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

Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort

BACKGROUND

Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear.

METHODS

The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed.

RESULTS

Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group.

LIMITATIONS

Relatively shorter follow-up duration, medications confounding, and modest sample size.

CONCLUSION

These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.

Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions

Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.

Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.

Premorbid intelligence quotient and school failure as risk markers for bipolar disorder and major depressive disorder

Mood disorders significantly impact global health, with MDD ranking as the second leading cause of disability in the United States and BD ranking 18th. Despite their prevalence and impact, the relationship between premorbid intelligence and the subsequent development of BD and MDD remains inconclusive. This study investigates the potential of premorbid Intelligence Quotient (IQ) and school failure frequency as risk factors for Bipolar Disorder (BD) and Major Depressive Disorder (MDD) in a birth cohort setting. We analyze data from the Pelotas population-based birth cohort study, comprising 3580 participants aged 22, who had no prior mood disorder diagnoses. Utilizing regression models and accounting for potential confounders, we assess the impact of IQ and school failure, measured at age 18, on the emergence of BD and MDD diagnoses at age 22, using individuals without mood disorders as comparators. Results reveal that lower IQ (below 70) at 18 is associated with an increased risk of BD (Adjusted Odds Ratio [AOR] 1.75, 95%CI: 1.00-3.09, p < 0.05), while higher IQ (above 120) is linked to MDD (AOR 2.16, 95%CI: 1.24-3.75, p < 0.001). Moreover, an elevated number of school failures is associated with increased BD risk (AOR 1.23, 95%CI: 1.11-1.41, p < 0.001), particularly for BD type 1 (AOR 1.36, 95% CI: 1.17-1.58, p < 0.001). These findings offer insights into the distinct premorbid intellectual characteristics of BD and MDD and contribute to a deeper understanding of their developmental trajectories, potentially informing the development of risk assessment tools for mood disorders.

Can neurological soft signs and neurocognitive deficits serve as a combined endophenotype for Han Chinese with bipolar disorder?

BACKGROUND

Bipolar disorder's (BD) potential endophenotypes include neurological soft signs (NSS) and neurocognitive disorders (ND). Few research, meanwhile, has coupled NSS and ND as combined endophenotypes of BD.

OBJECT

This study intends to investigate NSS and ND and compare their differences in euthymic patients with bipolar disorder (EBP), their unaffected first-degree relatives (FDR), and healthy controls (HC). Additionally, search for potential endophenotypic subprojects of NSS and ND and construct and verify a composite endophenotypic.

METHODS

The subjects were all Han Chinese and consisted of 86 EBP, 81 FDR, and 81HC. Cambridge Neurological Inventory and MATRICSTM Consensus Cognitive Battery tested NSS and ND independently.

RESULTS

All three groups displayed a trapezoidal distribution of NSS levels and cognitive abnormalities, with EBP having the most severe NSS levels and cognitive deficits, followed by FDR and HC. Among them, motor coordination in NSS and Information processing speed (IPS), Verbal learning (VL), and Working memory (WM) in neurocognitive function are consistent with the traits of the endophenotype of BD. The accuracy in differentiating EBP and HC or FDRs and HC was higher when these items were combined as predictor factors than in differentiating EBP and FDR.

CONCLUSION

These results provide more evidence that motor coordination, IPS, VL, and WM may be internal characteristics of bipolar disease. When these characteristics are combined into a complex endophenotype, it may be possible to distinguish BD patients and high-risk groups from normal populations.

Child and adolescent anxiety as a risk factor for bipolar disorder: A systematic review of longitudinal studies

OBJECTIVES

Several studies have suggested that anxiety disorders in childhood and adolescence often precede the onset of bipolar disorder. We therefore systematically reviewed the relationship between child and adolescent anxiety and later bipolar disorder.

METHODS

Online databases (Medline [for Ovid], EMBASE and PsychINFO) were searched for original, peer-reviewed studies examining the relationship between child and adolescent anxiety and later bipolar disorder. Studies in both community samples and bipolar offspring samples were included.

RESULTS

A total of 16 studies were included in the review. The results were broadly consistent and revealed that child and adolescent anxiety disorders are associated with later bipolar disorder in community samples. In bipolar offspring, child and adolescent anxiety disorders are a marker of increased risk and predict the onset of bipolar disorder and other major mood disorders.

CONCLUSIONS

There is evidence that anxiety disorders in childhood and adolescence increase the risk of later bipolar disorder. Anxiety disorders may be a useful target for early intervention in those at high-risk of bipolar disorder.

Development and Multi-Site External Validation of a Generalizable Risk Prediction Model for Bipolar Disorder

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.

Trauma history in veterans with bipolar disorder and its impact on suicidality

OBJECTIVES

Veterans are at increased risk for exposure to trauma, developing serious mental illnesses, and death by suicide. History of trauma correlates with worsening outcomes in patients with bipolar disorder. This study investigated associations between trauma exposure (type and timing) and suicide attempt in Veterans with bipolar disorder.

METHODS

One hundred six Veterans with a diagnosis of bipolar disorder and 815 Veterans with no psychiatric history (age rage = 20-72 years old) completed a clinical questionnaire, the Beck Scale for Suicide Ideation, and the Traumatic Live Events Questionnaire. Multinomial logistic regressions investigated correlations between diagnosis, time of trauma (before, during, or after the military), trauma type (attack, illness, accident, child violence, child sexual abuse, and adult sexual abuse), and suicide attempt.

RESULTS

Seventy-five Veterans with bipolar disorder had comorbid PTSD. Controlling for PTSD, Veterans with bipolar disorder had a higher prevalence of trauma including physical assault [odds ratio (OR) = 2.85; 95% confidence interval (CI) = 1.39-5.86] and child sexual trauma (OR = 2.89; CI = 1.38-6.05). Veterans with bipolar disorder who endorsed previous suicide attempts (n = 42) had significantly higher levels of exposure to childhood trauma (OR = 5.69; CI = 1.84-17.62).

CONCLUSIONS

Results support incorporating history of previous trauma exposure when assessing Veterans at risk for bipolar disorder. Especially, trauma characterized as attack and childhood sexual abuse. Particular attention should be given to Veterans with bipolar disorder and exposure to trauma during childhood, which may be associated with increased risk of suicidality.

Staging the bipolar disorders: Are early stages at too early a stage for intervention?

OBJECTIVES

A number of staging models have been generated for the bipolar disorders, which include pre-onset as well as post-onset stages. Some models propose treatments for those at the pre-onset stage, a recommendation which is critiqued here.

METHODS

Several exemplar staging models are overviewed, and a critique is provided.

RESULTS

The critique argues against intervention at a pre-onset stage, in light of there being limited risk factors, unquantified sensitivity and specificity data for most putative onset illness risk factors, and thus there is the risk of overtreatment. Also, it is possible that many of the recommended interventions for those at risk of a bipolar disorder may have general non-specific benefits for those at risk.

CONCLUSIONS

While retaining a pre-onset phase in the staging model, it would appear wiser for it to not be populated with recommended interventions until they have a firmer empirical base.

Correlates of suicidality in young people with depressive disorders: A systematic review

OBJECTIVE

Depression is one of the most prevalent and disabling mental health conditions among young people worldwide. The health and economic burdens associated with depressive illness are substantial. Suicide and depression are closely intertwined, yet a diagnosis of depression itself lacks predictive specificity for suicidal behaviour. To better inform suicide prevention and early intervention strategies for young people, improved identification of modifiable intervention targets is needed. The objective of this review was to identify clinical, psychosocial and biological correlates of suicidality in young people diagnosed with a broad range of unipolar and bipolar depressive disorders.

METHOD

Systematic searches were conducted across MEDLINE, Embase and PsycINFO to identify studies of young people aged 15-25 years diagnosed with unipolar or bipolar depressive disorders. An assessment of suicidality was required for inclusion. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 and Synthesis Without Meta-analysis guidelines.

RESULTS

We integrated findings from 71 studies including approximately 24,670 young people with clinically diagnosed depression. We identified 26 clinical, psychosocial and biological correlates of suicidality. Depression characteristics (type and severity), psychiatric comorbidity (particularly anxiety and substance use disorders) and neurological characteristics emerged as having the most evidence for being associated with suicidal outcomes. Our ability to pool data and conduct meaningful quantitative synthesis was hampered by substantial heterogeneity across studies and incomplete reporting; thus, meta-analysis was not possible.

CONCLUSION

Findings of this review reinforce the notion that suicidality is a complex phenomenon arising from the interplay of multiple contributing factors. Our findings question the utility of considering a diagnosis of depression as a specific risk factor for suicidality in young people. Suicidality itself is transdiagnostic; adoption of a transdiagnostic approach to investigating its aetiology and treatment is perhaps warranted. Future research investigating specific symptoms, or symptom networks, might help to further our understanding of suicidality among young people experiencing mental illness.

Substance use as a risk factor for bipolar disorder: A systematic review

Detecting substance use as a predictor of bipolar disorder (BD) is important for clinicians to perform accurate and early diagnosis, as well as better manage the treatment of individuals with BD. The aim of this systematic review was to describe whether substance use is a predictor of BD. A literature search was conducted using the following databases: PubMed, PsycINFO, and Embase. All eligible studies published up to February 9, 2021 were included. This systematic review included 22 studies. We found that 66.7% of the studies assessing overall substance use found that overall substance use was a risk factor for BD. Regarding the specific substances assessed, cannabis use was described as a risk factor for BD in 55.6% of the studies, nonmedical use of prescription medications was a risk factor for BD in 50% of the studies, nicotine was found as a risk factor for BD in 50% of the studies, and alcohol use was described as a risk factor for BD in 42.9% of the studies assessing it. Only one study assessed whether cocaine use was a risk factor for BD and found a significant association. Interestingly, some studies suggested that the greater frequency of cannabis use was associated with greater risk to develop BD or hypomanic/manic symptoms. In conclusion, there is evidence supporting that substance use is a risk factor for BD. Importantly, when assessing the risk factors for BD related to psychoactive substance use, special attention should be given for the frequency of cannabis use.

Risk factors for new-onset bipolar disorder in a community cohort: A five-year follow up study

The aim of this study was to assess the risk factors for new-onset Bipolar Disorder (BD) in a community sample of young adults. This is a prospective cohort study including a population-based sample of young adults aged between 18-24 years. The baseline took place from 2007 to 2009, and 1560 subjects were included. Five years after, 1244 individuals were re-evaluated (79.7% retention). Substance abuse/dependence was assessed using the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), and mental disorders were assessed using the Mini International Neuropsychiatric Interview 5.0 (MINI) at both waves. The cumulative incidence of BD in five years was 4.6%. There was no significant association between sociodemographic factors and BD incidence. Tobacco, cannabis, cocaine/crack, other substances abuse/dependence increased the relative risk for BD. Depressive, anxiety, post-traumatic stress disorders, and the suicide risk increased the relative risk to BD. Depressive episode was the strongest risk factor for BD, followed by other mental disorders and substance abuse/dependence in a probabilistic community sample of young adults. Preventive actions in mental health directed at the non-clinical population are needed for early detection and better management of BD.