Transitioning to bipolar disorder: A systematic review of prospective high-risk studies

Putative Risk Biomarkers of Bipolar Disorder in At-risk Youth

Bipolar disorder is a highly heritable and functionally impairing disease. The recognition and intervention of BD especially that characterized by early onset remains challenging. Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis. We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or (and) clinical risk of BD. Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses. Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores, epigenetic alterations, elevated immune parameters, front-limbic system deficits, and brain circuit dysfunction associated with emotion and reward processing. Future studies need to enhance machine learning integration, make clinical detection methods more objective, and improve the quality of cohort studies.

Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study

Early identification and intervention of individuals with an increased risk for bipolar disorder (BD) may improve the course of illness and prevent long‑term consequences. Early-BipoLife, a multicenter, prospective, naturalistic study, examined risk factors of BD beyond family history in participants aged 15-35 years. At baseline, positively screened help-seeking participants (screenBD at-risk) were recruited at Early Detection Centers and in- and outpatient depression and attention-deficit/hyperactivity disorder (ADHD) settings, references (Ref) drawn from a representative cohort. Participants reported sociodemographics and medical history and were repeatedly examined regarding psychopathology and the course of risk factors. N = 1,083 screenBD at-risk and n = 172 Ref were eligible for baseline assessment. Within the first two years, n = 31 screenBD at-risk (2.9 %) and none of Ref developed a manifest BD. The cumulative transition risk was 0.0028 at the end of multistep assessment, 0.0169 at 12 and 0.0317 at 24 months (p = 0.021). The transition rate with a BD family history was 6.0 %, 4.7 % in the Early Phase Inventory for bipolar disorders (EPIbipolar), 6.6 % in the Bipolar Prodrome Interview and Symptom Scale-Prospective (BPSS-FP) and 3.2 % with extended Bipolar At-Risk - BARS criteria). In comparison to help-seeking young patients from psychosis detection services, transition rates in screenBD at-risk participants were lower. The findings of Early-BipoLife underscore the importance of considering risk factors beyond family history in order to improved early detection and interventions to prevent/ameliorate related impairment in the course of BD. Large long-term cohort studies are crucial to understand the developmental pathways and long-term course of BD, especially in people at- risk.

Preventive interventions for individuals at risk of developing bipolar disorder: A systematic review and meta-analysis

BACKGROUND

This systematic review and meta-analysis aimed to explore whether early interventions can reduce affective symptoms and have long-term benefits among individuals at risk of bipolar disorder (BD).

METHODS

The PubMed, Embase, and Web of Science databases were searched. The primary outcome was continuous symptom scores before and after treatment. Random effects meta-analyses were conducted for each outcome arm studied and pooled mean difference estimates were calculated.

RESULTS

The search identified 10 controlled studies involving 425 participants and 6 single-arm studies involving 90 participants. For controlled trials, meta-analysis showed that the interventions led to greater reduction in clinical global score than placebo (standardized mean differences (SMD) = -0.96, 95 % CI:-1.32, -0.60), and supported a long-term longitudinal effect for pharmacotherapy (SMD = -0.42, 95 % CI: -0.79, -0.05). For single-arm trials, both pharmacotherapy and psychotherapy showed efficacy for depressive symptoms, while pharmacotherapy only showed efficacy for hypomania symptoms (effect size (ES) = -9.16, 95 % CI:-11.29, -7.04). Discontinuation of pharmacotherapy due to adverse effects did not show a difference.

LIMITATIONS

The primary limitations are the small number of RCTs and the influence of medication dosage.

CONCLUSIONS

Based on the limited available data, early interventions show efficacy for individuals at risk of BD. Psychological therapy might be more beneficial for depressive symptoms and have long-term benefits for hypomania. Pharmacotherapy may be appropriate in situations of severe hypomanic symptoms and the poor functioning. Large, well-designed, double-blind -controlled trials are needed to make solid conclusions about the efficacy of early interventions.

Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2-74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0-71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

Hippocampal cingulum white matter increases over time in young people at high genetic risk for bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a strongly familial psychiatric disorder associated with white matter (WM) brain abnormalities. It is unclear whether such abnormalities are present in relatives without BD, and little is known about WM trajectories in those at increased genetic risk.

METHODS

Diffusion magnetic resonance imaging (dMRI) data were acquired at baseline and after two years in 91 unaffected individuals with a first-degree relative with bipolar disorder (HR), and 85 individuals with no family history of mental illness (CON). All participants were aged between 12 and 30 years at baseline. We examined longitudinal change in Fractional Anisotropy (FA) using tract-based spatial statistics (TBSS).

RESULTS

Compared to the CON group, HR participants showed a significant increase in FA in the right cingulum (hippocampus) (CGH) over a two-year period (p < .05, FDR corrected). This effect was more pronounced in HR individuals without a lifetime diagnosis of a mood disorder than those with a mood disorder.

LIMITATIONS

While our study is well powered to achieve the primary objectives, our sub-group analyses were under powered.

CONCLUSIONS

In one of the very few longitudinal neuroimaging studies of young people at high risk for BD, this study reports novel evidence of atypical white matter development in HR individuals in a key cortico-limbic tract involved in emotion regulation. Our findings also suggest that this different white matter developmental trajectory may be stronger in HR individuals without affective psychopathology. As such, increases in FA in the right CGH of HR participants may be a biomarker of resilience to mood disorders.

Understanding the development of bipolar disorder and borderline personality disorder in young people: a meta-review of systematic reviews

BACKGROUND

There is ongoing debate on the nosological position of bipolar disorder (BD) and borderline personality disorder (BPD). Identifying the unique and shared risks, developmental pathways, and symptoms in emerging BD and BPD could help the field refine aetiological hypotheses and improve the prediction of the onset of these disorders. This study aimed to: (a) systematically synthesise the available evidence from systematic reviews (SRs) and meta-analyses (MAs) concerning environmental, psychosocial, biological, and clinical factors leading to the emergence of BD and BPD; (b) identify the main differences and common features between the two disorders to characterise their complex interplay and, (c) highlight remaining evidence gaps.

METHODS

Data sources were; PubMed, PsychINFO, Embase, Cochrane, CINAHL, Medline, ISI Web of Science. Overlap of included SRs/MAs was assessed using the corrected covered area process. The methodological quality of each included SR and MA was assessed using the AMSTAR.

RESULTS

22 SRs and MAs involving 249 prospective studies met eligibility criteria. Results demonstrated that family history of psychopathology, affective instability, attention deficit hyperactivity disorder, anxiety disorders, depression, sleep disturbances, substance abuse, psychotic symptoms, suicidality, childhood adversity and temperament were common predisposing factors across both disorders. There are also distinct factors specific to emerging BD or BPD.

CONCLUSIONS

Prospective studies are required to increase our understanding of the development of BD and BPD onset and their complex interplay by concurrently examining multiple measures in BD and BPD at-risk populations.