The Compelling and Persistent Problem of Bipolar Disorder Disguised as Major Depression Disorder: An Integrative Review [Formula: see text]

Opportunities for digital health technology: identifying unmet needs for bipolar misdiagnosis and depression care management

Introduction

Digital health technologies (DHTs) driven by artificial intelligence applications, particularly those including predictive models derived with machine learning methods, have garnered substantial attention and financial investment in recent years. Yet, there is little evidence of widespread adoption and scant proof of gains in patient health outcomes. One factor of this paradox is the disconnect between DHT developers and digital health ecosystem stakeholders, which can result in developing technologies that are highly sophisticated but clinically irrelevant. Here, we aimed to uncover challenges faced by psychiatrists treating patients with major depressive disorder (MDD). Specifically, we focused on challenges psychiatrists raised about bipolar disorder (BD) misdiagnosis.

Methods

We conducted semi-structured interviews with 10 United States-based psychiatrists. We applied text and thematic analysis to the resulting interview transcripts.

Results

Three main themes emerged: (1) BD is often misdiagnosed, (2) information crucial to evaluating BD is often occluded from clinical observation, and (3) BD misdiagnosis has important treatment implications.

Discussion

Using upstream stakeholder engagement methods, we were able to identify a narrow, unforeseen, and clinically relevant problem. We propose an organizing framework for development of digital tools based upon clinician-identified unmet need.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression

There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.

Common and differential neural mechanisms underlying mood disorders

BACKGROUND

Despite homogenous clinical presentations between bipolar and unipolar disorders, there are distinct neurobiological differences. Chronicity of illness may be a factor impacting and sustaining certain neural features. The goal of this study was to investigate common and shared neural mechanisms underlying mood disorders, and possible sustained neural changes relating to illness chronicity by investigating a cohort of euthymic patients with bipolar disorder (BD), unipolar depression who had responded to treatment (treatment-sensitive depression, TSD), and a chronically treatment-resistant depressed (TRD) group.

METHODS

One hundred and seventy-two participants (40 BD, 39 TSD, 40 TRD, and 53 age-gender-matched healthy controls) underwent resting-state fMRI scans. Seed-based and independent component analyses were performed to investigate group differences in resting-state connectivity between the four groups.

RESULTS

All three clinical groups had significantly lower connectivity within the frontoparietal network (FPN) relative to controls. TRD and BD were significantly different from TSD (TRD, BD > TSD) but were not significantly different from each other. TRDs were also significantly different from both BD and TSD for salience network connectivity with the posterior cingulate (DMN) and the FPN with frontal pole (DMN). Additionally, the BD group exhibited greater DMN-FPN (sgACC-RDLPFC) connectivity relative to TRD, TSD, and controls, which was correlated with a previous number of depressive episodes, in the BD group only.

CONCLUSIONS

BD demonstrated shared and differential connectivity features relative to symptomatic TRD and euthymic TSD groups. The increased sgACC-RDLPFC connectivity in BD and its correlation with a number of depressive episodes could be a neural feature associated with illness chronicity.

Associations between cannabis use, cannabis use disorder, and mood disorders: longitudinal, genetic, and neurocognitive evidence

RATIONALE

Cannabis use among people with mood disorders increased in recent years. While comorbidity between cannabis use, cannabis use disorder (CUD), and mood disorders is high, the underlying mechanisms remain unclear.

OBJECTIVES

We aimed to evaluate (1) the epidemiological evidence for an association between cannabis use, CUD, and mood disorders; (2) prospective longitudinal, genetic, and neurocognitive evidence of underlying mechanisms; and (3) prognosis and treatment options for individuals with CUD and mood disorders.

METHODS

Narrative review of existing literature is identified through PubMed searches, reviews, and meta-analyses. Evidence was reviewed separately for depression, bipolar disorder, and suicide.

RESULTS

Current evidence is limited and mixed but suggestive of a bidirectional relationship between cannabis use, CUD, and the onset of depression. The evidence more consistently points to cannabis use preceding onset of bipolar disorder. Shared neurocognitive mechanisms and underlying genetic and environmental risk factors appear to explain part of the association. However, cannabis use itself may also influence the development of mood disorders, while others may initiate cannabis use to self-medicate symptoms. Comorbid cannabis use and CUD are associated with worse prognosis for depression and bipolar disorder including increased suicidal behaviors. Evidence for targeted treatments is limited.

