Peripheral inflammatory biomarkers define biotypes of bipolar depression

The Gut Microbiota Affects Anti-TNF Responsiveness by Activating the NAD+ Salvage Pathway in Ulcerative Colitis

Approximately 50% of the patients with ulcerative colitis (UC) are primarily nonresponsive to anti-tumor necrosis factor (TNF) therapy or lose their responsiveness over time. The gut microbiota plays an important role in the resistance of UC to anti-TNF therapy; however, the underlying mechanism remains unknown. Here, it is found that the transplantation of gut fecal microbiota from patients with UC alters the diversity of the gut microbiota in dextran sulfate sodium-induced colitis mice and may affect the therapeutic responsiveness of mice to infliximab. Furthermore, the abundances of Romboutsia and Fusobacterium increase in the tissues of patients with UC who do not respond to anti-TNF therapy. Differentially abundant metabolites are mainly enriched in nicotinate and nicotinamide metabolism in NCM460 cells after Fusobacterium nucleatum infection. Mechanistically, F. nucleatum promotes the nicotinamide adenine dinucleotide (NAD+) salvage pathway by upregulating NAMPT expression, which subsequently leads to the activation of the p38 mitogen-activated protein kinase (MAPK) signaling pathway and promotes the secretion of inflammatory factors, ultimately inhibiting the therapeutic response to anti-TNF drugs. These findings demonstrate that the gut microbiota can influence the response to anti-TNF therapy in patients with UC and highlight the therapeutic potential of targeting F. nucleatum and its associated pathways for preventing and treating drug resistance in UC.

Metabolic factors modulate effort-based decision-making in major depressive disorder

BACKGROUND

Abnormalities in effort-based decision-making have been consistently reported in major depressive disorder (MDD). Evidence indicates that metabolic factors, such as insulin resistance and dyslipidemia, which are highly prevalent in MDD, are independently associated with reward disturbances. Herein, we investigate the moderating effect of metabolic factors on effort-based decision-making in individuals with MDD.

METHODS

Forty-nine adults with MDD completed the Effort Expenditure for Rewards Task (EEfRT). Anthropometric and laboratorial parameters were assessed in all participants. We conducted a factor analysis to identify combinations of correlated metabolic variables, and reduce the number of comparisons.

RESULTS

Proxy markers of elevated insulin resistance (OR: 0.816, p < 0.001) and hyperglycemia (OR: 0.898, p = 0.021) were associated with a lower willingness to exert physical effort for rewards in the EEfRT. In contrast, elevated HDL (OR: 1.165, p = 0.004), and elevated non-HDL cholesterol and triglycerides (OR: 1.184, p < 0.001) were associated with increased frequency of hard task choices. These associations were independent of age, sex, depressive symptoms severity and medication use. Computational modeling revealed that the insulin resistance (β = 0.275, p = 0.035) and cholesterol factors (β = 0.565, p < 0.001) were independently associated with increased effort discounting.

LIMITATIONS

Post-hoc analysis using a relatively small sample of convenience.

CONCLUSIONS

Metabolic factors significantly and independently modulated effort-based decision-making in patients with MDD. These results have implications for our understanding of reward disturbances in MDD, and offer insights for further mechanistic investigations.

Putative Risk Biomarkers of Bipolar Disorder in At-risk Youth

Bipolar disorder is a highly heritable and functionally impairing disease. The recognition and intervention of BD especially that characterized by early onset remains challenging. Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis. We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or (and) clinical risk of BD. Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses. Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores, epigenetic alterations, elevated immune parameters, front-limbic system deficits, and brain circuit dysfunction associated with emotion and reward processing. Future studies need to enhance machine learning integration, make clinical detection methods more objective, and improve the quality of cohort studies.

Comorbid physical health outcomes in patients with bipolar disorder: An umbrella review of systematic reviews and meta-analyses

BACKGROUND

Although several meta-analyses have examined the association between bipolar disorder (BD) and its comorbid health outcomes, this evidence has not been comprehensively assembled.

OBJECTIVE

We aimed to systematically review existing meta-analyses based on multiple physical outcomes and validate the evidence level by examining the existing certainty of evidence.

METHODS

We systematically searched databases, including PubMed/MEDLINE, Embase, Google Scholar, and CINAHL, for articles published up to July 2023. We included meta-analyses of cohort, case-control, and/or cross-sectional studies investigating any comorbid health outcomes in patients with BD. We conducted quality assessments of the included meta-analysis using AMSTAR2. The credibility of findings was categorized into five levels of class and quality of evidence (CE), including convincing, highly suggestive, suggestive, weak, or not significant.

RESULTS

We analyzed 12 meta-analyses, including 145 original articles, covering 14 unique health outcomes with over 60 million participants across 29 countries and five continents. Among 14 health outcomes, BD was significantly associated with eight comorbid health outcomes, including dementia (equivalent odds ratio [eOR], 2.96 [95 % confidence intervals {CI}, 1.69-5.17]; CE=suggestive), Parkinson's disease (3.35 [1.72-6.53]; CE=suggestive), asthma (1.86 [1.42-2.42]; CE=weak), toxoplasmosis (1.69 [1.21-2.37]; CE=weak), hypertension (1.28 [1.02-1.60]; CE=convincing), breast cancer (1.33 [1.15-1.55]; CE=weak), obesity (1.64 [1.30-1.99]; CE=suggestive), and type 2 diabetes mellitus (1.98 [1.55-2.52]; CE=weak).

CONCLUSION

Individuals with BD are predisposed to numerous comorbid physical conditions, though these links are supported by various evidence levels and necessitate further studies. It is imperative that physicians be aware of these potential comorbidities in patients with BD and take proactive measures to manage them.

Brain responses to intermittent fasting and the healthy living diet in older adults

Diet may promote brain health in metabolically impaired older individuals. In an 8-week randomized clinical trial involving 40 cognitively intact older adults with insulin resistance, we examined the effects of 5:2 intermittent fasting and the healthy living diet on brain health. Although intermittent fasting induced greater weight loss, the two diets had comparable effects in improving insulin signaling biomarkers in neuron-derived extracellular vesicles, decreasing the brain-age-gap estimate (reflecting the pace of biological aging of the brain) on magnetic resonance imaging, reducing brain glucose on magnetic resonance spectroscopy, and improving blood biomarkers of carbohydrate and lipid metabolism, with minimal changes in cerebrospinal fluid biomarkers for Alzheimer's disease. Intermittent fasting and healthy living improved executive function and memory, with intermittent fasting benefiting more certain cognitive measures. In exploratory analyses, sex, body mass index, and apolipoprotein E and SLC16A7 genotypes modulated diet effects. The study provides a blueprint for assessing brain effects of dietary interventions and motivates further research on intermittent fasting and continuous diets for brain health optimization. For further information, please see ClinicalTrials.gov registration: NCT02460783.

Associations between dietary habits and bipolar disorder: a diet-wide mendelian randomization study

Background

Diet/nutrition is critically important in the pathogenesis, progression, and treatment outcomes of various mental disorders. Current research predominantly focuses on the role of diet in the development and treatment of depression, with less attention given to the relationship between diet and Bipolar Disorder (BD).

Method

We employed Mendelian Randomization (MR) to investigate the relationship between 28 dietary habits and BD. An analysis was conducted using publicly available genome-wide association study data from the UK Biobank dataset. Various dietary habits were analyzed as exposures with BD as the outcome, mainly using the Inverse Variance Weighted (IVW) method.

Results

Intake of non-oily fish and sponge pudding both have a positive association with BD. Oily fish, dried fruit, apples, salt, and cooked vegetables intake also appeared potentially risky for BD, although the possibility of false positives cannot be ruled out. Sensitivity analysis further confirmed the robustness of these findings.

Conclusion

Our research provides evidence of a relationship between various dietary habits and BD. It underscores the need for careful dietary management and balance to reduce the risk of BD, suggesting caution with dietary preferences for fish and sponge pudding. Furthermore, more detailed studies are needed to further understand the potential impacts of high-sugar and high-protein diets on BD development.

Infliximab alleviates memory impairment in rats with chronic pain by suppressing neuroinflammation and restoring hippocampal neurogenesis

Patients with chronic pain commonly report impaired memory. Increasing evidence has demonstrated that inhibition of neurogenesis by neuroinflammation plays a crucial role in chronic pain-associated memory impairments. There is currently a lack of treatment strategies for this condition. An increasing number of clinical trials have reported the therapeutic potential of anti-inflammatory therapies targeting tumour necrosis factor-α (TNF-α) for inflammatory diseases. The present study investigated whether infliximab alleviates chronic pain-associated memory impairments in rats with chronic constriction injury (CCI). We demonstrated that infliximab alleviated spatial memory impairment and hyperalgesia induced by CCI. Furthermore, infliximab inhibited the activation of hippocampal astrocytes and microglia and decreased the release of proinflammatory cytokines in CCI rats. Furthermore, infliximab reversed the decrease in the numbers of newborn neurons and mature neurons in the dentate gyrus (DG) caused by chronic pain. Our data provide evidence that infliximab alleviates chronic pain-associated memory impairments, suppresses neuroinflammation and restores hippocampal neurogenesis in a CCI model. These facts indicate that infliximab may be a potential therapeutic agent for the treatment of chronic pain and associated memory impairments.

The Role of Histone Deacetylases in NLRP3 Inflammasomesmediated Epilepsy

Epilepsy is one of the most common brain disorders that not only causes death worldwide, but also affects the daily lives of patients. Previous studies have revealed that inflammation plays an important role in the pathophysiology of epilepsy. Activation of inflammasomes can promote neuroinflammation by boosting the maturation of caspase-1 and the secretion of various inflammatory effectors, including chemokines, interleukins, and tumor necrosis factors. With the in-depth research on the mechanism of inflammasomes in the development of epilepsy, it has been discovered that NLRP3 inflammasomes may induce epilepsy by mediating neuronal inflammatory injury, neuronal loss and blood-brain barrier dysfunction. Therefore, blocking the activation of the NLRP3 inflammasomes may be a new epilepsy treatment strategy. However, the drugs that specifically block NLRP3 inflammasomes assembly has not been approved for clinical use. In this review, the mechanism of how HDACs, an inflammatory regulator, regulates the activation of NLRP3 inflammasome is summarized. It helps to explore the mechanism of the HDAC inhibitors inhibiting brain inflammatory damage so as to provide a potential therapeutic strategy for controlling the development of epilepsy.

Neuron-derived extracellular vesicles in blood reveal effects of exercise in Alzheimer's disease

BACKGROUND

Neuron-derived extracellular vesicles (NDEVs) in blood may be used to derive biomarkers for the effects of exercise in Alzheimer's disease (AD). For this purpose, we studied changes in neuroprotective proteins proBDNF, BDNF, and humanin in plasma NDEVs from patients with mild to moderate AD participating in the randomized controlled trial (RCT) of exercise ADEX.

METHODS

proBDNF, BDNF, and humanin were quantified in NDEVs immunocaptured from the plasma of 95 ADEX participants, randomized into exercise and control groups, and collected at baseline and 16 weeks. Exploratorily, we also quantified NDEV levels of putative exerkines known to respond to exercise in peripheral tissues.

RESULTS

NDEV levels of proBDNF, BDNF, and humanin increased in the exercise group, especially in APOE ε4 carriers, but remained unchanged in the control group. Inter-correlations between NDEV biomarkers observed at baseline were maintained after exercise. NDEV levels of putative exerkines remained unchanged.

CONCLUSIONS

Findings suggest that the cognitive benefits of exercise could be mediated by the upregulation of neuroprotective factors in NDEVs. Additionally, our results indicate that AD subjects carrying APOE ε4 are more responsive to the neuroprotective effects of physical activity. Unchanged NDEV levels of putative exerkines after physical activity imply that exercise engages different pathways in neurons and peripheral tissues. Future studies should aim to expand upon the effects of exercise duration, intensity, and type in NDEVs from patients with early AD and additional neurodegenerative disorders.

TRIAL REGISTRATION

The Effect of Physical Exercise in Alzheimer Patients (ADEX) was registered in ClinicalTrials.gov on April 30, 2012 with the identifier NCT01681602.

Neuroimmune Mechanisms of Opioid Use Disorder and Recovery: Translatability to Human Studies, and Future Research Directions

Opioid use disorder (OUD) is a major current cause of morbidity and mortality. Long-term exposure to short-acting opioids (MOP-r agonists such as heroin or fentanyl) results in complex pathophysiological changes to neuroimmune and neuroinflammatory functions, affected in part by peripheral mechanisms (e.g., cytokines in blood), and by neuroendocrine systems such as the hypothalamic-pituitary-adrenal (HPA) stress axis. There are important findings from preclinical models, but their role in the trajectory and outcomes of OUD in humans is not well understood. The goal of this narrative review is to examine available data on immune and inflammatory functions in persons with OUD, and to identify major areas for future research. Peripheral blood biomarker studies revealed a pro-inflammatory state in persons with OUD in withdrawal or early abstinence, consistent with available postmortem brain studies (which show glial activation) and diffusion tensor imaging studies (indicating white matter disruptions), with gradual abstinence-associated recovery. The mechanistic roles of these neuroimmune and neuroinflammatory changes in the trajectory of OUD (including recovery and medication management) cannot be examined practically with postmortem data. Collection of longitudinal data in larger-scale human cohorts would allow examination of these mechanisms associated with OUD stage and progression. Given the heterogeneity in presentation of OUD, a precision medicine approach integrating multi-omic peripheral biomarkers and comprehensive phenotyping, including neuroimaging, can be beneficial in risk stratification, and individually optimized selection of interventions for individuals who will benefit, and assessments under refractory therapy.

