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Inflammation might play a role in bipolar disorder (BD), but it remains unclear the relationship between inflammation and brain structural and functional abnormalities in patients with BD. In this study, we focused on the alterations of functional connectivity (FC), peripheral pro-inflammatory cytokines and their correlations to investigate the role of inflammation in FC in BD depression.
Methods
In this study, 42 unmedicated patients with BD II depression and 62 healthy controls (HCs) were enrolled. Resting-state-functional magnetic resonance imaging was performed in all participants and independent component analysis was used. Serum levels of Interleukin-6 (IL-6) and Interleukin-8 (IL-8) were measured in all participants. Correlation between FC values and IL-6 and IL-8 levels in BD was calculated.
Results
Compared with the HCs, BD II patients showed decreased FC in the left orbitofrontal cortex (OFC) implicating the limbic network and the right precentral gyrus implicating the somatomotor network. BD II showed increased IL-6 (p = 0.039), IL-8 (p = 0.002) levels. Moreover, abnormal FC in the right precentral gyrus were inversely correlated with the IL-8 (r = −0.458, p = 0.004) levels in BD II. No significant correlation was found between FC in the left OFC and cytokines levels.
Conclusions
Our findings that serum IL-8 levels are associated with impaired FC in the right precentral gyrus in BD II patients suggest that inflammation might play a crucial role in brain functional abnormalities in BD.
Brain-derived neurotrophic factor (BDNF), which facilitates neuroplasticity and synaptogenesis, may be decreased in bipolar disorder, but has not been systematically investigated in people with newly diagnosed bipolar disorder and unaffected first-degree relatives.
Aims
To compare BDNF levels in patients with newly diagnosed bipolar disorder, their unaffected first-degree relatives and healthy controls.
Method
The study investigated plasma BDNF levels in patients (n = 371) with newly diagnosed bipolar disorder, their unaffected first-degree relatives (n = 98) and healthy controls (n = 200) using enzyme-linked immunosorbent assay. We further investigated associations between BDNF levels and illness-related variables and medication status.
Results
BDNF levels were found to be 22.0% (95% CI 1.107–1.343) higher in patients with bipolar disorder compared with healthy controls (P < 0.001) and 15.6% higher in unaffected first-degree relatives compared with healthy controls (95% CI 1.007–1.327, P = 0.04), when adjusting for age and gender. Further, BDNF levels were positively associated with duration of illness at a trend level (P = 0.05), age (P = 0.001) and use of anti-epileptic medication (P = 0.05).
Conclusions
These findings suggest that BDNF levels are not decreased in the early stages of bipolar disorder and in unaffected first-degree relatives contrasting with prior findings during later stages of the illness.
Whether smoking should be regarded as a risk factor for mental disorders remains unresolved. Prescribed psychotropic drugs can be used as indications for mental disorders. We investigated how smoking was prospectively related to prescription of antipsychotics, mood stabilizers, antidepressants, and anxiolytics.
Methods
Information about smoking, including the Fagerström Test for Nicotine Dependence, and relevant confounders, were obtained from the population-based Young in Norway Study (N = 2602), with four data collection waves between 1992 and 2006. These survey data were linked with information on prescriptions for psychotropic drugs from the comprehensive, nationwide Norwegian Prescription Database from 2007 to 2015.
Results
Daily smoking with high dependence in 2006 at age 28.5 (s.d. = 2.0) was associated with filling prescriptions of antipsychotics (OR, 6.57, 95% CI 2.19–19.70, p = 0.001), mood stabilizers (OR, 7.11, 95% CI 2.51–20.15, p < 0.001) and antidepressants (OR, 1.91, 95% CI 1.13–3.23, p = 0.016) 1–9 years later. Associations remained significant after adjustment for a variety of potential confounders measured before the assessment of smoking, including sociodemographic background, conduct problems, cannabis use, mental distress, and previous prescriptions for psychotropic medications. The association between smoking and prescription of anxiolytics was weaker and more unstable.
