Diagnosis Of Mood Disorder Research Articles

Differences between rural and urban residence in the detection and treatment of perinatal mood and anxiety disorders

BackgroundPerinatal mood and anxiety disorders are common, serious complications of pregnancy. Disparities exist by race and income in the prevalence and treatment of these conditions, and overall treatment rates remain low. Outside of pregnancy, a small body of literature suggests that rural residency may contribute to higher rates of depression for those who identify as women. Among more diverse populations, however, evidence suggests urban residency may be associated with higher rates of depression among women of color. It is not known whether these trends hold true for mood and anxiety disorders during pregnancy and postpartum. ObjectivesWe examined differences in the detection and treatment of perinatal mood and anxiety disorders by rural and urban residence and assessed if the observed differences varied by maternal race/ethnicity. Study DesignWe conducted a cross sectional study using linked Medicaid claims and birth certificate records from Oregon and South Carolina from 2016 to 2020. We identified perinatal mood and anxiety disorder diagnoses during the perinatal period (pregnancy and within 60 days postpartum) using International Classification of Disease 9/10 codes and enumerated receipt of pharmaco- and psychotherapy treatment using Medicaid claims. We used logistic regression models controlling for relevant clinical and sociodemographic characteristics to estimate associations between rural residence and mood disorder detection and treatment. ResultsAmong the 185,809 births in our sample, 27% of births (n=50,820) were to people who lived in rural areas and 73% (n=134,989) to those in urban areas. Prevalence of any perinatal mood and anxiety disorders diagnosis was higher for urban residents (19.5%) than for rural residents (18.0%, p<0.001). Overall treatment rates were low among people with a perinatal mood and anxiety disorder (42% (n=14,789)). In our adjusted models, those living in urban areas had higher odds of a perinatal mood and anxiety disorder diagnosis (aOR=1.059, 95% CI 1.059-1.059, p<0.001). We found a significant interaction between maternal race and rurality (p<0.001). When we stratified by race, we found that among those who identified as Black, the odds of a perinatal mood and anxiety disorder diagnosis were increased for urban residents (OR=1.188, 95% CI 1.188-1.188), whereas among those who identified as white there was no such increased odds (OR=1.027, 95% CI 0.843-1.252). ConclusionsWe saw small but meaningful differences between rural and urban residence in perinatal mood and anxiety disorder diagnosis rates. We detected an interaction between race and rural versus urban maternal residence that impacted the observed differences. By elucidating the intersection between race and other sociodemographic factors, we hope more targeted and meaningful investments can be made in the communities most at need.

Psychological Stressor Of End Stage Chronic Kidney Disease Patients On Dialysis. A Battle For Life

Introduction Patients with chronic kidney disease (CKD) may choose to undergo dialysis. Factors that may have led patients to prescribe psychological interventions related to dialysis are poorly understood in the literature. The purpose of this study was to explore multi-level factors surrounding dialysis modalities such as Diagnosed Mood Disorders, Existential crises, Triggering events, Social support, and Distrust towards the process of dialysis.ObjectivesThe study aims to investigate the psychological battle of the client while going through the process of dialysis. The study reveals multiple mood disorders and existential crises leading to depression among chronic kidney disease patients. Therefore the study was conducted with the aim of providing a therapeutic guide line in future once the factors are investigated in detail.MethodsSemi-structured qualitative interviews were conducted in a dialysis clinic in Karachi where 19 participants participated in this qualitative study. The age ranges from 40-76. Initiating with informed consent followed by surveys assessing demographic and clinical information were administered to participants following their interviews.ResultsQualitative findings suggested that patients were dealing with Clinical Mood Disorders without being provided treatment. Moreover, the cohesive family support enabled them to continue with daily living activities; however, the patients with low support triggering adverse events in life lost their lives in follow-up sessions. Furthermore, nephrology care doesn’t seem sufficient as they are dealing with existential crises of hopelessness, regret, condemnation, and elevated death anxiety. In CKD the misinterpretation of dialysis by cognitively substituting it as End of life increased the clinical symptoms of Mood disorders. Thus the risk factors increase disturbing the quality of life.ConclusionsFindings point to broader factors affecting dialysis modalities with Mood disorders. The low social support and adverse triggering events precipitate the risk factors of dialysis treatment. Furthermore, distrust towards dialysis and existential crisis are recommended for therapeutic interventionsDisclosure of InterestNone Declared

