Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease

Larger putamen in individuals at risk and with manifest bipolar disorder

BACKGROUND

Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

METHODS

In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

RESULTS

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

CONCLUSIONS

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Oxidatively-induced DNA base damage and base excision repair abnormalities in siblings of individuals with bipolar disorder DNA damage and repair in bipolar disorder

Previous evidence suggests elevated levels of oxidatively-induced DNA damage, particularly 8-hydroxy-2'-deoxyguanosine (8-OH-dG), and abnormalities in the repair of 8-OH-dG by the base excision repair (BER) in bipolar disorder (BD). However, the genetic disposition of these abnormalities remains unknown. In this study, we aimed to investigate the levels of oxidatively-induced DNA damage and BER mechanisms in individuals with BD and their siblings, as compared to healthy controls (HCs). 46 individuals with BD, 41 siblings of individuals with BD, and 51 HCs were included in the study. Liquid chromatography-tandem mass spectrometry was employed to evaluate the levels of 8-OH-dG in urine, which were then normalized based on urine creatinine levels. The real-time-polymerase chain reaction was used to measure the expression levels of 8-oxoguanine DNA glycosylase 1 (OGG1), apurinic/apyrimidinic endonuclease 1 (APE1), poly ADP-ribose polymerase 1 (PARP1), and DNA polymerase beta (POLβ). The levels of 8-OH-dG were found to be elevated in both individuals with BD and their siblings when compared to the HCs. The OGG1 and APE1 expressions were downregulated, while POLβ expressions were upregulated in both the patient and sibling groups compared to the HCs. Age, smoking status, and the number of depressive episodes had an impact on APE1 expression levels in the patient group while body mass index, smoking status, and past psychiatric history had an impact on 8-OH-dG levels in siblings. Both individuals with BD and unaffected siblings presented similar abnormalities regarding oxidatively-induced DNA damage and BER, suggesting a link between abnormalities in DNA damage/BER mechanisms and familial susceptibility to BD. Our findings suggest that targeting the oxidatively-induced DNA damage and BER pathway could offer promising therapeutic strategies for reducing the risk of age-related diseases and comorbidities in individuals with a genetic predisposition to BD.

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.

Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features

BACKGROUND

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

METHODS

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

RESULTS

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

CONCLUSIONS

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

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.

Morphological abnormalities in youth with bipolar disorder and their relationship to clinical characteristics

OBJECTIVES

To characterize the neuroanatomy of BD in youth and its correlation to clinical characteristics.

METHODS

The current study includes a sample of 105 unmedicated youth with first-episode BD, aged between 10.1 and 17.9 years, and 61 healthy comparison adolescents, aged between 10.1 and 17.7 years, who were matched for age, race, sex, socioeconomic status, intelligence quotient (IQ), and education level. T1-weighted magnetic resonance imaging (MRI) images were obtained using a 4 T MRI scanner. Freesurfer (V6.0) was used to preprocess and parcellate the structural data, and 68 cortical and 12 subcortical regions were considered for statistical comparisons. The relationship between morphological deficits and clinical and demographic characteristics were evaluated using linear models.

RESULTS

Compared with healthy youth, youth with BD had decreased cortical thickness in frontal, parietal, and anterior cingulate regions. These youth also showed decreased gray matter volumes in 6 of the 12 subcortical regions examined including thalamus, putamen, amygdala and caudate. In further subgroup analyses, we found that youth with BD with comorbid attention-deficit hyperactivity disorder (ADHD) or with psychotic symptoms had more significant deficits in subcortical gray matter volume.

LIMITATIONS

We cannot provide information about the course of structural changes and impact of treatment and illness progression.

CONCLUSIONS

Our findings indicate that youth with BD have significant neurostructural deficits in both cortical and subcortical regions mainly located in the regions related to emotion processing and regulation. Variability in clinical characteristics and comorbidities may contribute to the severity of anatomic alterations in this disorder.

Mania-related effects on structural brain changes in bipolar disorder - a narrative review of the evidence

Cross-sectional neuroimaging studies show that bipolar disorder is associated with structural brain abnormalities, predominantly observed in prefrontal and temporal cortex, cingulate gyrus, and subcortical regions. However, longitudinal studies are needed to elucidate whether these abnormalities presage disease onset or are consequences of disease processes, and to identify potential contributing factors. Here, we narratively review and summarize longitudinal structural magnetic resonance imaging studies that relate imaging outcomes to manic episodes. First, we conclude that longitudinal brain imaging studies suggest an association of bipolar disorder with aberrant brain changes, including both deviant decreases and increases in morphometric measures. Second, we conclude that manic episodes have been related to accelerated cortical volume and thickness decreases, with the most consistent findings occurring in prefrontal brain areas. Importantly, evidence also suggests that in contrast to healthy controls, who in general show age-related cortical decline, brain metrics remain stable or increase during euthymic periods in bipolar disorder patients, potentially reflecting structural recovering mechanisms. The findings stress the importance of preventing manic episodes. We further propose a model of prefrontal cortical trajectories in relation to the occurrence of manic episodes. Finally, we discuss potential mechanisms at play, remaining limitations, and future directions.

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.

Early cognitive comorbidities before disease onset: A common symptom towards prevention of related brain diseases?

