Ives Cavalcante Passos

Inflammatory markers in post-traumatic stress disorder: a systematic review, meta-analysis, and meta-regression

BACKGROUND Studies investigating inflammatory markers in post-traumatic stress disorder (PTSD) have yielded mixed results. The aim of our study was to compare concentrations of inflammatory markers in patients with PTSD compared with healthy controls. METHODS We did a meta-analysis and meta-regression of studies comparing inflammatory markers between patients with PTSD and healthy controls by searching PubMed, Embase, Scopus, Web of Science, and PsycINFO for articles published between Jan 1, 1960, and April 7, 2015. From eligible studies (ie, cross-sectional studies or baseline data from longitudinal studies of peripheral blood cytokine concentrations that compared adults with PTSD with healthy controls), we extracted outcomes of interest, such as mean and SD of peripheral blood cytokines, the time of day blood was collected, whether the study allowed patients with comorbid major depressive disorder in the PTSD group, whether patients were medication free, and severity of PTSD symptoms. We undertook meta-analyses whenever values of inflammatory markers were available in two or more studies. A random-effects model with restricted maximum-likelihood estimator was used to synthesise the effect size (assessed by standardised mean difference [SMD]) across studies. FINDINGS 8057 abstracts were identified and 20 studies were included. Interleukin 6 (SMD 0.88; p=0.0003), interleukin 1β (SMD 1.42; p=0.045), and interferon γ (SMD 0.49; p=0.002) levels were higher in the PTSD group than in healthy controls. Subgroup meta-analysis of patients who were not given medication showed higher tumour necrosis factor α (TNFα; SMD 0.69, 95% CI 0.35-1.02; p<0.0001) in the PTSD group than the control group in addition to the aforementioned cytokines. TNFα (SMD 1.32, 0.13-2.50; p=0.003), interleukin 1β (SMD 2.35, 0.01-4.68; p=0.048), and interleukin 6 (SMD 1.75, 0.97-2.53; p<0.0001) levels remained increased in the PTSD group in a subgroup meta-analysis of studies that excluded comorbid major depressive disorder. Illness duration was positively associated with interleukin 1β levels (b=0.33, p<0.0001) and severity with interleukin 6 (b=0.02, p=0.042). A model composed of several variables-presence of comorbid major depressive disorder, use of psychotropic medications, assay used, and time of day blood was collected-explained the large amount of heterogeneity between interleukin 1β, interleukin 6, and C-reactive protein studies. Eggers linear regression test revealed a potential publication bias for interleukin 1β. Additionally, for most inflammatory markers, study heterogeneity was reported to be high (I(2)>75%). INTERPRETATION PTSD is associated with increased interleukin 6, interleukin 1β, TNFα, and interferon γ levels. This information might be useful for consideration of chronic low-grade inflammation as a potential target or biomarker in PTSD treatment. Use of psychotropic medication and presence of comorbid major depressive disorder were important moderators that might explain the inconsistency between results of previous studies. Our search strategy used a range of databases and we made exhaustive effort to acquire data by contacting the authors. Notably, high levels of between-study heterogeneity were recorded for most cytokine variables measured in our analysis. However, meta-regression analysis could explain a large amount of this heterogeneity. FUNDING None.

Areas of controversy in neuroprogression in bipolar disorder.

We aimed to review clinical features and biological underpinnings related to neuroprogression in bipolar disorder (BD). Also, we discussed areas of controversy and future research in the field.

Big data analytics and machine learning: 2015 and beyond.