Racial And Ethnic Inequities In Postpartum Depressive Symptoms, Diagnosis, And Care In 7 US Jurisdictions.

Understanding whether racial and ethnic inequities exist along the postpartum mental health care continuum is vital because inequitable identification of depression can lead to inequitable referral to and receipt of care. We aimed to expand on existing cross-sectional and single-state data documenting potential racial and ethnic disparities in postpartum depression care. Using early (from two to six months) and late (from twelve to fourteen months) postpartum survey data from seven US jurisdictions, we documented patterns of early postpartum depressive symptoms, perinatal mood and anxiety disorder (PMAD) diagnosis, and receipt of postpartum mental health care overall and by racial and ethnic identity. Of 4,542 people who delivered live births in 2020, 11.8percent reported early postpartum depressive symptoms. Among the sample with these symptoms, only 25.4percent reported receiving a PMAD diagnosis, and 52.8percent reported receiving some form of postpartum mental health care. There were no significant differences in diagnosis by race and ethnicity. Respondents identifying as Asian; Native Hawaiian or Pacific Islander; Southwest Asian, Middle Eastern, or North African; Hispanic; and non-Hispanic Black were significantly less likely than non-Hispanic White respondents to receive mental health care, demonstrating stark inequities in the management of postpartum depressive symptoms. Policies mandating and reimbursing universal postpartum depression screening, facilitating connection to care, reducing insurance coverage gaps, and enhancing clinician training in culturally responsive care may promote equitable postpartum mental health care.

Perinatal Mood And Anxiety Disorders Rose Among Privately Insured People, 2008-20.

Nationwide, perinatal mood and anxiety disorder (PMAD) diagnoses among privately insured people increased by 93.3percent from 2008 to 2020, growing faster in 2015-20 than in 2008-14. Most states and demographic subgroups experienced increases, suggesting worsening morbidity in maternal mental health nationwide. PMAD-associated suicidality and psychotherapy rates also increased nationwide from 2008 to 2020. Relative to 2008-14, psychotherapy rates continued to rise in 2015-20, whereas suicidality rates declined.

Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders

BackgroundGeographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas.MethodsWe constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters.ResultsWe show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p = 5.67E-05). We find nine clusters/hotspots of inpatients.ConclusionsOur results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.

Late-life mood disorder as the initial presentation of progressive supranuclear palsy: A case series.

Progressive supranuclear palsy (PSP) is a rapidly progressive neurodegenerative disorder characterized by Parkinsonism, supranuclear ophthalmoplegia, postural instability, and cognitive impairment. This case series describes three patients initially diagnosed with late-life mood disorders (depression and bipolar disorder) who were later diagnosed with PSP because of the development of typical neurological symptoms. The diagnostic challenge of PSP is highlighted in this case report, particularly in the early stages, when characteristic symptoms may not be present. The importance of considering PSP in the differential diagnosis of late-life mood disorders, especially in the absence of response to standard antidepressant therapy, is also emphasized. The heterogeneity of PSP is described, with various subtypes and atypical variants presenting with different clinical features. The psychiatric symptoms of PSP include apathy, disinhibition, depression, and anxiety, whereas hallucinations and delusions are less frequent. Tau positron emission tomography imaging is discussed as a potential biomarker for atypical PSP. Early diagnosis and intervention are crucial for improved outcomes in PSP, necessitating further research to enhance the diagnostic and treatment strategies for PSP and other neurodegenerative diseases.

Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models

Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that integrates patient-oriented brain structural magnetic resonance imaging (sMRI) scans with DNA whole-exome sequencing (WES) data. Multimodal data fusion aims to improve the detection of mood disorders by employing established deep-learning architectures for computer vision and machine-learning strategies. We analyzed brain imaging genetic data of 321 East Asian individuals, including 147 patients with MDD, 78 patients with BD, and 96 healthy controls. We developed and evaluated six fusion models by leveraging common computer vision models in image classification: Vision Transformer (ViT), Inception-V3, and ResNet50, in conjunction with advanced machine-learning techniques (XGBoost and LightGBM) known for high-dimensional data analysis. Model validation was performed using a 10-fold cross-validation. Our ViT ⊕ XGBoost fusion model with MRI scans, genomic Single Nucleotide polymorphism (SNP) data, and unweighted polygenic risk score (PRS) outperformed baseline models, achieving an incremental area under the curve (AUC) of 0.2162 (32.03% increase) and 0.0675 (+8.19%) and incremental accuracy of 0.1455 (+25.14%) and 0.0849 (+13.28%) compared to SNP-only and image-only baseline models, respectively. Our findings highlight the opportunity to refine mood disorder diagnostics by demonstrating the transformative potential of integrating diverse, yet complementary, data modalities and methodologies.

The impact of the affordable care act on perinatal mood and anxiety disorder diagnosis and treatment rates among Michigan Medicaid enrollees 2012–2018

BackgroundPerinatal Mood and Anxiety Disorders (PMADs) affect one in five birthing individuals and represent a leading cause of maternal mortality. While these disorders are associated with a variety of poor outcomes and generate significant societal burden, underdiagnosis and undertreatment remain significant barriers to improved outcomes. We aimed to quantify whether the Patient Protection Affordable Care Act (ACA) improved PMAD diagnosis and treatment rates among Michigan Medicaid enrollees.MethodsWe applied an interrupted time series framework to administrative Michigan Medicaid claims data to determine if PMAD monthly diagnosis or treatment rates changed after ACA implementation for births 2012 through 2018. We evaluated three treatment types, including psychotherapy, prescription medication, and either psychotherapy or prescription medication. Participants included the 170,690 Medicaid enrollees who had at least one live birth between 2012 and 2018, with continuous enrollment from 9 months before birth through 3 months postpartum.ResultsACA implementation was associated with a statistically significant 0.76% point increase in PMAD diagnosis rates (95% CI: 0.01 to 1.52). However, there were no statistically significant changes in treatment rates among enrollees with a PMAD diagnosis.ConclusionThe ACA may have improved PMAD detection and documentation in clinical settings. While a higher rate of PMAD cases were identified after ACA Implementation, Post-ACA cases were treated at similar rates as Pre-ACA cases.

Differentiation between depression and bipolar disorder in child and adolescents by voice features

ObjectiveMajor depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features.MethodsThis study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve.ResultsThe three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD.ConclusionBy exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.

The effect of age on emotion processing in individuals with mood disorders and in healthy individuals.

Emotion processing is an essential part of interpersonal relationships and social interactions. Changes in emotion processing have been found in both mood disorders and in aging, however, the interaction between such factors has yet to be examined in detail. This is of interest due to the contrary nature of the changes observed in existing research - a negativity bias in mood disorders versus a positivity effect with aging. It is also unclear how changes in non-emotional cognitive function with aging and in mood disorders, interact with these biases. In individuals with mood disorders and in healthy control participants, we examined emotional processing and its relationship to age in detail. Data sets from two studies examining facial expression recognition were pooled. In one study, 98 currently depressed individuals (either unipolar or bipolar) were compared with 61 healthy control participants, and in the other, 100 people with bipolar disorder (in various mood states) were tested on the same facial expression recognition task. Repeated measures analysis of variance was used to examine the effects of age and mood disorder diagnosis alongside interactions between individual emotion, age, and mood disorder diagnosis. A positivity effect was associated with increasing age which was evident irrespective of the presence of mood disorder or current mood episode. Results suggest a positivity effect occurring at a relatively early age but with no evidence of a bias toward negative emotions in mood disorder or specifically, in depressed episodes. The positivity effect in emotional processing in aging appears to occur even within people with mood disorders. Further research is needed to understand how this fits with negative biases seen in previous studies in mood disorders.

