried out. Cut-off scores indicating the presence of dominant temperament were also calculated. RESULTS: Internal consistency was optimal for all temperament subscales (α: 0.682- 0.893). The questionnaire demonstrated good test-retest reliability (ρ: 0.594-0.754). The strongest positive associations were found between cyclothymic and anxious and between depressive and anxious temperaments. Hyperthymic and depressive as well as hyperthymic and anxious temperaments showed a strong negative correlation. LIMITATIONS: The HC sample was not matched with the BD group. There were some sociodemographic and clinical differences between groups that may impact on the obtained results. A portion of patients with BD was recruited from tertiary centers. CONCLUSIONS: The Spanish version of the Barcelona TEMPS-A questionnaire presents a good internal consistency and their results are stable in clinical population. The performance of the Barcelona TEMPS-A is as good as the original scale.

Impact of caregiver group psychoeducation on the course and outcome of bipolar patients in remission: a randomized controlled trial.

OBJECTIVE: Although there are some randomized controlled trials that highlight the positive role of family-focused treatment added to pharmacotherapy in bipolar disorder, no trials using contemporary methodologies have analyzed the specific effect of working with caregiver-only groups. The aim of this study was to assess the efficacy of a psychoeducational group intervention focused on caregivers of euthymic bipolar patients. METHOD: A total of 113 medicated euthymic bipolar outpatients who lived with their caregivers were randomized into an experimental and a control group. Caregivers in the experimental group received twelve 90-min group psychoeducation sessions focused on knowledge of bipolar disorder and training in coping skills. The patients did not attend the groups. Caregivers assigned to the control group did not receive any specific intervention. Patients were assessed monthly during both the intervention and the 12 months of follow-up. The primary outcome was time to any mood recurrence. RESULTS: Psychoeducation group intervention focused on the caregivers of bipolar patients carried a reduction of the percentage of patients with any mood recurrence (chi2 = 6.53; p = 0.011) and longer relapse-free intervals (log-rank chi(2) = 4.04; p = 0.044). When different types of episodes were analyzed separately, the effect was significant for both the number of patients who experienced a hypomanic/manic recurrence (chi2 = 5.65; p = 0.017) and the time to such an episode (log-rank chi2 = 5.84; p = 0.015). The differences in preventing depressive and mixed episodes were not significant. CONCLUSIONS: A psychoeducation group intervention for the caregivers of bipolar patients is a useful adjunct to usual treatment for the patients in reducing the risk of recurrences, particularly mania and hypomania, in bipolar disorder.