Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.

The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.

There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.

These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.

L’introduction de l’agomélatine est peu fréquente au sein de notre établissement, 39 patients sur les 925 patients sous antidépresseurs ont été répertoriés en un an, soit 4,21 %. L’objectif de cette étude était de comprendre pourquoi cet antidépresseur était si peu prescrit.

Une étude rétrospective a été réalisée sur 1 an (du 1/06/2014 au 1/06/2015) pour l’ensemble des services d’hospitalisation de l’établissement. Une analyse des prescriptions d’agomélatine a été effectuée à partir du logiciel Pharma® : posologie, durée, nombre de patients. La cause d’un éventuel arrêt ou d’un relais par une autre molécule a été évaluée à partir des informations recueillies dans les dossiers patients (logiciel Crossway®).

Sur les 39 patients ayant reçu de l’agomélatine, la posologie était de 1 comprimé par jour pour 82 % des patients et 2 comprimés par jour pour les 18 % restant. Seize patients (41 %) ont arrêté le traitement sur cette période, la moyenne de durée de leur traitement était de 25 jours [1–90]. Concernant les causes d’arrêt du traitement, 6 patients (37,5 %) l’ont arrêté à cause d’effets secondaires somatiques (5 pour des troubles hépatiques, 1 pour trouble du sommeil type insomnie), 5 (31,25 %) pour cause d’inefficacité de la molécule dont 3 patients pour lesquels le traitement a été arrêté avant les 15 jours recommandés. Pour 3 patients la cause d’arrêt n’a pas été retrouvée. L’arrêt du traitement a été suivi par la prescription d’un autre antidépresseur sauf pour 2 patients où l’indication n’était plus retrouvée.

Le peu de prescription d’agomélatine peut s’expliquer par une efficacité jugée discutable de la molécule sur les pathologies dépressives nécessitant une hospitalisation mais aussi des effets secondaires hépatiques fréquents imposant une surveillance accrue. L’indication de l’agomélatine pour la pathologie dépressive traitée en hospitalisation est donc « discutée ».

Postoperative cognitive impairment is among the most common medical complications associated with surgical interventions – particularly in elderly patients. In our aging society, it is an urgent medical need to determine preoperative individual risk prediction to allow more accurate cost–benefit decisions prior to elective surgeries. So far, risk prediction is mainly based on clinical parameters. However, these parameters only give a rough estimate of the individual risk. At present, there are no molecular or neuroimaging biomarkers available to improve risk prediction and little is known about the etiology and pathophysiology of this clinical condition. In this short review, we summarize the current state of knowledge and briefly present the recently started BioCog project (Biomarker Development for Postoperative Cognitive Impairment in the Elderly), which is funded by the European Union. It is the goal of this research and development (R&D) project, which involves academic and industry partners throughout Europe, to deliver a multivariate algorithm based on clinical assessments as well as molecular and neuroimaging biomarkers to overcome the currently unsatisfying situation.

Neuroimaging measures of behavioral and emotional dysregulation can yield biomarkers denoting developmental trajectories of psychiatric pathology in youth. We aimed to identify functional abnormalities in emotion regulation (ER) neural circuitry associated with different behavioral and emotional dysregulation trajectories using latent class growth analysis (LCGA) and neuroimaging.

A total of 61 youth (9–17 years) from the Longitudinal Assessment of Manic Symptoms study, and 24 healthy control youth, completed an emotional face n-back ER task during scanning. LCGA was performed on 12 biannual reports completed over 5 years of the Parent General Behavior Inventory 10-Item Mania Scale (PGBI-10M), a parental report of the child's difficulty regulating positive mood and energy.

There were two latent classes of PGBI-10M trajectories: high and decreasing (HighD; n = 22) and low and decreasing (LowD; n = 39) course of behavioral and emotional dysregulation over the 12 time points. Task performance was >89% in all youth, but more accurate in healthy controls and LowD versus HighD (p < 0.001). During ER, LowD had greater activity than HighD and healthy controls in the dorsolateral prefrontal cortex, a key ER region, and greater functional connectivity than HighD between the amygdala and ventrolateral prefrontal cortex (p's < 0.001, corrected).

Patterns of function in lateral prefrontal cortical–amygdala circuitry in youth denote the severity of the developmental trajectory of behavioral and emotional dysregulation over time, and may be biological targets to guide differential treatment and novel treatment development for different levels of behavioral and emotional dysregulation in youth.