A step-by-step and data-driven guide to index gender in psychiatry.

Beyond sex as a binary or biological variable, within-sex variations related to sociocultural gender variables are of increasing interest in psychiatric research to better understand individual differences. Using a data-driven approach, we developed a composite gender score based on sociodemographic and psychosocial variables showing sex differences in a sample of psychiatric emergency patients upon admission (N = 1708; 39.4% birth-assigned females; mean age = 40 years; age standard deviation = 14). This gender score was extracted from a confirmatory factor analysis (CFI = 0.966; RMSEA = 0.044, SRMR = 0.030) and could predict a person's birth-assigned sex with 67% accuracy. This score allowed the further identification of differences on impulsivity measures that were absent when looking solely at birth-assigned sex. Female birth-assigned sex was also associated with higher rates of mood and personality disorder diagnoses, while higher feminine gender scores were related to higher proportions of anxiety and mood disorder diagnoses. By contrast, male birth-assigned sex and higher masculine gender scores were associated with higher proportions of psychotic and substance use disorder diagnoses. Patients with undifferentiated gender scores (i.e., scoring between masculine and feminine threshold defined by terciles) were more represented in the psychotic disorder group. Considering both sex and gender in psychiatric research is essential and can be achieved even when using secondary data to index gender comprised of demographic and psychosocial variables.

Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models.

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.

Risk for diagnosis or treatment of mood or anxiety disorders in adults after SARS-CoV-2 infection, 2020–2022

COVID-19 is associated with increased risks for mood or anxiety disorders, but it remains uncertain how the association evolves over time or which patient groups are most affected. We conducted a retrospective cohort study using a nationwide database of electronic health records to determine the risk of depressive or anxiety disorder diagnoses after SARS-CoV-2 infection by 3-month blocks from January 2020 to April 2022. The study population comprised 822,756 patients (51.8% female; mean age 42.8 years) with COVID-19 and 2,034,353 patients with other respiratory tract infections (RTIs) (53.5% female, mean age 30.6 years). First time diagnoses of depressive or anxiety disorders 14 days to 3 months after infection, as well as new or new plus recurrent prescriptions of antidepressants or anxiolytics, were compared between propensity score matched cohorts using Kaplan-Meier survival analysis, including hazard ratio (HR) and 95% confidence interval (CI). Risk of a new diagnosis or prescription was also stratified by age, sex, and race to better characterize which groups were most affected. In the first three months of the pandemic, patients infected with SARS-CoV-2 had significantly increased risk of depression or anxiety disorder diagnosis (HR 1.65 [95% CI, 1.30-2.08]). October 2021 to January 2022 (HR, 1.12 [95% CI, 1.06–1.18]) and January to April 2022 (HR, 1.08 [95% CI, 1.01–1.14]). Similar temporal patterns were observed for antidepressant and anxiolytic prescriptions, when the control group was patients with bone fracture, when anxiety and depressive disorders were considered separately, when recurrent depressive disorder was tested, and when the test period was extended to 6 months. COVID-19 patients ≥65 years old demonstrated greatest absolute risk at the start of the pandemic (6.8%), which remained consistently higher throughout the study period (HR, 1.20 [95% CI, 1.13–1.27]), and overall, women with COVID-19 had greater risk than men (HR 1.35 [95% CI 1.30–1.40]).

