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OBJECTIVES: Persistent functional impairment is common in bipolar disorder (BD) and is influenced by a number of demographic, clinical, and cognitive features. The goal of this project was to estimate and compare the influence of key factors on community function in multiple cohorts of well-characterized samples of individuals with BD. METHODS: Thirteen cohorts from 7 countries included n = 5882 individuals with BD across multiple sites. The statistical approach consisted of a systematic uniform application of analyses across sites. Each site performed a logistic regression analysis with empirically derived higher versus lower function as the dependent variable and selected clinical and demographic variables as predictors. RESULTS: We found high rates of functional impairment, ranging from 41 to 75%. Lower community functioning was associated with depressive symptoms in 10 of 12 of the cohorts that included this variable in the analysis. Lower levels of education, a greater number of prior mood episodes, the presence of a comorbid substance use disorder, and a greater total number of psychotropic medications were also associated with low functioning. CONCLUSIONS: The bipolar clinical research community is poised to work together to characterize the multi-dimensional contributors to impairment and address the barriers that impede patients complete recovery. We must also identify the core features which enable many to thrive and live successfully with BD. A large-scale, worldwide, prospective longitudinal study focused squarely on BD and its heterogeneous presentations will serve as a platform for discovery and promote major advances toward optimizing outcomes for every individual with this illness.

Introduction: Bipolar disorders (BD) are characterized by mood symptoms that can worsen medication side effects. We aimed to study the association between residual mood signs and self‐reported side effects in the euthymic phase of BD. Methods: We assessed residual mood signs using the Montgomery–Asberg Depression Rating scale (MADRS) and Young Mania Rating scale (YMRS) and self‐reported side effects using the Patient‐Rated Inventory of Side Effects (PRISE‐M) for 880 males and 1369 females with BD. We conducted a network analysis to test the associations between 52 items of the three scales for males and females separately. We then identified clusters of nodes that fit the networks well. Results: We report only positive associations between residual mood signs and side effects. An elevated mood (YMRS) in females and increased energy (YMRS) in males were central nodes, strongly influencing the development of additional mood symptoms and side effects. Furthermore, we identified three clusters of nodes in both sexes: (1) a "mood cluster", including most YMRS and MADRS items and the PRISE‐M items evaluating sedation, sleep, and restlessness, (2) a cluster of nonsexual side effects (mostly PRISE‐M items), and (3) a cluster of sexual side effects. In both sexes, we identified bridge nodes that may favor the communication between mood and side effects, namely palpitations (PRISE‐M) and agitation (PRISE‐M). Conclusions: The results justify the particular attention of practitioners to monitor elevated moods or increased energy to try to reduce self‐reported side effects and other residual mood symptoms in the euthymic phase of BD. Our findings suggest that clinicians could consider patient‐reported loss of energy, difficulty in falling asleep, and restlessness as mood symptoms rather than medications' side effects. Palpitations and agitation may contribute to the development of additional mood symptoms or somatic complaints. [ABSTRACT FROM AUTHOR]

Background: Individuals with bipolar disorders (BD) have an estimated loss of life expectancy around 10–15 years. Several laboratory‐measured biomarkers of accelerated aging exist (e.g., telomere length), however with a questionable transferability to bedside. There is a need for easily and inexpensively measurable markers of aging, usable in routine practice, such as BioAge. Methods: We calculated BioAge that estimates biological age based on routine blood tests and a physical exam, in a sample of 2220 outpatients with BD. We investigated associations between BioAge Acceleration (BioAgeAccel), which is an indicator of accelerated aging, and sociodemographic variables, clinical variables, and current psychotropic medication use. Results: Mean chronological age was 40.2 (±12.9). Mean BioAge was 39.1 (±12.4). Mean BioAgeAccel was 0.08 (±1.8). A minority of individuals (15%) had a BioAgeAccel above 2 years. Multivariable analyses suggested strong associations between a higher BioAgeAccel and younger age, male sex, overweight and sleep disturbances. Regarding current psychotropic medication use, discrepancies between univariate and multivariate analyses were observed. Conclusions: A minority of individuals with BD had an accelerated aging as measured by BioAge. We identified associations with potentially modifiable factors, such as higher body mass index and sleep disturbances, that are however nonspecific to BD. These results require replications in independent samples of individuals with BD, and comparisons with a control group matched for age and gender. Longitudinal studies are also required to test whether any change in metabolic health, or sleep might decrease BioAgeAccel. [ABSTRACT FROM AUTHOR]

Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from −6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from −4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium’s clearance in only two models. To conclude, most of lithium’s PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.

