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.

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.

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.

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 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.

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<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������.

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.

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.

Tratamiento con aripiprazol en la práctica clínica: consenso de un panel de expertos / Treatment with aripiprazole in clinical practice: Expert consensus panel

Objetivo: Revisión de la evidencia científica sobre el manejo clínico del aripiprazol. Metodología: Un panel de expertos formado por 7 miembros discutió una serie de casos clínicos. Cuando se llegó a un consenso, sacaron sus conclusiones. Además, se revisaron e incluyeron los datos y la evidencia clínica de los ensayos clínicos de aripiprazol más relevantes de los últimos 10años. Resultados: El aripiprazol por vía oral es eficaz para el tratamiento de los pacientes con esquizofrenia y trastorno bipolar, tanto en la fase aguda como en la fase de mantenimiento. También demostró ser eficaz para evitar las recaídas. La administración intramuscular es útil en el manejo de la agitación en esos pacientes. La presentación inyectable de liberación prolongada ha tenido resultados positivos en el retraso de la recaída en pacientes esquizofrénicos y bipolares, y mejora el cumplimiento del tratamiento. El aripiprazol es un fármaco bien tolerado y los efectos adversos, como somnolencia, aumento de peso, trastornos metabólicos o eventos cardiovasculares, son menos frecuentes que para otros fármacos antipsicóticos. El aripiprazol ha sido bien tolerado cuando se usa en combinación con otros antipsicóticos, debido a sus interacciones limitadas. Conclusiones: Los datos de los estudios revisados y el consenso del panel de expertos mostraron que el aripiprazol es un tratamiento efectivo y bien tolerado para los pacientes con esquizofrenia, trastorno esquizoafectivo, trastorno bipolar moderado a grave y episodios maníacos. Su uso también conduce a una mejor adherencia. La menor frecuencia de sedación y el hecho de que no afecte a la función cognitiva del paciente mejoran la adherencia y colocan al aripiprazol en una buena posición como opción terapéutica

Objective: To review the scientific evidence on the clinical management of aripiprazole. Methodology: A seven-member expert panel discussed a series of clinical cases. When a consensus was reached, they drew their conclusion. They also reviewed, and included data and clinical evidence from the most relevant aripiprazole clinical trials from the last 10years. Results: Oral aripiprazole is effective for the treatment of patients with schizophrenia and bipolar disorder, both in the acute and maintenance phase. It was also shown to be effective to prevent relapses. Intramuscular administration is useful in the management of agitation in these patients. The presentation of prolonged action has had positive results in the delay of a relapse in schizophrenic and bipolar patients, and improves treatment compliance. Aripiprazole is a well-tolerated drug and the secondary effects, such as drowsiness, increase in weight, metabolic disorders, or cardiovascular events are less common than in other antipsychotic drugs. Aripiprazole has been well-tolerated when it was used in combination with other antipsychotic drugs, due to their limited interaction. Conclusions: The data from the reviews studied and the consensus of the Expert Panel showed that Aripiprazole is an effective and well-tolerated treatment for patients with schizophrenia, schizo-affective disorders, moderate to severe bipolar disorder, and manic episodes. Its use leads to improved adherence. The lower sedation frequency and the fact that it does affect the cognitive function of the patient improves adherence and places aripiprazole in a good position as a therapeutic option

Association between genetic variation in the myo-inositol monophosphatase 2 (IMPA2) gene and age at onset of bipolar disorder.

INTRODUCTION: The age at onset of bipolar disorder (BD) has significant implications for severity, duration of affective episodes, response to treatment, and psychiatric comorbidities. It has been suggested that early-onset BD (EO-BD) could represent a clinically distinct subtype with probable genetic risk factors different from those of late-onset BD (LO-BD). To date, several genes have been associated with BD risk but few studies have investigated the genetic differences between EO-BD and LO-BD. The aim of this study was to evaluate if variants of the gene coding for myo-inositol monophosphatase (IMPA2) are linked to age at onset of BD. METHOD: 235 bipolar patients were recruited and assessed. The final sample consisting of 192 euthymic individuals, was compared according to the age at onset. Polymorphisms were genotyped in the IMPA2 gene (rs669838, rs1020294, rs1250171, and rs630110). Early-onset was defined by the appearance of a first affective episode before the age of 18. RESULTS: The analyses showed that in the genotype distribution rs1020294 (p = .01) and rs1250171 (p = .01) were associated with the age at onset. The significant effect remained only in the rs1020294 SNP in which G carriers were more likely to debut later compared to patients presenting the AA genotype (p = .002; OR = 9.57, CI95%[2.37-38.64]). The results also showed that EO-BD tended to experience more alcohol misuse (p = .003; OR = .197, CI95%[.07-.58]) compared to LO-BD. CONCLUSIONS: Our results provide evidence for genetic differences between EO-BD and LO-BD at the IMPA2 gene as well as clinical differences between subgroups with therapeutic implications.

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.

Ketamina asociada a terapia electroconvulsiva en depresión resistente al tratamiento en pacientes de edad avanzada: a propósito de 2 casos / Ketamine associated with electroconvulsive therapy for treatment-resistant depression in the elderly: two case reports

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