Conclusions
In this study of young adults, daily smoking with high dependence was associated with later prescriptions of antipsychotics, mood stabilizers and antidepressants, indicating smoking as a risk factor for mental disorders treated with these drugs.
Previous studies suggested that a disturbance of the dopamine system underlies the pathophysiology of bipolar disorder (BD). In addition, the therapeutic action of medications for treating BD, such as valproate (VPA), might modulate dopamine system activity, but it remains unclear. Here, we aimed to investigate the role of the striatal dopamine transporter (DAT) in BD patients and in social defeat (SD) mice treated with VPA.
Methods
We enrolled community-dwelling controls (N = 18) and BD patients (N = 23) who were treated with VPA in a euthymic stage. The striatal DAT availabilities were approached by TRODAT-1 single photon emission computed tomography. We also established a chronic SD mouse model and treated mice with 350 mg/kg VPA for 3 weeks. Behavioral tests were administered, and striatal DAT expression levels were determined.
Results
In humans, the level of striatal DAT availability was significantly higher in euthymic BD patients (1.52 ± 0.17 and 1.37 ± 0.23, p = 0.015). Moreover, the level of striatal DAT availability was also negatively correlated with the VPA concentration in BD patients (r = −0.653, p = 0.003). In SD mice, the expression of striatal DAT significantly increased (p < 0.001), and the SD effect on DAT expression was rescued by VPA treatment.
Conclusions
The striatal DAT might play a role in the pathophysiology of BD and in the therapeutic mechanism of VPA. The homeostasis of DAT might represent a new therapeutic strategy for BD patients.
Objective: Previous studies have shown differences in the regional brain structure and function between patients with bipolar disorder (BD) and healthy subjects, but little is known about the structural connectivity between BD patients and healthy subjects. In this study, we evaluated the disease-related changes in regional structural connectivity derived from gray matter magnetic resonance imaging (MRI) scans. Methods: The subjects were 73 patients with BD and 80 healthy volunteers who underwent 3-Tesla MRI. Network metrics, such as the small world properties, were computed. We also performed rendering of the network metric images such as the degree, betweenness centrality, and clustering coefficient, on individual brain image. Then, we estimated the differences between them, and evaluate the relationships between the clinical symptoms and the network metrics in the patients with BD. Results: BD patients showed a lower clustering coefficient in the right parietal region and left occipital region, compared with healthy subjects. A weak negative correlation between Young mania rating scale and clustering coefficient was found in left anterior cingulate cortex. Conclusions: We found differences in gray matter structural connectivity between BD patients and healthy subjects by a similarity-based approach. These points may provide objective biological information as an adjunct to the clinical diagnosis of BD.
The prefrontal deficits in psychiatric disorders have been investigated using functional neuroimaging tools; however, no studies have tested the related characteristics across psychiatric disorders considering various demographic and clinical confounders.
Methods
We analyzed 1558 functional brain measurements using a functional near-infrared spectroscopy during a verbal fluency task from 1200 participants with three disease spectra [196 schizophrenia, 189 bipolar disorder (BPD), and 394 major depressive disorder (MDD)] and 369 healthy controls along with demographic characteristics (age, gender, premorbid IQ, and handedness), task performance during the measurements, clinical assessments, and medication equivalent doses (chlorpromazine, diazepam, biperiden, and imipramine) in a consistent manner. The association between brain functions and demographic and clinical variables was tested using a general linear mixed model (GLMM). Then, the direction of relationship between brain activity and symptom severity, controlling for any other associations, was estimated using a model comparison of structural equation models (SEMs).
Results
The GLMM showed a shared functional deficit of brain activity and a schizophrenia-specific delayed activity timing in the prefrontal cortex (false discovery rate-corrected p < 0.05). Comparison of SEMs showed that brain activity was associated with the global assessment of functioning scores in the left inferior frontal gyrus opercularis (IFGOp) in BPD group and the bilateral superior temporal gyrus and middle temporal gyrus, and the left superior frontal gyrus, inferior frontal gyrus triangularis, and IFGOp in MDD group.
