Affective temperaments mediate aggressive dimensions in bipolar disorders: A cluster analysis from a large, cross-sectional, international study

Background: Affective temperaments show potential for aggressive behavior (AB) preventive strategies in bipolar disorder (BD). We aim to define intra-diagnostic subgroups of patients with BD based on homogeneous behaviors related to AB. Subsequently, to assess whether affective temperament dimensions may contribute to the presence and severity of AB. Methods: Patients with BD were recruited. AB was evaluated through the modified overt aggression scale (MOAS); affective temperaments were assessed with the TEMPS-A. A cluster analysis was conducted based on TEMPS-A and MOAS scores. Stepwise backward logistic regression models were used to identify the predictive factors of cluster membership. Results: 799 patients with BD were enrolled. Three clusters were determined: non-aggressive (55.5 %), self-aggressive (18 %), and hetero-aggressive (26.5 %). Depressive, irritable, and anxious temperament scores significantly increased from the non-aggressive (lower) to the self-aggressive (intermediate) and the hetero-aggressive group (highest). A positive history of a suicide attempt (B = 5.131; OR = 169.2, 95 % CI 75.9; 377) and rapid cycling (B = -0.97; OR = 0.40, 95 % CI 0.17; 0.95) predicted self-aggressive cluster membership. Atypical antipsychotics (B = 1.19; OR = 3.28, 95 % CI 2.13; 5.06) or SNRI treatment (B = 1.09; OR = 3, 95 % CI 1.57; 5.71), psychotic symptoms (B = 0.73; OR = 2.09, 95 % CI 1.34; 3.26), and history of a suicide attempt (B = -1.56; OR = 0.20, 95 % CI 0.11; 0.38) predicted hetero-aggressive cluster membership. Limitations: Recall bias might have affected the recollection of AB. Conclusions: Clinical factors orientate the prevention of different ABs in BD. Affective temperaments might play a role in preventing AB since patients with more pronounced affective temperaments might have an increased risk of showing AB, in particular hetero-AB

The U-shaped relationship between parental age and the risk of bipolar disorder in the offspring: A systematic review and meta-analysis

Parenthood age may affect the risk for the development of different psychiatric disorders in the offspring, including bipolar disorder (BD). The present systematic review and meta-analysis aimed to appraise the relationship between paternal age and risk for BD and to explore the eventual relationship between paternal age and age at onset of BD. We searched the MEDLINE, Scopus, Embase, PsycINFO online databases for original studies from inception, up to December 2021. Random-effects meta-analyses were conducted. Sixteen studies participated in the qualitative synthesis, of which k = 14 fetched quantitative data encompassing a total of 13,424,760 participants and 217,089 individuals with BD. Both fathers [adjusted for the age of other parent and socioeconomic status odd ratio - OR = 1.29(95%C.I. = 1.13-1.48)] and mothers aged ≤ 20 years [(OR = 1.23(95%C.I. = 1.14-1.33)] had consistently increased odds of BD diagnosis in their offspring compared to parents aged 25-29 years. Fathers aged ≥ 45 years [adjusted OR = 1.29 (95%C.I. = 1.15-1.46)] and mothers aged 35-39 years [OR = 1.10(95%C.I. = 1.01-1.19)] and 40 years or older [OR = 1.2(95% C.I. = 1.02-1.40)] likewise had inflated odds of BD diagnosis in their offspring compared to parents aged 25-29 years. Early and delayed parenthood are associated with an increased risk of BD in the offspring. Mechanisms underlying this association are largely unknown and may involve a complex interplay between psychosocial, genetic and biological factors, and with different impacts according to sex and age range. Evidence on the association between parental age and illness onset is still tentative but it points towards a possible specific effect of advanced paternal age on early BD-onset

Lithium therapy and weight change in people with bipolar disorder: A systematic review and meta-analysis

