Depressive Bipolar Disorder Research Articles

Behavioral skill practice as a predictor of mood and family functioning in adolescents with bipolar and depressive mood disorders: Results of a 6-month randomized trial of family-focused therapy.

Behavioral interventions require considerable practice of treatment skills in between therapy sessions. The effects of these treatments may vary with the degree to which patients are able to implement these practices. In offspring of parents with bipolar and major depressive disorders, we examined whether youth who frequently practiced communication and problem-solving skills between family-focused therapy (FFT) sessions had less severe mood symptoms and better psychosocial functioning over 6 months than youth who practiced less frequently. We randomly assigned offspring (ages 12-19) of parents with mood disorders to 12 sessions of FFT plus a mobile app that encouraged the practice of communication, problem-solving and mood management skills (FFT-MyCoachConnect [MCC] condition) or 12 sessions of FFT with an app that only allowed for tracking of symptoms and stress (FFT-Track condition). Independent evaluators assessed youths' mood and psychosocial functioning at 9-week intervals over 27 weeks. Clinicians rated participants' between-session skill practice at each FFT session. FFT-MCC was associated with more frequent skill practice than FFT-Track over 18 weeks of treatment. Skill practice was associated with reductions in youths' mood instability and perceptions of family conflict over 27 weeks in both app conditions. Skill practice mediated the effects of app condition on youths' mood instability and family functioning. Mobile applications as adjuncts to family therapy for youth with mood disorders can help increase skill practice. These findings provide preliminary causal evidence for behavioral skill practice improving mood symptoms and family functioning among youth with mood disorders.

Borderline personality features in relationship to childhood trauma in unipolar depressive and bipolar disorders

BackgroundChildhood trauma, including emotional neglect, emotional abuse, physical abuse, and sexual abuse, may contribute to borderline personality features like affective instability, identity problems, negative relationships, and self-harm. This study aims to explore how different types of childhood trauma affect these features in bipolar versus unipolar depressive disorders. MethodsWe included 839 participants of the Netherlands Study of Depression and Anxiety (NESDA) with a lifetime diagnosis of major depressive disorder single episode (MDDS; N = 443), recurrent major depressive disorder (MDD-R; N = 331), or bipolar disorder (BD; N = 65). Multivariate regression was used to analyze data from the Childhood Trauma Interview and borderline features (from the self-report Personality Assessment Inventory). ResultsOn average, participants were 48.6 years old (SD: 12.6), with 69.2 % being women, and 50.3 % of participants assessed positive for childhood trauma. Adjusted analyses revealed that participants diagnosed with BD, followed by MDD-R, exhibited the highest number of borderline personality features. Additionally, within the entire group, a strong association was found between childhood trauma, especially emotional neglect, and the presence of borderline personality features. ConclusionGiven the high prevalence of childhood trauma and borderline personality features, screening for these factors in individuals with mood disorders is crucial. Identifying these elements can inform and enhance the management of the often fluctuating and complex nature of these comorbid conditions, leading to more effective and tailored treatment strategies.

Cognitive reserve and cognition in mood disorders: A systematic review and meta-analysis

Cognitive functioning heterogeneity is a well-recognized phenomenon in individuals diagnosed with mood disorders. Cognitive Reserve (CR) has been linked to multiple positive outcomes, including cognitive performance in these patients. This systematic review and meta-analysis aim to provide a comprehensive analysis of the relationship between CR and cognitive functioning in individuals with mood disorders, including bipolar disorder and depressive disorders. Following PRISMA guidelines, a systematic review and meta-analysis was conducted of original research exploring the relationship between CR and cognitive performance in adult individuals with mood disorders. The literature search was conducted on PubMed, Scopus, and Web of Science, from 2002 to September 2023, and the Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of studies. Overall, 17 studies met the inclusion criteria for the systematic review and 11 for the meta-analysis. Both qualitative and quantitative findings suggested a positive relationship between CR measures and cognitive domains. CR emerges as a possible protective factor for cognitive functioning in adult individuals with mood disorders, potentially helping to mitigate the cognitive impairments associated with the disorder. These findings underscore the importance of the fact that promoting and enhancing CR could help in the cognitive prognosis of this population.