Background: Treatment optimization is mandatory in psychiatric diseases and the use of population pharmacokinetics (popPK) models through model informed precision dosing (MIPD) has the potential to improve patient medical care. In this perspective, meta-modelling methods could provide popPK models with improved predictive performances and most of covariates of interest. The aims of this study were to develop meta-models of clozapine and lithium, assess their predictability and propose optimized dosing regimens for both drugs. Methods: Two popPK models for each drug were retained to develop the meta-models. For clozapine, the model with the best predictive performances and gender as a covariate and one with smoking status were retained. For lithium, the model with the best predictive performances and fat-free mass as covariate and one with glomerular filtration rate were retained. Results: Both meta-models showed improved predictability compared to the original models. Clozapine meta-model simulations allowed us to propose dosing regimen according to gender and smoking status. Steady-state doses ranged from 375 to 725 mg/day for clozapine once daily, and from 350 to 650 mg/day for clozapine twice daily. Lithium meta-model simulations allowed us to propose dosing regimen according to weight, body mass index, gender and GFR. Our steady-state dose propositions ranged from 625 to 1125 mg/day for males, and from 375 to 750 mg/day for females. Conclusion: Both meta-models met the acceptability criteria for use in clinical practice on all subpopulations of interest. Those models could be used in the perspective of MIPD for clozapine and lithium. [ABSTRACT FROM AUTHOR]

Background. Physical pain is a common issue in people with bipolar disorder (BD). It worsens mental health and quality of life, negatively impacts treatment response, and increases the risk of suicide. Lithium, which is prescribed in BD as a mood stabilizer, has shown promising effects on pain. Methods. This naturalistic study included 760 subjects with BD (FACE-BD cohort) divided in two groups: with and without self-reported pain (evaluated with the EQ-5D-5L questionnaire). In this sample, 176 subjects were treated with lithium salts. The objectives of the study were to determine whether patients receiving lithium reported less pain, and whether this effect was associated with the recommended mood-stabilizing blood concentration of lithium. Results. Subjects with lithium intake were less likely to report pain (odds ratio [OR] = 0.59, 95% confidence interval [CI], 0.35-0.95; p = 0.036) after controlling for sociodemographic variables, BD type, lifetime history of psychiatric disorders, suicide attempt, personality traits, current depression and anxiety levels, sleep quality, and psychomotor activity. Subjects taking lithium were even less likely to report pain when lithium concentration in blood was =0.5 mmol/l (OR = 0.45, 95% CI, 0.24-0.79; p = 0.008). Conclusions. This is the first naturalistic study to show lithium's promising effect on pain in subjects suffering from BD after controlling for many confounding variables. This analgesic effect seems independent of BD severity and comorbid conditions. Randomized controlled trials are needed to confirm the analgesic effect of lithium salts and to determine whether lithium decreases pain in other vulnerable populations. [ABSTRACT FROM AUTHOR]

Depression, a devastating psychiatric disorder, is a leading cause of disability worldwide. Current antidepressants address specific symptoms of the disease, but there is vast room for improvement.sup.1. In this respect, new compounds that act beyond classical antidepressants to target signal transduction pathways governing synaptic plasticity and cellular resilience are highly warranted.sup.2-4. The extracellular signal-regulated kinase (ERK) pathway is implicated in mood regulation.sup.5-7, but its pleiotropic functions and lack of target specificity prohibit optimal drug development. Here, we identified the transcription factor ELK-1, an ERK downstream partner.sup.8, as a specific signaling module in the pathophysiology and treatment of depression that can be targeted independently of ERK. ELK1 mRNA was upregulated in postmortem hippocampal tissues from depressed suicides; in blood samples from depressed individuals, failure to reduce ELK1 expression was associated with resistance to treatment. In mice, hippocampal ELK-1 overexpression per se produced depressive behaviors; conversely, the selective inhibition of ELK-1 activation prevented depression-like molecular, plasticity and behavioral states induced by stress. Our work stresses the importance of target selectivity for a successful approach for signal-transduction-based antidepressants, singles out ELK-1 as a depression-relevant transducer downstream of ERK and brings proof-of-concept evidence for the druggability of ELK-1. The transcription factor ELK-1 is upregulated in patients with major depressive disorder, and selective inhibition of hippocampal ELK-1 produces rapid antidepressive effects in rodent models of depression., Author(s): Kallia Apazoglou [sup.1] , Séverine Farley [sup.1] , Victor Gorgievski [sup.1] , Raoul Belzeaux [sup.2] [sup.3] [sup.4] , Juan Pablo Lopez [sup.2] , Julien Grenier [sup.5] , El Chérif [...]