Diagnosis Of Depressive Disorder Research Articles

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging. To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022. In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment. The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity. The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response. In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Multi-view graph network learning framework for identification of major depressive disorder

Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) exhibits non-Euclidean topological structures, which have pathological foundations and serve as ideal objective data for intelligent diagnosis of major depressive disorder (MDD) patients. Additionally, the fully connected FC demonstrates uniform spatial structures. To learn and integrate information from these two structural forms for a more comprehensive identification of MDD patients, we propose a novel hierarchical learning structure called Multi-View Graph Neural Network (MV-GNN). In MV-GNN, the collaborative FC of subjects is filtered and reconstructed from topological view to obtain the reconstructed FC, incorporating various threshold values to calculate the topological attributes of brain regions. ROC analysis is performed on the average scores of these attributes for MDD and healthy control (HC) groups to determine an efficient threshold. Group differences analysis is conducted on the efficient topological attributes of brain regions, followed by their selection. These efficient attributes, along with the reconstructed FC, are combined to construct a graph view using self-attention graph pooling and graph convolutional neural networks, enabling efficient embedding. To extract efficient FC pattern difference information from spatial view, a dual leave-one-out cross-feature selection method is proposed. It selects and extracts relevant information from uniformly sized FC structures’ high-dimensional spatial features, constructing a relationship view between brain regions. This approach incorporates both the whole graph topological view and spatial relationship view in a multi-layered structure, fusing them using gating mechanisms. By incorporating multiple views, it enhances the inference of whether subjects suffer from MDD and reveals differential information between MDD and HC groups across different perspectives. The proposed model structure is evaluated through leave-one-site cross-validation and achieves an average accuracy of 65.61% in identifying MDD patients at a single-center site, surpassing state-of-the-art methods in MDD recognition. The model provides valuable discriminatory information for objective diagnosis of MDD and serves as a reference for pathological foundations.

Deiodinase Types 1 and 3 and Proinflammatory Cytokine Values May Discriminate Depressive Disorder Patients from Healthy Controls.

Depressive disorders are multifactorial diseases in that a variety of factors may play a role in their etiology, including inflammation and abnormalities in the thyroid hormone (TH) metabolism and levels. The purpose of this study was to evaluate iodothyronine deiodinases (DIOs) and DIO-interacting cytokines as possible biomarkers in the diagnosis of depressive disorders. This study enrolled 73 patients diagnosed with recurrent depressive disorder (rDD) and 54 controls. The expressions of DIO1, DIO2, DIO3, IL1B, IL6, TNFA, and IFNG genes, encoding three types of DIOs (1, 2, and 3), interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ, were assessed using the polymerase chain reaction in blood cells and an enzymatic immunoassay method in serum. The levels of examined molecules between patients and controls were compared, and correlations and diagnostic values were evaluated. Lower levels of DIO2 and higher levels of IL1B, IL6, and TNFA were found in patients compared to controls. The protein concentrations of DIO1 and DIO2 were lower, while that of DIO3 was higher, in patients than in controls. Serum IL-1β, IL-6, and TNF-α were also higher in patients than in controls. The area under the curve (AUC) of the IL-1β, IL-6, DIO1, and DIO3 proteins was >0.7 for discriminating patients with rDD from controls. The expressions of genes for DIO2, IL-1β, IL-6, and TNF-α may have a role in the estimation of processes present in depressive disorders. We can cautiously claim that DIO1 and DIO3 and pivotal cytokines, mainly IL-1β and IL-6, may play a role in depression diagnosis, and further studies are suggested to explain the exact role of these molecules in larger samples with more precise methods.

Exploring the prevalence of antidepressant medication discontinuation among pregnant veterans.