www.thelancet.com/psychiatry Vol 3 January 2016 13 defi cit). These fi ndings, like those highlighted above, have drawn into serious question the usefulness of symptom-based diagnostic constructs if they fail to provide information or insight about the underlying psycho patholgical mechanisms. Another long-standing challenge has been the limited contact between neuroimaging and other areas of psychiatric neuroscience, especially genetic and molecular investigations. This too has seen progress in 2015, in part related to larger neuroimaging sample sizes that can allow such interfaces. Findings from a large twin study of white matter structure in schizophrenia showed a strong association between white matter integrity and genetic risk for schizophrenia, suggesting a potential common causal genetic mechanism. Another study of amygdala volume and emotion recognition task performance in a very large sample (n=858) showed a common relationship of both to a polymorphism in the PDE5 gene using a genome-wide quantitative trait locus analysis. Particularly exciting is that this association suggests potential pharmacological treatment insights for many neuropsychiatric disorders in which emotion recognition is impaired, as drugs that target PDE5 (a phosphodiesterase) already exist. Finally, even the organising theme of anatomically distributed, large-scale connectivity networks, which has been central to many neuroimaging studies during the past decade, has made contact with genomics. Specifi cally, using a post-mortem gene expression dataset drawn from many regions across the human brain, a relationship has been found between spatial patterns of functional connectivity and gene expression . Perhaps the biggest obstacle to the clinical utility of neuroimaging, however, has been the dependence on group-level analyses, which generally preclude insights about individual patients. This too has seen striking progress, both with respect to methods that can readily parcellate functional regions within individual brains, and demonstrations that functional connectivity-based analyses are powerful and specifi c enough to reliably identify an individual from a group. Thus, looking forward to 2016, it would seem that a greater focus on individuals will be a crucial area of progress that can fi nally bring neuroimaging into the clinical sphere in psychiatry. We must therefore also demand more of study design so that it can be relevant to individuals, such as use of control arms when studying interventions, which still remains rare in neuroimaging.

Hippocampal subfield volumes in mood disorders

Volume reduction and shape abnormality of the hippocampus have been associated with mood disorders. However, the hippocampus is not a uniform structure and consists of several subfields, such as the cornu ammonis (CA) subfields CA1–4, the dentate gyrus (DG) including a granule cell layer (GCL) and a molecular layer (ML) that continuously crosses adjacent subiculum (Sub) and CA fields. It is known that cellular and molecular mechanisms associated with mood disorders may be localized to specific hippocampal subfields. Thus, it is necessary to investigate the link between the in vivo hippocampal subfield volumes and specific mood disorders, such as bipolar disorder (BD) and major depressive disorder (MDD). In the present study, we used a state-of-the-art hippocampal segmentation approach, and we found that patients with BD had reduced volumes of hippocampal subfields, specifically in the left CA4, GCL, ML and both sides of the hippocampal tail, compared with healthy subjects and patients with MDD. The volume reduction was especially severe in patients with bipolar I disorder (BD-I). We also demonstrated that hippocampal subfield volume reduction was associated with the progression of the illness. For patients with BD-I, the volumes of the right CA1, ML and Sub decreased as the illness duration increased, and the volumes of both sides of the CA2/3, CA4 and hippocampal tail had negative correlations with the number of manic episodes. These results indicated that among the mood disorders the hippocampal subfields were more affected in BD-I compared with BD-II and MDD, and manic episodes had focused progressive effect on the CA2/3 and CA4 and hippocampal tail.

Hippocampal volume and verbal memory performance in late-stage bipolar disorder

Studies about changes in hippocampal volumes in subjects with bipolar disorder (BD) have been contradictory. Since the number of manic episodes and hospitalization has been associated with brain changes and poor cognitive outcomes among BD patients, we have hypothesized that these variables could clarify this issue. We stratified subjects with BD in early (BD-Early), intermediate (BD-intermediate) and late (BD-Late) stages as a function of number of manic episodes and prior hospitalization. Then, we compared their hippocampal volumes and California Verbal Learning Test-II (CVLT-II) scores with healthy controls (HC) using the general linear model. A total of 173 subjects were included in the study (112 HC, 15 BD-Early, 30 BD-Intermediate, and 16 BD-Late). We found a significant group effect on hippocampus volume (F(3,167) = 3.227, p = 0.024). Post-hoc analysis showed that BD-Late subjects had smaller hippocampus than HC (p = 0.017). BD-Early and BD-Intermediate subjects showed no significant difference in hippocampus volume compared to HC and BD-Late subjects. The CVLT trial 1 to 5 scores were significantly different across the groups (F(3,167) = 6.371, p < 0.001). Post-hoc analysis showed that BD-Intermediate (p = 0.006) and BD-Late (p = 0.017) subjects had worse memory performance during immediate recall than HC, while the performance difference between BD-Early subjects and HC was not significant (p = 0.208). These findings add to the notion that BD is a neuroprogressive disorder with brain changes and cognitive impairment according to prior morbidity (number of manic episodes and hospitalization). Also, they suggest that hippocampus is a brain marker and a potential therapeutic target for patients at late stage.

