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The neuropathology of mood disorders, including the diagnostic transition from major depressive disorder (MDD) to bipolar disorder (BD), is poorly understood. This study investigated resting-state electroencephalography (EEG) activity in patients with MDD and those whose diagnosis changed from MDD to BD. Among sixty-eight enrolled patients with MDD, the diagnosis of 17 patients converted to BD during the study period. We applied machine learning techniques to differentiate the two groups using sensor- and source-level EEG features. At the sensor level, patients with BD showed higher theta band power at the AF3 channel and low-alpha band power at the FC5 channel compared to patients with MDD. At the source level, patients with BD showed higher theta band activity in the right anterior cingulate and low-alpha band activity in the left parahippocampal gyrus. These four EEG features were selected for discriminating between BD and MDD with the best classification performance showing an accuracy of 80.88%, a sensitivity of 76.47%, and a specificity of 82.35%. Our findings revealed distinct theta and low-alpha band activities in patients with BD and MDD. These differences could potentially serve as candidate neuromarkers for the diagnosis and diagnostic transition between the two distinct mood disorders., (© 2024. The Author(s).)

Objective: Recent research has provided strong evidence supporting the use of uric acid-related ratios as potential biomarkers for disease diagnosis. Notable ratios among these include uric acid-to-albumin ratio (UAR), uric acid-to-creatinine ratio (UCR), uric acid-to-high-density lipoprotein ratio (UHR), and uric acid-to-lymphocyte ratio (ULR). The aim of the study is to analyze these biomarkers in bipolar disorder (BD) patients., Methods: 5465 BD patients were enrolled in this study and 6629 healthy subjects served as controls. We compared differences of the above ratio indicators among different BD diagnostic groups and analyzed factors leading to the onset of BD., Results: BD-M (manic episodes of BD) group had higher ULR, UHR, UCR, UAR than HC (Heathy Controls) and BD-D (depressive episodes of BD) groups, while BD-D group showed lower ULR and UCR than HC group. P-BD-M (BD-M with psychotic symptoms) group displayed higher ULR, UHR, UCR, UAR than HC group, while even higher ULR, UHR, UAR than NP-BD-M (BD-M without psychotic symptoms) group. High UAR was risk factor in BD-M., Conclusions: Our study suggests BD-M has more intense inflammatory response than BD-D. Furthermore, there is heterogeneity in the pathogenesis of psychotic symptoms between BD-M and BD-D. Additionally, High UAR was risk factor in BD-M., Competing Interests: Declaration of competing interest The authors declared no potential conflict of interest in this article., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The diagnostic criteria of bipolar disorder (BD) have changed over time. To test if these changes are reflected in the polygenic profile in BD, we studied the association between first BD diagnosis year (during 1972-2016) and polygenic scores (PGS) for psychiatric disorders in BD patients (N = 3,818). We found significant associations between diagnosis year and PGS for BD, depression, and attention deficit hyperactivity disorder (ADHD). The PGS remained largely stable over time in BD type 1, while changes were observed in BD type 2. These findings bear significance not only for genetic research but also for clinical practise, as shifts in patient characteristics can influence treatment response., Competing Interests: Declaration of competing interest ML declares that he has received lecture honoraria from Lundbeck pharmaceutical outside the scope of this work. EJ is currently employed at IQVIA, Mölndal, Sweden., (Copyright © 2024. Published by Elsevier B.V.)

Background: Catatonia, as a transdiagnostic construct, manifests across various psychiatric and non-psychiatric conditions. Understanding how symptom variations impact the catatonia construct and differ across primary diagnoses (schizophrenia, bipolar disorder, unipolar depression, and neurological/metabolic/immunological condition) is essential to refine diagnostic and therapeutic approaches. This study aims to compare the symptom networks and centrality measures of these diagnoses., Methods: We conducted a network analysis using Bush-Francis Catatonia Rating Scale (BFCRS) data from 118 patients, examining centrality measures and network comparisons across the four primary diagnostic groups., Results: In the general catatonia network, the three most central symptoms identified were Excitement (1.462), Perseveration (1.456), and Impulsivity (1.332). While the overall structure of the catatonia networks did not show significant differences between diagnoses in terms of symptom connections and centrality, variations in centrality measures were observed among the different networks., Conclusions: The study reinforces the notion of catatonia as an independent syndrome relatively to psychiatric or non-psychiatric diagnoses. However, the variation in centrality of symptoms across different primary diagnoses provides critical insights that could aid clinicians in tailoring diagnostic and therapeutic strategies. Future research should further explore these relationships and develop more refined approaches to managing catatonia., Competing Interests: Declaration of competing interest The Authors have declared that there are no conflicts of interest in relation to the subject of this study., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Changes in EEG have been reported in both major depressive disorder (MDD) and bipolar disorder (BD). Specifically, power changes in EEG alpha and theta frequency bands during rest and task are known in both disorders. This leaves open whether there are changes in yet another component of the electrophysiological EEG signal, namely phase-related processes that may allow for distinguishing MDD and BD. For that purpose, we investigate EEG-based spontaneous phase in the resting state of MDD, BD and healthy controls. Our main findings show: (i) decreased spontaneous phase variability in frontal theta of both MDD and BD compared to HC; (ii) decreased spontaneous phase variability in central-parietal alpha in MDD compared to both BD and HC; (iii) increased delays or lags of alpha phase cycles in MDD (but not in BD), which (iv) correlate with the decreased phase variability in MDD. Together, we show similar (decreased frontal theta variability) and distinct (decreased central-parietal alpha variability with increased lags or delays) findings in the spontaneous phase dynamics of MDD and BD. This suggests potential relevance of theta and alpha phase dynamics in distinguishing MDD and BD in clinical differential-diagnosis., Competing Interests: Declaration of competing interest None of the authors declare any conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

Abstract ., Background: There is growing evidence that understanding the role of sleep disturbance in bipolar disorder (BD) and major depressive disorder (MDD) is helpful when studying the high heterogeneity of patients across psychiatric disorders., Objective: The present study was designed to investigate the transdiagnostic role of sleep disturbance measured by polysomnography (PSG) in differentiating from MDD with BD., Methods: A total of 256 patients with MDD and 107 first-episode and never medicated patients with BD using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria were recruited. All patients completed 1 night of PSG recording, and the changes in objective sleep structure parameters were determined by PSG analysis., Results: We showed that patients with MDD had statistically longer rapid eye movement (REM) latency, a higher percentage of stage N2 sleep, and lower percentages of stage N3 sleep and REM sleep than those with BD after controlling for confounding factors (all P < .05). Moreover, using the logistic regression analysis, we identified that REM latency was associated with BD diagnosis among the PSG sleep features. The cutoff value for PSG characteristics to differentiate BD from MDD was 261 in REM latency (sensitivity: 41.4% and specificity: 84.1%)., Conclusions: Our findings suggest that PSG-measured sleep abnormalities, such as reduced REM latency, may be a diagnostic differentiating factor between MDD and BD, indicating their roles in identifying homogeneous transdiagnostic subtypes across psychiatric disorders., (© Copyright 2024 Physicians Postgraduate Press, Inc.)

Objectives: To examine the reliability and validity of a semi-structured interview assessing the features of the DSM-5 mixed features specifier. Our goal was to develop an instrument that could be used for both diagnostic and severity measurement purposes., Methods: Four hundred fifty-nine psychiatric patients in a depressive episode were interviewed by a trained diagnostic rater who administered semi-structured interviews including the DSM-5 Mixed Features Specifier Interview (DMSI). We examined the inter-rater reliability and psychometric properties of the DMSI. The patients were rated on clinician rating scales of depression, anxiety, and irritability, and measures of psychosocial functioning, suicidality, and family history of bipolar disorder., Results: The DMSI had excellent joint-interview interrater reliability. More than twice as many patients met the DSM-5 mixed features specifier criteria during the week before the assessment than for the majority of the episode (9.4% vs. 3.9%). DMSI total scores were more highly correlated with a clinician-rated measure of manic symptoms than with measures of depression and anxiety. More patients with bipolar depression met the mixed features specifier than patients with MDD. Amongst patients with MDD, those with mixed features more frequently had a family history of bipolar disorder, were more frequently diagnosed with anxiety disorders, attention deficit disorder, and borderline personality disorder, more frequently had attempted suicide, and were more severely depressed, anxious, and irritable., Conclusion: The DMSI is a reliable and valid measure of the presence of the DSM-5 mixed features specifier in depressed patients as well as the severity of the features of the specifier., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: The aim of this study was to determine whether the clinical profiles of bipolar disorder (BD) patients could be differentiated more clearly using the existing classification by diagnostic subtype or by lithium treatment responsiveness., Methods: We included adult patients with BD-I or II (N = 477 across four sites) who were treated with lithium as their principal mood stabilizer for at least 1 year. Treatment responsiveness was defined using the dichotomized Alda score. We performed hierarchical clustering on phenotypes defined by 40 features, covering demographics, clinical course, family history, suicide behaviour, and comorbid conditions. We then measured the amount of information that inferred clusters carried about (A) BD subtype and (B) lithium responsiveness using adjusted mutual information (AMI) scores. Detailed phenotypic profiles across clusters were then evaluated with univariate comparisons., Results: Two clusters were identified (n = 56 and n = 421), which captured significantly more information about lithium responsiveness (AMI range: 0.033 to 0.133) than BD subtype (AMI: 0.004 to 0.011). The smaller cluster had disproportionately more lithium responders (n = 47 [83.8%]) when compared to the larger cluster (103 [24.4%]; p = 0.006)., Conclusions: Phenotypes derived from detailed clinical data may carry more information about lithium responsiveness than the current classification of diagnostic subtype. These findings support lithium responsiveness as a valid approach to stratification in clinical samples., (© 2024 The Authors. Acta Psychiatrica Scandinavica published by John Wiley & Sons Ltd.)

Introduction: Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes., Methods: Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants' mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes., Results: Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 h/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days., Conclusion: The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder., (© 2024 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)

Management of depressive episodes in bipolar disorder remains challenging for clinicians despite the availability of treatment guidelines. In other contexts, large language models have yielded promising results for supporting clinical decisionmaking. We developed 50 sets of clinical vignettes reflecting bipolar depression and presented them to experts in bipolar disorder, who were asked to identify 5 optimal next-step pharmacotherapies and 5 poor or contraindicated choices. The same vignettes were then presented to a large language model (GPT4-turbo; gpt-4-1106-preview), with or without augmentation by prompting with recent bipolar treatment guidelines, and asked to identify the optimal next-step pharmacotherapy. Overlap between model output and gold standard was estimated. The augmented model prioritized the expert-designated optimal choice for 508/1000 vignettes (50.8%, 95% CI 47.7-53.9%; Cohen's kappa = 0.31, 95% CI 0.28-0.35). For 120 vignettes (12.0%), at least one model choice was among the poor or contraindicated treatments. Results were not meaningfully different when gender or race of the vignette was permuted to examine risk for bias. By comparison, an un-augmented model identified the optimal treatment for 234 (23.0%, 95% CI 20.8-26.0%; McNemar's p < 0.001 versus augmented model) of the vignettes. A sample of community clinicians scoring the same vignettes identified the optimal choice for 23.1% (95% CI 15.7-30.5%) of vignettes, on average; McNemar's p < 0.001 versus augmented model. Large language models prompted with evidence-based guidelines represent a promising, scalable strategy for clinical decision support. In addition to prospective studies of efficacy, strategies to avoid clinician overreliance on such models, and address the possibility of bias, will be needed., (© 2024. The Author(s).)

Bipolar disorder (BD) is a chronic psychiatric condition characterized by large episodic changes in mood and energy. Recently, BD has been proposed to be conceptualized as chronic cyclical mood instability, as opposed to the traditional view of alternating discrete episodes with stable periods in-between. Recognizing this mood instability may improve care and call for high-frequency measures coupled with advanced statistical models. To uncover empirically derived mood states, a multilevel hidden Markov model (HMM) was applied to 4-month ecological momentary assessment data in 20 patients with BD, yielding ∼9,820 assessments in total. Ecological momentary assessment data comprised self-report questionnaires (5 × daily) measuring manic and depressive constructs. Manic and depressive symptoms were also assessed weekly using the Altman Self-Rating Mania Scale and the Quick Inventory for Depressive Symptomatology Self-Report. Alignment between HMM-uncovered momentary mood states and weekly questionnaires was assessed with a multilevel linear model. HMM uncovered four mood states: neutral, elevated, mixed, and lowered, which aligned with weekly symptom scores. On average, patients remained < 25 hr in one state. In almost half of the patients, mood instability was observed. Switching between mood states, three patterns were identified: patients switching predominantly between (a) neutral and lowered states, (b) neutral and elevated states, and (c) mixed, elevated, and lowered states. In all, elevated and lowered mood states were interspersed by mixed states. The results indicate that chronic mood instability is a key feature of BD, even in "relatively" euthymic periods. This should be considered in theoretical and clinical conceptualizations of the disorder. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: Bipolar depression (BPD) is often misdiagnosed as a major depressive disorder (MDD) in clinical practice, which may be attributed to a lack of robust biomarkers indicative of differentiated diagnosis. This study analysed the differences in various hormones and inflammatory markers to explore peripheral biomarkers that differentiate BPD from MDD patients., Methods: A total of 2,048 BPD and MDD patients were included. A panel of blood tests was performed to determine the levels of sex hormones, stress hormones, and immune-related indicators. Propensity score matching (PSM) was used to control for the effect of potential confounders between two groups and further a receiver operating characteristic (ROC) curve was used to analyse the potential biomarkers for differentiating BPD from MDD., Results: Compared to patients with MDD, patients with BPD expressed a longer duration of illness, more hospitalisations within five years, and an earlier age of onset, along with fewer comorbid psychotic symptoms. In terms of biochemical parameters, MDD patients presented higher IgA and IgM levels, while BPD patients featured more elevated neutrophil and monocyte counts. ROC analysis suggested that combined biological indicators and clinical features could moderately distinguish between BPD and MDD. In addition, different biological features exist in BPD and MDD patients of different ages and sexes., Conclusions: Differential peripheral biological parameters were observed between BPD and MDD, which may be age-sex specific, and a combined diagnostic model that integrates clinical characteristics and biochemical indicators has a moderate accuracy in distinguishing BPD from MDD., (© 2024. The Author(s).)

Objective: The objective of this study is to explore and enhance the diagnostic process of unipolar and bipolar disorders. The primary focus is on leveraging automated processes to improve the accuracy and accessibility of diagnosis. The study aims to introduce an audio corpus collected from patients diagnosed with these disorders, annotated using the Clinical Global Impressions Scale (CGI) by psychiatrists., Methods and Procedures: Traditional diagnostic methods rely on the clinician's expertise and consideration of co-existing mental disorders. However, this study proposes the implementation of automated processes in the diagnosis, providing quantitative measures and enabling prolonged observation of patients. The paper introduces a speech signal pipeline for CGI state classification, with a specific focus on selecting the most discriminative features. Acoustic features such as prosodies, MFCC, and LPC coefficients are examined in the study. The classification process utilizes common machine learning methods., Results: The results of the study indicate promising outcomes for the automated diagnosis of bipolar and unipolar disorders using the proposed speech signal pipeline. The audio corpus annotated with CGI by psychiatrists achieved a classification accuracy of 95% for the two-class classification. For the four- and seven-class classifications, the results were 77.3% and 73%, respectively, demonstrating the potential of the developed method in distinguishing different states of the disorders.

Increased immune-inflammatory activation has been repeatedly linked to etiopathogenesis and the progression of both major depressive disorder (MDD) and bipolar depression (BD). We explore the role of soluble intercellular cell adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1) in diagnostic differentiation and disorder progression in patients with MDD and BD. Serum levels of sICAM-1 and sVCAM-1 were measured in 137 patients (MDD = 93 and BD = 44) and compared with 73 healthy controls. The severity of psychopathology was assessed using the Hamilton Depression Rating Scale and Clinical Global Impression Scale. After adjustment for multiple confounders, we noticed significant downregulation of sVCAM-1 and upregulation of sICAM-1 levels in both patient groups. Decreased sVCAM-1 levels were detected in patients with acute episodes of BD when compared to MDD. Immune mediators were related to indicators of progression in both mood disorders. They also followed different post-treatment normalization patterns in MDD and BD and in relation to the stage of each disorder. Adhesion molecules could potentially be useful in discriminating between patients with MDD and BD and determining the possible progression of the disorders. Future nosological methods should include time-dependent pathoplasticity and biological correlates, at least for affective disorders.

Background: Bipolar disorder is challenging to diagnose. In Rwanda, a sub-Saharan country with a limited number of psychiatrists, the number of people with an undetected diagnosis of bipolar disorder could be high. Still, no screening tool for the disorder is available in the country. This study aimed to adapt and validate the Mood Disorder Questionnaire in the Rwandan population., Methods: The Mood Disorder Questionnaire was translated into Kinyarwanda. The process involved back-translation, cross-cultural adaptation, field testing of the pre-final version, and final adjustments. A total of 331 patients with either bipolar disorder or unipolar major depression from two psychiatric outpatient hospitals were included. The statistical analysis included reliability and validity analyses and receiver operating characteristic curve (ROC) analysis. The optimal cut-off was chosen by maximizing Younden's index., Results: The Rwandese version of The Mood Disorder Questionnaire had adequate internal consistency (Cronbach's alpha =0.91). The optimal threshold value was at least six positive items, which yielded excellent sensitivity (94.7%), and specificity (97.3%). The ROC area under the curve (AUC) was 0.99., Conclusion: The adapted tool showed good psychometric properties in terms of reliability and validity for the screening of bipolar disorder, with a recommended cutoff value of six items on the symptom checklist for a positive score and an exclusion of items 14 and 15. The tool has the potential to be a crucial instrument to identify otherwise undetected cases of bipolar disorder in Rwanda, improving access to mental health treatment, thus enhancing the living conditions of people with bipolar disorder., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Abstract: The DSM-III symptomatic criteria for major depression (MD) were derived from those proposed by Feighner and colleagues in 1972, which closely resembled those published by Cassidy in 1957. I here present a counter-factual history in which Feighner carefully read a key reference in Cassidy, a large 1953 follow-up study by Campbell of depressed patients with detailed tables of depressive signs and symptoms. In this alternative timeline, the Feighner criteria for MD were modified by Campbell's results, which then changed DSM-III and subsequent MD criteria sets. The historical pathway to the current DSM MD criteria was contingent on a range of historical events and could easily have been different. This story is not meant to criticize DSM MD criteria that perform well. Rather, it suggests that these criteria represent a useful but fallible set of symptoms/signs that index but do not constitute MD and therefore are not to be reified., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Bipolar disorder (BD) is often accompanied by persistent cognitive impairment. However, screening for cognitive impairment in the clinic is challenged by a lack of consensus on screening procedures. This study assesses cognitive impairment prevalence and screening feasibility in alignment with the International Society for Bipolar Disorder Targeting Cognition Task Force recommendations. Between January 2022 and May 2023, 136 newly diagnosed BD outpatients were assessed with the Screen for Cognitive Impairment in Psychiatry after 15-20 months of specialised care at the Copenhagen Affective Disorder Clinic. Cognitive impairment patterns and associations with cognitive complaints, perceived stress, and functioning were examined. Most screened patients (73 %) achieved full or partial remission, with 51 % being cognitively normal, 38 % showing global impairments, and 11 % displaying selective impairments. Among remitted patients, 56 % were cognitively normal, while 31 % and 13 % exhibited global or selective impairments, respectively. Both objectively impaired patient groups reported more subjective cognitive difficulties than those who were cognitively normal. The globally impaired group also demonstrated poorer functioning, more depressive symptoms and lower quality of life than cognitively normal patients. Across all patients, lower cognitive performance correlated with more cognitive complaints, lower functioning, lower quality of life, and more depressive symptoms. Cognitive screenings were relatively easily implementable, involving only a 1.5 h session including mood ratings, feedback and cognitive strategy discussion. The study highlights the clinical relevance and feasibility of cognitive screenings in BD patients, emphasizing the need for tailored interventions given frequent cognitive impairment in clinically stable individuals., Competing Interests: Declaration of competing interest KWM reports having received consultancy fees from Lundbeck, Angelini, Gideon Richter and Janssen-Cilag in the past three years. LVK reports having received consultancy fees from Lundbeck and Teva in the past three years. JM, TR, JLB, ZKO, LH and report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: The WHO Adult ADHD Self-report Scale (ASRSv1.1 and ASRS-S) is used for screening for attention-deficit/hyperactivity disorder (ADHD). The capacity of the Swedish version of the scale to discriminate ADHD from borderline personality disorder (BPD) and bipolar disorder (BP) has not been tested., Aim: Evaluate scoring methods, psychometric properties, and diagnostic accuracy of the Swedish versions of ASRSv1.1/ASRS-S in a group of patients with ADHD and/or BPD and/or BP., Method: A total of 151 young adult psychiatric patients diagnosed with ADHD, BPD and/or BD completed ASRSv1.1 and the Wender Utah Rating Scale (WURS) for ADHD symptoms, and the Sheehan Disability Scale (SDS) for functional impairment. ADHD diagnoses were assessed with the Schedule for Affective Disorders and Schizophrenia (K-SADS) interview. Both versions of the scale were analysed through dichotomised and non-dichotomised scoring for diagnostic accuracy analysis., Results: The internal consistency for ASRSv1.1/ASRS-S was satisfactory with α 0.913 and 0.743, respectively. The two-factor structure of the ASRSv1.1 and the one factor structure of ASRS-S were supported by the confirmatory factor analyses. A strong positive correlation was found between ASRSv1.1 and WURS and a moderate level of correlation was found between ASRSv1.1 and SDS. The area under the curve for both scoring methods were excellent with an area under the curve (AUC) of 0.808 and 0.817, respectively. Optimal cut-off scores were in line with the original recommendations., Conclusion: The Swedish translation of ASRSv1.1/ASRS-S has psychometric properties comparable to other populations and the capacity to screen for ADHD in patients with overlapping symptoms.

Background: Major depressive disorder (MDD) and bipolar disorder (BD) are psychiatric disorders with overlapping symptoms, leading to high rates of misdiagnosis due to the lack of biomarkers for differentiation. This study aimed to identify metabolic biomarkers in urine samples for diagnosing MDD and BD, as well as to establish unbiased differential diagnostic models., Methods: We utilized a metabolomics approach employing ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) to analyze the metabolic profiles of urine samples from individuals with MDD (n = 50), BD (n = 12), and healthy controls (n = 50). The identification of urine metabolites was verified using MS data analysis tools and online metabolite databases., Results: Two diagnostic panels consisting of a combination of metabolites and clinical indicators were identified-one for MDD and another for BD. The discriminative capacity of these panels was assessed using the area under the receiver operating characteristic (ROC) curve, yielding an area under the curve (AUC) of 0.9084 for MDD and an AUC value of 0.9017 for BD., Conclusions: High-resolution mass spectrometry-based assays show promise in identifying urinary biomarkers for depressive disorders. The combination of urine metabolites and clinical indicators is effective in differentiating healthy controls from individuals with MDD and BD. The metabolic pathway indicating oxidative stress is seen to significantly contribute to depressive disorders., Competing Interests: Declaration of competing interest The authors have nothing to disclose. The authors declare no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Research support: This research received no external financial or non-financial support. Relationship: There are no additional relationship to disclose. Patents and Intellectual Property: SALVETAT NICOLAS and WEISSMANN DINAH has patent #WO2021089866 pending to ALCEDIAG. Other Activities: There are no additional activities to disclose., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Repetitive negative thinking (RNT) is a transdiagnostic process involving perseverative, unproductive, and uncontrollable thoughts. Although RNT may impede adaptive psychosocial functioning by prolonging negative mood states, strengthening cognitive biases, and preventing effective problem-solving, the extent to which RNT is associated with risk for poor psychosocial outcomes is unclear. Given that this has clear transdiagnostic treatment implications, the present study aimed to isolate the unique relationship of RNT with social functioning and life satisfaction in a mixed clinical and non-clinical sample., Methods: In 201 mid-to-later life adult participants (27 with primary diagnoses of bipolar disorder, 84 with major depressive disorder, and 90 healthy volunteers), we measured RNT, social functioning, life satisfaction, trait rumination, DSM-5 diagnoses, depressive symptoms, manic symptoms, cognitive control performance, and global cognitive functioning., Results: Linear regression models revealed that RNT, but not rumination, was significantly associated with poorer social functioning (β = 0.42 p < .001) and reduced life satisfaction (β = -0.42, p < .001) after controlling for clinical and cognitive covariates., Limitations: Limited demographic diversity, cross-sectional design, self-reporting of outcomes., Conclusions: Results suggest that RNT may confer risk for key psychosocial outcomes during middle to later adulthood, over and above the effects of clinical and cognitive variables and independent of diagnostic status. Findings lend support to the notion of RNT as a transdiagnostic process and suggest that RNT may be an important therapeutic target for adults with poor social functioning and/or reduced life satisfaction., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: KEB receives honorarium from the Breakthrough Discoveries for thriving with Bipolar Disorder for her role as Scientific Director of the Integrated Network and as an advisory board member for Merck & Co. FMD receives financial support from the American Medical Association that is unrelated to the work presented in this manuscript. All other authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: There is limited evaluation of approaches to identify patients with new onset bipolar affective disorder (BPAD) when using administrative datasets., Methods: Using the Massachusetts All-Payer Claims Database (APCD), we identified individuals with a 2016 diagnosis of bipolar disorder with mania and examined patterns of psychiatric and medical care over the preceding 48 months., Results: Among 4806 individuals aged 15-35 years with a 2016 BPAD with mania diagnosis, 3066 had 48 months of historical APCD data, and of those, 75 % involved information from ≥2 payors. After excluding individuals with historical BPAD or mania diagnoses, there were 583 individuals whose 2016 BPAD with mania diagnosis appeared to be new (i.e., 34 new diagnoses per 100,000 individuals aged 15-35 years). Most individuals received medical care, e.g., 98 % had outpatient visits, 76 % had Emergency Department (ED) visits, and 50 % had mental health-related ED visits during the 48 months prior to their first mania diagnosis. One-third (37.2 %) had a depressive episode before their initial BPAD with mania diagnosis., Limitations: Study was conducted in one state among insured individuals. We used administrative data, which permits evaluation of large populations but lacks rigorous, well-validated claims-based definitions for BPAD. There could be diagnostic uncertainty during illness course, and clinicians may differ in their diagnostic thresholds., Conclusions: Careful examination of multiple years of patient history spanning all payors is essential for identifying new onset BPAD diagnoses presenting with mania, which in turn is critical to estimating population rates of new disease and understanding the early course of disease., Competing Interests: Declaration of competing interest Dr. Benson reports volunteering for the Epic Behavioral Health Specialty Steering Board. Dr. Smoller is a member of the Scientific Advisory Board of Sensorium Therapeutics (with options), and has received grant support from Biogen, Inc. He is PI of a collaborative study of the genetics of depression and bipolar disorder sponsored by 23andMe for which 23andMe provides analysis time as in-kind support but no payments. Dr. Hsu consults for Cambridge Health Alliance, Brandeis University, University of Southern California, and Columbia University, and has received compensation for keynote speeches from the University of South Carolina and Invitrx., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine learning-based studies have shown encouraging results using diagnostic biomarkers in predictive models, but few have focused on immune-based markers. Our main objective was to develop supervised machine learning models to predict diagnosis and illness state in schizophrenia and bipolar disorder using only a panel of peripheral kynurenine metabolites and cytokines., Methods: The cross-sectional I-GIVE cohort included hospitalized acute bipolar patients (n = 205), stable bipolar outpatients (n = 116), hospitalized acute schizophrenia patients (n = 111), stable schizophrenia outpatients (n = 75) and healthy controls (n = 185). Serum kynurenine metabolites, namely tryptophan (TRP), kynurenine (KYN), kynurenic acid (KA), quinaldic acid (QUINA), xanthurenic acid (XA), quinolinic acid (QUINO) and picolinic acid (PICO) were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS), while V-plex Human Cytokine Assays were used to measure cytokines (interleukin-6 (IL-6), IL-8, IL-17, IL-12/IL23-P40, tumor necrosis factor-alpha (TNF-ɑ), interferon-gamma (IFN-γ)). Supervised machine learning models were performed using JMP Pro 17.0.0. We compared a primary analysis using nested cross-validation to a split set as sensitivity analysis. Post-hoc, we re-ran the models using only the significant features to obtain the key markers., Results: The models yielded a good Area Under the Curve (AUC) (0.804, Positive Prediction Value (PPV) = 86.95; Negative Prediction Value (NPV) = 54.61) for distinguishing all patients from controls. This implies that a positive test is highly accurate in identifying the patients, but a negative test is inconclusive. Both schizophrenia patients and bipolar patients could each be separated from controls with a good accuracy (SCZ AUC 0.824; BD AUC 0.802). Overall, increased levels of IL-6, TNF-ɑ and PICO and decreased levels of IFN-γ and QUINO were predictive for an individual being classified as a patient. Classification of acute versus stable patients reached a fair AUC of 0.713. The differentiation between schizophrenia and bipolar disorder yielded a poor AUC of 0.627., Conclusions: This study highlights the potential of using immune-based measures to build predictive classification models in schizophrenia and bipolar disorder, with IL-6, TNF-ɑ, IFN-γ, QUINO and PICO as key candidates. While machine learning models successfully distinguished schizophrenia and bipolar disorder from controls, the challenges in differentiating schizophrenic from bipolar patients likely reflect shared immunological pathways by the both disorders and confounding by a larger state-specific effect. Larger multi-centric studies and multi-domain models are needed to enhance reliability and translation into clinic., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Unrelated to the submitted work, LDP reports grants from Boehringer-Ingelheim and Janssen R&D, MM reports grants from Janssen R&D, Boehringer Ingelheim Pharma GmbH & Co. and Takeda Pharmaceutical Company. RT, LDP and ML are members of the ECNP Immuno-NeuroPsychiatry Network. The I-GIVE study received funding from Agence Nationale de la Recherche (I-GIVE ANR-13-SAMA-0004-01), INSERM (Institut National de la Santé et de la Recherche Médicale) and Fondation FondaMental in France., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

We sought to derive an objective measure of psychomotor slowing from speech analytics during a psychiatric interview to avoid potential burden of dedicated neurophysiological testing. Speech latency, which reflects response time between speakers, shows promise from the literature. Speech data was obtained from 274 subjects with a diagnosis of bipolar I depression enrolled in a randomized, doubleblind, 6-week phase 2 clinical trial. Audio recordings of structured Montgomery-Åsberg Depression Rating Scale (MADRS) interviews at 6 time points were examined (k = 1,352). We evaluated speech latencies, and other aspects of speech, for temporal stability, convergent validity, sensitivity/responsivity to clinical change, and generalization across seven socio-linguistically diverse countries. Speech latency was minimally associated with demographic features, and explained nearly a third of the variance in depression (categorically defined). Speech latency significantly decreased as depression symptoms improved over time, explaining nearly 20 % of variance in depression remission. Classification for differentiating people with versus without concurrent depression was high (AUCs > 0.85) both cross-sectionally and longitudinally. Results replicated across countries. Other speech features offered modest incremental contribution. Neurophysiological speech parameters with face validity can be derived from psychiatric interviews without the added patient burden of additional testing., Competing Interests: Declaration of competing interest This project reflects a collaboration by Sumitomo Pharma America, Inc, Quantic Innovation, WCG Inc., Louisiana State University, and the Psychiatric Research Institute at the University of Arkansas for Medical Sciences. Potential conflicts of interest between private and academic interests were formally reviewed and mitigated by an LSU review board. Potential conflicts of interest were addressed by transparent declaration of potentially competing interests, and by publishing these results such that the scientific community might be able to critically review, replicate and extend them. S.M, S.T.S, S.T., A.O., K.S.K, D.P. and S.H. are employees of Sumitomo Pharma America Inc. M.O. is a full-time employee and shareholder in WCG. In the past three years, A.S.C has received honoraria/support from Indivior and Boehringer Ingelheim. A.S.C., B.K., and M.O. have stock or related interests in Quantic Innovation which develops and validates digital health measurements. B.K receives licensing royalties from ProPhase for use of the Brief Negative Symptom Scale (BNSS) by for-profit groups; these fees are donated to the Brain and Behavior Research Foundation. In the last three years he has also received honoraria and travel support from ProPhase for training pharmaceutical company raters on the BNSS; consulting fees and/or travel support from Lundbeck, Acadia, ProPhase, Otsuka, and Minerva Neurosciences; fees from anonymized investors through Guideposts and Decision Resources Group; and an honorarium from Otsuka for preparation of educational materials., (Copyright © 2024. Published by Elsevier B.V.)

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Nazari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Bipolar Disorder is one of the most incapacitating diseases among young persons, leading to cognitive and functional impairment and raised mortality, particularly death by suicide. Managing a manic episode and developing new and more effective treatment modalities requires sensitive and reliable instruments. This study aims to translate the English version of the YMRS questionnaire into Kinyarwanda, adapt it to the Rwandan context, and assess its validity., Methods: The original English version of The Young Mania Rating Scale questionnaire was translated into Kinyarwanda. The translation process followed a standardized approach, including back-translation, cross-cultural adaptation, and final adjustments. A total of 130 inpatients with bipolar disorder in a manic episode from CARAES Ndera Teaching Hospital were included. The descriptive statistics and test-retest correlations were carried out, as well as the CFA for validation and Rasch-analysis., Results: The Rwandese version of The Young mania rating scale had an adequate internal consistency (Cronbach's alpha = 0.90). Item 11 provided the lowest standardized loading in both ratings (0.51 and 0.55). The second lowest loading involved the highly correlated item pairs 5 & 9, with item 5 loading 0.51 in rating 1 and item 9 loading 0.57 in rating 2. The remaining loadings ranged from 0.59 to 0.79. This relatively narrow range indicated that a fit to a Rasch model was plausible if excluding item 11., Conclusion: The findings demonstrate that the translated YMRS, the R-YMRS, can be used as a reliable and valid instrument for assessing mania in the Rwandese population in clinical and research settings. However, the results supported using an unweighted total score of 32 and removing items 5, 9, and 11. Studies on this revised scale with an added interview guide for less-trained clinical staff are recommended., (© 2024. The Author(s).)

Background: Individuals with psychiatric disorders have an increased risk of developing dementia. Most cross-sectional studies suffer from selection bias, underdiagnosis and poor population representation, while there is only limited evidence from longitudinal studies on the role of anxiety, bipolar and psychotic disorders. Electronic health records (EHRs) permit large cohorts to be followed across the lifespan and include a wide range of diagnostic information., Objective: To assess the association between four groups of psychiatric disorders (schizophrenia, bipolar disorder/mania, depression and anxiety) with dementia in two large population-based samples with EHR., Methods: Using EHR on nearly 1 million adult individuals in Wales, and from 228 937 UK Biobank participants, we studied the relationships between schizophrenia, mania/bipolar disorder, depression, anxiety and subsequent risk of dementia., Findings: In Secure Anonymised Information Linkage, there was a steep increase in the incidence of a first diagnosis of psychiatric disorder in the years prior to the diagnosis of dementia, reaching a peak in the year prior to dementia diagnosis for all psychiatric diagnoses. Psychiatric disorders, except anxiety, were highly significantly associated with a subsequent diagnosis of dementia: HRs=2.87, 2.80, 1.63 for schizophrenia, mania/bipolar disorder and depression, respectively. A similar pattern was found in the UK Biobank (HRs=4.46, 3.65, 2.39, respectively) and anxiety was also associated with dementia (HR=1.34). Increased risk of dementia was observed for all ages at onset of psychiatric diagnoses when these were divided into 10-year bins., Conclusions: Psychiatric disorders are associated with an increased risk of subsequent dementia, with a greater risk of more severe disorders., Clinical Implications: A late onset of psychiatric disorders should alert clinicians of possible incipient dementia., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: The study of clinical biological indicators in bipolar disorder (BD) is important. In recent years, basic experiments have associated the pathophysiological mechanism of BD is related to mitochondrial dysfunction, but few clinical studies have confirmed this finding., Object: The present study aimed to evaluate whether plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) levels, which can represent the degree of mitochondrial damage in vivo, are altered in patients with BD in early onset and during treatment compared with controls., Method: A total of 75 first-diagnosed drug-naive patients with BD and 60 HCs were recruited and followed up for 1 month. The clinical symptoms were assessed using HAMD, HAMA, and YMRS, and ccf-mtDNA levels were measured by qPCR before and after drug treatment in BD., Result: (1) The plasma ccf-mtDNA levels in first-diagnosed drug-naive patients with BD increased compared with those in HCs (p = 0.001). (2) Drug treatment for 1 month can decrease the expression of ccf-mtDNA in BD (p < 0.001). (3) No significant correlation was observed between the changes in ccf-mtDNA levels and the improvement of clinical symptoms in BD after drug treatment., Conclusion: The plasma ccf-mtDNA level was increased in BD, and decreased after pharmacological treatment. These outcomes suggested that plasma ccf-mtDNA level is likely to be sensitive to the drug response in BD, and mitochondrial pathway is a potential target for further therapy., Competing Interests: Declaration of competing interest We declare that none of the authors holds any actual or potential conflict of interest for this study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis., (© 2024. The Author(s).)

