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Background Despite thorough and validated clinical guidelines based on bipolar disorders subtypes, large pharmacological treatment heterogeneity remains in these patients. There is limited knowledge about the different treatment combinations used and their influence on patient outcomes. We attempted to determine profiles of patients based on their treatments and to understand the clinical characteristics associated with these treatment profiles. Methods This multicentre longitudinal study was performed on a French nationwide bipolar cohort database. We performed hierarchical agglomerative clustering to search for clusters of individuals based on their treatments during the first year following inclusion. We then compared patient clinical characteristics according to these clusters. Results Four groups were identified among the 1795 included patients: group 1 (“heterogeneous” n = 1099), group 2 (“lithium” n = 265), group 3 (“valproate” n = 268), and group 4 (“lamotrigine” n = 163). Proportion of bipolar 1 disorder, in groups 1 to 4 were: 48.2%, 57.0%, 48.9% and 32.5%. Groups 1 and 4 had greater functional impact at baseline and a less favorable clinical and functioning evolution at one-year follow-up, especially on GAF and FAST scales. Limitations The one-year period used for the analysis of mood stabilizing treatments remains short in the evolution of bipolar disorder. Conclusions Treatment profiles are associated with functional evolution of patients and were not clearly determined by bipolar subtypes. These profiles seem to group together common patient phenotypes. These findings do not seem to be influenced by the duration of disease prior to inclusion and neither by the number of treatments used during the follow-up period.

International audience; Objectives: The comorbidity of alcohol use disorder (AUD) and bipolar disorder (BD) has been repeatedly associated with poorer clinical outcomes than BD without AUD. We aimed to extend these findings by focusing on the characteristics associated with the sequence of onset of BD and AUD. Methods: 3,027 outpatients from the Fondamental Advanced Centres of Expertise were ascertained for BD-1, BD-2 and AUD diagnoses, including their respective ages at onset (AAOs, N =2,804). We selected the variables associated with both the presence and sequence of onset of comorbid AUD using bivariate analyses corrected for multiple testing to enter a binary regression model with the sequence of onset of BD and AUD as the dependent variable (AUD first - which also included 88 same-year onsets, vs. BD first). Results: BD patients with comorbid AUD showed more severe clinical profile than those without. Compared to BD-AUD (N =269), AUD-BD (N =276) was independently associated with a higher AAO of BD (OR =1.1, p

International audience; Background: Bipolar disorder (BD) is a chronic and severe mental illness. It requires a non-discontinued pharmacological treatment to prevent mood recurrences but nonadherence to medication is frequent. To this date, medication adherence in BD has been mostly evaluated in cross-sectional studies and often considered as a stable trait. We aimed to study medication adherence using a prospective person-oriented approach. Methods: 1627 BD patients were followed on a 2 years period and assessed every 6 months. Medication adherence was evaluated at each visit with the Medication Adherence Rating Scale (MARS). A latent class mixed model (LCMM) was used to identify trajectory classes of adherence over time. Regression analyses and linear mixed model were used to search for predictors and covariables of the trajectories. Results: Three distinct and robust trajectories of medication adherence have been identified: one that starts poorly and keeps deteriorating (4.8%), one that starts poorly but improves (9%) and one that starts well and keeps improving (86.2%). A good tolerance to psychotropic medications, low depressive symptoms, the absence of comorbid eating disorders and anticonvulsant medication were associated to a better prognosis of adherence. Along the follow-up, the lower were the depressive symptoms, the better was the medication adherence (p

BACKGROUND: Tobacco smoking increases the global burden of bipolar disorder (BD). We examined markers of physical and mental health that are associated with tobacco smoking, controlling for confounders that have not always been considered in previous studies of BD. METHODS: Over 600 individuals with BD I or II referred to the French Network for bipolar disorder (FACE-BD) who completed standardized assessments, and could be reliably classified as current (CS) or former smokers (FS), were compared with those who were never smokers (NS) on: BD symptom load and psychiatric comorbidities; prevalence of alcohol and substance use disorders (ASUD); medication usage; functioning and physical health parameters. The bivariate and multivariate analyses took into account age and gender. RESULTS: 300 cases (49%) were CS, 78 (13%) FS and 238 (39%) had never smoked. Rates were similar across genders regardless of BD subtype. Compared with NS, CS were more likely to have an ASUD (Odds Ratio (OR) 5.18), BD I (OR 2.09), and lower abdominal obesity (OR 0.97), and FS were more likely to have an ASUD (OR 6.32) and higher abdominal obesity (OR 1.03). LIMITATIONS: The sample comprised of white Europeans; the FS subgroup was relatively small and we did not apply any statistical correction for the bivariate analyses. CONCLUSIONS: The increased risk of physical and mental health burden in CS and FS compared to NS represents avoidable morbidity in BD. This study offers support to the argument that individuals with BD should be routinely offered support to prevent or stop tobacco smoking.