Refractory bipolar disorder and neuroprogression.

Immune activation and failure of physiologic compensatory mechanisms over time have been implicated in the pathophysiology of illness progression in bipolar disorder. Recent evidence suggests that such changes are important contributors to neuroprogression and may mediate the cross-sensitization of episode recurrence, trauma exposure and substance use. The present review aims to discuss the potential factors related to bipolar disorder refractoriness and neuroprogression. In addition, we will discuss the possible impacts of early therapeutic interventions as well as the alternative approaches in late stages of the disorder.

Predictors of psychiatric readmission among patients with bipolar disorder at an academic safety-net hospital

Objective: Even with treatment, approximately one-third of patients with bipolar disorder relapse into depression or mania within 1 year. Unfavorable clinical outcomes for patients with bipolar disorder include increased rates of psychiatric hospitalization and functional impairment. However, only a few studies have examined predictors of psychiatric hospital readmission in a sample of patients with bipolar disorder. The purpose of this study was to examine predictors of psychiatric readmission within 30 days, 90 days and 1 year of discharge among patients with bipolar disorder using a conceptual model adapted from Andersen’s Behavioral Model of Health Service Use. Methods: In this retrospective study, univariate and multivariate logistic regression analyses were conducted in a sample of 2443 adult patients with bipolar disorder who were consecutively admitted to a public psychiatric hospital in the United States from 1 January to 31 December 2013. Results: In the multivariate models, several enabling and need factors were significantly associated with an increased risk of readmission across all time periods examined, including being uninsured, having ⩾3 psychiatric hospitalizations and having a lower Global Assessment of Functioning score. Additional factors associated with psychiatric readmission within 30 and 90 days of discharge included patient homelessness. Patient race/ethnicity, bipolar disorder type or a current manic episode did not significantly predict readmission across all time periods examined; however, patients who were male were more likely to readmit within 1 year. The 30-day and 1-year multivariate models showed the best model fit. Conclusion: Our study found enabling and need factors to be the strongest predictors of psychiatric readmission, suggesting that the prevention of psychiatric readmission for patients with bipolar disorder at safety-net hospitals may be best achieved by developing and implementing innovative transitional care initiatives that address the issues of multiple psychiatric hospitalizations, housing instability, insurance coverage and functional impairment.

Lifespan Gyrification Trajectories of Human Brain in Healthy Individuals and Patients with Major Psychiatric Disorders

Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex. How the brain cortical gyrification changes from childhood to old age in healthy human subjects is still unclear. Additionally, studies have shown regional gyrification alterations in patients with major psychiatric disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, whether the lifespan trajectory of gyrification over the brain is altered in patients diagnosed with major psychiatric disorders is still unknown. In this study, we investigated the trajectories of gyrification in three independent cohorts based on structural brain images of 881 subjects from age 4 to 83. We discovered that the trajectory of gyrification during normal development and aging was not linear and could be modeled with a logarithmic function. We also found that the gyrification trajectories of patients with MDD, BD and SCZ were deviated from the healthy one during adulthood, indicating altered aging in the brain of these patients.

Neuroprogression and illness trajectories in bipolar disorder

ABSTRACT Introduction: The longitudinal course of bipolar disorder is highly variable, and a subset of patients seems to present a progressive course associated with brain changes and functional impairment. Areas covered: We discuss the theory of neuroprogression in bipolar disorder. This concept considers the systemic stress response that occurs within mood episodes and late-stage deficits in functioning and cognition as well as neuroanatomic changes. We also discuss treatment refractoriness that may take place in some cases of bipolar disorder. We searched PubMed for articles published in any language up to June 4th, 2016. We found 315 abstracts and included 87 studies in our review. Expert commentary: We are of the opinion that the use of specific pharmacological strategies and functional remediation may be potentially useful in bipolar patients at late-stages. New analytic approaches using multimodal data hold the potential to help in identifying signatures of subgroups of patients who will develop a neuroprogressive course.

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning

BACKGROUND Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC). METHODS A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC. RESULTS The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p=0.0053. The LASSO algorithm assigned individual subjects with a probability score (0-healthy, 1-patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC. LIMITATIONS Our study sample was small as we only considered euthymic BD patients and demographically matched HC. CONCLUSION Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients.