Treatment delay is associated with more episodes and more severe illness staging progression in patients with bipolar disorder

Accelerated brain aging as a biomarker for staging in bipolar disorder: an exploratory study

BACKGROUND

Two established staging models outline the longitudinal progression in bipolar disorder (BD) based on episode recurrence or inter-episodic functioning. However, underlying neurobiological mechanisms and corresponding biomarkers remain unexplored. This study aimed to investigate if global and (sub)cortical brain structures, along with brain-predicted age difference (brain-PAD) reflect illness progression as conceptualized in these staging models, potentially identifying brain-PAD as a biomarker for BD staging.

METHODS

In total, 199 subjects with bipolar-I-disorder and 226 control subjects from the Dutch Bipolar Cohort with a high-quality T1-weighted magnetic resonance imaging scan were analyzed. Global and (sub)cortical brain measures and brain-PAD (the difference between biological and chronological age) were estimated. Associations between individual brain measures and the stages of both staging models were explored.

RESULTS

A higher brain-PAD (higher biological age than chronological age) correlated with an increased likelihood of being in a higher stage of the inter-episodic functioning model, but not in the model based on number of mood episodes. However, after correcting for the confounding factors lithium-use and comorbid anxiety, the association lost significance. Global and (sub)cortical brain measures showed no significant association with the stages.

CONCLUSIONS

These results suggest that brain-PAD may be associated with illness progression as defined by impaired inter-episodic functioning. Nevertheless, the significance of this association changed after considering lithium-use and comorbid anxiety disorders. Further research is required to disentangle the intricate relationship between brain-PAD, illness stages, and lithium intake or anxiety disorders. This study provides a foundation for potentially using brain-PAD as a biomarker for illness progression.

Early identification of bipolar disorder among young adults - a 22-year community birth cohort

OBJECTIVE

We set forth to build a prediction model of individuals who would develop bipolar disorder (BD) using machine learning techniques in a large birth cohort.

METHODS

A total of 3748 subjects were studied at birth, 11, 15, 18, and 22 years of age in a community birth cohort. We used the elastic net algorithm with 10-fold cross-validation to predict which individuals would develop BD at endpoint (22 years) at each follow-up visit before diagnosis (from birth up to 18 years). Afterward, we used the best model to calculate the subgroups of subjects at higher and lower risk of developing BD and analyzed the clinical differences among them.

RESULTS

A total of 107 (2.8%) individuals within the cohort presented with BD type I, 26 (0.6%) with BD type II, and 87 (2.3%) with BD not otherwise specified. Frequency of female individuals was 58.82% (n = 150) in the BD sample and 53.02% (n = 1868) among the unaffected population. The model with variables assessed at the 18-year follow-up visit achieved the best performance: AUC 0.82 (CI 0.75-0.88), balanced accuracy 0.75, sensitivity 0.72, and specificity 0.77. The most important variables to detect BD at the 18-year follow-up visit were suicide risk, generalized anxiety disorder, parental physical abuse, and financial problems. Additionally, the high-risk subgroup of BD showed a high frequency of drug use and depressive symptoms.

CONCLUSIONS

We developed a risk calculator for BD incorporating both demographic and clinical variables from a 22-year birth cohort. Our findings support previous studies in high-risk samples showing the significance of suicide risk and generalized anxiety disorder prior to the onset of BD, and highlight the role of social factors and adverse life events.

Lifetime treatment of DSM-IV mental disorders in the Saudi National Mental Health Survey

OBJECTIVES

To estimate lifetime treatment rates of mental disorders in the Saudi National Mental Health Survey (SNMHS).

METHODS

The SNMHS is a face-to-face community epidemiological survey in a nationally representative household sample of citizens ages 15-65 in the Kingdom of Saudi Arabia (KSA) (n = 4,004). The World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) was used to produce estimates of lifetime prevalence and treatment of common DSM-IV mental disorders.

RESULTS

Lifetime treatment ranged from 52.2% for generalized anxiety disorder to 20.3% for attention deficit/hyperactivity disorder, had a median (interquartile range) of 35.5% (30.6-39.5%) across disorders, and was 28.3% for people with any lifetime DSM-IV/CIDI disorder. Half (49.0%) of patients received treatment in the mental health specialty sector, 35.9% in the general medical sector, 35.2% in the human services sector, and 15.7% in the complementary-alternative medical sector. Median (interquartile range) delays in help-seeking after disorder onset among respondents who already sought treatment were 8 (3-15) years. Odds of seeking treatment are positively related to age-of-onset and comorbidity.

CONCLUSIONS

Unmet need for treatment of lifetime mental disorders is a major problem in KSA. Interventions to ensure prompt help-seeking are needed to reduce the burdens and hazards of untreated mental disorders.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

OBJECTIVES

The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement.

METHOD

We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019.

RESULT

We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%.

CONCLUSIONS

Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.

Exploring the clinical utility of two staging models for bipolar disorder

OBJECTIVE

To assess the clinical utility of two staging models for bipolar disorder by examining distribution, correlation, and the relationship to external criteria. These are primarily defined by the recurrence of mood episodes (model A), or by intra-episodic functioning (model B).

METHODS

In the Dutch Bipolar Cohort, stages according to models A and B were assigned to all patients with bipolar-I-disorder (BD-I; N = 1396). The dispersion of subjects over the stages was assessed and the association between the two models calculated. For both models, change in several clinical markers were concordant with the stage was investigated.

RESULTS

Staging was possible in 87% of subjects for model A and 75% for model B. For model A, 1079 participants (93%) were assigned to stage 3c (recurrent episodes). Subdividing stage 3c with cut-offs at 5 and 10 episodes resulted in subgroups containing 242, 510, and 327 subjects. For model B, most participants were assigned to stage II (intra-episodic symptoms, N = 431 (41%)) or stage III (inability to work, N = 451 (43%)). A low association between models was found. For both models, the clinical markers "age at onset," "treatment resistance," and "episode acceleration" changed concordant with the stages.

CONCLUSION

The majority of patients with BD-I clustered in recurrent stage 3 of Model A. Model B showed a larger dispersion. The stepwise change in several clinical markers supports the construct validity of both models. Combining the two staging models and sub-differentiating the recurrent stage into categories with cut-offs at 5 and 10 lifetime episodes improves the clinical utility of staging for individual patients.

Delays in making initial treatment contact after the first onset of mental health disorders in the Argentinean Study of Mental Health Epidemiology

AIMS

While there are effective treatments for psychiatric disorders, many individuals with such disorders do not receive treatment and those that do often take years to get into treatment. Information regarding treatment contact failure and delay in Argentina is needed to guide public health policy and planning. Therefore, this study aimed to provide data on prompt treatment contact, lifetime treatment contact, median duration of treatment delays and socio-demographic predictors of treatment contact after the first onset of a mental disorder.

METHODS

The Argentinean Study of Mental Health Epidemiology (EAESM) is a multistage probability sample representative of adults (aged 18+) living in large urban areas of Argentina. A total of 2116 participants were evaluated with the World Mental Health Composite International Diagnostic Interview to assess psychiatric diagnosis, treatment contact and delay.

RESULTS

Projections of cases that will make treatment contact by 50 years taken from a survival curve suggest that the majority of individuals with a mood (100%) or anxiety disorder (72.5%) in Argentina whose disorder persist for a sufficient period of time eventually make treatment contact while fewer with a substance disorder do so (41.6%). Timely treatment in the year of onset is rare (2.6% for a substance disorder, 14.6% for an anxiety disorder and 31.3% of those with a mood disorder) with mean delays between 8 years for mood disorders and 21 years for anxiety disorders. Younger cohorts are more likely to make treatment contact than older cohorts, whereas those with earlier ages of disorder onset are least likely to make treatment contact. Those with anxiety disorders and major depressive disorder are more likely to make treatment contact when they have comorbid disorders, whereas those with substance use disorders are less likely.

CONCLUSIONS

Argentina needs to implement strategies to get individuals with substance use disorders into treatment, and to reduce treatment delays for all, but particularly to target early detection and treatment among children and adolescents.

Twelve-month prevalence rates of mental disorders and service use in the Argentinean Study of Mental Health Epidemiology

PURPOSE

Community surveys of mental disorders and service use are important for public health policy and planning. There is a dearth of information for Latin America. This is the first representative community survey in the Argentinean population. The purpose is to estimate the 12-month prevalence and severity of mental disorders, socio-demographic correlates and service use in a general population survey of adults from urban areas of Argentina.

METHODS

The World Mental Health Composite International Diagnostic Interview was administered to 3927 individuals aged 18 years and older participating in a multistage clustered area probability household survey. The response rate was 77%.

RESULTS

The 12-month prevalence of any disorder was 14.8%, and a quarter of those disorders were classified as severe. Younger participants and those with lower education had greater odds of any disorder and most classes of disorder. 11.6% of the total population received treatment in the prior 12 months and only 30.2% of those with a severe disorder. Women and those never married were more likely to receive or seek treatment, whereas those with low and low-average education were less likely.

CONCLUSION

Most individuals with a mental disorder in the past year, even those with a severe disorder, have not received treatment. Because low education is a barrier to treatment, initiatives aimed at mental health education might help timely detection and treatment of these disorders in Argentina.

Infectious and immunogenetic factors in bipolar disorder

OBJECTIVE

Despite the evidence supporting the association between infection and bipolar disorder (BD), the genetic vulnerability that mediates its effects has yet to be clarified. A genetic origin for the immune imbalance observed in BD, possibly involved in the mechanisms of pathogen escape, has, however, been suggested in recent studies.

METHOD

Here, we present a critical review based on a systematic literature search of articles published until December 2016 on the association between BD and infectious/immunogenetic factors.

RESULTS

We provide evidence suggesting that infectious insults could act as triggers of maladaptive immune responses in BD and that immunogenetic vulnerability may amplify the effects of such environmental risk factors, increasing susceptibility to subsequent environmental encounters. Quality of evidence was generally impaired by scarce attempt of replication, small sample sizes and lack of high-quality environmental measures.

CONCLUSION

Infection has emerged as a potential preventable cause of morbidity in BD, urging the need to better investigate components of the host-pathogen interaction in patients and at-risk subjects, and thus opening the way to novel therapeutic opportunities.

Right hippocampus size is negatively correlated with leptin serum levels in bipolar disorder

Obesity is more frequent in bipolar disorder. Adipokines are associated with depression and obesity via the inflammatory process. Twenty-six DSM-IV patients with BD and 39 controls were enrolled to assess the relationship between serum leptin and adiponectin with hippocampal volumes. Among patients, there was a significant negative correlation between right hippocampal volume and serum leptin levels. This result sum for the hypothesis of a pro-inflammatory state associated with BD and the prevalent co-morbid obesity.