Conclusion
This cross-disease large-sample neuroimaging study with high-quality clinical data reveals a robust relationship between prefrontal function and behavioral outcomes across three major psychiatric disorders.
Difficulties with decision making and risk taking in individuals with bipolar disorder (BD) have been associated with mood episodes. However, there is limited information about these experiences during euthymia, the mood state where people with BD spent the majority of their time.
Aims:
To examine how individuals with BD consider risk in everyday decisions during their euthymic phase.
Method:
We conducted a qualitative study that used semi-structured audio recorded interviews. Eight euthymic participants with confirmed BD were interviewed, and we used interpretative phenomenological analysis to analyse the data.
Results:
We identified four themes. The first theme, ‘Who I really am’, involves the relationship between individual identity and risks taken. The second theme, ‘Taking back control of my life’, explored the relationship between risks taken as participants strove to keep control of their lives. The third theme, ‘Fear of the “what ifs”’, represents how the fear of negative consequences from taking risks impacts risk decisions. Finally, the fourth theme, ‘The role of family and friends’, highlights the important role that a supporting network can play in their lives in the context of taking risks.
Conclusions:
The study highlights aspects that can impact on an individual with BD’s consideration of risk during euthymia. Identity, control, fear and support all play a role when a person considers risk in their decision-making process, and they should be taken into consideration when exploring risk with individuals with BD in clinical settings, and inform the design of future interventions.
An understanding of the current state of mental health services in the United Arab Emirates (UAE) from a clinical perspective is an important step in advising government and stakeholders on addressing the mental health needs of the fast-growing population. We conducted a retrospective study of data on all patients admitted to a regional psychiatric in-patient unit between June 2012 and May 2015. More Emiratis (UAE nationals) were admitted compared with expatriates. Emiratis were diagnosed more frequently with substance use disorders and expatriates with stress-related conditions. Psychotic and bipolar disorders were the most common causes for admission and had the longest in-patient stays; advancing age was associated with longer duration of in-patient stay.
Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR).
Methods
This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders.
Results
The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity.
Conclusions
These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.
Changes in inflammatory and metabolic markers are implicated in the pathogenesis in both the development and progression of bipolar disorder (BD). Notwithstanding, these markers have not been investigated in newly diagnosed BD.
Methods
We compared high-sensitive C-reactive protein (hs-CRP) and homocysteine (Hcy) levels in 372 patients with newly diagnosed BD, 106 unaffected first-degree relatives (URs), and 201 healthy control persons (HCs). Within the patient group, we also investigated possible associations between hs-CRP and Hcy, respectively, with illness-related characteristics and psychotropic medication.
Results
No statistically significant differences in Hcy and hs-CRP levels were found when comparing BD and URs with HCs. Similarly, there were no differences when comparing only patients in remission or patients with affective symptoms, respectively, with HCs. Hcy levels were found to be 11.9% (95% CI: 1.030–1.219) higher in patients with BD when compared with their URs (p = 0.008), when adjusting for folate and cobalamin status, age, sex, and self-reported activity levels. Hcy levels were significantly associated with folate, cobalamin, gender, and age in all models (p < 0.05).
Conclusion
Our results do not support hs-CRP or Hcy as markers in newly diagnosed BD.
An aberrant neural connectivity has been known to be associated with bipolar disorder (BD). Local gyrification may reflect the early neural development of cortical connectivity and has been studied as a possible endophenotype of psychiatric disorders. This study aimed to investigate differences in the local gyrification index (LGI) in each cortical region between patients with BD and healthy controls (HCs).
Methods
LGI values, as measured using FreeSurfer software, were compared between 61 patients with BD and 183 HCs. The values were also compared between patients with BD type I and type II as a sub-group analysis. Furthermore, we evaluated whether there was a correlation between LGI values and illness duration or depressive symptom severity in patients with BD.
