Trajectories of medication adherence in patients with Bipolar Disorder along 2 years-follow-up.

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 < .001) LIMITATIONS: The use of a single measure of medication adherence although it is a validated instrument and a possible positive selection bias that might limit the generalization of our findings. CONCLUSIONS: This study demonstrates that medication adherence in BD patients is a heterogeneous and potentially variable phenomenon.

Serotonin transporter gene expression predicts the worsening of suicidal ideation and suicide attempts along a long-term follow-up of a Major Depressive Episode.

The quest for biomarkers in suicidal behaviors has been elusive so far, despite their potential utility in clinical practice. One of the most robust biological findings in suicidal behaviors is the alteration of the serotonin transporter function in suicidal individuals. Our main objective was to investigate the predictive value of the serotonin transporter gene expression (SLC6A4) for suicidal ideation and as secondary, for suicide attempts in individuals with a major depressive episode (MDE). A 30-week prospective study was conducted on 148 patients with a MDE and 100 healthy controls including 4 evaluation times (0, 2, 8 and 30 weeks). Blood samples and clinical data were collected and SLC6A4 mRNA levels were measured from peripheral blood mononuclear cells using RT-qPCR. We first demonstrated the stability and reproducibility of SLC6A4 mRNA expression measures over time in healthy controls (F=0.658; p=0.579; η2=0.008; ICC=0.91, 95% CI [0.87-0.94]). Baseline SLC6A4 expression level (OR=0.563 [0.340-0.932], p=0.026) as well as early changes in SLC6A4 expression between baseline and the 2nd week (ß=0.200, p=0.042) predicted the worsening of suicidal ideation (WSI) in the following 8 weeks. Moreover, changes in SLC6A4 expression between the 2nd and 8th weeks predicted the occurrence of a suicide attempt within 30 weeks (OR=10.976 [1.438-83.768], p=0.021). Altogether, the baseline level and the changes in SLC6A4 mRNA expression during a MDE might predict the WSI and the occurrence of suicidal attempts and could be a useful biomarker in clinical practice.

Neuropsychological functioning, age, and medication adherence in bipolar disorder.

OBJECTIVES: Poor adherence to medication is frequent in bipolar disorder (BD) and has been associated with several factors. To date, the relationship between low adherence and neuropsychological functioning in BD is still unclear. As age and neuropsychological functioning might have opposing influences on adherence, our aim was to investigate this link with a particular focus on the effect of age. METHODS: In a cross-sectional study, we included 353 patients divided into two age-groups (16-46; 47-71) from a French cohort diagnosed with BD (type I, II, NOS) and strictly euthymic. All patients had a standardized clinical and neuropsychological assessment and were categorized as high (n = 186) or low (n = 167) adherent based on their score from the Medication Adherence Rating Scale. Clinical information was collected based on a standardized interview and clinical validated scales. Neuropsychological performances were evaluated with an established standardized neuropsychological battery for bipolar disorder patients. After univariate analysis, neuropsychological and clinical predictors of low adherence were included in two age-specific stepwise multiple logistic regressions. RESULTS: A smaller number of hospitalizations (OR = 0.846, p = 0.012), a shorter illness duration (OR = 0.937, p = 0.003) and higher adverse effects (OR = 1.082, p<0.001) were associated with a greater risk of low adherence in the younger patients. In the older patients, low adherence was also predicted by a smaller number of hospitalizations (OR = 0.727, p = 0.008) and higher adverse effects (OR = 1.124, p = 0.005). Interestingly poor inhibition performance was also a significant predictor of low adherence in older patients (OR = 0.924, p = 0.030). CONCLUSIONS: We found an age-specific relationship between cognitive functioning and adherence in patients with BD. Poor inhibition performances predicted low adherence in older patients only. Our results highlight the need to provide age-adapted therapeutic interventions to improve adherence in patients with BD.

Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response.

Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.

How to: Measuring blood cytokines in biological psychiatry using commercially available multiplex immunoassays.

Cytokines produced by both immune and non-immune cells are likely to play roles in the development and/or progression of psychiatric disorders. Indeed, many investigators have compared the blood cytokine levels in psychiatric patients with those of healthy controls or monitored their levels in patients during disease progression to identify biomarkers. Nevertheless, very few studies have confirmed that such cytokines remain stable in healthy individuals through periods of weeks and months. This is an important issue to consider before using blood cytokine levels as biomarkers of disease traits, disease state, or treatment response. Although multiplex assay technology represents an advance in identifying biomarkers because it allows simultaneous examination of large panels of analytes from a small volume of sample, it is necessary to verify whether these assays yield enough sensitivity and reproducibility when applied to the blood from neuropsychiatric patients. Therefore, we compared two multiplex immunoassays, the bead-based Luminex® (Bio-Rad) and the electro-chemiluminescence-based V-plex® (MesoScaleDiscovery), for the detection and quantification of 31 cytokines, chemokines and growth factors in both the sera and plasma of patients with major depressive episodes (MDE) and age- and sex-matched healthy control subjects during a 30-week period. Although both platforms exhibited low coefficients of variability (CV) between the duplicates in the calibration curves, the linearity was better in general for the V-PLEX® platform. However, neither platform was able to detect the absolute values for all of the tested analytes. Among the 16 analytes that were detected by both assays, the intra-assay reproducibility was in general better with the V-PLEX® platform. Although it is not a general rule that the results from sera and plasma will be correlated, consistent results were more frequent with the V-PLEX® platform. Furthermore, the V-PLEX® results were more consistent with the gold standard ELISA simplex assay for IL-6 in both sera and plasma. The intra-individual variability of the measurements, among the sera and plasma for the 4 samples harvested from each healthy individual, was low for Eotaxin, G-CSF, IL-4, IL-7, IL-9, IL-12p40, IL-12p70, IL-15, MIP-1ß, PDGF-BB, TNF, TNF-ß and VEGF, but intermediate or high for IFN-γ, IL-6, IL-8, IL-10, and IP10. Together, these data suggest that extreme caution is needed in translating the results of multiplex cytokine profiling into biomarker discovery in psychiatry.