US female veterans have higher rates of mental health (MH) disorders compared to US civilian females and, consequently, are at risk for poor MH outcomes during pregnancy. This study evaluated the MH burden and identified the prevalence of antidepressant prescription and discontinuation among pregnant veterans (PGVets). The electronic health records (EHR) of PGVets using the US Veterans Administration's (VA) maternity care benefits over a two-year period were retrospectively reviewed. Inclusion criteria for this study were a current MH diagnosis of depression, anxiety, or posttraumatic stress disorder (PTSD) at the onset of pregnancy (n=351). Outcomes examined included antidepressant use prior to pregnancy, the use and discontinuation of antidepressants during pregnancy, and risk factors for discontinuation. PGVets had a high MH burden, as indicated by multiple comorbid diagnoses of unipolar depression, anxiety, and PTSD in 67% of the sample. At the onset of pregnancy, 163 (46%) were treated with an antidepressant. Only 56 (34%) continued using antidepressants through the pregnancy. Self-discontinuation (34%) and VA provider discontinuation (31%) of antidepressants were found. Among PGVets with documented past suicidal behaviors, 90% discontinued their active antidepressants. PGVets with indicators for more severe MH diagnoses were most likely to discontinue. The MH burden of PGVets and high rates of antidepressant discontinuation have implications for engaging this population in a higher level of perinatal monitoring and intervention. The findings suggest that VA providers and veterans would benefit from risks and benefits education regarding antidepressant use during pregnancy as well as the provision of alternative therapies.

Association of interleukin-6 with suicidal ideation in veterans: a longitudinal perspective.

Studies showing associations between inflammation in suicide are typically cross-sectional. Present study investigated how cytokine levels track with suicidal ideation and severity longitudinally. Veterans with a diagnosis of major depressive disorder (MDD) with or without suicide attempt history (MDD/SA n = 38, MDD/NS n = 41) and non-psychiatric non-attempter controls (HC n = 33) were recruited, MDD/SA and HC groups were followed longitudinally at 3 months and 6 months. Blood plasma was collected and processed using Luminex Immunology Multiplex technology. Significant differences in depression severity (BDI) and suicidal ideation severity (SSI) were observed across all groups at study entry, wherein MDD/SA group had the highest scores followed by MDD/NS and HC, respectively. Cytokines IL-1β, IL-4, TNF-α, IFN-γ, and IL-6 were examined at study entry and longitudinally, with IL6 levels differing significantly across the groups (p = 0.0123) at study entry. Significant differences in changes in cytokine levels between depressed attempters and the control group were detected for IL-6 (interaction F1,91.77 = 5.58, p = 0.0203) and TNF-α (F1,101.73 = 4.69, p = 0.0327). However, only depressed attempters showed a significant change, in IL-6 and TNF-α levels, decreasing over time [IL-6: b = -0.04, 95% CI = (-0.08, -0.01), p = 0.0245 and TNF-α: b = -0.02, 95% CI = (-0.04, -0.01), p = 0.0196]. Although IL-6 levels were not predictive of suicidal ideation presence [OR = 1.34, 95% CI = (0.77, 2.33), p = 0.3067], IL-6 levels were significantly associated with suicidal ideation severity (b = 0.19, p = 0.0422). IL-6 was not associated with presence of suicidal ideation. IL-6 however, was associated with severity of ideation, suggesting that IL-6 may be useful in clinical practice, as an objective marker of heightened suicide risk.

Mental illness and cardiovascular health: observational and polygenic score analyses in a population-based cohort study.

Individuals with serious mental illness have a markedly shorter life expectancy. A major contributor to premature death is cardiovascular disease (CVD). We investigated associations of (genetic liability for) depressive disorder, bipolar disorder and schizophrenia with a range of CVD traits and examined to what degree these were driven by important confounders. We included participants of the Dutch Lifelines cohort (N = 147 337) with information on self-reported lifetime diagnosis of depressive disorder, bipolar disorder, or schizophrenia and CVD traits. Employing linear mixed-effects models, we examined associations between mental illness diagnoses and CVD, correcting for psychotropic medication, demographic and lifestyle factors. In a subsample (N = 73 965), we repeated these analyses using polygenic scores (PGSs) for the three mental illnesses. There was strong evidence that depressive disorder diagnosis is associated with increased arrhythmia and atherosclerosis risk and lower heart rate variability, even after confounder adjustment. Positive associations were also found for the depression PGSs with arrhythmia and atherosclerosis. Bipolar disorder was associated with a higher risk of nearly all CVD traits, though most diminished after adjustment. The bipolar disorder PGSs did not show any associations. While the schizophrenia PGSs was associated with increased arrhythmia risk and lower heart rate variability, schizophrenia diagnosis was not. All mental illness diagnoses were associated with lower blood pressure and a lower risk of hypertension. Our study shows widespread associations of (genetic liability to) mental illness (primarily depressive disorder) with CVD, even after confounder adjustment. Future research should focus on clarifying potential causal pathways between mental illness and CVD.

Characterizing positive and negative valence systems function in adolescent depression: An RDoC-informed approach integrating multiple neural measures

Depression is a prevalent, debilitating, and costly disorder that often manifests in adolescence. There is an urgent need to understand core pathophysiological processes for depression to inform more targeted intervention efforts. The Research Domain Criteria (RDoC) Positive Valence Systems (PVS) and Negative Valence Systems (NVS) have both been implicated in depression symptomatology and vulnerability; however, the nature of NVS alterations is unclear across studies, and associations between single neural measures and symptoms are often small in magnitude and inconsistent. The present study advances characterization of depression in adolescence via an innovative data-driven approach to identifying subgroups of PVS and NVS function by integrating multiple neural measures (assessed by electroencephalogram [EEG]) relevant to depression in adolescents oversampled for clinical depression and depression risk based on maternal history (N = 129; 14–17 years old). Results of the k-means cluster analysis supported a two-cluster solution wherein one cluster was characterized by relatively attenuated reward and emotion responsiveness across valences and the other by relatively intact responsiveness. Youth in the attenuated responsiveness cluster reported significantly greater depressive symptoms and were more likely to have major depressive disorder diagnoses than youth in the intact responsiveness cluster. In contrast, associations of individual neural measures with depressive symptoms were non-significant. The present study highlights the importance of innovative neuroscience approaches to characterize emotional processing in depression across domains, which is imperative to advancing the clinical utility of RDoC-informed research.

Psychosocial Consequences of Hand Eczema-A Prospective Cross-Sectional Study.

Hand eczema (HE) is a chronic inflammatory disease with a high prevalence, negatively influencing patients' quality of life (QoL). It may also affect patients' psychological status. The aim of this study was to assess and characterize the psychological burden of HE, its influence on patients' QoL, and the presence and severity of anxiety and depressive disorders in HE patients. The study group consisted of 100 adult HE individuals. To assess the severity of the disease, two instruments were used: the Investigator Global Assessment for Chronic Hand Eczema (IGA-CHE) scale and the Hand Eczema Severity Index (HECSI). Assessment of patients' quality of life (QoL) was obtained with the use of the DLQI tool. Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) questionnaires were employed to assess depression and anxiety, respectively, as well as a modified version of the Hospital Anxiety and Depression Scale (HADS-M). The mean DLQI value for the whole group reached 11.62 ± 6.35 points (13.27 ± 6.67 points in females and 9.15 ± 4.95 points in males; p = 0.023). A decrease in QoL correlated positively with the severity of the disease and the severity of itch and pain. In 17 patients (17%), a possible diagnosis of depressive disorder was found. Patients scoring higher results on the PHQ-9 and HADS-M depression (D) questionnaires reported greater intensity of the itch (r = 0.363, p < 0.001, and r = 0.237, p = 0.017, respectively) and the pain (r = 0.445, p < 0.001, and r = 0.287, p = 0.004, respectively). The anxiety disorder might possibly be diagnosed in 25% of patients (n = 25). This study revealed a positive correlation between the severity of the anxiety symptoms, measured with the use of both GAD-7 and HADS-M anxiety (A) tools, and the intensity of the pain (r = 0.248, p = 0.013, and r = 0.342, p = 0.001, respectively). The severity of depressive and anxiety symptoms correlated positively with the severity of the disease. The psychosocial burden of HE is an undeniable phenomenon. The disorder influences patients' QoL and may cause mental disturbances such as depression and anxiety disorders.

PSYCHOPATHOLOGICAL SYMPTOMS IN THE MENTAL HEALTH OF PRISONERS (AZERBAIJAN CASE)

The main object of this study is to assess the prevalence of mental illness (diagnosis) and symptoms of psychopathology, evaluate the correlation between variables. (Independent variables: age, imprisonment years, marital status. Dependent variables: psychopathological symptoms). The survey study was conducted among inmates at correctional facilities in Azerbaijan. Participants were conducted from January 2023-March 2023. A total of participants was 45 male prisoners. Prisoners reported lifetime or current diagnosis of depression, anxiety, guilt feelings, tension, mental retardation and other disorder, as well as current mental health symptoms. Brief Psychiatric Rating Scale-BPRS was used to measure the symptoms, and SPSS program for analyzing the statistics. Of the 50 inmates invited to participate, 45 (90%) of them were consistent with research (mean [SD] age, 41,1 [5.3] years; 45 [100%] male; imprisonment years 9,51 [2.9]; 26 [57,8%] of them have previous imprisonment experience, 19 [42,2%] of them don’t have any punishment before). A statistically significant positive correlation was examined between the increasing age factor of the prisoner and the symptoms of somatic anxiety, thought disorders, tension, mannerism and posturing, slowness of movements, disruption of social cooperation, unusual content of thoughts, blunting of affect (r= 0.633* *,*p&lt;0.05, **p&lt;0.01); a positive significance statistical correlation was examined between the increasing sentence duration and the mental health problems (r= 0.459**,*p&lt;0.05, **p&lt;0.01); Mental health problems of persons who had previous prison experience were higher than persons convicted for the first time, respectively: 31.7±10.1 and 28.53±9.4 (F=2.64; p=0.111; t=-1,08; df=43); Lack of family support was followed by negative consequences in their mental health. Thus, the total index of mental disorders in unmarried persons is 25.4±9.8; 27.06±6.4 in married people; 40±5.6 was obtained in divorced and isolated individuals. Moreover, educational factor of the prisoners shows its effect in maintaining their emotional stability and knowledge about mental health problems.

Anamnesis y exploración psicopatológica en el paciente con síntomas ansiosos y/o depresivos

Anxiety and depression disorders are the most prevalent mental health problems at present and those that most contribute to morbidity and mortality due to the suffering they entail. Therefore, they are often present in non-psychiatric medical consultations. Symptoms of anxiety or depression may indicate the presence of primary anxiety/depression disorder, but they may also appear in the context of adaptive changes in response to life stressors or as accessory symptoms of other psychiatric/medical illness/substance use disorder. An essential tool for the differential diagnosis of anxiety and/or depression disorders is a psychiatric case history of the patient accompanied by a psychopathological examination that encompasses the different manifestations of these types of symptoms as well as conducting additional tests to rule out organic disease, if necessary. The use of screening scales in order to detect patients who may have a mental illness can also be evaluated.

Daily Affective Dynamics in Major Depressive Disorder: The Role of Daily Stressors and Positive Events.

This study examined daily affective dynamic indices among individuals with a major depressive disorder (MDD) diagnosis in the past one year at the time of the interview, focusing on affective variability and change in affect in response to daily events (affective reactivity). Data were from the main survey and daily diary project of the Midlife in the United States (MIDUS) study. Participants (N = 1,970; nMDD = 202; nnon-MDD = 1,768) completed structured clinical interviews on mental health and telephone interviews about their daily experiences spanning eight consecutive days. Multilevel models revealed that the MDD group experienced greater positive (PA) and negative affect (NA) variability than the non-MDD group. On days that at least one stressful event was reported, the MDD group experienced a greater decrease in PA and a greater increase in NA. On days that at least one positive event was reported, the MDD group experienced a greater increase in PA and a greater decrease in NA. Changes in affect to daily events, particularly the mood brightening effect, may be indicators of depression and potential targets for intervention. Limitations of the study include a community sample, reliance on self-reported measures of daily stressors and positive events, inclusion of remitted and current MDD participants, and the DSM-III-R based criteria for MDD diagnosis.

Estimated Prevalence of Depressive Disorders in Children From 2004 to 2019

Depression during childhood (ie, age <13 years) poses a major health burden. Recent changes in environmental and lifestyle factors may increase children's risk of mental health problems. This has been reported for anxiety disorders, but it is unclear whether this occurs for depressive disorders. To provide prevalence estimates for the depressive disorders (ie, major depressive disorder [MDD], dysthymia, disruptive mood dysregulation disorder [DMDD], and overall) in children, and whether they have changed over time. The MEDLINE, PsycINFO, Embase, Scopus, and Web of Science databases were searched using terms related to depressive disorders, children, and prevalence. This was supplemented by a systematic gray literature search. Studies were required to provide population prevalence estimates of depressive disorder diagnoses (according to an established taxonomy and standardized interviews) for children younger than 13 years, information about participants' year of birth, and be published in English. Data extraction was compliant with the Meta-Analysis of Observational Studies in Epidemiology guidelines. A total of 12 985 nonduplicate records were retrieved, and 154 full texts were reviewed. Data were analyzed from 2004 (the upper limit of a previous review) to May 27, 2023. Multiple proportional random-effects meta-analytic and mixed-effects meta-regression models were fit. Pooled prevalence rates of depressive disorders, prevalence rate differences between males vs females and high-income countries (HICs) vs low-and middle-income countries (LMICs), and moderating effects of time or birth cohort. A total of 41 studies were found to meet the inclusion criteria. Pooled prevalence estimates were obtained for 1.07% (95% CI, 0.62%-1.63%) for depressive disorders overall, 0.71% (95% CI, 0.48%-0.99%) for MDD, 0.30% (95% CI, 0.08%-0.62%) for dysthymia, and 1.60% (95% CI, 0.28%-3.90%) for DMDD. The meta-regressions found no significant evidence of an association with birth cohort, and prevalence rates did not differ significantly between males and females or between HICs and LMICs. There was a low risk of bias overall, except for DMDD, which was hindered by a lack of studies. In this systematic review and meta-analysis, depression in children was uncommon and did not increase substantially between 2004 and 2019. Future epidemiologic studies using standardized interviews will be necessary to determine whether this trend will continue into and beyond the COVID-19 pandemic.

Psychotherapy for adult depression in low- and middle-income countries: an updated systematic review and meta-analysis.

Previous meta-analyses on psychotherapy for adult depression have found a larger treatment effect in non-Western trials compared to Western trials (i.e. North America, Europe, and Australia). However, factors contributing to this difference remain unclear. This study investigated different study characteristics between Western and non-Western trials and examined their association with effect size estimates. We systematically searched PubMed, PsycINFO, Embase, and Cochrane Library (01-09-2022). We included randomized-controlled trials (RCTs) that compared psychotherapy with a control condition. The validity of included RCTs was assessed by the Cochrane risk of bias assessment tool (RoB 1). Effect sizes were pooled using the random-effects model. Subgroup analyses and meta-regressions were also conducted. We identified 405 eligible trials, among which 105 trials (117 comparisons, 16 304 participants) were from non-Western countries. We confirmed that non-Western trials had a larger treatment effect (g = 1.10, 95% CI 0.90-1.31) than Western trials (g = 0.57, 95% CI 0.52-0.62). Trials from non-Western countries also had more usual care controls, higher risk of bias, larger sample sizes, lower mean ages, younger adults, more group-based interventions, and other recruitment methods (e.g. systematic screening; p < 0.05). The larger effect sizes found in non-Western trials were related to the presence of wait-list controls, high risk of bias, cognitive-behavioral therapy, and clinician-diagnosed depression (p < 0.05). The larger treatment effects observed in non-Western trials may result from the high heterogeneous study design and relatively low validity. Further research on long-term effects, adolescent groups, and individual-level data are still needed.

Shoulder arthroplasty outcomes in patients with major depressive disorder or generalized anxiety disorder

BackgroundAnxiety and depression are among the most common mental health disorders in the United States and have been linked to increased health-care utilization and poorer clinical outcomes. However, data on the relationship between anxiety and depression and outcomes after shoulder arthroplasty (SA) are both limited and conflicting. MethodsThis retrospective cohort study examined the influence of a preoperative diagnosis of anxiety and/or major depressive disorder (MDD) on outcomes following shoulder arthroplasty, such as range of motion, pain, perioperative and postoperative complications, and readmission rates. A retrospective chart review of our health system’s electronic medical record was performed for dates of service January 2014–December 2019. Patients who underwent primary total shoulder arthroplasty or reverse total shoulder arthroplasty were considered for inclusion. These patients were then divided into two groups based on the presence or absence of an anxiety disorder and/or MDD in their past medical history. Results595 patients with a minimum follow-up of one year were considered for analysis. Of these, 205 patients (34.5%) had diagnoses of an anxiety and/or MDD and were assigned to the psychiatric comorbidity group, while 390 (65.5%) were assigned to the control group. The psychiatric comorbidity group was significantly younger (68.3 ± 9.3 vs. 70.5 ± 10.0, P = .008) and more heavily female (68.7% vs. 48.7%, P < .001). The length of stay was significantly longer in the psychiatric comorbidity group than in the control group (1.9 ± 2.4 vs. 1.5 ± 0.9 days, P = .023). Postoperative range of motion, visual analog scale pain scores (1.8 ± 2.7 vs. 1.3 ± 2.3, P = .059), complication rates (7.8% vs. 6.9%, P = .681), and revision rates (4.9% vs. 3.6%, P = .448) were not significantly different between the psychiatric comorbidity and control groups, respectively. All-cause emergency department visitation and hospital readmission rates were also similar between the two groups at 30, 60, and 90 days postoperatively. ConclusionOur results suggest that a preoperative diagnosis of MDD or anxiety does not affect pain, postoperative outcomes, complications, or readmission rates following shoulder arthroplasty.