Serum Copper and Zinc Levels in Bipolar and Major Depressive Disorders

Serum Copper and Zinc Levels in Bipolar and Major Depressive Disorders

A phenomenological inquiry into the costs and consequences of insomnia for veterans with serious mental illness

SummaryMany individuals with serious mental illness (i.e. schizophrenia spectrum, bipolar or major depressive disorders, with serious functional impairments) have insomnia symptoms. Insomnia is a common reason for mental health referrals in the Veterans Health Administration. The primary aim of this study was to explore the costs (what participants lose or what trade‐offs they make due to insomnia) and consequences (how insomnia impacts functioning) of insomnia for veterans with serious mental illness. Semi‐structured interviews of 20 veterans with insomnia and serious mental illness were collected as data using an inductive phenomenological approach. Two main themes were identified: Sleep Affects Mental Health and Functioning; and Compromising to Cope. Results illuminate pathways by which sleep effort destabilizes functional recovery, and illustrate how sleep has multiplicative positive impacts on functioning and mood. Researchers and clinicians alike must explore supporting people with serious mental illness in replacing sleep effort with the recovery of meaningful identity‐driven, values‐based experiences formerly conceded due to serious mental illness, insomnia or both.

Shared genetic effect of kidney function on bipolar and major depressive disorders: a large-scale genome-wide cross-trait analysis

BackgroundEpidemiological studies have revealed a significant association between impaired kidney function and certain mental disorders, particularly bipolar disorder (BIP) and major depressive disorder (MDD). However, the evidence regarding shared genetics and causality is limited due to residual confounding and reverse causation.MethodsIn this study, we conducted a large-scale genome-wide cross-trait association study to investigate the genetic overlap between 5 kidney function biomarkers (eGFRcrea, eGFRcys, blood urea nitrogen (BUN), serum urate, and UACR) and 2 mental disorders (MDD, BIP). Summary-level data of European ancestry were extracted from UK Biobank, Chronic Kidney Disease Genetics Consortium, and Psychiatric Genomics Consortium.ResultsUsing LD score regression, we found moderate but significant genetic correlations between kidney function biomarker traits on BIP and MDD. Cross-trait meta-analysis identified 1 to 19 independent significant loci that were found shared among 10 pairs of 5 kidney function biomarkers traits and 2 mental disorders. Among them, 3 novel genes: SUFU, IBSP, and PTPRJ, were also identified in transcriptome-wide association study analysis (TWAS), most of which were observed in the nervous and digestive systems (FDR < 0.05). Pathway analysis showed the immune system could play a role between kidney function biomarkers and mental disorders. Bidirectional mendelian randomization analysis suggested a potential causal relationship of kidney function biomarkers on BIP and MDD.ConclusionsIn conclusion, the study demonstrated that both BIP and MDD shared genetic architecture with kidney function biomarkers, providing new insights into their genetic architectures and suggesting that larger GWASs are warranted.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study.

Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

The incidence risk of gynecological cancer by antipsychotic use: a meta-analysis of 50,402 patients

BackgroundFemale gynecological cancers represent a serious public health problem, with 1,398,601 new diagnoses and 671,875 deaths per year worldwide. Antipsychotics are often used in psychiatric disorders, including schizophrenia, bipolar disorder, and major depression. It is estimated that the prescription of these drugs is linked to 1,800 deaths a year in the United States, but their association with cancer remains controversial.MethodsWe searched PubMed, Scopus, and Web of Science databases for studies reporting the correlation in the incidence risk of gynecological cancer by antipsychotic use. We used DerSimonian and Laird random-effect models to compute logit transformed odds ratio (OR) for the primary binary endpoint with 95% confidence interval (CI). Heterogeneity was assessed through effect size width along with I-squared and Tau-squared statistics. Review Manager 5.4.1. was used for statistical analyses. A p-value of < 0.05 denoted statistically significant.Results50,402 patients were included, of whom 778 (1,54%) took antipsychotic medication for at least 1 year. 1,086 (2,15%) with ovarian cancer and 49,316 (97,85%) with endometrial cancer. Antipsychotic use (OR 1.50; 1.06 to 2.13 95% CI; p-value 0.02), hypertension (OR 1.50; 95% CI 1.06 to 2.13; p-value < 0.01), nulliparity (OR 1.98; 95% CI 1.53 to 2.57; p-value < 0.01) and multiparity (OR 0.53; 95% CI 0.41 to 0.69; p-value < 0.01) showed significantly different distributions between groups of cancer and cancer-free patients. The primary endpoint of incidence risk of gynecological cancer by antipsychotic therapy showed a statistically significant difference (OR 1.67; 95% CI 1.02 to 2.73; p-value < 0.05) against the use of antipsychotic drugs.ConclusionsOur meta-analysis showed that the use of antipsychotic drugs increases the risk of gynecological cancers, particularly endometrial cancer. This result should be weighed against the potential effects of treatment for a balanced prescribing decision.

The potential inflammatory biomarker of bipolar disorder: Neutrophil-to-lymphocyte Ratio – A hospital-based cross-sectional study

Objectives: Inflammation has been hypothesized as one of the pathophysiological factor for mood disorders. The neutrophil-to-lymphocyte ratio (NLR) has been proposed as a potential peripheral biomarker of mood episodes, as this is an economical and accessible marker of inflammation. This study aims to determine the role of inflammation in the pathophysiology of bipolar disorder (BD) and the potential of NLR as a marker for differentiating mood disorders. Materials and Methods: A cross-sectional study was conducted on 195 patients who met the inclusion criteria, of whom 80 were diagnosed with BD (mania), 47 with BD (depression), and 68 with major depressive disorder (MDD). Sociodemographic details and a blood sample were taken for hemogram measures. The NLR and MLR were calculated using the following formula: NLR = neutrophil count/lymphocyte count and MLR = monocyte count/lymphocyte count, and statistical analysis was done. Results: The mean age (±standard deviation) of patients with mania, bipolar depression, and MDD were 35.97 (±13.14), 39.27 (±14.28), and 33.41 (±13.21) years, respectively, with an almost equal representation of male and female gender. The NLR ratio of 2.41 ± 0.84 was highest in BD (mania), followed by 1.75 ± 0.41 in bipolar depression and 1.67 ± 0.45 in MDD, which was significant (P &lt; 0.001), while MLR ratio in BD (mania) was highest, followed by MDD and bipolar depression. Conclusion: Our result indicates NLR as a marker of differentiation and, thus, strengthens the pathophysiological importance of inflammation in mood disorders.

Acute neural effects of the mood stabiliser lamotrigine on emotional processing in healthy volunteers: a randomised control trial.

Lamotrigine is an effective mood stabiliser, largely used for the management and prevention of depression in bipolar disorder. The neuropsychological mechanisms by which lamotrigine acts to relieve symptoms as well as its neural effects on emotional processing remain unclear. The primary objective of this current study was to investigate the impact of an acute dose of lamotrigine on the neural response to a well-characterised fMRI task probing implicit emotional processing relevant to negative bias. 31 healthy participants were administered either a single dose of lamotrigine (300 mg, n = 14) or placebo (n = 17) in a randomized, double-blind design. Inside the 3 T MRI scanner, participants completed a covert emotional faces gender discrimination task. Brain activations showing significant group differences were identified using voxel-wise general linear model (GLM) nonparametric permutation testing, with threshold free cluster enhancement (TFCE) and a family wise error (FWE)-corrected cluster significance threshold of p < 0.05. Participants receiving lamotrigine were more accurate at identifying the gender of fearful (but not happy or angry) faces. A network of regions associated with emotional processing, including amygdala, insula, and the anterior cingulate cortex (ACC), was significantly less activated in the lamotrigine group compared to the placebo group across emotional facial expressions. A single dose of lamotrigine reduced activation in limbic areas in response to faces with both positive and negative expressions, suggesting a valence-independent effect. However, at a behavioural level lamotrigine appeared to reduce the distracting effect of fear on face discrimination. Such effects may be relevant to the mood stabilisation effects of lamotrigine.

Association of cancer and schizophrenia, major depression and bipolar disorder: A Mendelian randomization study

BackgroundSchizophrenia, bipolar disorder and major depression have been reported to be associated with some cancers. However, the magnitude of the causal relationship between them remains unclear. This study aims to explore the potential association between three major mental diseases and the risk of some cancers. MethodsWe performed the two-sample Mendelian randomization(MR) analysis using publicly available genome-wide association studies (GWAS) statistics to investigate the causal relationship between these three mental diseases and some common types of cancers, including breast cancer, ovarian cancer, lung cancer, colorectal cancer, bladder cancer, prostate cancer, thyroid cancer, pancreatic cancer, malignant melanoma and glioma. We obtained genetic association estimates for schizophrenia, bipolar disorder and depression from the Psychiatric Genomics Consortium.The genetic association estimates for cancers were obtained from the UK Biobank, the MRC-IEU consortium and the GliomaScan consortium. ResultsAfter correction for heterogeneity and horizontal pleiotropy, we detected suggestive evidence for the association between thyroid cancer and genetically predicted schizophrenia (OR = 1.543, 95% CI: 1.023–2.328, P = 0.039), and thyroid cancer and major depression (OR = 3.573, 95% CI: 1.068–11.953, P = 0.039). No evidence of causal effects of schizophrenia, major depression and bipolar disorder on other types of cancers. ConclusionsOur findings suggest the association of schizophrenia and major depression and the development of thyroid cancer.

External Validation of a Machine Learning Model for Schizophrenia Classification.

Background and Objective: Excellent generalizability is the precondition for the widespread practical implementation of machine learning models. In our previous study, we developed the schizophrenia classification model (SZ classifier) to identify potential schizophrenia patients in the Japanese population. The SZ classifier has exhibited impressive performance during internal validation. However, ensuring the robustness and generalizability of the SZ classifier requires external validation across independent sample sets. In this study, we aimed to present an external validation of the SZ classifier using outpatient data. Methods: The SZ classifier was trained by using online survey data, which incorporate demographic, health-related, and social comorbidity features. External validation was conducted using an outpatient sample set which is independent from the sample set during the model development phase. The model performance was assessed based on the sensitivity and misclassification rates for schizophrenia, bipolar disorder, and major depression patients. Results: The SZ classifier demonstrated a sensitivity of 0.75 when applied to schizophrenia patients. The misclassification rates were 59% and 55% for bipolar disorder and major depression patients, respectively. Conclusions: The SZ classifier currently encounters challenges in accurately determining the presence or absence of schizophrenia at the individual level. Prior to widespread practical implementation, enhancements are necessary to bolster the accuracy and diminish the misclassification rates. Despite the current limitations of the model, such as poor specificity for certain psychiatric disorders, there is potential for improvement if including multiple types of psychiatric disorders during model development.

Response to electroconvulsive therapy in elderly patients with late-onset bipolar disorder: The impact of cerebral small vessel disease.

Cerebral Small Vessel Disease (CSVD) is a chronic, progressive vascular disorder that confers increased vulnerability to psychiatric syndromes, including late-life mood disorders. In this study, we investigated the impact of CSVD on electroconvulsive therapy (ECT) outcomes in patients with late-onset bipolar disorder (BD). A sample of 54 non-demented elderly patients (≥60years) with late-onset BD and treatment-resistant major depression, mixed state, or catatonia who underwent bilateral ECT were included in this naturalistic observational study. A diagnosis of CSVD was established based on brain neuroimaging performed before ECT. All patients were evaluated before and after ECT using the Brief Psychiatric Rating Scale (BPRS), the Hamilton Rating Scale for Depression (HAM-D), and the Clinical Global Impression scale (CGI). Of the total sample, 19 patients were diagnosed with CSVD (35.2%). No significant differences were observed at baseline between patients with and without CSVD. Overall, a response was obtained in 66%-68.5% of patients, with remission in 56.2%. No significant differences in ECT outcomes were found between those with and without CSVD, and both groups exhibited substantial improvements in symptom severity following ECT. The outcome of ECT in late-onset BD was not influenced by the presence of CSVD. This finding aligns with previous research on unipolar depression. Accordingly, ECT should be considered for elderly patients with late-onset BD, regardless of the presence of CSVD.

