Consequences of misdiagnosis: inaccurate treatment and poor patient outcomes in bipolar disorder

Biomarkers of bipolar disorder based on metabolomics: A systematic review

Bipolar disorder (BD) is a severe affective disorder characterized by recurrent episodes of depression or mania/hypomania, which significantly impair cognitive function, life skills, and social abilities of patients. There is little understanding of the neurobiological mechanisms of BD. The diagnosis of BD is primarily based on clinical assessment and psychiatric examination, highlighting the urgent need for objective markers to facilitate the diagnosis of BD. Metabolomics can be used as a diagnostic tool for disease identification and evaluation. This study summarized the altered metabolites in BD and analyzed aberrant metabolic pathways, which might contribute to the diagnosis of BD. Search of PubMed and Web of science for human BD studies related to metabolism to identify articles published up to November 19, 2022 yielded 987 articles. After screening and applying the inclusion and exclusion criteria, 16 untargeted and 11 targeted metabolomics studies were included. Pathway analysis of the potential differential biometabolic markers was performed using the Kyoto encyclopedia of genes and genomes (KEGG). There were 72 upregulated and 134 downregulated biomarkers in the untargeted metabolomics studies using blood samples. Untargeted metabolomics studies utilizing urine specimens revealed the presence of 78 upregulated and 54 downregulated metabolites. The targeted metabolomics studies revealed abnormalities in the metabolism of glutamate and tryptophan. Enrichment analysis revealed that the differential metabolic pathways were mainly involved in the metabolism of glucose, amino acid and fatty acid. These findings suggested that certain metabolic biomarkers or metabolic biomarker panels might serve as a reference for the diagnosis of BD.

The prevalence and clinical correlates of suicide attempts in patients with bipolar disorder misdiagnosed with major depressive disorder: Results from a national survey in China

BACKGROUND AND AIM

Suicide is nearly always associated with underlying mental disorders. Risk factors for suicide attempts (SAs) in patients with bipolar disorder (BD) misdiagnosed with major depressive disorder (MDD) remain unelucidated. This study was to evaluate the prevalence and clinical risk factors of SAs in Chinese patients with BD misdiagnosed with MDD.

METHODS

A total of 1487 patients with MDD from 13 mental health institutions in China were enrolled. Mini International Neuropsychiatric Interview (MINI) was used to identify patients with BD who are misdiagnosed as MDD. The general sociodemographic and clinical data of the patients were collected and MINI suicide module was used to identify patients with SAs in these misdiagnosed patients.

RESULTS

In China, 20.6% of patients with BD were incorrectly diagnosed as having MDD. Among these misdiagnosed patients, 26.5% had attempted suicide. These patients tended to be older, had a higher number of hospitalizations, and were more likely to experience frequent and seasonal depressive episodes with atypical features, psychotic symptoms, and suicidal thoughts. Frequent depressive episodes and suicidal thoughts during depression were identified as independent risk factors for SAs. Additionally, significant sociodemographic and clinical differences were found between individuals misdiagnosed with MDD in BD and patients with MDD who have attempted suicide.

CONCLUSIONS

This study highlights the importance of accurate diagnosis in individuals with BD and provide valuable insights for the targeted identification and intervention of individuals with BD misdiagnosed as having MDD and those with genuine MDD, particularly in relation to suicidal behavior.

A diagnostic model based on bioinformatics and machine learning to differentiate bipolar disorder from schizophrenia and major depressive disorder

Bipolar disorder (BD) showed the highest suicide rate of all psychiatric disorders, and its underlying causative genes and effective treatments remain unclear. During diagnosis, BD is often confused with schizophrenia (SC) and major depressive disorder (MDD), due to which patients may receive inadequate or inappropriate treatment, which is detrimental to their prognosis. This study aims to establish a diagnostic model to distinguish BD from SC and MDD in multiple public datasets through bioinformatics and machine learning and to provide new ideas for diagnosing BD in the future. Three brain tissue datasets containing BD, SC, and MDD were chosen from the Gene Expression Omnibus database (GEO), and two peripheral blood datasets were selected for validation. Linear Models for Microarray Data (Limma) analysis was carried out to identify differentially expressed genes (DEGs). Functional enrichment analysis and machine learning were utilized to identify. Least absolute shrinkage and selection operator (LASSO) regression was employed for identifying candidate immune-associated central genes, constructing protein-protein interaction networks (PPI), building artificial neural networks (ANN) for validation, and plotting receiver operating characteristic curve (ROC curve) for differentiating BD from SC and MDD and creating immune cell infiltration to study immune cell dysregulation in the three diseases. RBM10 was obtained as a candidate gene to distinguish BD from SC. Five candidate genes (LYPD1, HMBS, HEBP2, SETD3, and ECM2) were obtained to distinguish BD from MDD. The validation was performed by ANN, and ROC curves were plotted for diagnostic value assessment. The outcomes exhibited the prediction model to have a promising diagnostic value. In the immune infiltration analysis, Naive B, Resting NK, and Activated Mast Cells were found to be substantially different between BD and SC. Naive B and Memory B cells were prominently variant between BD and MDD. In this study, RBM10 was found as a candidate gene to distinguish BD from SC; LYPD1, HMBS, HEBP2, SETD3, and ECM2 serve as five candidate genes to distinguish BD from MDD. The results obtained from the ANN network showed that these candidate genes could perfectly distinguish BD from SC and MDD (76.923% and 81.538%, respectively).

Distinguishing functional and structural MRI abnormalities between bipolar and unipolar depression

Background

This study aims to investigate the underlying characteristics of spontaneous brain activity by analyzing the volumes of the hippocampus and parahippocampal gyrus, as well as the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), in order to differentiate between bipolar disorder (BD) and unipolar depressive disorder.

Methods

A total of 46 healthy controls, 58 patients with major depressive disorder (MDD), and 61 patients with BD participated in the study and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. The researchers calculated the differences in volume, fALFF, and ReHo values among the three groups. Additionally, they conducted correlation analyses to examine the relationships between clinical variables and the aforementioned brain measures.

Results

The results showed that the BD group exhibited increased fALFF in the hippocampus compared to the healthy control (HC) and MDD groups. Furthermore, the ReHo values in the hippocampus and parahippocampal gyrus were significantly higher in the BD group compared to the HC group. The findings from the person correlation analysis indicated a positive relationship between ReHo values in the hippocampus and both HAMD and HAMA scores. Moreover, there was no correlation between the volumes, fALFF, and ReHo values in the hippocampus and parahippocampal gyrus, and cognitive function levels (RBANS).

Conclusion

Taken together, these aberrant patterns of intrinsic brain activity in the hippocampus and parahippocampal gyrus may serve as quantitative indicators for distinguishing between BD and unipolar depression.

Untargeted Lipidomics Study of Bipolar Disorder Patients in Serbia

The Lipidomic profiles of serum samples from patients with bipolar disorder (BD) and healthy controls (C) were explored and compared. The sample cohort included 31 BD patients and 31 control individuals. An untargeted lipidomics study applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) was conducted to achieve the lipid profiles. Multivariate statistical analyses (principal component analysis and partial least squares discriminant analysis) were performed, and fifty-six differential lipids were confirmed in BD and controls. Our results pointed to alterations in lipid metabolism, including pathways of glycerophospholipids, sphingolipids, glycerolipids, and sterol lipids, in BD patient sera. This study emphasized the role of lipid pathways in BD, and comprehensive research using the LC-HRMS platform is necessary for future application in the diagnosis and improvement of BD treatments.

Future Directions in the Mental Health of Transgender Youth: Towards a Social-Affective Developmental Model of Health Disparity

Mental health disparities in transgender and gender diverse (TGD) youth are well-documented. These disparities are often studied in the context of minority stress theory, and most of this research focuses on experiences of trauma and discrimination TGD youth experience after coming out. However, TGD youth may be targets of violence and victimization due to perceived gender nonconformity before coming out. In this Future Directions, we integrate research on attachment, developmental trauma, and effects of racism and homophobia on mental health to propose a social-affective developmental framework for TGD youth. We provide a clinical vignette to highlight limitations in current approaches to mental health assessment in TGD youth and to illustrate how using a social-affective developmental framework can improve clinical assessment and treatment approaches and deepen our understanding of mental health disparities in TGD people.

Comparison of oxidative stress parameters, thiol-disulfide homeostasis, and pro-inflammatory cytokines levels in patients with bipolar disorder and their first-degree relatives

BACKGROUND

In this study, we aimed to compare the oxidative stress parameters, thiol-disulfide homeostasis, and plasma pro-inflammatory cytokines levels of patients with bipolar disorder (BD), BD patients' first-degree relatives (FDRs), and the healthy controls (HCs).

METHODS

Thirty-five patients with BD, 35 FDRs of BD, and 35 healthy controls (HCs) were included. The individuals' ages varied from 28 to 58, and the groups were well-matched in terms of age and gender. The total thiol (TT), native thiol (NT), disulfide (DIS), total oxidant status (TOS), total antioxidant status (TAS), IL-1β, IL-6, and TNF-α concentrations were measured from serum samples. The oxidative stress index (OSI) was calculated using mathematical formulas.

RESULTS

TOS was significantly higher in both patients and FDRs than HCs (p < 0.01 for all pairwise comparisons). OSI, DIS, oxidized thiol, and the ratio of thiol oxidation-reduction levels were significantly higher in both patients with BD and FDRs than HCs (p < 0.01 for all pairwise comparisons). TAS, TT, NT, and reduced thiol levels were significantly lower in both patients with BD and FDRs than HCs (p < 0.01 for all pairwise comparisons). IL-1β, IL-6, and TNF-α were significantly higher in both patients and FDRs than HCs (p < 0.01 for all pairwise comparisons).

LIMITATIONS

Small sample size.

CONCLUSIONS

Early diagnosis is important for treating of bipolar disorder. TT, NT, DIS, TOS, TAS, OSI, IL1-β, IL-6, and TNF-α can be used as potential biomarkers in the early diagnosis and intervention of BD. Furthermore, oxidative/antioxidative markers and plasma pro-inflammatory cytokine parameters may guide the determination of the disease's activity and response to treatment.

Biomarkers and treatments for mood disorders encompassing the neurosteroid and endocannabinoid systems

Mood disorders, including major depressive disorder, postpartum depression, post-traumatic stress disorder and suicidality are highly prevalent, associated with a significant economic burden, and remain poorly diagnosed and poorly treated psychiatric conditions. In part, this may result from the lack of biomarkers that can guide precision medicine with individualized treatments for millions of individuals who suffer these debilitating conditions worldwide. While several biomarker candidates have been proposed for mood disorders, none has been implemented in clinical practice and the treatment still relies in the prescription of selective serotonin reuptake inhibitors that shows mixed efficacy and significant side effects. Both neurosteroid biosynthesis and the endocannabinoid system have recently provided evidence for pharmacological targets to improve mood symptoms and the neuroactive steroid allopregnanolone has recently been approved by the USA Food and Drug Administration for the treatment of post-partum depression. Clinical studies also show efficacy for the management of major depression and more studies are being conducted to study efficacy in post-traumatic stress disorder. Likewise, the endocannabinoid-like modulator, N-palmioyl ethanolamide (PEA) has shown efficacy in the treatment of major depression and bipolar disorder. While these new agents are coming forward in the field of neuropsychopharmacology as a new generation of fast-acting antidepressants, the hypothesis of whether their deficits underlying mood disorders could constitute valid predictive biomarkers to facilitate diagnosis and treatment of these conditions is under consideration.

Use of the 33-Item Hypomania Checklist (HCL-33) to Distinguish Bipolar Disorder From Major Depressive Disorder in Older Adults

BACKGROUND

Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in older patients. This study examined the psychometric properties of the 33-item Hypomania Checklist (HCL-33) and its accuracy to differentiate BD from MDD among older adults.

METHOD

A total of 215 depressed older patients were recruited; 107 were diagnosed with BD (71 with BD-type I and 36 with BD-type II) and 108 with MDD. Principal components analysis (PCA) was used to explore the factor structure of the HCL-33. Cronbach's alpha was calculated to test the internal consistency. Intra-class correlation coefficient (ICC) was used to measure test-retest reliability. The receiver operating characteristic (ROC) analysis was used to generate the optimal cut-off value to differentiate between BD and MDD.

RESULTS

Two factors were identified in the PCA analysis accounting for 33.9% of the total variance. The Cronbach's alpha value for the HCL-33 was 0.912, with 0.922 for factor I and 0.664 for factor II. The test-retest reliability was excellent (ICC: 0.891). The optimal cut-off of the HCL-33 total score for discriminating between MDD and BD was 14, with a sensitivity of 88.8% and specificity of 82.4%.

CONCLUSION

The HCL-33 had satisfactory reliability and validity and could be used to distinguish BD from MDD in older adults.

Divergent and convergent imaging markers between bipolar and unipolar depression based on Machine Learning

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain high-order functional connectivity (HOFC) network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex chronnectome metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthy controls (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.

Sex differences in the blood-brain barrier: Implications for mental health

Prevalence of mental disorders, including major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ) are increasing at alarming rates in our societies. Growing evidence points toward major sex differences in these conditions, and high rates of treatment resistance support the need to consider novel biological mechanisms outside of neuronal function to gain mechanistic insights that could lead to innovative therapies. Blood-brain barrier alterations have been reported in MDD, BD and SZ. Here, we provide an overview of sex-specific immune, endocrine, vascular and transcriptional-mediated changes that could affect neurovascular integrity and possibly contribute to the pathogenesis of mental disorders. We also identify pitfalls in current literature and highlight promising vascular biomarkers. Better understanding of how these adaptations can contribute to mental health status is essential not only in the context of MDD, BD and SZ but also cardiovascular diseases and stroke which are associated with higher prevalence of these conditions.

Magnetic Resonance Spectroscopy and Bipolar Disorder: How Feasible Is This Pairing?

Bipolar disorder is a psychiatric disorder that affects a significant part of the world's population; however, its diagnosis is difficult, mainly because of the lack of biomarkers and objective tests that aid the clinical evaluation. Proton magnetic resonance spectroscopy (MRS) is a tool that is relatively unused in the medical field. Its application arises from conventional magnetic resonance, and allows non-invasive, in vivo, the study of various metabolites and compounds in the human brain. This method may allow the assessment of neurobiochemical alterations in bipolar patients. One of the main advantages of this study type is the simplicity in its use since it only needs a standard magnetic resonator. All these characteristics make it an attractive diagnostic tool that can be used anywhere, including in low-middle-income countries. In conclusion, MRS has potential as a diagnostic tool for bipolar disorder; nevertheless, using it for this purpose still requires additional steps.

A piece of the puzzle: Does bipolarity partly explain the high prevalence of treatment resistance in depression?

In this cross-sectional study we examined whether the prevalence of treatment resistant depression (TRD) can be partly attributed to level of bipolarity. We included data of 201 patients with either episodic depression or TRD, who received treatment for their depression at either an outpatient or 2nd opinion/daytime setting, within a specialised mental healthcare department in the Netherlands. Whether level of TRD, assessed by the 'Dutch Measure for quantification of Treatment Resistance in Depression', can be partly explained by level of bipolarity, assessed by 'the Bipolarity Index', was examined using linear regression. We found no direct association between level of TRD and level of bipolarity, nor did comorbid anxiety disorders obscure an existing association. In this study we found no evidence for overlooked bipolarity contributing to the high prevalence of TRD. If replicated, we could state that additional screening on bipolarity with an instrument such as the 'Bipolarity Index' in the specialised mental health care is unnecessary.

Changes in major psychiatric disorders in children and adolescents from 2001 to 2020: A retrospective single-center study

OBJECTIVE

This study aimed to determine the hospitalization rates, length of stay, age at the time of admission, and sex distribution for major psychiatric disorders in children and adolescents and provide a reference for early intervention for these diseases and distribution of medical resources in hospitals.

METHODS

We screened 4,423 patients in the child and adolescent wards of the Anhui Provincial Mental Health Center from 2001 to 2020, and examined the top four (81.1%) mental health disorders that accounted for the overall proportion of patients admitted, namely schizophrenia (SCZ) (45.7%), depressive disorder (DD) (14.5%), bipolar disorder (BD) (9.3%), and childhood emotional disorder (CED) (11.6%), and for each disorder, the percentage of hospitalization, length of stay, age at admission, and sex distribution were analyzed.

RESULTS

From 2001 to 2020, there was a significantly decreasing trend in the proportion of hospitalizations for SCZ (p < 0.001) and an increasing trend for depression and CED (p < 0.001). In terms of length of stay, SCZ was significantly longer than the other three disorders (p < 0.001), whereas there was no significant difference between DD, BD, and CED, and there was no significant trend in length of stay for any of the four disorders. The age at admission for CED was significantly lower than that for the other three disorders (p < 0.001). There was a decreasing trend in the age at admission for DD (p = 0.011) and an increasing trend for BD (p = 0.001). A significant increase in the number of female patients admitted for SCZ, DD, and CED was observed, while there was no significant change in the sex ratio for BD.

CONCLUSION

Although there is a significant downward trend in the percentage of hospitalizations for SCZ, it is still the most common psychiatric disorder in children and adolescents. We observed a significant increase in the percentage of hospitalizations for DD and CED. In addition, the proportion of female patients being hospitalized is on the rise, and this aspect requires continuous attention.

Development and validation of a nomogram based on lymphocyte subsets to distinguish bipolar depression from major depressive disorder

OBJECTIVE

Bipolar depression (BD) and major depressive disorder (MDD) are both common affective disorders. The common depression episodes make it difficult to distinguish between them, even for experienced clinicians. Failure to properly diagnose them in a timely manner leads to inappropriate treatment strategies. Therefore, it is important to distinguish between BD and MDD. The aim of this study was to develop and validate a nomogram model that distinguishes BD from MDD based on the characteristics of lymphocyte subsets.

MATERIALS AND METHODS

A prospective cross-sectional study was performed. Blood samples were obtained from participants who met the inclusion criteria. The least absolute shrinkage and selection operator (LASSO) regression model was used for factor selection. A differential diagnosis nomogram for BD and MDD was developed using multivariable logistic regression and the area under the curve (AUC) with 95% confidence interval (CI) was calculated, as well as the internal validation using a bootstrap algorithm with 1,000 repetitions. Calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical utility of the nomogram, respectively.

RESULTS

A total of 166 participants who were diagnosed with BD (83 cases) or MDD (83 cases), as well as 101 healthy controls (HCs) between June 2018 and January 2022 were enrolled in this study. CD19⁺ B cells, CD3⁺ T cells, CD3^-CD16/56⁺ NK cells, and total lymphocyte counts were strong predictors of the diagnosis of BD and MDD and were included in the differential diagnosis nomogram. The AUC of the nomogram and internal validation were 0.922 (95%; CI, 0.879-0.965), and 0.911 (95% CI, 0.838-0.844), respectively. The calibration curve used to discriminate BD from MDD showed optimal agreement between the nomogram and the actual diagnosis. The results of DCA showed that the net clinical benefit was significant.

CONCLUSION

This is an easy-to-use, repeatable, and economical nomogram for differential diagnosis that can help clinicians in the individual diagnosis of BD and MDD patients, reduce the risk of misdiagnosis, facilitate the formulation of appropriate treatment strategies and intervention plans.

Prospective Study of a Novel Risk Stratification Process for Opioid-Related Harm Reduction in Cancer Patients Seen in an Outpatient Palliative Care Clinic

Background: Oncologists and palliative specialists prescribe opioids for millions of cancer patients despite limited research on effective screening and mitigation strategies to reduce risk of opioid-related harm in that population. Objective: To evaluate the efficacy of a novel opioid risk stratification process for predicting significant aberrant behaviors (SABs) related to prescribed opioid medications. Design and Setting/Subjects: This is a prospective, longitudinal study of 319 consecutive patients referred to an outpatient palliative care clinic between 2010 and 2012, a period during which prescription opioid-related deaths began to increase in the United States. Measures: Patients completed a psychodiagnostic/substance use risk assessment with a licensed clinical psychologist or social worker at the initial palliative clinic visit. Patients were assigned to Low-, Moderate-, or High-Risk groups based on predetermined stratification criteria and were managed via an opioid harm reduction approach. The primary dependent measure was the presence of at least one SAB after the initial visit. Results: Eighteen percent of patients (n = 56) demonstrated at least one major aberrant behavior. Odds of future aberrant behavior was 15 times greater in the High-Risk versus the Low-Risk category. Five risk factors significantly enhanced our risk model: age 18 to 45 years, job instability, history of bipolar diagnosis, history of substance abuse, and theft. Conclusion: Our risk stratification process provides a useful model for predicting those at greatest risk of future aberrant behaviors and most in need of comanagement. We recommend additional studies to test our proposed streamlined risk stratification tool.

Transcriptomic profiling as biological markers of depression - A pilot study in unipolar and bipolar women

OBJECTIVES

A significant challenge in psychiatry is the differential diagnosis of depressive episodes in the course of mood disorders. Gene expression profiling may provide an opportunity for such distinguishment.

METHODS

We studied differentially expressed genes in women with a depressive episode in the course of unipolar depression (UD) (n = 24) and bipolar disorder types I (BDI) (n = 13) and II (BDII) (n = 19), and healthy controls (n = 15).

RESULTS

Different types of depression varied in the number and type of up or down-regulated genes. The pathway analysis showed: in UD, up-regulated rheumatoid arthritis pathway (including ITGB2, CXCL8, TEK, TLR4 genes), and down-regulated taste transduction pathway (TAS2R10, TAS2R46, TAS2R14, TAS2R43, TAS2R45, TAS2R19, TAS2R13, TAS2R20, GNG13); in BDI, eight down-regulated pathways: glutamatergic synapse, retrograde endocannabinoid signalling, axon guidance, calcium signalling, nicotine addiction, PI3K-Akt signalling, drug metabolism - cytochrome P450, and morphine addiction; in BDII, up-regulated osteoclast differentiation and Notch signalling pathway, and down-regulated type I diabetes mellitus pathway. Distinct expression markers analysis uncovered the unique for UD, up-regulated bladder cancer pathway (HBEGF and CXCL8 genes).

CONCLUSIONS

This pilot study suggests a probability of differentiating depression in the course of UD, BDI, and II, based on transcriptomic profiling.

Which factors delay treatment in bipolar disorder? A nationwide study focussed on duration of untreated illness

AIM

The aim of the present study was to detect factors associated with duration of untreated illness (DUI) in bipolar disorder (BD).

METHOD

A total of 1575 patients were selected for the purposes of the study. Correlation analyses were performed to analyse the relation between DUI and quantitative variables. The length of DUI was compared between groups defined by qualitative variables through one-way analyses of variance or Kruskal-Wallis's tests according to the distribution of the variable. Linear multivariable regressions were used to find the most parsimonious set of variables independently associated with DUI: to this aim, qualitative variables were inserted with the numeric code of their classes by assuming a proportional effect moving from one class to another.

RESULTS

An inverse significant correlation between length of DUI and time between visits in euthymic patients was observed (r = -.52, P < .001). DUI resulted to be longer in patients with: at least one lifetime marriage/partnership (P = .009), a first psychiatric diagnosis of major depressive disorder or substance abuse (P < .001), a depressive polarity of first episode (P < .001), no lifetime psychotic symptoms (P < .001), BD type 2 (P < .001), more lifetime depressive/hypomanic episodes (P < .001), less lifetime manic episodes (P < .001), presence of suicide attempts (P = .004), depressive episodes (P < .001), hypomanic episodes (P = .004), hospitalizations (P = .011) in the last year.

CONCLUSIONS

Different factors resulted to increase the length of DUI in a nationwide sample of bipolar patients. In addition, the DUI was found to show a negative long-term effect in terms of more suicidal behaviour, more probability of hospitalization and depressive/hypomanic episodes.

Challenges Faced by New Psychiatric-Mental Health Nurse Practitioner Prescribers

One of the main challenges that psychiatric-mental health nurse practitioner (PMNHP) students experience is preparing to prescribe medications by demonstrating psychopharmacological competency. To examine the challenges as they relate to this issue, self-reflective journaling narratives were evaluated from two cohorts of Post-Master's PMHNP program graduates, across each of two semesters of pediatric and adult clinical experience. The most prominent challenges reported by students were in regard to medication treatment adherence, decision making, and monitoring symptom-related outcomes. The narratives also demonstrate that reflection, combined with faculty- and preceptor-supported clinical education, assists PMHNP students in developing psychopharmacological competency. All PMHNP students described in this article were nurse practitioners before they began the program. By seeking to augment their competencies and through continued self-reflective learning and practice, they will improve access to mental health care for the populations they serve. [Journal of Psychosocial Nursing and Mental Health Services, 58(10), 7-11.].

Validation of the 33-item Hypomania Checklist (HCL-33) in screening adolescents with bipolar disorder

BACKGROUND

Adolescents with bipolar disorder (BD) are often misdiagnosed with major depressive disorder (MDD), which delays the introduction of appropriate treatment resulting in adverse outcomes. This study examined the psychometric properties of the 33-item Hypomania Checklist (HCL-33) and its accuracy to distinguish BD from MDD in adolescents.

METHOD

A total of 248 participants (171 MDD and 77 BD patients) were recruited from a university-affiliated hospital in China. Depression was measured with the Hamilton Depression Rating Scale. All participants completed the assessment with the HCL-33.

RESULTS

A two-factor structure was found for the HCL-33, which explained 30.2% of the total variance. The internal consistency and split half reliability of the total scale were acceptable. The optimal cut-off value of 18 generated sensitivity of 0.52 and specificity of 0.73 for distinguishing BD from MDD.

CONCLUSION

The HCL-33 seems to be a useful screening instrument to distinguish BD from depressed adolescents. However, considering certain less than robust psychometric properties, the HCL-33 needs to be modified and further refined for adolescent patients.

Misdiagnosis, detection rate, and associated factors of severe psychiatric disorders in specialized psychiatry centers in Ethiopia

BACKGROUND

There are limited studies on the prevalence of misdiagnosis as well as detection rates of severe psychiatric disorders in specialized and non-specialized healthcare settings. To the best of our knowledge, this is the first study to determine the prevalence of misdiagnosis and detection rates of severe psychiatric disorders including schizophrenia, schizoaffective, bipolar, and depressive disorders in a specialized psychiatric setting.

METHOD

In this cross-sectional study, a random sample of 309 patients with severe psychiatric disorders was selected by systematic sampling technique. Severe psychiatric disorders were assessed using the Structured Clinical Interview for DSM-IV (SCID). The potential determinates of misdiagnosis were explored using univariable and multivariable logistic regression models, adjusting for the potential confounding factors.

RESULT

This study revealed that more than a third of patients with severe psychiatric disorders were misdiagnosed (39.16%). The commonly misdiagnosed disorder was found to be a schizoaffective disorder (75%) followed by major depressive disorder (54.72%), schizophrenia (23.71%), and bipolar disorder (17.78%). Among the patients detected with the interview by SCID criteria, the highest level of the correct diagnosis was recorded in the medical record for schizophrenia (76.29%) followed by bipolar (72.22%), depressive (42.40%), and schizoaffective (25%) disorders with detection rate (sensitivity) of 0.76 (95% CI 0.69-0.84), 0.42 (95% CI 0.32-0.53), 0.72 (95% CI 0.60-0.84), and 0.25 (95% CI 0.09-0.41), respectively for schizophrenia, depressive, bipolar, and schizoaffective disorders. Patients with bipolar disorder were more likely to be misdiagnosed as having schizophrenia (60%), whereas schizophrenic patients were more likely to be misdiagnosed as having bipolar disorder (56.25%) and patients with depressive disorders were more likely to be misdiagnosed as having schizophrenia (54.72%). Having a diagnosis of schizoaffective and depressive disorders, as well as suicidal ideation, was found to be significant predictors of misdiagnosis.

CONCLUSION

This study showed that roughly four out of ten patients with severe psychiatric disorders had been misdiagnosed in a specialized psychiatric setting in Ethiopia. The highest rate of misdiagnosis was observed for schizoaffective disorder (3 out of 4), followed by major depressive disorder (1 out of 2), schizophrenia (1 out of 4), and bipolar disorders (1 in 5). The detection rates were highest for schizophrenia, followed by bipolar, depressive, and schizoaffective disorders. Having a diagnosis of schizoaffective and depressive disorders as well as suicidal ideation was found to be significant predictors of misdiagnosis.

Comparing the Self- and External Assessment Versions of the HCL-33 as Screening Instruments for Bipolar Disorder in Older Depressed Patients

Objectives: The misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD) is common in depressed older adults. The self-rated HCL-33 and its external assessment version (HCL-33-EA) have been developed to screen for hypomanic symptoms. This study compared the screening ability of these two instruments to discriminate BD from MDD. Methods: A total of 215 patients (107 with BD and 108 with MDD) and their carers were recruited. Patients and their carers completed the HCL-33 and HCL-33-EA, respectively. The consistency of the total score and the positive response to each item between the two scales was calculated with the intraclass correlation coefficient (ICC) and Cohen's kappa coefficient separately. Receiver operating characteristics (ROC) curves were drawn for both instruments. The optimal cut-off points were determined according to the maximum Youden's Index. The areas under the ROC curve (AUC) of the HCL-33 and HCL-33-EA were calculated separately and compared. The sensitivity and specificity at the optimal cut-off values were also calculated separately for the HCL-33 and HCL-33-EA. Results: The intraclass correlation coefficient (ICC) between the total scores of the HCL-33 and HCL-33-EA was 0.823 (95% CI = 0.774-0.862). The positive response rate on all items showed high agreement between the two instruments. ROC curve analysis demonstrated that the total scores of both HCL-33 and HCL-33-EA differentiated well between MDD and BD, while there was no significant difference in the AUCs between the two scales (Z = 0.422, P = 0.673). The optimal cutoff values for the HCL-33 and HCL-33-EA were 14 and 12, respectively. With the optimal cutoff value, the sensitivities of the HCL-33 and HCL-33-EA were 88.8% and 93.5%, and their specificities were 82.4% and 79.6%. Conclusion: Both the HCL-33 and HCL-33-EA had good screening ability for discriminating BD from MDD in depressed older adults.

Effects of smartphone-based interventions and monitoring on bipolar disorder: A systematic review and meta-analysis

BACKGROUND

Recently, there has been a range of studies about smartphone-based interventions and monitoring for reducing symptoms of bipolar disorder (BD). However, their efficacy for BD remains unclear.

AIM

To compare the effect of smartphone-based interventions and monitoring with control methods in treating patients with BD.

METHODS

A systematic literature search was performed on PubMed, Embase, Clinical trials, psycINFO, Web of Science, and Cochrane Library. Randomized clinical trials (RCTs) or single-group trials in which smartphone-based interventions and monitoring were compared with control methods or baseline in patients with symptoms of BD were included. Data were synthesized using a random-effects or a fixed-effects model to analyze the effects of psychological interventions and monitoring delivered via smartphone on psychiatric symptoms in patients with BD. The primary outcome measures were set for mania and depression symptoms. Subgroups were created to explore which aspects of smartphone interventions are relevant to the greater or lesser efficacy of treating symptoms.

RESULTS

We identified ten articles, including seven RCTs (985 participants) and three single-group trials (169 participants). Analysis of the between-group study showed that smartphone-based interventions were effective in reducing manic [g = -0.19, 95% confidence interval (CI): -0.33 to -0.04, P = 0.01] and depressive (g = -0.28, 95%CI: -0.55 to -0.01, P < 0.05) symptoms. In within-group analysis, smartphone-based interventions significantly reduced manic (g = 0.17, 95%CI: 0.04 to 0.30, P < 0.01) and depressive (g = 0.48, 95%CI: 0.18 to 0.78) symptoms compared to the baseline. Nevertheless, smartphone-based monitoring systems significantly reduced manic (g = 0.27, 95%CI: 0.02 to 0.51, P < 0.05) but not depressive symptoms. Subgroup analysis indicated that the interventions with psychoeducation had positive effects on depressive (g = -0.62, 95%CI: -0.81 to -0.43, P < 0.01) and manic (g = -0.24, 95%CI: -0.43 to -0.06, P = 0.01) symptoms compared to the controlled conditions, while the interventions without psychoeducation did not (P > 0.05). The contacts between therapists and patients that contributed to the implementation of psychological therapy reduced depression symptoms (g = -0.47, 95%CI: -0.75 to -0.18, P = 0.01).

CONCLUSION

Smartphone-based interventions and monitoring have a significant positive impact on depressive and manic symptoms of BD patients in between-group and within-group analysis.

Telepsychiatry and integrated primary care: setting expectations and creating an effective process for success

Telepsychiatry, especially in the form of live interactive videoconferencing, has greatly advanced the availability and use of specialist psychiatric consultations in primary care settings. Nevertheless, reliance on telepsychiatry, with corresponding decreases in direct face-to-face interaction between primary care providers and psychiatrists, can create unique challenges such as reducing the availability of non-verbal cues, and preventing the informal interactions that are so necessary for clarifying clinical and process details and for building essential team-based trust and rapport. Written from the perspective of an integrated psychiatrist, this article offers recommendations for a formal process to optimize virtual care coordination by setting clear expectations and providing communication tools for an effective and efficient telepsychiatry enabled integrated service.

Diagnose, indicate, and treat severe mental illness (DITSMI) as appropriate care: A three-year follow-up study in long-term residential psychiatric patients on the effects of re-diagnosis on medication prescription, patient functioning, and hospital bed utilization

Background.

While polypharmacy is common in long-term residential psychiatric patients, prescription combinations may, from an evidence-based perspective, be irrational. Potentially, many psychiatric patients are treated on the basis of a poor diagnosis. We therefore evaluated the DITSMI model (i.e., Diagnose, Indicate, and Treat Severe Mental Illness), an intervention that involves diagnosis (or re-diagnosis) and appropriate treatment for severely mentally ill long-term residential psychiatric patients. Our main objective was to determine whether DITSMI affected changes over time regarding diagnoses, pharmacological treatment, psychosocial functioning, and bed utilization.

Methods.

DITSMI was implemented in a consecutive patient sample of 94 long-term residential psychiatric patients during a longitudinal cohort study without a control group. The cohort was followed for three calendar years. Data were extracted from electronic medical charts. As well as diagnoses, medication use and current mental status, we assessed psychosocial functioning using the Health of the Nations Outcome Scale (HoNOS). Bed utilization was assessed according to length of stay (LOS). Change was analyzed by comparing proportions of these data and testing them with chi-square calculations. We compared the numbers of diagnoses and medication changes, the proportions of HoNOS scores below cut-off, and the proportions of LOS before and after provision of the protocol.

Results.

Implementation of the DITSMI model was followed by different diagnoses in 49% of patients, different medication in 67%, some improvement in psychosocial functioning, and a 40% decrease in bed utilization.

Conclusions.

Our results suggest that DITSMI can be recommended as an appropriate care for all long-term residential psychiatric patients.

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

OBJECTIVE

This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls.

METHODS

We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor necrosis factor-α, interferon-γ, interleukin-17A, brain-derived neurotrophic factor, lipid peroxidation and oxidative protein damage in 54 outpatients with bipolar depression, 54 outpatients with unipolar depression and 54 healthy controls, matched by sex and age. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Variable selection was performed with recursive feature elimination with a linear support vector machine kernel, and the leave-one-out cross-validation method was used to test and validate our model.

RESULTS

Bipolar vs unipolar depression classification achieved an area under the receiver operating characteristics (ROC) curve (AUC) of 0.69, with 0.62 sensitivity and 0.66 specificity using three selected biomarkers (interleukin-4, thiobarbituric acid reactive substances and interleukin-10). For the comparison of bipolar depression vs healthy controls, the model retained five variables (interleukin-6, interleukin-4, thiobarbituric acid reactive substances, carbonyl and interleukin-17A), with an AUC of 0.70, 0.62 sensitivity and 0.7 specificity. Finally, unipolar depression vs healthy controls comparison retained seven variables (interleukin-6, Carbonyl, brain-derived neurotrophic factor, interleukin-10, interleukin-17A, interleukin-4 and tumor necrosis factor-α), with an AUC of 0.74, a sensitivity of 0.68 and 0.70 specificity.

CONCLUSION

Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

Comprehensive phenotyping of depression disease trajectory and risk: Rationale and design of Texas Resilience Against Depression study (T-RAD)

Depression has a chronic and recurrent course often with early onset and is the leading cause of disability worldwide. In contrast to diagnoses for other conditions which rely on precise medical tests, the diagnosis of depression still focuses exclusively on symptom reports. As a result, heterogeneous patient groups are included under broad categories. Furthermore, in the absence of companion diagnostic tests, choosing specific treatments for patients remains imprecise with only one-third of patients entering remission with initial treatment, with others requiring multiple intervention steps to achieve remission. In addition to improving treatment outcomes, disease prevention is essential to reduce overall disease burden. Adolescence is a critical window where complex emotional, social, familial, and biological shifts may predispose to lifelong depression. Thus, personalized medicine, integrating individual variability in genes, brain function, and clinical phenotypes, can offer a comprehensive approach to provide precise diagnosis, novel drug development, optimal treatment assignment, and prevention of illness and its associated burden. Texas Resilience Against Depression study (T-RAD) encompasses two natural history, longitudinal (10 + years), prospective studies (D2K and RAD), each enrolling 2500 participants. The D2K study follows participants (ages 10 years and older) who have a current or past diagnosis of depression or bipolar disorder. The RAD study follows participants aged 10-24 years who are at risk for depression but not yet suffering from the disease. The T-RAD study will help to uncover the socio-demographic, lifestyle, clinical, psychological, and neurobiological factors that contribute to mood disorder onset, recurrence, progression, and differential treatment response.

The Influence of Current Mood States on Screening Accuracy of the Mood Disorder Questionnaire

Objective

In this study we investigated whether current mood states of patients with bipolar disorder have an influence on the screening accuracy of the Mood Disorder Questionnaire (MDQ).

Methods

A total of 452 patients with mood disorder (including 192 with major depressive disorder and 260 with bipolar disorder completed the Korean version of the MDQ. Patients with bipolar disorder were subdivided into three groups (bipolar depressed only, bipolar euthymic only, bipolar manic/hypomanic only) according to current mood states. The screening accuracy of the MDQ including sensitivity, specificity and area under the curve (AUC) of receiver operating characteristic (ROC) curves were evaluated according to current mood states.

Results

The optimal cutoff of MDQ was 5 in this study sample. Sensitivity and specificity were not significantly different according to current mood states. Significant differences in AUCs of four independent ROC curves were not found (ROC 1st curve included all bipolar patients; ROC 2nd curve included only bipolar depressed patients; ROC 3rd curve included only bipolar manic/hypomanic patients; ROC 4th curve included only bipolar euthymic patients).

Conclusion

The study results showed that current mood states (either euthymic state, depressed or manic/hypomanic) did not significantly influence the screening accuracy of the MDQ suggesting that the MDQ could be a useful screening instrument for detecting bipolar disorder in clinical practice regardless of the current mood symptoms of subjects.

The use of structured reporting of head and neck ultrasound ensures time-efficiency and report quality during residency

PURPOSE

Free text reports (FTR) of head and neck ultrasound studies are currently deployed in most departments. Because of a lack of composition and language, these reports vary greatly in terms of quality and reliability. This may impair the learning process during residency. The purpose of the study was to analyze the longitudinal effects of using structured reports (SR) of head and neck ultrasound studies during residency.

METHODS

Attending residents (n = 24) of a tripartite course on head and neck ultrasound, accredited by the German Society for Ultrasound in Medicine (DEGUM), were randomly allocated to pictures of common diseases. Both SRs and FTRs were compiled. All reports were analyzed concerning completeness, acquired time and legibility. Overall user contentment was evaluated by a questionnaire.

RESULTS

SRs achieved significantly higher ratings regarding completeness (95.6% vs. 26.4%, p < 0.001), description of pathologies (72.2% vs. 58.9%, p < 0.001) and legibility (100% vs. 52.4%, p < 0.001) with a very high inter-rater reliability (Fleiss' kappa 0.9). Reports were finalized significantly faster (99.1 s vs. 115.0 s, p < 0.001) and user contentment was significantly better when using SRs (8.3 vs. 6.3, p < 0.001). In particular, only SRs showed a longitudinally increasing time efficiency (- 20.1 s, p = 0.036) while maintaining consistent completeness ratings.

CONCLUSIONS

The use of SRs of head and neck ultrasound studies results in an increased longitudinal time-efficiency while upholding the report quality at the same time. This may indicate an additive learning effect of structured reporting. Superior outcomes in terms of comprehensiveness, legibility and time-efficiency can be observed immediately after implementation.

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.

Findings From a Trial of the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) Application: What Do Apps Really Tell Us About Patients with Depression? Concordance Between App-Generated Data and Standard Psychiatric Questionnaires for Depression and Anxiety

OBJECTIVE

Depression imposes a notable societal burden, with limited treatment success despite multiple available psychotherapy and medications choices. Potential reasons may include the heterogeneity of depression diagnoses and the presence of comorbid anxiety symptoms. Despite technological advances and the introduction of many mobile phone applications (apps) claiming to relieve depression, major gaps in knowledge still exist regarding what apps truly measure and how they correlate with psychometric questionnaires. The goal of this study was to evaluate whether mobile daily mood self-ratings may be useful in monitoring and classifying depression symptoms in a clinically depressed population compared with standard psychometric instruments including the Patient Health Questionaire-9 (PHQ-9), the Hamilton Rating Scale for Depression (HAM-D), and the Hamilton Anxiety Rating Scale (HAM-A).

METHOD

For this study, 22 patients with major depressive disorder with or without comorbid anxiety disorder were recruited. The diagnosis of depression was confirmed through the Mini International Neuropsychiatric Interview (MINI). Over an 8-week period, daily moods were self-reported through the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) application, a custom-designed application that was downloaded onto patients' mobile devices. Depression and anxiety symptoms were also measured biweekly using the HAM-D, HAM-A, and PHQ-9.

RESULTS

Significant correlations were observed among self-evaluated mood, daily steps taken, SMS (text) frequency, average call duration, and biweekly psychometric scores (|r|>0.5, P<0.05). The correlation coefficients were higher in individuals with more severe depressive symptoms.

CONCLUSIONS

Although this study, given its limited sample size, was exploratory in nature, it helps fill a significant gap in our knowledge of the concordance between ratings obtained on the Ham-D, Ham-A, and the PHQ-9 psychometric instruments and data obtained via a smartphone app. These questionnaires represent gold-standard, commonly used psychiatric research/clinical instruments, and, thus, this information can serve as a foundation for digital phenotyping for depression and pave the way for interventional studies using smartphone applications.

Mitochondrial DNA Copy Number Raises the Potential of Left Frontopolar Hemodynamic Response as a Diagnostic Marker for Distinguishing Bipolar Disorder From Major Depressive Disorder

Background: Given a lack of markers, diagnoses of bipolar disorder (BD) and major depressive disorder (MDD) rely on clinical assessment of symptoms. However, the depressive mood states of BD and depressive symptoms of MDD are often difficult to distinguish, which leads to misdiagnoses, which in turn leads to inadequate treatment. Previous studies have shown that the hemodynamic responses of the left frontopolar cortex measured by near-infrared spectroscopy (NIRS) differ between BD and MDD; these hemodynamic responses are associated with altered mitochondrial metabolism; and mitochondrial DNA copy number (mtDNAcn), an index of mitochondrial dysfunction, tends to decrease in BD and increase in MDD patients. In this study, we confirmed that mtDNAcn trends in opposite directions in BD and MDD. We then determined whether mtDNAcn could enhance the utility of NIRS as a diagnostic marker to distinguish between BD and MDD. Methods: We determined mtDNAcn in peripheral blood samples from 58 healthy controls, 79 patients with BD, and 44 patients with MDD. Regional hemodynamic responses during a verbal fluency task (VFT) in 24 BD patients and 44 MDD patients, matched by age and depression severity, were monitored using NIRS. Results: Measurements of mtDNAcn were lower in BD patients and higher in MDD patients than in controls. The left frontopolar region exhibited the most significant differences in mean VFT-related oxy-Hb changes between the BD and MDD groups. Multivariate logistic regression analysis with variables including age, sex, hemodynamic response of the left frontopolar region, and mtDNAcn showed high accuracy for distinguishing BD from MDD (area under the curve = 0.917; 95% confidence interval = 0.849-0.985). For the BD group, we observed a positive correlation between hemodynamic responses in the left frontopolar region and mtDNAcn, while for the MDD group, we observed a negative correlation. Conclusions: Our findings suggest that the association between hemodynamic response and mitochondrial dysfunction in BD or MDD plays an important role in differentiating the pathophysiological mechanisms of BD from those of MDD.

Modelling mood disorders: An ACE solution?

OBJECTIVES

The treatment of mood disorders remains sub-optimal. A major reason for this is our lack of understanding of the underlying pathophysiology of depression and bipolar disorder. A core problem is the lack of specificity of our current diagnoses. This paper discusses the history of this problem and posits a solution in the form of a more sophisticated model.

METHOD

The authors review the notable historical works that laid the foundations of mood disorder nosology; discuss the more recent influences that shaped modern diagnoses; and examine the evidence that mood disorders are characterised by multidimensional and longitudinal symptom profiles.

RESULTS

The ACE model considers mood disorders as a combination of symptoms across three domains: Activity, Cognition, and Emotion; that vary over time. This multidimensional and longitudinal perspective is consistent with the prevalence of complex clinical presentations, such as mixed states, and highlights the importance of recurrence in mood disorders.

CONCLUSIONS

The ACE model encourages researchers to characterise patients from a number of equally important perspectives and, by doing so, add specificity to the treatment of mood disorders.

The circulating levels of CD4+ t helper cells are higher in bipolar disorder as compared to major depressive disorder

INTRODUCTION

Clinical differentiation between bipolar disorder (BD) and major depressive disorder (MDD) is difficult. Research has therefore focused on discriminatory biological markers. Previous studies in MDD reported T cell deficits, while the limited studies in BD reported T cell activation. Studies directly comparing circulating numbers of T cells and T cell subsets between BD and MDD are lacking. The studies in the MOODINFLAME consortium make such a comparison possible.

METHODS

The number of circulating leukocyte populations (lymphocytes, monocytes, NK cells, B cells, T cells, CD3+CD8+ T cytotoxic cells, CD3+CD4+ T helper cells, Th1, Th2, Th17 and T regulatory cells) was determined using FACS technology in a cohort of 83 euthymic BD patients, 8 BD patients with a current mood episode and 165 healthy controls (HC). Data were compared to those of 34 moderately and 56 severely depressed MDD patients.

RESULTS

Compared to MDD patients, BD patients showed significantly increased levels of Th17, Th2, Th1 and T regulatory cells (all p < .02). In BD patients, levels of Th17 and T regulatory cells were increased compared to HC (p = .03, p = .02, respectively), while MDD patients showed decreased levels of Th17 and Th2 compared to HC (p = .03, p = .01, respectively). Of the various medications only SSRI/SNRI usage could explain part of the Th2 decrease in MDD.

CONCLUSION

This study shows CD4+ T helper cell deficits in MDD patients, while normal or even raised levels of these cells were found in BD patients. The differences in CD4+ T helper cell differentiation was most outspoken for Th17 cells.

The utility of pharmacogenetic testing to support the treatment of bipolar disorder

Background

Bipolar disorder (BD) is a frequent cause of disability, health care costs, and risk of suicide. Pharmacogenetic tests (PGTs) could help clinicians to identify those patients predisposed to the occurrence of adverse events (AEs) improving the understanding of the correlation between genetic variants and drug response.

Materials and methods

The study evaluated 30 patients affected by BD type I or II (according to Diagnostic and Statistical Manual of Mental Disorders, version 5) who underwent the PGT Neurofarmagen^® (AB-BIOTICS SA, Barcelona, Spain) between March 2016 and March 2017. The primary aim of this study was to identify if the treatment prescribed by the psychiatrists was consistent with the treatment suggested by the PGT at T0 (corresponding to the test report communication). As a secondary aim, we wanted to assess if clinicians had changed the treatment (in case of discordance) at T1 (3-month follow-up visit) according to the results of the PGT.

Results

At T0, only 4 patients (13%) had an optimal therapy in line with the PGT suggestions. At 3-month follow-up, 13 patients (40%) had received a change of therapy consistent to the test, showing a significant statistical improvement in the Clinical Global Impression item Severity (CGI-S) score over time compared to those not having changes consistent with the test. Regarding AEs, at baseline 9 out of 10 (90%) of the patients who received a therapy modification according to the test presented AEs, and a significant within-group reduction was observed after 3 months (p = 0.031).

Conclusion

Despite the small sample size, the study shows promising data about the usefulness of PGT to support clinicians in reaching a more effective and tolerated treatment in the routine approach of BD.

Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder

BACKGROUND

Bipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD.

METHODS

This study extends our previous work by performing individual level classification of BD or MDD in an expanded, currently unmedicated, cohort using gray matter volume (GMV) based on Magnetic Resonance Imaging and a Support Vector Machine. All patients were in a Major Depressive Episode and a leave-two-out analysis was performed.

RESULTS

Nineteen out of 26 BD subjects and 20 out of 26 MDD subjects were correctly identified, for a combined accuracy of 75%. The three brain regions contributing to the classification were higher GMV in bilateral supramarginal gyrus and occipital cortex indicating MDD, and higher GMV in right dorsolateral prefrontal cortex indicating BD.

LIMITATIONS

This analysis included scans performed with two different headcoils and scan sequences, which limited the interpretability of results in an independent cohort analysis.

CONCLUSIONS

Our results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.

Assessment of Complement Cascade Components in Patients With Bipolar Disorder

Introduction: The immune system is undoubtedly involved in the pathogenesis of various psychiatric disorders, such as schizophrenia, bipolar disorder, or depression. Although its role is not fully understood, it appears that this area of research can help to understand the etiology of mental illness. One of the components of the human immune system is the complement system, which forms a part of the innate immune response. Physiologically, except for its essential protective role, it is a vital element in the regeneration processes, including neurogenesis. To date, few studies have tried to clarify the role of the complement cascade in mental disorders. Materials and Methods: We evaluated concentrations of C3a, C5a, and C5b-9 complement cascade components in the peripheral blood of 30 patients suffering from bipolar disorder (BD) for at least 10 years, in euthymia, who were not treated with lithium salts. In addition, we divided our study sample into BD type I (BD-I, 22 persons), and BD type II (BD-II, 8 patients). The control group consisted of 30 healthy volunteers matched for age, sex, BMI, and smoking habits. Results: Compared to healthy controls, BD patients had elevated concentrations of all the investigated components. Furthermore, in patients with BD-II, we observed higher concentrations of C5b-9 as compared to patients with BD-I. However, there was a significant effect of BD diagnosis only on the levels of C3a and C5a but not on the level of C5b-9 after adjustment for potential confounding factors. Conclusions: Increased concentrations of components C3a and C5a of the complement system in the investigated group as compared to healthy controls suggest involvement of the complement cascade in the pathogenesis of BD, and provides further evidence of immune system dysregulation in BD patients.

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

BACKGROUND

Up to 40% of patients with bipolar disorder are misdiagnosed, usually with major depression disorder.

OBJECTIVE

The purpose was to describe the current state of the science of the misdiagnosis of bipolar disorder, with the ultimate goal of improving psychiatric diagnostic workups including screening.

DESIGN

An integrative review was conducted using standard criteria for evaluating research articles.

RESULTS

Forty-nine articles met the eligibility criteria. Articles explored patient-related and health care provider-related factors contributing to the misdiagnosis of bipolar disorder as well as consequences of misdiagnosis. Clinically oriented, reliable, and valid screening tools for bipolar disorder also were reviewed.

CONCLUSIONS

Awareness of multiple, challenging patient-related factors and more comprehensive assessment and screening by health care providers may reduce misdiagnosis.

A preliminary study of bipolar disorder type I by mass spectrometry-based serum lipidomics

The present study aimed at investigating possible alterations in the serum lipid profile of euthymic patients with bipolar disorder type I (BD) compared to healthy controls (HC). Thirty-five individuals from both genders were recruited, with 14 diagnosed and treated as BD patients (BD group) and 21 healthy subjects (HC group). Clinical assessment was based on the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), Young Mania Rating Scale (YMRS), and 17-items of Hamilton Depression Rating Scale (HDRS-17) data, which were used to confirm diagnosis, to verify psychiatric comorbidities, and to estimate the severity of manic and depressive symptoms. Ultra-high performance liquid chromatography (UHPLC) coupled to high resolution mass spectrometry (HRMS) was applied to analyze the lipids extracted from all serum samples from both studied groups. In this pioneer and exploratory study, we observed different serum lipid profiles for BD and HC groups, especially regarding glycerophospholipid, glycerolipid, and sphingolipid distribution. Multivariate statistical analyses indicated that 121 lipids were significantly different between BD and HC. Phosphatidylinositols were identified as the most altered lipids in BD patient sera. The results of this preliminary study reinforce the role of lipid abnormalities in BD and offer additional methodological possibilities for investigation in the field.

Bipolar II disorder as the initial presentation of CADASIL: an underdiagnosed manifestation

Mood disturbances have been documented in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). The highly varied morbidity indicates that the affective symptoms in CADASIL have not been cataloged systematically, leading to ineffective treatment, affecting the patients' quality of life, and possibly resulting in suicide. We present a case of CADASIL with bipolar II disorder as the first manifestation. A middle-aged female reported recurrent depressive episodes and appeared treatment resistant to adequate dosages and durations of antidepressants. Following a structured psychiatric interview and neuropsychological assessment, a past episode of hypomania was identified. Added treatment with sodium valproate alleviated most symptoms. Considering late-onset bipolar disorder with unexplained decline in cognition, a medical history of migraine, and a suspected family history of stroke, further cranial magnetic resonance imaging scan was performed and revealed severe leukoencephalopathy, prompting further investigation. The diagnosis was revised to CADASIL after Arg587Cys NOTCH3 mutation was confirmed. This case highlights the evolving process of affective disorder diagnosis and underlying organic etiologies. Based on the overlap of white matter hyperintensities, NOTCH3 mutation, and valproate therapy in bipolar disorder and CADASIL, bipolar II depression may be a poorly recognized manifestation of CADASIL. Well-designed clinical trials are warranted to verify the current findings.

Contribution of long duration of undiagnosed bipolar disorder to high frequency of relapse: A naturalistic study in China

BACKGROUND

With attention to misdiagnosis of bipolar disorder (BP), long duration of undiagnosed bipolar disorder (DUBP) had been reported recently in years. This study aims to investigate the contributions of long DUBP to the frequency of relapse in bipolar patients, and explore affect factors of DUBP.

METHOD

From 26 hospitals throughout China, 3896 participants diagnosed with BP according to International Classification of Diseases 10th criteria were enrolled in this study. Socio-demographic and clinical data were collected from medical records and specific questionnaires through clinical interviews with patients and their relatives.

RESULTS

(1) Our results showed that the mean of DUBP was 40.52months. In total, 779 patients (19.995%) reported DUBP greater than 5years, and 1931 patients (49.564%) reported their DUBP greater than 2years. The number of mood episodes was averaged 5.44, and the frequency ratio of (hypo) mania to depressive episodes was 1.49 (3.27/2.19). (2) Multiple linear regression analysis revealed that DUBP was significantly contributed to the number of relapse (Beta=0.072, p<0.001) after considering the confounding including gender, age at study entry, age of onset, age of first (hypo) manic episodes, age of first depressive episodes, type of first episodes and family history of mental illness. (3) Factors including age at the study entry (Beta=0.526, p<0.001), age of onset (Beta=-1.654, p<0.001), age of first (hypo) manic episode (Beta=0.348, p<0.001), age of first depressive episode (Beta=0.983, p<0.001), depression as the type of first episode (Beta=0.058, p<0.001) and family history of mental illness (Beta=0.029, p<0.05) were significantly contributed to long DUBP.

CONCLUSION

It was concluded that long DUBP might lead to high frequent relapse in bipolar patients. The factors correlated with long DUBP include older age, early age of onset, depression as the type of first episode and family history of mental illness. The findings of our study suggest emergency task to early reorganization of bipolar disorder, and improving clinicians' recognition of bipolar disorder from patients with depressive episodes, especially in children and adolescents.