The recovery process in bipolar disorder is a subjective and multidimensional experience that seeks to develop new meanings and purposes for living a satisfying life despite the limitations imposed by the disorder. Thus, this qualitative study aimed to explore the perceptions of recovery and the meanings attributed by individuals undergoing treatment for bipolar disorder to the elements considered relevant in this process. Semi-structured interviews with open-ended questions were conducted to explore the experiences and perspectives of recovery in individuals undergoing treatment for bipolar disorder. Grounded Theory was used as the method for qualitative analysis. The study included 26 participants aged between 18 and 65 years. Based on the analysis of participant reports, we identified two main themes: living with the illness and what it means to be in recovery. The perception of recovery is an individual process and can differ from the medical model. Participants suggest that accepting the diagnosis of bipolar disorder and finding meaning in life are essential to their recovery. They also describe how mental health professionals can facilitate or hinder this process. Understanding patients' perceptions can facilitate access to healthcare services and treatment adherence., (© 2024. The Author(s).)

Competing Interests: All authors have disclosed no conflicts of interest.

Children of people with a diagnosis of schizophrenia or bipolar disorder encounter great difficulties in coping with the symptoms of the disorders. The study was conducted to determine the feelings, opinions, life experiences, and needs of the children of parents with a diagnosis of schizophrenia or bipolar disorder. This is a descriptive study conducted using the in-depth interview design, a qualitative method. The sample consisted of 19 children who agreed to participate in the study with parental consent. Data were collected using a personal information form and a semi-structured qualitative interview form. The data were analyzed using the thematic analysis method. As a result of the thematic analysis, five main themes were obtained: Parents from children's eyes, living with parents, social pressure, coping strategies, this life with one word. The study concluded that children of parents followed up for schizophrenia or bipolar disorder struggle with many individual and social difficulties. These children have feelings of fear, embarrassment, or anger with this life experience; encounter social exclusion; and are overwhelmed with heavy responsibilities at an early age. Their coping strategies can be maladaptive, such as smoking or alcohol consumption, thinking of eloping, becoming introverted, and so on., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: The longitudinal course of bipolar disorder (BD) is associated with an active process of neuroprogression, characterized by structural brain alterations and progressive functional impairment. In the last decades, a growing need of a standardized staging model for BD arose, with the aim of a more appropriate definition of stage-specific clinical manifestations and the identification of more customized therapeutic tools., Areas Covered: The authors review the literature on clinical aspects, neurobiological correlates and treatment issues related to BD progression. Thereafter, they address the definition, constructs, and evolution of the staging concept, focusing on the clinical applications of BD staging models available in literature., Expert Opinion: Although several staging models for BD have been proposed to date, their application in clinical practice is still relatively scant. This may have a detrimental impact on the clinical and therapeutic management of BD, in terms of early and proper diagnosis as well as tailored treatment interventions according to the different stages of illness. Future research efforts should tend to the integration of recent insights on neuroimaging and epigenetic markers, toward a standardized and multidimensional staging model.

Aim: To develop a typology of care trajectories (CTs) 1 year before and after a first dementia diagnosis in individuals aged ≥65 years, with prevalent schizophrenia or bipolar disorder., Methods: This was a longitudinal, retrospective cohort study using health administrative data (1996-2016) from Quebec (Canada). We selected patients aged ≥65 years with an incident diagnosis of dementia between 1 January 2014 and 31 December 2016, and a diagnosis of schizophrenia and/or or bipolar disorder. A CT typology was generated by a multidimensional state sequence analysis based on the "6 W" model of CTs. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why") and the specialty of care providers ("which")., Results: In total, 3868 patients were categorized into seven distinct types of CTs, with varying patterns of healthcare use and comorbidities. Healthcare use differed in terms of intensity, but also in its distribution around the diagnosis. For instance, whereas one group showed low healthcare use, healthcare use abruptly increased or decreased after the diagnosis in other groups, or was equally distributed. Other significant differences between CTs included mortality rates and use of long-term care after the diagnosis. Most patients (67%) received their first dementia diagnosis during hospitalization., Conclusions: Our innovative approach provides a unique insight into the complex healthcare patterns of people living with serious mental illness and dementia, and provides an avenue to support data-driven decision-making by highlighting fragility areas in allocating care resources. Geriatr Gerontol Int 2024; 24: 577-586., (© 2024 The Authors. Geriatrics & Gerontology International published by John Wiley & Sons Australia, Ltd on behalf of Japan Geriatrics Society.)

Background: Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD)., Method: We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo., Results: Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group., Conclusions: HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Competing Interests: L'auteur déclare n'avoir aucun lien d'intérêts.