Major depressive disorder diagnosis from electroencephalogram data and potential treatment with dimethyltryptamine

In recent times, there has been increasing interest in utilizing EEG-based techniques for studying Major Depressive Disorder as a dynamic method. Although it is frequently used for identifying depression, the method is still difficult to interpret. The conventional treatment of MDD involves medications such as Selective Serotonin Reuptake Inhibitors, which often have adverse effects. On the other hand, the use of dimethyltryptamine to stimulate brain activity in regions where MDD patients show lower activity has demonstrated promising results. This study analyzed resting-state EEG signals from MDD patients, DMT users, and healthy controls to evaluate and validated a computer-aided approach. The brain activity of DMT users was recorded and compared with MDD individuals and healthy controls. Using Welch's method, the power of several frequency bands was analyzed from the EEG dataset for comparison and diagnosis. The extracted EEG data underwent noise removal and feature extraction. The features from all controls were concatenated to form a data matrix. Furthermore, the data matrix was standardized using the Z-score standardization method. The classifier model logistic regression was employed to train and test the extracted features. The results of the investigations have demonstrated the most important features, such as signal power of the EEG data from the frontal, temporal, parietal, and occipital brain areas, to be significant.

Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment

BackgroundThe high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume. MethodsWe enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors. ResultsIn contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146–0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800–0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination. ConclusionsCapturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.

Rest-activity rhythm associated with depressive symptom severity and attention among patients with major depressive disorder: a 12-month follow-up study.

Patients with depressive disorder demonstrate rest-activity rhythm disturbances and cognitive function impairment. This study examined the association of individual rest-activity rhythm changes over time with mood symptoms and attention. We recruited 15 adult outpatients with a diagnosis of major depressive disorder from a single medical center and observed them for 12 months. Weekly rest-activity parameters, including rhythm characteristics generated from nonparametric circadian rhythm analysis, were retrieved from actigraphy data. Attention was evaluated weekly with a smartphone-based psychomotor vigilance test upon awakening. Depressive symptom severity was evaluated using the Beck Depression Inventory (BDI) fortnightly. The association of rest-activity parameters with BDI score and attention was examined using generalized linear mixed regression. A fixed-effects analysis was used to examine the association between rest-activity parameters and depressive episodes. An advanced bedtime and most active continuous 10 h starting time were associated with depressive symptom severity but also associated with higher vigilance test performance. A longer sleep duration, mainly due to an earlier bedtime, was associated with depressive symptom severity. Compared to remission, sleep duration was 27.8 min longer during depressive episodes, and bed time was 24 min earlier. A shorter sleep duration and increased activity during sleep were associated with poorer attention. Rest-activity rhythms change with mood symptoms among patients with depressive disorder. The circadian rhythms of rest-activity among patients with depressive disorder should be distinguished during various mood states in future studies.

Diabulimia: A Risky Trend Among Adults with Type 1 Diabetes Mellitus

ObjectivePoor adherence leads to worse glycemic control and increased complications in patients with type 1 diabetes mellitus (T1DM). Diabulimia characterizes patients with T1DM who skip or use less insulin for weight loss purposes. The study objectives were to determine: (1) the prevalence of diabulimia among adult patients with T1DM, (2) compare patients with and without diabulimia, and (3) identify factors that may place individuals at higher risk of diabulimia. MethodsA 40-item, web-based survey was administered to 21 T1DM discussion boards, Listservs, and social media outlets. The survey assessed demographics, diabetes management, psychiatric diagnoses, and screened for diabulimia. Individuals who reported intentionally skipping or using less insulin than directed for the purpose of weight loss or to prevent weight gain in the past 12 months were classified as having diabulimia. ResultsOf the 225 participants who completed the survey, 8.9% had diabulimia. Patients with diabulimia had elevated hemoglobin A1C (A1C) levels (8.4% vs 6.9%; P = .014), higher rates of a diabetes-related emergency department visits or hospitalization (30.0% vs 13.2%; P = .042), and higher rates of a major depressive disorder diagnosis (40.0% vs 11.5%; P < .001) than patients without diabulimia. Factors associated with diabulimia included high A1C levels (odds ratio, 1.43; 95% CI [1.08-1.91]; P = .014) and a major depressive disorder diagnosis (odds ratio, 4.87; 95% CI [1.31-18.22]; P = .018). ConclusionApproximately 1 in 11 adult patients with T1DM screened positive for diabulimia. Higher A1C levels and a diagnosis of major depressive disorder were associated with diabulimia.

The effect of obstructive sleep apnea severity on PTSD symptoms during the course of esketamine treatment: a retrospective clinical study.

Intranasal administration of esketamine is Food and Drug Administration-approved for treatment-resistant depression. In a recent retrospective case series, we show that it has promise in reducing symptoms of posttraumatic stress disorder (PTSD) as well. Untreated obstructive sleep apnea (OSA) is prevalent among veterans with PTSD and has been shown to interfere with other PTSD treatments. In the current study, we examined whether OSA impacts esketamine's effectiveness in reducing symptoms of PTSD or depression. Participants were 60 veterans with a diagnosis of major depressive disorder and PTSD who received intranasal esketamine treatment at the San Diego Veterans Affairs (VA) Medical Center. We used growth-curve modeling to examine changes in depression and PTSD symptoms following esketamine treatments and, in the subset of individuals screened for OSA (n = 24, all prescribed positive airway pressure therapy), examined the impacts of OSA severity on these trajectories. We first showed that both PTSD and depressive symptoms significantly decreased over the course of esketamine treatment. In the subset of veterans screened for OSA, individuals with lower OSA severity reported the greatest reduction in PTSD symptoms, while veterans with the most severe OSA reported the least reduction in PTSD symptoms. Depression response was not affected by severity of OSA in this analysis. Veterans with PTSD and depression tend to benefit from esketamine treatment, but OSA may interfere with esketamine effectiveness. Comorbid OSA should be assessed for and treated to maximize esketamine's benefits in PTSD. Titone MK, Hunt C, Bismark A, etal. The effect of obstructive sleep apnea severity on PTSD symptoms during the course of esketamine treatment: a retrospective clinical study. J Clin Sleep Med. 2023;19(12):2043-2051.

Identifying circulating biomarkers for major depressive disorder.

To date, the current diagnosis of major depressive disorder (MDD) still depends on clinical symptomatologic criteria, misdiagnosis and ineffective treatment are common. The study aimed to explore circulating biomarkers for MDD diagnosis. A high-throughput antibody array technology was utilized to detect 440 circulating cytokines in eight MDD patients and eight age-and gender-matched healthy controls. LASSO regression was conducted for MDD-related characteristic proteins selection. Enzyme-linked immunosorbent assay (ELISA) was used to validate the characteristic proteins in 40 MDD patients and 40 healthy controls. Receiver operating characteristic (ROC) curve was employed to evaluate the diagnostic values of characteristic proteins for discriminating MDD patients from healthy controls. Correlations between the levels of characteristic proteins and depression severity (HAMD-17 scores) were evaluated using linear regression. The levels of 59 proteins were found aberrant in MDD patients compared with healthy controls. LASSO regression found six MDD-related characteristic proteins including insulin, CD40L, CD155, Lipocalin-2, HGF and LIGHT. ROC curve analysis showed that the area under curve (AUC) values of six characteristic proteins were more than 0.85 in discriminating patients with MDD from healthy controls. Furthermore, significant relationship was found between the levels of insulin, CD155, Lipocalin-2, HGF, LIGHT and HAMD-17 scores in MDD group. These results suggested that six characteristic proteins screened from 59 proteins differential in MDD may hold promise as diagnostic biomarkers in discriminating patients with MDD. Among six characteristic proteins, insulin, CD155, Lipocalin-2, HGF and LIGHT might be useful to estimate the severity of depressive symptoms.