Lipid and Glucose Profile across Different Mental Disorders.

Objectives: Schizophrenia, unipolar depression, bipolar disorder, bipolar mania, and bipolar depression are a few of the severe psychiatric diseases that affect millions of individuals and their overall life quality. This study aimed to look at differences in TGA, TC, HDL, LDL, and FPG levels in people who were going through acute episodes of listed diseases. Materials and methods: A cross-sectional prospective study was carried out in Jordan between January and November of 2023, involving all patients with the aforementioned diseases who attended three psychiatric clinics. This study encompassed results from 1187 patients (women N = 675, 56.87%) who were classified into the following ranges: <25, 25-45, 45-65, and >65. Results: The average level of LDL was the highest in bipolar depression (112.442 ± 36.178 mg/dL) and the lowest in bipolar mania (111.25 ± 33.14 mg/dL). The average level of HDL was the highest in schizophrenia (58.755 ± 16.198 mg/dL) and the lowest in bipolar depression (45.584 ± 12.128 mg/dL). Both average levels of TC and TGA were the highest in patients with bipolar depression (188.403 ± 37.396 mg/dL and 149.685 ± 96.951 mg/dL, respectively) and the lowest in bipolar mania (164.790 ± 40.488 mg/dL and 100.679 ± 54.337 mg/dL, respectively). The average level of FPG was the highest in unipolar depression (94.00 ± 21.453 mg/dL) and the lowest in bipolar mania (89.492 ± 14.700 mg/dL). Conclusions: The results confirmed that lipid and glucose abnormalities were more common in people with schizophrenia and mood disorders (unipolar and bipolar).

Study of the different sleep disturbances during the prodromal phase of depression and mania in bipolar disorders.

One of the challenges in bipolar disorder (BD) lies in early detection of the illness and its recurrences, to improve prognosis. Sleep disturbances (SD) have been proposed as reliable predictive markers of conversion. While preliminary studies have explored the relationship between SD and the onset of mood episodes, the results remain heterogeneous and a few have specifically examined patients' perception of prodromal symptoms and their progression until the episode occurs. Identifying prodromes represents a crucial clinical challenge, as it enables early intervention, thereby reducing the severity of BD. Therefore, the objective of this study is to better characterize and evaluate the progressive nature of SD as prodromal symptoms of mood episodes, and patients' perception of it. Patients diagnosed with BD, either hospitalized or seeking treatment for a (hypo)manic or depressive episode benefited from standardized questionnaires, structured interviews, and self-report questionnaires to evaluate SD prior to the current episode, as well as sociodemographic and clinical information. Out of the 41 patients included, 59% spontaneously reported SD prior to the episode, appearing 90 days before depression and 35 days before mania (pre-indexed/spontaneous reports: 51.22% insomnia complaints, 4.88% hypersomnolence complaints, 7.32% parasomnias, 2.44% sleep movements). After inquiry about specific SD, the percentage of patients reporting prodromal SD increased significantly to 83%, appearing 210 days before depression and 112.5 days before mania (post-indexed reports: 75.61% presented with insomnia complaints appearing 150 days before depression and 20 days before mania, 46.34% had hypersomnolence complaints appearing 60 days before depression, 43.9% had parasomnias appearing 210 days before depression and 22.5 days before mania, 36.59% had sleep movements appearing 120 days before depression and 150 days before mania). Of note, bruxism appeared in 35% of patients before mania, and restless legs syndrome in 20% of patients before depression. This study highlights the very high prevalence of SD prior to a mood episode in patients with BD with differences between depressive and manic episodes. The more systematic screening of sleep alterations of the prodromal phase improved the recognition and characterization of different symptoms onset by patients. This underscores the need for precise questioning regarding sleep patterns in patients, to better identify the moment of transition toward a mood episode, referred to as "Chronos syndrome". The study emphasizes the importance of educating patients about the disorder and its sleep prodromal symptoms to facilitate early intervention and prevent recurrences.