Brain diseases are very heterogeneous; however they also display multiple common risk factors and comorbidities. With a paucity of disease-modifying therapies, prevention became a health priority. Towards prevention, one strategy is to focus on similar symptoms of brain diseases occurring before disease onset. Cognitive deficits are a promising candidate as they occur across brain diseases before disease onset. Based on recent research, this review highlights the similarity of brain diseases and discusses how early cognitive deficits can be exploited to tackle disease prevention. After briefly introducing common risk factors, I review common comorbidities across brain diseases, with a focus on cognitive deficits before disease onset, reporting both experimental and clinical findings. Next, I describe network abnormalities associated with early cognitive deficits and discuss how these abnormalities can be targeted to prevent disease onset. A scenario on brain disease etiology with the idea that early cognitive deficits may constitute a common symptom of brain diseases is proposed.

The inferior frontal gyrus and familial risk for bipolar disorder

Bipolar disorder (BD) is a familial disorder with high heritability. Genetic factors have been linked to the pathogenesis of BD. Relatives of probands with BD who are at familial risk can exhibit brain abnormalities prior to illness onset. Given its involvement in prefrontal cognitive control and in frontolimbic circuitry that regulates emotional reactivity, the inferior frontal gyrus (IFG) has been a focus of research in studies of BD-related pathology and BD-risk mechanism. In this review, we discuss multimodal neuroimaging findings of the IFG based on studies comparing at-risk relatives and low-risk controls. Review of these studies in at-risk cases suggests the presence of both risk and resilience markers related to the IFG. At-risk individuals exhibited larger gray matter volume and increased functional activities in IFG compared with low-risk controls, which might result from an adaptive brain compensation to support emotion regulation as an aspect of psychological resilience. Functional connectivity between IFG and downstream limbic or striatal areas was typically decreased in at-risk individuals relative to controls, which could contribute to risk-related problems of cognitive and emotional control. Large-scale and longitudinal investigations on at-risk individuals will further elucidate the role of IFG and other brain regions in relation to familial risk for BD, and together guide identification of at-risk individuals for primary prevention.

Neural Correlates of the Risk for Schizophrenia and Bipolar Disorder: A Meta-analysis of Structural and Functional Neuroimaging Studies

BACKGROUND

Clinical features and genetics overlap in schizophrenia (SCZ) and bipolar disorder (BD). Identifying brain alterations associated with genetic vulnerability for SCZ and BD could help to discover intermediate phenotypes, quantifiable biological traits with greater prevalence in unaffected relatives (RELs), and early recognition biomarkers in ultrahigh risk populations. However, a comprehensive meta-analysis of structural and functional magnetic resonance imaging (MRI) studies examining relatives of patients with SCZ and BD has not been performed yet.

METHODS

We systematically searched PubMed, Scopus, and Web of Science for structural and functional MRI studies investigating relatives and healthy control subjects. A total of 230 eligible neuroimaging studies (6274 SCZ-RELs, 1900 BD-RELs, 10,789 healthy control subjects) were identified. We conducted coordinate-based activation likelihood estimation meta-analyses on 26 structural MRI and 81 functional MRI investigations, including stratification by task type. We also meta-analyzed regional and global volumetric changes. Finally, we performed a meta-analysis of all MRI studies combined.

RESULTS

Reduced thalamic volume was present in both SCZ and BD RELs. Moreover, SCZ-RELs showed alterations in corticostriatal-thalamic networks, spanning the dorsolateral prefrontal cortex and temporal regions, while BD-RELs showed altered thalamocortical and limbic regions, including the ventrolateral prefrontal, superior parietal, and medial temporal cortices, with frontoparietal alterations in RELs of BD type I.

CONCLUSIONS

Familiarity for SCZ and BD is associated with alterations in the thalamocortical circuits, which may be the expression of the shared genetic mechanism underlying both disorders. Furthermore, the involvement of different prefrontocortical and temporal nodes may be associated with a differential symptom expression in the two disorders.

COVID-19 and first manic episodes: a systematic review

Sars-CoV-2 is a respiratory virus that can access the central nervous system, as indicated by the presence of the virus in patients' cerebrospinal fluid and the occurrence of several neurological syndromes during and after COVID-19. Growing evidence indicates that Sars-CoV-2 can also trigger the acute onset of mood disorders or psychotic symptoms. COVID-19-related first episodes of mania, in subjects with no known history of bipolar disorder, have never been systematically analyzed. Thus, the present study assesses a potential link between the two conditions. This systematic review analyzes cases of first appearance of manic episodes associated with COVID-19. Clinical features, pharmacological therapies, and relationships with pre-existing medical conditions are also appraised. Medical records of twenty-three patients fulfilling the current DSM-5 criteria for manic episode were included. Manic episodes started, on average, after 12.71±6.65 days from the infection onset. Psychotic symptoms were frequently reported. 82.61% of patients exhibited delusions, whereas 39.13% of patients presented hallucinations. A large discrepancy in the diagnostic workups was observed. Mania represents an underestimated clinical presentation of COVID-19. Further studies should focus on the pathophysiological substrates of COVID-19-related mania and pursue appropriate and specific diagnostic and therapeutic workups.