Inflammation as an aetiological trigger for depressive symptoms in a prospective cohort of patients with inflammatory bowel disease

ObjectiveInflammatory bowel disease (IBD) is often comorbid with mood disorders and depressive symptoms. The aetiology of depressive symptoms in IBD, however, remains largely unknown. Consistent with the inflammatory hypothesis of depression, the aim of this study was to explore the prospective associations between inflammatory biomarkers and depressive symptoms in a cohort of IBD patients with and without a previous clinical diagnosis of mood disorder. MethodIBD clinical activity was determined using the Harvey-Bradshaw Index for CD and the Partial Mayo score for UC; serum C-reactive protein (CRP) and faecal calprotectin (fCAL) were used as biomarkers of systemic and intestinal inflammation, respectively. Participants were administered the Hospital Anxiety and Depression Scale-depression (HADS-D) at baseline and 1-year follow-up. ResultsEighty-four participants (50 ± 16 years; 75% UC and 25% CD) were included in the main analyses. Longitudinal moderated regression models showed that baseline CRP significantly predicted follow-up HADS-D scores among individuals with a previous mood disorder diagnosis (β = 0.843, p < .001), but not among individuals without (β = −0.013, p = .896), after controlling for baseline HADS-D scores, body mass index, IBD phenotype, sex, and perceived stress. Likely due to lower power, results on FCAL (n = 31) were not statistically significant. ConclusionThis study suggests that IBD patients with previous diagnosis of mood disorder may be at higher risk of inflammation-related depressive symptoms.

Cannabis use in the UK: a quantitative comparison of individual differences in medical and recreational cannabis users.

There is a paucity of research, especially in the UK, that investigates individual differences in both medical and recreational cannabis users. A cross-sectional survey study design was used to assess recreational cannabis users and medical cannabis users currently living in the UK. Recreational cannabis users were invited to take part via social media. Medical cannabis users were recruited from Sapphire Medical Clinics, London, UK, which provides treatment with prescribed cannabis-based medicinal products. Demographic data and cannabis use frequency, as well as post-traumatic stress disorder symptoms (PCL-5), depression symptoms (Centre for Epidemiological Studies Depression Scale), trait and state anxiety (State-Trait Anxiety Inventory), and cannabis use motives [Comprehensive Marijuana Motives Questionnaire (CMMQ)] were collected. The Chi-square and independent-sample t-tests were used for the comparison of categorical variables and normally distributed continuous variables. Data were analyzed using analyses of variance (ANOVAs) and t-tests. Statistical significance was considered where the value of p was <0.05. The survey was completed by 161 participants. Medical cannabis users were older, consumed cannabis more often, had a higher "Sleep" motive on the CMMQ, and had a higher prevalence in self-reporting current diagnoses of neurological problems, mood disorders, and anxiety disorders (p < 0.05). Recreational cannabis users had higher scores on several motives for use (e.g., "Enjoyment," "Coping," "Experimentation," "Boredom," and "Celebration") and higher state anxiety scores (p < 0.05). The most common motives for cannabis use in both groups were "Enjoyment," "Low Risk," and "Sleep." There were no differences between groups in gender, "Low-Risk" motive, post-traumatic stress disorder symptoms, depression scores, trait anxiety scores, self-reported prevalence of substance use-related disorders, and past consumption of alcohol, tobacco, or caffeine (p > 0.05). The current study not only demonstrates a difference in age and motivations for cannabis consumption between recreational and medical cannabis users but also shows areas of potential overlap, including mental health outcomes, past substance use, and gender. These UK-specific findings indicate that recreational cannabis users experience higher state anxiety, highlighting the need for further evaluation of potential anxiogenic/anxiolytic properties of cannabis. These findings demonstrate the importance of researching individual differences in cannabis users and hold significant implications for future research, clinical practice, and legislation.

Bariatric surgery efficacy in patients with mood disorders

Mood disorders are comorbid in patients with obesity and found in approximately 22.0% to 54.8% of patients who are eligible for bariatric surgery. Given the unclear effect of mood disorders on bariatric surgery outcomes, we aimed this study to assess the impact of mood disorders index bariatric surgery weight loss outcomes. A retrospective study institutional database of index bariatric surgery patients at University Hospitals Cleveland Medical Center between 2016 and 2018. The primary outcome of body mass index was followed over a 4-year period. The secondary outcomes measured were mortality and suicide rates. Mood disorders defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, included depressive and bipolar disorders obtained from electronic medical records International Classification of Diseases, Tenth Revision, coding. A total of 790 patients underwent bariatric surgery between 2016 and 2018. Of these, 15 patients were excluded due to death in the postoperative period or insufficient weight loss data, and a total of 775 patients (620 women and 155 men) were included. Two hundred and ninety-five (38.1%) had an electronic medical record mood disorder diagnosis before surgery, while 480 (61.9%) did not. Both groups had a significant decrease in postoperative body mass index; however, there was no significant difference in the body mass index change between the mood disorder group (mean= 37.63, standard deviation= 9.88) and the control group (mean= 38.72, standard deviation= 9.54; t[294]= 1.40; P= .1634). Patients with mood disorders are as successful with weight loss after index bariatric surgery as those without mood disorders. There was no significant difference in mortality rates between the mood disorder group and the control group. Hence, mood disorders should not be prohibitive for weight loss surgery.

The prevalence and impact of psychiatric comorbidities on hospitalized inflammatory bowel disease patients in the United States: insights from the National Inpatient Sample from 2009-2018.

Patients with inflammatory bowel disease (IBD) are at increased risk of anxiety and mood disorders. This study examines the temporal trends and clinical impact of anxiety and mood disorder diagnoses in hospitalized IBD patients in the United States during a 10-year period. Using the National Inpatient Sample from 2009-2018, all IBD-related discharges in adults were analyzed. Primary outcomes were the prevalence and temporal trends of mood disorder and anxiety diagnoses for IBD-related admissions. The impact of the psychiatric comorbidities on clinical outcomes was also evaluated. A total of 1,718,736 IBD-related discharged were identified. A diagnosis of anxiety or a mood disorder was found to have a prevalence of 16.44% and 18.97%, respectively, amongst IBD-related admissions. The prevalence of anxiety disorders amongst hospitalized IBD patients increased significantly (from 12.13% to 20.26%), whereas the prevalence of mood disorders did not (17.46% and 18.9%). IBD admissions with psychiatric comorbidities had lower rates of IBD-related complications or mortality during hospitalization compared to IBD admissions without comorbid psychiatric diagnoses. This population, however, was more likely to experience certain comorbidities such as Clostridioides difficile, pneumonia, and venous thromboembolism, as well as a longer hospitalization. The prevalence of comorbid anxiety among hospitalized IBD patients in the United States matches or exceeds the prevalence of anxiety in the general hospitalized population. Given its association with more in-hospital complications and a longer hospital stay, it is important to further understand how psychological screening and mental health services can improve the management of hospitalized IBD patients.

Prevalence of childhood trauma in patients with psychiatric disorders and its association with perceived social support and suicide attempts: A cross-sectional observational study in a tertiary hospital in South India

Background: Adverse childhood experiences include exposure of children to physical abuse, physical neglect, emotional neglect, emotional abuse, and sexual abuse. Children exposed to severe maltreatment and trauma during their early childhood are at a higher risk of early onset of psychiatric disorders. Aim: To find the prevalence of childhood adverse experiences in psychiatric patients and its association with perceived social support and suicidal attempts. Materials and Methods: A cross-sectional observational study was conducted in the psychiatry outpatient department at a tertiary care hospital in Kerala. Patients with a diagnosis of substance use disorders, psychotic disorders, mood disorders, and neurotic disorders according to ICD-10 (F10-F45) and in remission were included in the study. Institutional Ethical Committee approval and informed consent from the participants were obtained. Socio-demographic and clinical details were obtained. Childhood Trauma Questionnaire-Short Form and Multidimensional scale of Perceived Social Support were administered. Descriptive statistics and Chi-square test were employed for data analysis. Results: Eighty-five per cent of the subjects had a history of at least one type of childhood trauma. Emotional abuse was the most commonly reported type of trauma. Among patients with childhood trauma, 47.2% reported high levels of perceived social support, whereas 18.5% individuals reported a history of at least one suicide attempt. Conclusions: A significant proportion of patients with psychiatric disorders in an Indian setting have experienced childhood trauma. Childhood trauma in psychiatric disorders is associated with a higher risk of attempting suicide.