Patients with major depressive disorder (MDD) present with highly heterogeneous symptom profiles. We aimed to examine whether individual differences in amygdala activity to emotionally salient stimuli were related to heterogeneity in lifetime levels of depressive and subthreshold manic symptoms among adults with MDD.

We compared age- and gender-matched adults with MDD (n = 26) with healthy controls (HC, n = 28). While undergoing functional magnetic resonance imaging, participants performed an implicit emotional faces task: they labeled a color flash superimposed upon initially neutral faces that dynamically morphed into one of four emotions (angry, fearful, sad, happy). Region of interest analyses examined group differences in amygdala activity. For conditions in which adults with MDD displayed abnormal amygdala activity versus HC, within-group analyses examined amygdala activity as a function of scores on a continuous measure of lifetime depression-related and mania-related pathology.

Adults with MDD showed significantly greater right-sided amygdala activity to angry and happy conditions than HC (p < 0.05, corrected). Multiple regression analyses revealed that greater right-amygdala activity to the happy condition in adults with MDD was associated with higher levels of subthreshold manic symptoms experienced across the lifespan (p = 0.002).

Among depressed adults with MDD, lifetime features of subthreshold mania were associated with abnormally elevated amygdala activity to emerging happy faces. These findings are a first step toward identifying biomarkers that reflect individual differences in neural mechanisms in MDD, and challenge conventional mood disorder diagnostic boundaries by suggesting that some adults with MDD are characterized by pathophysiological processes that overlap with bipolar disorder.

Individuals with bipolar disorder demonstrate abnormal social function. Neuroimaging studies in bipolar disorder have shown functional abnormalities in neural circuitry supporting face emotion processing, but have not examined face identity processing, a key component of social function. We aimed to elucidate functional abnormalities in neural circuitry supporting face emotion and face identity processing in bipolar disorder.

Twenty-seven individuals with bipolar disorder I currently euthymic and 27 healthy controls participated in an implicit face processing, block-design paradigm. Participants labeled color flashes that were superimposed on dynamically changing background faces comprising morphs either from neutral to prototypical emotion (happy, sad, angry and fearful) or from one identity to another identity depicting a neutral face. Whole-brain and amygdala region-of-interest (ROI) activities were compared between groups.

There was no significant between-group difference looking across both emerging face emotion and identity. During processing of all emerging emotions, euthymic individuals with bipolar disorder showed significantly greater amygdala activity. During facial identity and also happy face processing, euthymic individuals with bipolar disorder showed significantly greater amygdala and medial prefrontal cortical activity compared with controls.

This is the first study to examine neural circuitry supporting face identity and face emotion processing in bipolar disorder. Our findings of abnormally elevated activity in amygdala and medial prefrontal cortex (mPFC) during face identity and happy face emotion processing suggest functional abnormalities in key regions previously implicated in social processing. This may be of future importance toward examining the abnormal self-related processing, grandiosity and social dysfunction seen in bipolar disorder.

One aim of personalized medicine is to determine which treatment is to be preferred for an individual patient, given all patient information available. Particularly in mental health, however, there is a lack of a single objective, reliable measure of outcome that is sensitive to crucial individual differences among patients.

We examined the feasibility of quantifying the total clinical value provided by a treatment (measured by both harms and benefits) in a single metric. An expert panel was asked to compare 100 pairs of patients, one from each treatment group, who had participated in a randomized clinical trial (RCT) involving interpersonal psychotherapy (IPT) and escitalopram, selecting the patient with the preferred outcome considering both benefits and harms.

From these results, an integrated preference score (IPS) was derived, such that the differences between any two patients' IPSs would predict the clinicians' preferences. This IPS was then computed for all patients in the RCT. A second set of 100 pairs was rated by the panel. Their preferences were highly correlated with the IPS differences (r=0.84). Finally, the IPS was used as the outcome measure comparing IPT and escitalopram. The 95% confidence interval (CI) for the effect size comparing treatments indicated clinical equivalence of the treatments.

A metric that combines benefits and harms of treatments could increase the value of RCTs by making clearer which treatments are preferable and, ultimately, for whom. Such methods result in more precise estimation of effect sizes, without increasing the required sample size.