Developing a prediction model to identify people with severe mental illness without regular contact to their GP - a study based on data from the Danish national registers

IntroductionPeople with severe mental illness (SMI) face a higher risk of premature mortality due to physical morbidity compared to the general population. Establishing regular contact with a general practitioner (GP) can mitigate this risk, yet barriers to healthcare access persist. Population initiatives to overcome these barriers require efficient identification of those persons in need.ObjectiveTo develop a predictive model to identify persons with SMI not attending a GP regularly.MethodFor individuals with psychotic disorder, bipolar disorder, or severe depression between 2011 and 2016 (n = 48,804), GP contacts from 2016 to 2018 were retrieved. Two logistic regression models using demographic and clinical data from Danish national registers predicted severe mental illness without GP contact. Model 1 retained significant main effect variables, while Model 2 included significant bivariate interactions. Goodness-of-fit and discriminating ability were evaluated using Hosmer-Lemeshow (HL) test and area under the receiver operating characteristic curve (AUC), respectively, via cross-validation.ResultsThe simple model retained 11 main effects, while the expanded model included 13 main effects and 10 bivariate interactions after backward elimination. HL tests were non-significant for both models (p = 0.50 for the simple model and p = 0.68 for the extended model). Their respective AUC values were 0.789 and 0.790.ConclusionLeveraging Danish national register data, we developed two predictive models to identify SMI individuals without GP contact. The extended model had slightly better model performance than the simple model. Our study may help to identify persons with SMI not engaging with primary care which could enhance health and treatment outcomes in this group.

Distinguishing bipolar depression, bipolar mania, and major depressive disorder by gut microbial characteristics.

Gut microbial disturbance has been widely confirmed in mood disorders. However, little is known about whether gut microbial characteristics can distinguish major depressive disorder (MDD), bipolar depression (BP-D), and bipolar mania (BP-M). This was a prospective case-control study. The composition of gut microbiota was profiled using 16S ribosomal RNA (rRNA) gene sequencing of fecal samples and compared between healthy controls (HC; n = 46), MDD (n = 51), BP-D (n = 44), and patients with BP-M (n = 45). Gut microbial compositions were remarkably changed in the patients with MDD, BP-D, and BP-M. Compared to HC, distinct gut microbiome signatures were found in MDD, BP-D, and BP-M, and some gut microbial changes were overlapping between the three mood disorders. Furthermore, we identified a signature of 7 operational taxonomic units (OUT; Prevotellaceae-related OUT22, Prevotellaceae-related OUT31, Prevotellaceae-related OTU770, Ruminococcaceae-related OUT70, Bacteroidaceae-related OTU1536, Propionibacteriaceae-related OTU97, Acidaminococcaceae-related OTU34) that can distinguish patients with MDD from those with BP-D, BP-M, or HC, with area under the curve (AUC) values ranging from 0.910 to 0.996. Our results provide the clinical rationale for the discriminative diagnosis of MDD, BP-D, and BP-M by characteristic gut microbial features.

Comorbidity of depression and anxiety with obstructive sleep apnea in a sample of Egyptian patients

Abstract Background Over the past 2 decades, sleep researchers have made significant advances in understanding sleep disorders including obstructive sleep apnea (OSA). OSA commonly coexists with psychiatric disorders encompassing mental health issues like depression, anxiety, bipolar, schizophrenia an others. This study aims to assess the comorbidity of OSA with depression and anxiety disorders among Egyptian patients. During the study, we conducted a cross-sectional study involving 92 adults diagnosed with OSA. Those patients were interviewed using a checklist to gather information regarding their chief complaints and other associated symptoms. The psychological status was assessed utilizing the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID I), Beck Anxiety Inventory (BAI), and Beck Depression Inventory (BDI) scoring. The severity of the breathing disorder was classified as mild, moderate, or severe based on the respiratory disturbance index (RDI), which was confirmed through overnight polysomnography. Daytime sleepiness was assessed by the Epworth Sleepiness Scale (ESS). Results The sample of patients as a whole shows that witnessed apneas represent the highest percentage (70.7%), followed by fatigue (69.6%), then non-refreshing sleep (67.4%), and snores (63.0%). The least frequent presenting symptom is frequent naps. The majority of OSA patients have a comorbid psychiatric disorder (65.0%). Among these, depressive disorders are the most prevalent (31.5%), followed by anxiety disorders (23.9%) and psychoses (6.5%). It is worth noting that 38% of OSA patients do not have any psychiatric disorders. In terms of anxiety disorders, the most common subtype observed is OCD (8.7%), followed by GAD (6.5%), panic (5%), and PTSD (3.3%). As regards depressive disorders, the predominant subtype present is depressive disorder not otherwise specified (11.9%), followed by dysthymic disorder and major depressive disorder (8.7% for each), and then bipolar disorder (depression) at 2.2%. Conclusions We conclude the following: 1. OSA patients are likely to present with comorbid depression and/or anxiety disorder(s). 2. Fatigue was found to be the most prevalent presenting symptom in OSA patients with comorbid anxiety compared to those without anxiety.