Impulsivity, decision-making, and risk behavior in bipolar disorder and major depression from bipolar multiplex families.

Bipolar disorder (BD) and major depressive disorder (MDD) are characterized by specific alterations of mood. In both disorders, alterations in cognitive domains such as impulsivity, decision-making, and risk-taking have been reported. Identification of similarities and differences of these domains in BD and MDD could give further insight into their etiology. The present study assessed impulsivity, decision-making, and risk-taking behavior in BD and MDD patients from bipolar multiplex families. Eighty-two participants (BD type I, n=25; MDD, n=26; healthy relatives (HR), n=17; and healthy controls (HC), n=14) underwent diagnostic interviews and selected tests of a cognitive battery assessing neurocognitive performance across multiple subdomains including impulsivity (response inhibition and delay aversion), decision-making, and risk behavior. Generalized estimating equations (GEEs) were used to analyze whether the groups differed in the respective cognitive domains. Participants with BD and MDD showed higher impulsivity levels compared to HC; this difference was more pronounced in BD participants. BD participants also showed lower inhibitory control than MDD participants. Overall, suboptimal decision-making was associated with both mood disorders (BD and MDD). In risk-taking behavior, no significant impairment was found in any group. As sample size was limited, it is possible that differences between BD and MDD may have escaped detection due to lack of statistical power. Our findings show that alterations of cognitive domains-while present in both disorders-are differently associated with BD and MDD. This underscores the importance of assessing such domains in addition to mere diagnosis of mood disorders.

Whole blood mitochondrial copy number in clinical populations with mood disorders: A meta-analysis: Blood mitochondrial copy number and mood disorders

Major depressive disorder (MDD) and bipolar disorder (BD), are globally prevalent, contributing to significant disease burden and adverse health outcomes. These mood disorders are associated with changes in many aspects of brain reward pathways, yet cellular and molecular changes in the brain are not readily available in clinical populations. Therefore, the use of biomarkers as proxies for changes in the brain are necessary. The proliferation of mitochondria in blood has emerged as a potentially useful biomarker, yet a clear consensus on how these mood disorders impact mitochondrial DNA copy number (mtDNAcn) has not been reached. To determine the current available consensus on the relationship of mood disorder diagnosis and blood mtDNcn, we performed a meta-analysis of available literature measuring this biomarker. Following PRISMA guidelines for a systematic search, 22 papers met inclusion criteria for meta-analysis (10 MDD, 10 BD, 2 both MDD and BD). We extracted demographic, disorder, and methodological information with mtDNAcn. Using the metafor package for R, calculated effect sizes were used in random effects or meta regression models for MDD and BD. Overall, our data suggest blood mtDNAcn may be a useful biomarker for mood disorders, with MDD and BD Type II associated with higher mtDNAcn, and BD Type I associated with lower mtDNAcn. Initially, we observed a trending increase in mtDNAcn in patients with MDD, which reached significance when one study with outlying demographic characteristics was excluded. Subgroup and meta-regression analysis indicated the relationship between mtDNAcn and diagnosis in patients with BD is dependent on BD type, while no relationship is detectable when BD types are mixed. Further study of blood mtDNAcn could predict downstream health outcomes or treatment responsivity in individuals with mood disorders.