Lithium remains the gold standard maintenance treatment for Bipolar Disorder (BD). However, weight gain is a side effect of increasing relevance due to its metabolic implications. We conducted a systematic review and metaanalysis aimed at summarizing evidence on the use of lithium and weight change in BD. We followed the PRISMA methodology, searching Pubmed, Scopus and Web of Science. From 1003 screened references, 20 studies were included in the systematic review and 9 included in the meta-analysis. In line with the studies included in the systematic review, the meta-analysis revealed that weight gain with lithium was not significant, noting a weight increase of 0.462 Kg (p = 0158). A shorter duration of treatment was significantly associated with more weight gain. Compared to placebo, there were no significant differences in weight gain. Weight gain was significantly lower with lithium than with active comparators. This work reveals a low impact of lithium on weight change, especially compared to some of the most widely used active comparators. Our results could impact clinical decisions

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

Efficacy and tolerability of monoamine oxidase inhibitors for the treatment of depressive episodes in mood disorders:A systematic review and network meta-analysis

BACKGROUND: Monoamine oxidase inhibitors (MAOIs) are considered third-line treatments for treatment resistant depression; however, they are underused in clinical practice.AIMS: This study aimed to assess the efficacy, tolerability, and acceptability of MAOIs for the treatment of depression in comparison with other antidepressant treatments.METHODS: A systematic review and network meta-analysis of randomised clinical trials was performed to compare the efficacy, tolerability and acceptability between MAOIs and other antidepressant treatments for the treatment of depressive episodes.RESULTS: A total of 83 double-blinded, randomised controlled trials were included in the analysis, with 7765 participants assigned to an active treatment and 1844 assigned to placebo. Several MAOIs, including isocarboxazid, phenelzine, tranylcypromine and moclobemide, showed significantly higher efficacy compared with placebo. The tolerability and acceptability of MAOIs was comparable to other antidepressants.LIMITATIONS: A disproportionate number of studies investigating the most commonly used MAOIs, such as moclobemide and phenelzine, and a lack of specific studies focusing on treatment-resistant and atypical depression.CONCLUSIONS: MAOIs are similar in efficacy to other antidepressants for the treatment of depression. However, more studies are needed comparing MAOI treatment in people with treatment-resistant, atypical and bipolar depression.</p

Shaped before birth: Obstetric complications identify a more severe clinical phenotype among patients presenting a first affective or non-affective episode of psychosis

Obstetric complications (OCs) may contribute to the heterogeneity that characterizes psychiatric illness, particularly the phenotypic presentation of first episode psychoses (FEP). Our aim was to examine the relationship between OCs and socio-demographic, clinical, functioning and neuropsychological characteristics in affective and non-affective FEP. We performed a cross-sectional,study where we recruited participants with FEP between 2011 and 2021, and retrospectively assessed OCs using the Lewis-Murray scale. OCs were used as a dichotomous variable and further stratified into three subtypes: complications of pregnancy, abnormal fetal growth and development, and difficulties in delivery. We performed a logistic stepwise forward regression analysis to examine variables associated with the presence of OCs. Of the 104 participants (67 affective FEP and 37 non-affective FEP), 31.7% (n = 33) had experienced OCs. Subjects with OCs showed a more gradual emergence of prodromal symptoms as well as higher negative and total Positive and Negative Syndrome Scale (PANSS) scores. In the multivariate analysis, the presence of OCs was independently associated with a younger age at first episode of any type (OR = 0.904, p = 0.003) and slower emergence of prodromal symptoms (OR = 0.274, p = 0.011). When considering specific types of OCs, those related with fetal growth were associated with worse neuropsychological performance, while OCs at delivery were related to earlier onset of illness and more severe symptoms. In conclusion, OCs signaled a specific FEP phenotype characterized by earlier and more protracted onset of illness as well as more burdensome symptoms, independently of FEP type (i.e., affective vs non-affective). These results indicate a potential target of early intervention in FEP