CONCLUSIONS

The current evidence base is limited by the lack of well-controlled prospective longitudinal studies and clinical studies including comorbid individuals. Future studies in humans examining the causal pathways and potential mechanisms of the association between cannabis use, CUD, and mood disorder comorbidity are crucial for optimizing harm reduction and treatment strategies.

Functional near-infrared spectroscopy during the verbal fluency task of English-Speaking adults with mood disorders: A preliminary study

Functional near-infrared spectroscopy (fNIRS) provides a direct and objective assessment of cerebral cortex function. It may be used to determine neurophysiological differences between psychiatric disorders with overlapping symptoms, such as major depressive disorder (MDD) and bipolar disorder (BD). Therefore, this preliminary study aimed to compare fNIRS signals during the verbal fluency task (VFT) of English-speaking healthy controls (HC), patients with MDD and patients with BD. Fifteen HCs, 15 patients with MDD and 15 patients with BD were recruited. Groups were matched for age, gender, ethnicity and education. Relative oxy-haemoglobin and deoxy-haemoglobin changes in the frontotemporal cortex was monitored with a 52-channel fNIRS system. Integral values of the frontal and temporal regions were derived as a measure cortical haemodynamic response magnitude. Both patient groups had lower frontal and temporal region integral values than HCs, and patients with MDD had lower frontal region integral value than patients with BD. Moreover, patients could be differentiated from HCs using the frontal and temporal integral values, and patient groups could be differentiated using the frontal region integral values. VFT performance, clinical history and symptom severity were not associated with integral values. These results suggest that prefrontal cortex haemodynamic dysfunction occurs in mood disorders, and it is more extensive in MDD than BD. The fNIRS-VFT paradigm may be a potential tool for differentiating MDD from BD in clinical settings, and these findings need to be verified in a larger sample of English-speaking patients with mood disorders.

Impact of bipolar disorder on health-related quality of life and work productivity: Estimates from the national health and wellness survey in Japan

BACKGROUND

Previous studies have shown that bipolar disorder (BD) patients frequently present with difficulties in interpersonal relationships, education or employment and suffer poorer quality of life. This study aimed to estimate the impact of BD on health-related quality of life (HRQOL), work productivity loss and indirect costs.

METHODS

Data was from the online, self-administered 2019 National Health and Wellness Survey. Outcomes were compared for those who self-reported a physician diagnosis of BD (N=179), major depressive disorder (MDD, N=1,549) and controls who have never experienced BD, MDD and schizophrenia (N=27,485).

RESULTS

The lifetime prevalence was estimated to be 0.60% for BD and 5.16% for MDD. Significantly lower Mental Component Summary (MCS), Role Component Summary (RCS) scores and EuroQol 5-dimension scale (EQ-5D-5L) summary index and significantly higher presenteeism, total work productivity impairment and activity impairment assessed by Work Productivity and Activity Impairment questionnaire and indirect costs for BD versus controls and BD PHQ-9≥10 versus PHQ-9<10 were observed. Compared to MDD patients, BD patients had significantly lower RCS score and greater work productivity loss and activity impairment. The national morbidity cost of BD in Japan was estimated to be Japanese yen 1,236 billion using a human-capital approach.

LIMITATIONS

The data used were self-reported and is cross-sectional in nature, thus causal relationship cannot be assumed.

CONCLUSION

BD patients and those with severe depressive symptoms experience significantly poorer HRQOL and greater work productivity loss and indirect costs. These findings highlight the importance of proper screening, diagnosis and treatment of BD and bipolar depression.

Default-mode and fronto-parietal network connectivity during rest distinguishes asymptomatic patients with bipolar disorder and major depressive disorder

Bipolar disorder (BD) is commonly misdiagnosed as major depressive disorder (MDD). This is understandable, as depression often precedes mania and is otherwise indistinguishable in both. It is therefore imperative to identify neural mechanisms that can differentiate the two disorders. Interrogating resting brain neural activity may reveal core distinguishing abnormalities. We adopted an a priori approach, examining three key networks documented in previous mood disorder literature subserving executive function, salience and rumination that may differentiate euthymic BD and MDD patients. Thirty-eight patients with BD, 39 patients with MDD matched for depression severity, and 39 age-gender matched healthy controls, completed resting-state fMRI scans. Seed-based and data-driven Independent Component analyses (ICA) were implemented to examine group differences in resting-state connectivity (pFDR < 0.05). Seed analysis masks were target regions identified from the fronto-parietal (FPN), salience (SN) and default-mode (DMN) networks. Seed-based analyses identified significantly greater connectivity between the subgenual cingulate cortex (DMN) and right dorsolateral prefrontal cortex (FPN) in BD relative to MDD and controls. The ICA analyses also found greater connectivity between the DMN and inferior frontal gyrus, an FPN region in BD relative to MDD. There were also significant group differences across the three networks in both clinical groups relative to controls. Altered DMN-FPN functional connectivity is thought to underlie deficits in the processing, management and regulation of affective stimuli. Our results suggest that connectivity between these networks could potentially distinguish the two disorders and could be a possible trait mechanism in BD persisting even in the absence of symptoms.

Investigating neural circuits of emotion regulation to distinguish euthymic patients with bipolar disorder and major depressive disorder

BACKGROUND

Up to 40% of patients with bipolar disorder (BD) are initially diagnosed as having major depressive disorder (MDD), and emotional lability is a key aspect of both sets of mood disorders. However, it remains unknown whether differences in the regulation of emotions through cognitive reappraisal may serve to distinguish BD and MDD. Therefore, we examined this question in euthymic BD and MDD patients.

METHODS

Thirty-eight euthymic BD, 33 euthymic MDD and 37 healthy control (HC) participants, matched for age, gender and depression severity, engaged in an emotion regulation (ER) cognitive reappraisal task during an fMRI scan were examined. Participants either reappraised (Think condition) or passively watched negative (Watch condition) or neutral (Neutral condition) pictures and rated their affect. Activation and connectivity analyses were used to examine group differences in reappraisal (Think vs Watch) and reactivity (Watch vs Neutral) conditions in ER-specific neural circuits.

RESULTS

Irrespective of group, participants rated most negatively the images during the Watch condition relative to Think and Neutral conditions, and more negatively to Think relative to Neutral. Notably, BD participants exhibited reduced subgenual anterior cingulate activation (sgACC) relative to MDD during reappraisal, but exhibited greater sgACC activation relative to MDD during reactivity, whereas MDD participants elicited greater activation in right amygdala relative to BD during reactivity. We found no group differences in task-related connectivity.

CONCLUSIONS

Euthymic BD and MDD patients engage differential brain regions to process and regulate emotional information. These differences could serve to distinguish the clinical groups and provide novel insights into the underlying pathophysiology of BD.

Short versions of the 32-item Hypomania Checklist: A systematic review

PURPOSE

Bipolar disorder (BD) is frequently misdiagnosed, which can lead to negative outcomes. The 32-item Hypomania Checklist (HCL-32) is one of the most widely used self-reported screening instruments for hypomanic symptoms, which has several short versions. This systematic review examined the psychometric properties of HCL-32 short versions.

DESIGN AND METHODS

Five international databases were systematically and independently searched by two researchers for studies that developed the HCL short versions. Basic demographic and clinical characteristics and the psychometric properties of the HCL short versions were recorded.

FINDINGS

Eighteen studies were identified. The majority of the HCL short versions showed satisfactory to good psychometric properties.

PRACTICE IMPLICATIONS

Validated HCL short versions with satisfactory psychometric properties may be helpful in screening for BD.

Identifying the Complexity of Diagnosing Bipolar Disorder: A Focused Ethnography

Purpose: To identify and describe the complexity of diagnosing bipolar disorder, including the diagnostic process and patient experiences of being newly diagnosed with bipolar disorder. Design: A mixed-methods focused ethnography was conducted, grounded in a post-positivist foundation. Methods: Medical records (n = 100) of patients whose diagnosis had been switched to bipolar disorder were examined. Six weeks post-hospitalization, ten outpatients with the diagnosis of bipolar disorder underwent an in-depth interview. Findings: Four diagnostic processes were identified during the retrospective record review. Two patterns and five themes were identified from the interviews. The first pattern, living with undiagnosed bipolar disorder, demonstrated common experiences of distinguishing impulsive moods and behavior, suffering life challenges, and seeking relief. The second pattern, acclimating to a new diagnosis of bipolar disorder, demonstrated participants' ways of understanding the diagnosis and reconciling the diagnosis. Patterns in the interviews corroborated data from the record review. Conclusions: The rendering of an appropriate diagnosis is key. Many participants' lives were significantly improved when diagnosis was made, and treatment recommendations for bipolar disorder (BPD) were initiated. These findings offer clinicians and researchers new ways to think about the complexity of the diagnosis of BPD including contrasting decision-making outcomes along a screening, diagnosis, and treatment continuum, as well as using the diagnostic event to instigate meaningful life change in the patient.