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

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