Results
Patients with BD showed significant hypogyria in various cortical regions, including the left inferior frontal gyrus (pars opercularis), precentral gyrus, postcentral gyrus, superior temporal cortex, insula, right entorhinal cortex, and both transverse temporal cortices, compared to HCs after the Bonferroni correction (p < 0.05/66, 0.000758). LGI was not associated with clinical factors such as illness duration, depressive symptom severity, and lithium treatment. No significant differences in cortical gyrification according to the BD subtype were found.
Conclusions
BD appears to be characterized by a significant regionally localized hypogyria, in various cortical areas. This abnormality may be a structural and developmental endophenotype marking the risk for BD, and it might help to clarify the etiology of BD.
Despite evidence of gender differences in bipolar disorder characteristics and comorbidity, there is little research on the differences in treatment and service use between men and women with bipolar disorder.
Aims
To use routine data to describe specialist mental health service contact for bipolar disorder, including in-patient, community and support service contacts; to compare clinical characteristics and mental health service use between men and women in contact with secondary services for bipolar disorder.
Method
Cross-sectional analysis of mental health patients with bipolar disorder in New Zealand, based on complete national routine health data.
Results
A total of 3639 individuals were in contact with specialist mental health services with a current diagnosis of bipolar disorder in 2015. Of these 58% were women and 46% were aged 45 and over. The 1-year prevalence rate of bipolar disorder leading to contact with specialist mental health services was 1.56 (95% CI 1.50–1.63) per 100 000 women and 1.20 (95% CI 1.14–1.26) per 100 000 men. Rates of bipolar disorder leading to service contact were 30% higher in women than men (rate ratio 1.30, 95% CI 1.22–1.39). The majority (68%) had a diagnosis of bipolar I disorder. Women were more likely to receive only out-patient treatment and have comorbid anxiety whereas more men had substance use disorder, were convicted for crimes when unwell, received compulsory treatment orders and received in-patient treatment.
Conclusions
Although the prevalence of bipolar disorder is equal between men and women in the population, women were more likely to have contact with specialist services for bipolar disorder but had a lower intensity of service interaction.
People with bipolar disorder have moderate cognitive difficulties that tend to be more pronounced during mood episodes but persist after clinical remission and affect recovery. Recent evidence suggests heterogeneity in these difficulties, but the factors underlying cognitive heterogeneity are unclear.
Aims
To examine whether distinct cognitive profiles can be identified in a sample of euthymic individuals with bipolar disorder and examine potential differences between subgroups.
Method
Cognitive performance was assessed across four domains (i.e. processing speed, verbal learning/memory, working memory, executive functioning) in 80 participants. We conducted a hierarchical cluster analysis and a discriminant function analysis to identify cognitive profiles and considered differences in cognitive reserve, estimated cognitive decline from premorbid cognitive functioning, and clinical characteristics among subgroups.
Results
Four discrete cognitive profiles were identified: cognitively intact (n = 25; 31.3%); selective deficits in verbal learning and memory (n = 15; 18.8%); intermediate deficits across all cognitive domains (n = 30; 37.5%); and severe deficits across all domains (n = 10; 12.5%). Cognitive decline after illness onset was greater for the intermediate and severe subgroups. Cognitive reserve scores were increasingly lower for subgroups with greater impairments. A smaller proportion of cognitively intact participants were using antipsychotic medications compared with all other subgroups.
Conclusions
Our findings suggest that individuals with cognitively impaired profiles demonstrate more cognitive decline after illness onset. Cognitive reserve may be one of the factors underlying cognitive variability across people with bipolar disorder. Patients in the intermediate and severe subgroups may be in greater need of interventions targeting cognitive difficulties.
Patients with severe mental illness (SMI), such as schizophrenia or bipolar disorders, are more frequently affected by metabolic syndrome and cardiovascular (CV) diseases than the general population, with a significant reduction in life expectancy. Beyond metabolic syndrome, quantifying the risk of CV morbidity in the long-term may help clinicians to put in place preventive strategies. In this study, we assessed 10-year CV risk in patients with SMI and healthy individuals using an algorithm validated on the Italian general population.