Levomilnacipran ameliorates lipopolysaccharide-induced depression-like behaviors and suppressed the TLR4/Ras signaling pathway

Levomilnacipran, a serotonin and norepinephrine reuptake inhibitor, has been reported to have anti-depressive effects. However, the detailed mechanisms underlying these effects are still unclear. This study aimed to investigate the antidepressant mechanisms of levomilnacipran to discover new perspectives on the treatment of depression in male rats. Intraperitoneal injection of lipopolysaccharide (LPS) was used to induce depressive behaviors in rats. Activation of microglia and apoptosis of neurons verified by immunofluorescence. Inflammatory related proteins and neurotrophic related proteins were verified by immunoblotting. The mRNA expression of apoptosis markers was verified by real-time quantitative PCR. Finally, electron microscopy analysis was used to observe the ultrastructural pathology of neuron. Here, we found that the anti-depression and anti-anxiety effects of levomilnacipran in the LPS-induced rat model of depression was resulted from the suppression of neuroinflammation and neuronal apoptosis within prefrontal cortex of rats. Furthermore, we found that levomilnacipran could decrease the number of microglia and suppress its activation in prefrontal cortex of rats. This effect may be mediated by suppressing the TLR4/NF-κB and Ras/p38 signaling pathways. In addition, levomilnacipran plays a neuroprotective role by increasing the expression of neurotrophic factors. Taken together, these results suggest that levomilnacipran exerts antidepressant effects by attenuating neuroinflammation to inhibit the damage in central nervous system and plays a neuroprotective role to improve depressive behaviors. These findings suggest that suppression of neuroinflammation in prefrontal cortex could ameliorate depressive behavioral disorder of rats induced by LPS, which provided a new perspective for the treatment of depression.

Non-canonical pathways in the pathophysiology and therapeutics of bipolar disorder

Bipolar disorder (BD) is characterized by extreme mood swings ranging from manic/hypomanic to depressive episodes. The severity, duration, and frequency of these episodes can vary widely between individuals, significantly impacting quality of life. Individuals with BD spend almost half their lives experiencing mood symptoms, especially depression, as well as associated clinical dimensions such as anhedonia, fatigue, suicidality, anxiety, and neurovegetative symptoms. Persistent mood symptoms have been associated with premature mortality, accelerated aging, and elevated prevalence of treatment-resistant depression. Recent efforts have expanded our understanding of the neurobiology of BD and the downstream targets that may help track clinical outcomes and drug development. However, as a polygenic disorder, the neurobiology of BD is complex and involves biological changes in several organelles and downstream targets (pre-, post-, and extra-synaptic), including mitochondrial dysfunction, oxidative stress, altered monoaminergic and glutamatergic systems, lower neurotrophic factor levels, and changes in immune-inflammatory systems. The field has thus moved toward identifying more precise neurobiological targets that, in turn, may help develop personalized approaches and more reliable biomarkers for treatment prediction. Diverse pharmacological and non-pharmacological approaches targeting neurobiological pathways other than neurotransmission have also been tested in mood disorders. This article reviews different neurobiological targets and pathophysiological findings in non-canonical pathways in BD that may offer opportunities to support drug development and identify new, clinically relevant biological mechanisms. These include: neuroinflammation; mitochondrial function; calcium channels; oxidative stress; the glycogen synthase kinase-3 (GSK3) pathway; protein kinase C (PKC); brain-derived neurotrophic factor (BDNF); histone deacetylase (HDAC); and the purinergic signaling pathway.