Background DSM-IV criteria for mixed states may be too restrictive and may actually exclude patients who do not meet the full criteria for a manic and depressive state. Using this DSM-IV definition, many patients who are considered depressed may have mixed features, which can explain why some bipolar depressive states can worsen with antidepressants and can be improved by mood stabilizers or atypical antipsychotics. A dimensional approach not exclusively focused on the tonality of affect would help to define a broader entity of mixed states. The aim of this study was to apply a dimensional model to bipolar episodes and to assess the overlap between the groups defined using this model and using categorical diagnosis. Method We assessed 139 DSM-IV acutely ill bipolar I patients with MAThyS (Multidimensional Assessment of Thymic States by Henry et al. in press), a scale that assesses five quantitative dimensions exploring excitatory and inhibition processes, and that is not focused on tonality of mood but on emotional reactivity. We studied the relationship between clusters defined by statistical analyses and DSM-IV bipolar mood states. Results This study showed the existence of three clusters. Cluster 1 was characterized by an inhibition in all dimensions and corresponded to the depressive cluster (more than 90% of patients met the criteria for DSM-IV Major Depressive Episode (MDE)). Cluster 2 showed a general excitation and was mainly DSM-IV manic or hypomanic patients (90%). Cluster 3 (Mixed) was more complex and the diagnosis included MDE (56%) in most of the cases associated with manic or hypomanic symptoms, mixed states (18%) defined by DSM-IV criteria, and manic or hypomanic states (25%). Emotional reactivity was relevant to distinguish Cluster 1 (Depressive), exhibiting emotional hypo-reactivity, from Cluster 2 (Manic) and 3 (Mixed), characterized by emotional hyper-reactivity. Sadness was reported equally in all three clusters. Conclusion A dimensional approach using the concept of emotional reactivity seems appropriate to define a broad mixed state entity in patients who would be diagnosed with MDE according to DSM-IV. Further studies are needed to test the relevance of this model in therapeutic strategies.

Introduction The aim of this study was to investigate the psychometric properties of the original 54 item version (ALS-54) and the short 18 item version (ALS-18) of the Affective Lability Scale (ALS) in patients with bipolar disorders, their first-degree relatives and healthy controls. Internal Consistency and Confirmatory Factor Analysis were performed, comparing clinical and non-clinical group comparisons on ALS scores. Methods A total of 993 participants (patients with bipolar disorders [ n =422], first-degree relatives [ n =201] and controls [ n =370]) were recruited from France and Norway. Diagnosis and clinical characteristics were assessed using the Structured Clinical Interview for DSM-IV Axis I disorders (SCID-I), or the Diagnostic Interview for Genetic Studies (DIGS). Affective lability was measured using the ALS-54 and ALS-18. Results Both ALS-54 and ALS-18 showed high internal consistency, but the subdimensions of both versions were highly inter-correlated. From confirmatory factor analysis both versions revealed acceptable to good model fit. Patients had significantly higher ALS scores compared to controls, with affected first-degree relatives presenting intermediate scores. Conclusion Both the original ALS-54 version and the short ALS-18 version showed good psychometric properties. They also discriminated between patients with a bipolar disorder (high ALS), first degree relatives (intermediate ALS), and healthy controls (low ALS). A high correlation between ALS items for both versions was observed. Our study supports reducing the scale from 54 to 18 items.

Background Poor adherence to medication is frequent in Bipolar Disorder (BD). It is associated with illness severity and increases total medical cost. Several factors are associated with poor adherence but previous studies included heterogeneous cohorts of patients with and without current mood episode, with and without SUD. Methods We conducted a cross-sectional study, based on the Fondamental Advanced Centers of Expertise in Bipolar Disorders. 382 patients diagnosed with BD (type I, II or NOS) according to DSM-IV, with partial or complete remission and without comorbid SUD, were included. All patients had a large standardized clinical evaluation with structured interview and self reports. Side effects were evaluated with Patient Rated Inventory of Side Effects (PRISE). Adherence behavior was measured by a self reported scale, Medication Adherence Rating Scale (MARS). Univariate analyses and linear regression models were undertaken to determine factors associated with adherence. Results Residual depressive symptoms ( β =−0.155, p =0.004), and side effects ( β =−0.142, p =0.008) were the main factors associated with adherence behavior in linear regression model. We found no association with residual manic symptoms, age at assessment, marital status, number of past mood episodes as well as past psychotic symptoms. Limitation We used no other assessment than self-rating scale for adherence behavior evaluation. We had no information concerning treatment regimen and patient/family knowledge about BD. Conclusions Adherence behavior in bipolar patients appears to be mainly influenced by the presence of residual depressive symptoms in patients without SUD. Improvement in diagnosis and pharmacotherapy of residual depressive symptoms has to be kept in mind to face low adherence to medication.