Objectives: Although free from characterized manic and depressive episodes, the euthymic period in bipolar disorder (BD) remains characterized by a whole host of difficulties, particularly relational. These difficulties are factors of vulnerabilities and relapses. People's perception of their own relationships has an impact on their symptomatology, their responses to treatment and on relapse rates. Young's early maladaptive schemas (EMS) approach proves to be relevant for understanding the construction of these perceptions and working on them. Nevertheless, to this date, few studies have investigated which EMS are related to relationship dysfunction in this particular population. Determining the link between EMS and relationship difficulties would be relevant to understand psychosocial impairment of people with BD in euthymic states. The present study aims to investigate the associations between the different domains of EMS and relationship dysfunction among patients with bipolar disorder in the euthymic phase., Methods: Data are extracted from the FACE-BD cohort, within the BD Expert Center in Versailles. Inclusion criteria were to be aged between 18 and 65 and to be an outpatient with a diagnosis of bipolar disorder (DSM-IV-TR). Patients had to be euthymic at the time of inclusion, according to DSM-IV-TR criteria with a cut-off score of 14 on the Montgomery-Asberg Depression Rating Scale and the Young Mania Rating Scale. Non-inclusion criteria were meeting at the time of the study the criteria for characteristic depressive episode, hypomania or mania according to the DSM-IV-TR. Sociodemographic data, clinical features associated with relationship dysfunction were assessed. EMS and EMS domains were assessed with the YSQ-R short form (Young Schema Questionnaire 3 Short Form) and current relationship dysfunction were assessed with the FAST (Functioning assessment short test subscale). Successive simple linear regression analyses were performed to investigate the association between the severity scores of each EMS and the intensity of relationship dysfunction. Furthermore, successive simple linear regression analyses investigated the association between EMS domain scores and intensity of relationship dysfunction. Multiple linear regression analyses were performed to test the association between EMS scores, then EMS domains, and the intensity of relationship dysfunction after adjusting for age as well as the intensity of residual depressive and manic symptoms., Results: Relationship dysfunction is partly associated with EMS activation in particular in the separation and rejection domain (P<0.0001), the other-directedness domain (P=0.031) and the over-vigilance and inhibition domain (P=0.005). Having residual depressive symptoms is also among the factors contributing to the relationship dysfunctions of people with bipolar disorder in the euthymic phase., Discussion: This is the first study demonstrating that the activation of several domains of EMS is a risk factor of relationships difficulties for people in euthymic phase of bipolar disorder. It is necessary to identify which EMS are specifically activated and their domains of belonging in order to prevent and reduce them. EMS are a lever for functional remission. It is therefore relevant to refer people reporting relationship problems to schema therapy consistent with a personalized care. Finally, future studies should focus on the mechanisms underlying the complex relationship between EMS domains and relationship dysfunction in people with bipolar disorder in the euthymic phase. It may also be relevant for future research to control for different types of relationship dysfunction. EMS may be differentially associated with several types of interpersonal problems. The relations between different adaptation styles and EMS should be further investigated to offer more personalized care, with the aim to improve functional remission., (Copyright © 2023 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.)

Introduction: Owing to the heterogenic picture of bipolar disorder, it takes approximately 8.8 years to reach a correct diagnosis. Early recognition and early intervention might not only increase quality of life, but also increase life expectancy as a whole in individuals with bipolar disorder. Therefore, we hypothesize that implementing machine learning techniques can be used to support the diagnostic process of bipolar disorder and minimize misdiagnosis rates., Materials and Methods: To test this hypothesis, a de-identified data set of only demographic information and the results of cognitive tests of 196 patients with bipolar disorder and 145 healthy controls was used to train and compare five different machine learning algorithms., Results: The best performing algorithm was logistic regression, with a macro-average F1-score of 0.69 [95% CI 0.66-0.73]. After further optimization, a model with an improved macro-average F1-score of 0.75, a micro-average F1-score of 0.77, and an AUROC of 0.84 was built. Furthermore, the individual amount of contribution per variable on the classification was assessed, which revealed that body mass index, results of the Stroop test, and the d2-R test alone allow for a classification of bipolar disorder with equal performance., Conclusion: Using these data for clinical application results in an acceptable performance, but has not yet reached a state where it can sufficiently augment a diagnosis made by an experienced clinician. Therefore, further research should focus on identifying variables with the highest amount of contribution to a model's classification., (© 2024 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)

Aim: To examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans-diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full-threshold disorders., Methods: Using data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans-diagnostic clinical caseness (onset of major mental disorders) at follow-up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on)., Results: Of 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non-cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings., Conclusions: The findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth., (© 2023 John Wiley & Sons Australia, Ltd.)

Background: Bell's mania was first described in 1849, and other terms have been used to describe this condition, including delirious mania, mania with delirium, and excited delirium. However, no international diagnostic manual has included mania as an independent diagnostic tool. The criteria for delirious mania were proposed by Bond et al., Methods: We present a case of a man without a personal or family psychiatric history who experienced his first manic episode of delirium and psychosis at 76 years old., Conclusions: The case described in this study is compatible with mood disorders, the original description of Bell's mania, and Bond's definition of delirious mania. Although rare, extremely late-onset primary mania can occur without personal or family psychiatric history. The initial clinical presentation of delirium requires a thorough medical investigation, including magnetic resonance imaging (MRI) and lumbar puncture with neuronal antibodies. The addition of delirious mania to the group of bipolar disorders in future editions of The International Classification of Diseases (ICD) and Diagnostic and Statistical Manual of Mental Disorders (DSM) has therapeutic and prognostic implications. The Bond criteria can provide valuable information in this respect. Further investigations are necessary to clarify the pathophysiology and epidemiology of delirious mania., (© 2024 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)

Multiplex immunoassays have been developed to detect multiple proteins simultaneously and are used to search for biomarkers, including those present in major psychiatric disorders. This study aimed to review multiplex immunoassay studies on cerebrospinal fluid (CSF) biomarkers in patients with schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD) and examine future research directions using improved proteomic techniques. According to the results of previous multiplex immunoassay studies, increased CSF IFN-β, IL-8, MCP-2, MMP-2, PAI-1, sICAM-1, and sVCAM-1 and decreased CSF ACE, APP, fibrinogen, and GDNF were observed in patients with schizophrenia, while CSF HGF and S100B were positively correlated with psychotic symptom and CSF IL-11, IL-29/IFN-λ 1 , and TSLP were negatively correlated. Increased CSF IFN-β and IL-1β and decreased CSF Aβ 42 , APP, IL-6, and NCAM-1 were observed, while CSF S100B was positively correlated with manic symptom in patients with BD. Increased CSF IL-4, MCP-1, MIP-1β, and MMP-2 were observed in patients with MDD, while CSF HGF and MMP-2 were positively correlated with depressive symptom and CSF IL-15 and MCP-1 were negatively correlated. However, signal cross-talk and cross-reactivity problems have been observed in previous studies using multiplex immunoassay. The proximity extension assay can be used to overcome cross-reactivity and enable ultrasensitive multiplexed detection and quantification of more than 1000 target proteins. However, proteomic studies using proximity extension assay technology in patients with schizophrenia, BD, or MDD are still scarce. Therefore, future high-quality proteomic studies are required to identify CSF biomarkers for larger sets of target proteins in patients with major psychiatric disorders., (© 2024 The Authors. Neuropsychopharmacology Reports published by John Wiley & Sons Australia, Ltd on behalf of The Japanese Society of Neuropsychopharmacology.)