Differences and correlations of biochemical index levels in patients with bipolar disorder and major depressive disorder during a stable period

The differences and correlation of biochemical indexes between bipolar disorder (BPD) and major depressive disorder (MDD) in stable stage were analyzed and discussed. Patients diagnosed with BPD and MDD in the Third People's Hospital of Foshan from January 2019 to December 2021 were selected as the research subjects, with 200 cases in each. Fasting serum was collected from patients and then detected regarding TC, TG, high-density lipoprotein, low-density lipoprotein (LDL), aspartate aminotransferase, lactic dehydrogenase, creatine kinase, creatine kinase-MB, urea, creatinine, uric acid, alanine aminotransferase, glucose (GLU), hemoglobin A1c, prolactin, high-sensitivity C-reactive protein, homocysteine. The results showed that the mean age and serum LDL, GLU, and HbAc1 levels of the MDD group were significantly higher than those of the BPD group (P < .05), while there was no significant difference in other indexes (P > .05). The prevalence of BPD was significantly negatively correlated with patient age (r = -0.164, P = .020), LDL (r = -0.150, P = .034), GLU (r = -0.140, P = .048), and HbAc1 (r = -0.215, P = .002) (P < .05). There were no significant differences in serum Hcy and high-sensitivity C-reactive protein levels between the BPD and MDD groups. The age, fasting blood glucose, glycosylated hemoglobin, and LDL of BPD patients were negatively correlated with their incidence.

The human neurosecretome: extracellular vesicles and particles (EVPs) of the brain for intercellular communication, therapy, and liquid-biopsy applications

Emerging evidence suggests that brain derived extracellular vesicles (EVs) and particles (EPs) can cross blood-brain barrier and mediate communication among neurons, astrocytes, microglial, and other cells of the central nervous system (CNS). Yet, a complete understanding of the molecular landscape and function of circulating EVs & EPs (EVPs) remain a major gap in knowledge. This is mainly due to the lack of technologies to isolate and separate all EVPs of heterogeneous dimensions and low buoyant density. In this review, we aim to provide a comprehensive understanding of the neurosecretome, including the extracellular vesicles that carry the molecular signature of the brain in both its microenvironment and the systemic circulation. We discuss the biogenesis of EVPs, their function, cell-to-cell communication, past and emerging isolation technologies, therapeutics, and liquid-biopsy applications. It is important to highlight that the landscape of EVPs is in a constant state of evolution; hence, we not only discuss the past literature and current landscape of the EVPs, but we also speculate as to how novel EVPs may contribute to the etiology of addiction, depression, psychiatric, neurodegenerative diseases, and aid in the real time monitoring of the "living brain". Overall, the neurosecretome is a concept we introduce here to embody the compendium of circulating particles of the brain for their function and disease pathogenesis. Finally, for the purpose of inclusion of all extracellular particles, we have used the term EVPs as defined by the International Society of Extracellular Vesicles (ISEV).

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Extracellular Vesicles in Mental Disorders: A State-of-art Review

Extracellular vesicles (EVs) are nanoscale particles with various physiological functions including mediating cellular communication in the central nervous system (CNS), which indicates a linkage between these particles and mental disorders such as schizophrenia, bipolar disorder, major depressive disorder, etc. To date, known characteristics of mental disorders are mainly neuroinflammation and dysfunctions of homeostasis in the CNS, and EVs are proven to be able to regulate these pathological processes. In addition, studies have found that some cargo of EVs, especially miRNAs, were significantly up- or down-regulated in patients with mental disorders. For many years, interest has been generated in exploring new diagnostic and therapeutic methods for mental disorders, but scale assessment and routine drug intervention are still the first-line applications so far. Therefore, underlying the downstream functions of EVs and their cargo may help uncover the pathogenetic mechanisms of mental disorders as well as provide novel biomarkers and therapeutic candidates. This review aims to address the connection between EVs and mental disorders, and discuss the current strategies that focus on EVs-related psychiatric detection and therapy.

Systematic metabolic characterization of mental disorders reveals age-related metabolic disturbances as potential risk factors for depression in older adults

Mental disorders are associated with dysregulated metabolism, but comprehensive investigations of their metabolic similarities and differences and their clinical relevance are few. Here, based on the plasma metabolome and lipidome of subcohort1, comprising 100 healthy participants, 55 cases with anxiety, 52 persons with depression, and 41 individuals with comorbidity, which are from WCHAT, a perspective cohort study of community-dwelling older adults aged over 50, multiple metabolites as potential risk factors of mental disorders were identified. Furthermore, participants with mental illnesses were classified into three subtypes (S1, S2, and S3) by unsupervised classification with lipidomic data. Among them, S1 showed higher triacylglycerol and lower sphingomyelin, while S2 displayed opposite features. The metabolic profile of S3 was like that of the normal group. Compared with S3, individuals in S1 and S2 had worse quality of life, and suffered more from sleep and cognitive disorders. Notably, an assessment of 6,467 individuals from the WCHAT showed an age-related increase in the incidence of depression. Seventeen depression-related metabolites were significantly correlated with age, which were validated in an independent subcohort2. Collectively, this work highlights the clinical relevance of metabolic perturbation in mental disorders, and age-related metabolic disturbances may be a bridge-linking aging and depressive.

The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.

Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches

Background

Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established.

Methods

Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD. Supervised classifiers (e.g., support vector machine (SVM), k-nearest neighbors (KNN)) cooperated with cross-validation were implemented to evaluate the predictive capability of selected features in a training set. Another test set that was not involved in developing the algorithms enabled the independent assessment of the model's generalization.

Findings

For the classification with the optimal fusion features, the SVM classifier achieved the highest accuracy of 75.6% ± 4.7% in the nested cross-validation, and the correct prediction rate of 78.0% with a sensitivity of 75.0% and a specificity of 81.4% in the test set. Moreover, the multiway ANOVA test on clinical and demographic factors confirmed that twenty out of 39 optimal features were significantly correlated with the MDD-distinctive consequence.

Interpretation

The abnormal prefrontal activity of MDD may be quantified as diminished relative intensity and inappropriate activation timing of hemodynamic response, resulting in an objectively measurable biomarker for assessing cognitive deficits and screening MDD at the early stage.

Funding

This study was funded by NUS iHeathtech Other Operating Expenses (R-722-000-004-731).

Registered clinical trials investigating treatment of long COVID: a scoping review and recommendations for research

Background

A considerable proportion of individuals report persistent, debilitating and disparate symptoms despite resolution of acute COVID-19 infection (i.e. long COVID). Numerous registered clinical trials investigating treatment of long COVID are expected to be completed in 2021–2022. The aim of this review is to provide a scope of the candidate treatments for long COVID. A synthesis of ongoing long COVID clinical trials can inform methodologic approaches for future studies and identify key research vistas.

Methods

Scoping searches were conducted on multiple national and international clinical trial registries. Interventional trials testing treatments for long COVID were selected. The search timeline was from database inception to 28 July 2021.

Results

This scoping review included 59 clinical trial registration records from 22 countries with a total projected enrolment of 6718. Considerable heterogeneity was exhibited amongst component records with respect to the characterization of long COVID (i.e. name, symptoms- including frequency, intensity, trajectory and duration- mode of ascertainment, and definition of acute phase). In addition, the majority of proposed interventions were non-pharmacological and either targeted multiple long COVID symptoms simultaneously, or focussed on treatment of respiratory/pulmonary sequelae. Multiple interventions targeted inflammation, as well as tissue oxygenation and cellular recovery, and several interventions were repurposed from analogous conditions.

Conclusions

The results of this scoping review investigating ongoing clinical trials testing candidate treatments for long COVID suggest that a greater degree of definitional stringency and homogeneity is needed insofar as the characterization of long COVID and inclusion/exclusion criteria.

Secondary Mania induced by TNF-α inhibitors: A systematic review

A growing number of studies support a bidirectional relationship between inflammation and bipolar disorders. Tumor necrosis factor-α (TNF-α) inhibitors have recently attracted interest as potential therapeutic compounds for treating depressive symptoms, but the risk for triggering mood switches in patients with or without bipolar disorders remains controversial. Thus, we conducted a systematic review to study the anti-TNF-α medication-induced manic or hypomanic episodes. PubMed, Scopus, Medline, and Embase databases were screened for a comprehensive literature search from inception until November 2020, using The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Out of the initial 75 references, the screening resulted in the inclusion of four case reports (each describing one patient) and a cohort study (in which 40 patients out of 7600-0.53% - experienced elated mood episodes after infliximab administration). Of these 44 patients, 97.7% experienced a manic episode and 2.3% hypomania. 93.2% of patients had no history of psychiatric disorder or psychotropic treatment. Only 6.8% had a history of psychiatric disorders with the affective spectrum (4.6% dysthymia and 2.3% bipolar disorder). The time of onset of manic or hypomanic symptoms varied across TNF-α inhibitors with an early onset for Infliximab and a later onset for Adalimumab and Etanercept. These findings suggest that medications targeting the TNF-α pathway may trigger a manic episode in patients with or without affective disorders. However, prospective studies are needed to evaluate the relative risk of such side effects and identify the population susceptible to secondary mania.