AI algorithm combined with RNA editing-based blood biomarkers to discriminate bipolar from major depressive disorders in an external validation multicentric cohort

Bipolar disorder (BD) is a leading cause of disability worldwide, as it can lead to cognitive and functional impairment and premature mortality. The first episode of BD is usually a depressive episode and is often misdiagnosed as major depressive disorder (MDD). Growing evidence indicates that peripheral immune activation and inflammation are involved in the pathophysiology of BD and MDD. Recently, by developing a panel of RNA editing-based blood biomarkers able to discriminate MDD from depressive BD, we have provided clinicians a new tool to reduce the misdiagnosis delay observed in patients suffering from BD. The present study aimed at validating the diagnostic value of this panel in an external independent multicentric Switzerland-based cohort of 143 patients suffering from moderate to major depression. The RNA-editing based blood biomarker (BMK) algorithm developped allowed to accurately discriminate MDD from depressive BD in an external cohort, with high accuracy, sensitivity and specificity values (82.5 %, 86.4 % and 80.8 %, respectively). These findings further confirm the important role of RNA editing in the physiopathology of mental disorders and emphasize the possible clinical usefulness of the biomarker panel for optimization treatment delay in patients suffering from BD.

Treatment of Refractory Bipolar Depression With Stereotactic Radiosurgery Targeting the Subgenual Cingulate Cortex.

Background The subgenual cingulate cortex (SGC) has been identified as a key structure within multiple neural circuits whose dysfunction is implicated in the neurobiology of depression. Deep brain stimulation in the SGC is thought to reduce and normalize local metabolism, causing normalization of circuit behavior and an improvement in depressive symptoms. We hypothesized that nonablative stereotactic radiosurgery (SRS) to the SGC would reduce local metabolism and reduce the severity of depression in patients with treatment-resistant bipolar depression. Methods Under the FDA's Humanitarian Device Exemption program, patients were screened for inclusion and exclusion criteria. Three volunteers meeting the criteria provided informed consent. Bilateral SGC targets were irradiated to a maximum dose of 75 Gy in one fraction. Subjects were followed for one year following the procedure with mood assessments (Hamilton Depression Rating Scale (HDRS), Clinical Global Impression-Improvement, Clinical Global Impression-Severity, and Young Mania Rating Scale), neurocognitive testing (Delis-Kaplan Executive Function System, Wechsler Adult Intelligence Scale III digit span, and California Verbal Learning Test II), and imaging. Further imaging was completed approximately two years after the procedure. Clinical improvement was defined as a ≥50% reduction in HDRS. Results Two of the three subjects showed clinical improvement in depressive symptoms during the follow-up period, while one subject showed no change in symptom severity. One of three subjects was hospitalized for the emergence of an episode of psychotic mania after discontinuing antipsychotic medications against medical advice but promptly recovered with the reinstitution of an antipsychotic. Sequential assessments did not reveal impairment in any cognitive domain assessed. For one of the three subjects, MRI imaging showed evidence of edema at 12 months post-SRS, which resolved at 22 months post-procedure. In a second of three patients, there was evidence of local edema at the target site at long-term follow-up. All imaging changes were asymptomatic. Conclusion Radiosurgical targeting of the SGC may be a noninvasive strategy for the reduction of severe depression in treatment-resistant bipolar disorder. Two out of three patients showed clinical improvement. While these results are promising, further study, including improvements in target selection and dosing considerations, is needed.