Methods
Patients aged 35–69 years diagnosed with SMI were consecutively recruited from psychiatric acute care units. Single CV risk factors were assessed, and 10-year CV risk calculated by means of the CUORE Project 10-year CV risk algorithm, based on the combination of the following risk factors: age, systolic blood pressure, total and high-density lipoprotein cholesterol, diabetes, smoking habit, and hypertensive treatment. Patients’ data were compared with those from the general population. The 10-year CV risk was log-transformed, and multivariable linear regression was used to estimate mean ratios, adjusting for age, and education.
Results
Three hundred patients and 3,052 controls were included in the analysis. Among men, the 10-year CV risk score was very similar between patients with SMI and the general population (mean ratio [MR]: 1.02; 95%CI 0.77–1.37), whereas a 39% increase in 10-year CV risk was observed in women with SMI compared to the general population (MR: 1.39; 95%CI 1.16–1.66).
Conclusions
In our study, women with SMI were consistently more at risk than the general population counterpart, even at younger age.
To investigate how individuals with a history of affective disorder use and perceive their use of social media and online dating.
Methods:
A questionnaire focusing on affective disorders and the use of social media and online dating was handed out to outpatients from unipolar depression and bipolar disorder clinics and general practice patients with or without a history of affective disorders (latter as controls). The association between affective disorders and use of social media and online dating was analysed using linear/logistic regression.
Results:
A total of 194 individuals with a history of unipolar depression, 124 individuals with a history of bipolar disorder and 196 controls were included in the analysis. Having a history of unipolar depression or bipolar disorder was not associated with the time spent on social media compared with controls. Using the controls as reference, having a history bipolar disorder was associated with use of online dating (adjusted odds ratio: 2.2 (95% CI: 1.3; 3.7)). The use of social media and online dating had a mood-congruent pattern with decreased and more passive use during depressive episodes, and increased and more active use during hypomanic/manic episodes. Among the respondents with a history of affective disorder, 51% reported that social media use had an aggravating effect on symptoms during mood episodes, while 10% reported a beneficial effect. For online dating, the equivalent proportions were 49% (aggravation) and 20% (benefit), respectively.
Conclusion:
The use of social media and online dating seems related to symptom deterioration among individuals with affective disorder.
Subjects with bipolar disorder (BD) show heterogeneous cognitive profile and that not necessarily the disease will lead to unfavorable clinical outcomes. We aimed to identify clinical markers of severity among cognitive clusters in individuals with BD through data-driven methods.
Methods
We recruited 167 outpatients with BD and 100 unaffected volunteers from Brazil and Spain that underwent a neuropsychological assessment. Cognitive functions assessed were inhibitory control, processing speed, cognitive flexibility, verbal fluency, working memory, short- and long-term verbal memory. We performed hierarchical cluster analysis and discriminant function analysis to determine and confirm cognitive clusters, respectively. Then, we used classification and regression tree (CART) algorithm to determine clinical and sociodemographic variables of the previously defined cognitive clusters.
Results
We identified three neuropsychological subgroups in individuals with BD: intact (35.3%), selectively impaired (34.7%), and severely impaired individuals (29.9%). The most important predictors of cognitive subgroups were years of education, the number of hospitalizations, and age, respectively. The model with CART algorithm showed sensitivity 45.8%, specificity 78.4%, balanced accuracy 62.1%, and the area under the ROC curve was 0.61. Of 10 attributes included in the model, only three variables were able to separate cognitive clusters in BD individuals: years of education, number of hospitalizations, and age.
Conclusion
These results corroborate with recent findings of neuropsychological heterogeneity in BD, and suggest an overlapping between premorbid and morbid aspects that influence distinct cognitive courses of the disease.
Relapse rates among individuals with psychotic disorders are high. In addition to the financial burden placed on clinical services, relapse is associated with worse long-term prognosis and poorer quality of life. Robust evidence indicates that stressful life events commonly precede the onset of the first psychotic episode; however, the extent to which they are associated with relapse remains unclear. The aim of this systematic review is to summarize available research investigating the association between recent stressful life events and psychotic relapse or relapse of bipolar disorder if the diagnosis included psychotic symptoms. PsycINFO, Medline and EMBASE were searched for cross-sectional, retrospective and prospective studies published between 01/01/1970 and 08/01/2020 that investigated the association between adult stressful life events and relapse of psychosis. Study quality was assessed using the Effective Public Health Practice Project guidelines. Twenty-three studies met eligibility criteria (prospective studies: 14; retrospective studies: 6; cross-sectional: 3) providing data on 2046 participants in total (sample size range: 14–240 participants). Relapse was defined as a return of psychotic symptoms (n = 20), a return of symptoms requiring hospitalization (n = 2) and a return of symptoms or hospitalization (n = 1). Adult stressful life events were defined as life events occurring after the onset of psychosis. Stressful life events included but were not limited to adult trauma, bereavement, financial problems and conflict. Eighteen studies found a significant positive association between adult stressful life events and psychotic relapse and five studies found a non-significant association. We conclude that adult stressful life events, occurring after psychosis onset, appear to be associated with psychotic relapse.
Approximately half of the individuals diagnosed with schizophrenia or bipolar disorder also suffer from SUD. Disorders of unipolar depression and anxiety are associated with an incidence of SUD double or triple that seen in the general population. Self-medication of distressing states with addictive drugs results in SUD for some individuals. Conversely, heavy drug use can cause or worsen non-drug psychiatric disorders. The co-occurrence of both disorders could also result from a third factor such as genetic predisposition. The term “addictive personality” has little scientific credibility, but certain personality disorders (antisocial, bipolar) and factors (impulsivity, negativity) are also SUD risk factors. However, many with SUD do not display these troublesome extremes of emotion and behavior. A major risk factor for SUD is regular use of an addictive drug before age 15 – an early indicator of addictive vulnerability associated with a four-fold increase in the probability of eventual SUD. Other adolescent risk factors include deviant and drug-using friends, expectations of positive drug effects, and impaired self-regulation of behavior, emotions, and cognition.
Family coaggregation of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD) and schizophrenia have been presented in previous studies. The shared genetic and environmental factors among psychiatric disorders remain elusive.
Methods
This nationwide population-based study examined familial coaggregation of major psychiatric disorders in first-degree relatives (FDRs) of individuals with ASD. Taiwan's National Health Insurance Research Database was used to identify 26 667 individuals with ASD and 67 998 FDRs of individuals with ASD. The cohort was matched in 1:4 ratio to 271 992 controls. The relative risks (RRs) and 95% confidence intervals (CI) of ADHD, ASD, BD, MDD and schizophrenia were assessed among FDRs of individuals with ASD and ASD with intellectual disability (ASD-ID).
Results
FDRs of individuals with ASD have higher RRs of major psychiatric disorders compared with controls: ASD 17.46 (CI 15.50–19.67), ADHD 3.94 (CI 3.72–4.17), schizophrenia 3.05 (CI 2.74–3.40), BD 2.22 (CI 1.98–2.48) and MDD 1.88 (CI 1.76–2.00). Higher RRs of schizophrenia (4.47, CI 3.95–5.06) and ASD (18.54, CI 16.18–21.23) were observed in FDRs of individuals with both ASD-ID, compared with ASD only.
Conclusions
The risk for major psychiatric disorders was consistently elevated across all types of FDRs of individuals with ASD. FDRs of individuals with ASD-ID are at further higher risk for ASD and schizophrenia. Our results provide leads for future investigation of shared etiologic pathways of ASD, ID and major psychiatric disorders and highlight the importance of mental health care delivered to at-risk families for early diagnoses and interventions.