Diagnosis Of Major Depressive Disorder Research Articles

Suicidal ideation following ketamine prescription in patients with recurrent major depressive disorder: a nation-wide cohort study

Ketamine has gained attention for its effective treatment for patients with major depressive disorder (MDD) and suicidal ideation; Despite numerous studies presenting the rapid efficacy, long-term benefit in real-world populations remains poorly characterized. This is a retrospective cohort study using TriNetX US Collaborative Network, a platform aggregating electronic health records (EHRs) data from 108 million patients from 62 health care organizations in the US, and the study population includes 514,988 patients with a diagnosis of recurrent MDD who were prescribed relevant treatment in their EHRs. The prescription of ketamine was associated with significantly decreased risk of suicidal ideation compared to the prescription of other common antidepressants: HR = 0.63 (95% CI: 0.53–0.76) at 1 day – 7 days, 0.67 (95% CI: 0.59–0.77) at 1 day – 30 days, 0.69 (95% CI: 0.62–0.77) at 1 day – 90 days, 0.74 (95% CI: 0.67–0.81) at 1 day – 180 days, and 0.78 (95% CI: 0.69–0.83) at 1 day – 270 days. This trend was especially robust among adults over 24 years of age, females, males, and White patients with recurrent MDD. This study provides real-world evidence that ketamine has long-term benefits in mitigating suicidal ideation in patients with recurrent MDD. Future work should focus on optimizing dosage regimens for ketamine, understanding the mechanism, and the difference in various demographic subpopulations.

Association between a selective 5-HT4 receptor agonist and incidence of major depressive disorder: emulated target trial.

The serotonin 4 receptor (5-HT4R) is a promising target for the treatment of depression. Highly selective 5-HT4R agonists, such as prucalopride, have antidepressant-like and procognitive effects in preclinical models, but their clinical effects are not yet established. To determine whether prucalopride (a 5-HT4R agonist and licensed treatment for constipation) is associated with reduced incidence of depression in individuals with no past history of mental illness, compared with anti-constipation agents with no effect on the central nervous system. Using anonymised routinely collected data from a large-scale USA electronic health records network, we conducted an emulated target trial comparing depression incidence over 1 year in individuals without prior diagnoses of major mental illness, who initiated treatment with prucalopride versus two alternative anti-constipation agents that act by different mechanisms (linaclotide and lubiprostone). Cohorts were matched for 121 covariates capturing sociodemographic factors, and historical and/or concurrent comorbidities and medications. The primary outcome was a first diagnosis of major depressive disorder (ICD-10 code F32) within 1 year of the index date. Robustness of the results to changes in model and population specification was tested. Secondary outcomes included a first diagnosis of six other neuropsychiatric disorders. Treatment with prucalopride was associated with significantly lower incidence of depression in the following year compared with linaclotide (hazard ratio 0.87, 95% CI 0.76-0.99; P = 0.038; n = 8572 in each matched cohort) and lubiprostone (hazard ratio 0.79, 95% CI 0.69-0.91; P < 0.001; n = 8281). Significantly lower risks of all mood disorders and psychosis were also observed. Results were similar across robustness analyses. These findings support preclinical data and suggest a role for 5-HT4R agonists as novel agents in the prevention of major depression. These findings should stimulate randomised controlled trials to confirm if these agents can serve as a novel class of antidepressant within a clinical setting.

Safety and effectiveness of vortioxetine in patients with major depressive disorder in a real-life clinical setting in India: results from an interventional, flexible-dose study

Objective Vortioxetine has demonstrated safety and efficacy in improving symptoms of major depressive disorder (MDD), including overall functioning in real-world settings. This is the first study in a real-life clinical setting in India to evaluate effectiveness and safety of vortioxetine in patients with MDD. Methods This interventional, open-label study consisted of a 12-week treatment period with flexible doses of vortioxetine (5–20 mg/day) in adult patients (aged 18–65 years) with a confirmed MDD diagnosis. Effectiveness outcomes included change from baseline to week 12 in Patient Health Questionnaire-9 (PHQ-9) and Clinical Global Impression–Severity (CGI-S) scores, along with CGI-Improvement (CGI-I) scores at week 12, using a mixed model for repeated measures. Adverse events (AEs) were recorded for safety outcome assessments. Results Of 395 patients who received vortioxetine, 42.3% were women mean age 38.9 years; 322 patients completed the study. Significant improvement in depressive symptoms was observed in change from baseline to week 12 least squares (LS) mean (SE) PHQ-9 total score (−9.36 [0.276]; p<.0001) and CGI-S score (−2.14 [0.065]; p<.0001). LS mean (SE) CGI-I score showed significant improvement at week 12 (1.93 [0.067]; p<.0001). Subgroup analysis across age, sex, disease severity, and body mass index showed significant improvements in depression symptoms and severity. A total of 35.4% (n = 140) of patients experienced treatment-emergent AEs (mostly mild-moderate); nausea and pruritus were the most frequent (6.6%, n = 26 each). Conclusion Safety and effectiveness of vortioxetine in improving symptoms of MDD over a 12-week period was demonstrated in a real-life clinical setting in India. Clinical trial registration information Open-label, flexible-dose study of vortioxetine in patients with major depressive disorder in India; Clinical Trials.gov ID: NCT04288895; https://www.clinicaltrials.gov/study/NCT04288895

Racial disparities in prosthesis abandonment and mobility outcomes after lower limb amputation from a dysvascular etiology in a veteran population.

Non-Hispanic Black (NHB) individuals have higher rates of amputation and increased risk of a transfemoral amputation due to dysvascular disease than non-Hispanic White (NHW) individuals. However, it is unclear if NHB individuals have differences in prosthesis use or functional outcomes following an amputation. To determine if there are racial disparities in prosthesis abandonment and mobility outcomes in veterans who have undergone their first major unilateral lower extremity amputation (LEA) due to diabetes and/or peripheral artery disease. National cohort study that identified individuals retrospectively through the Veterans Affairs (VA) Corporate Data Warehouse (CDW) from March 1, 2018, to November 30, 2020, then prospectively collected their self-reported prosthesis abandonment and mobility. Multiple logistic regression was used to control for potential confounders and identify potential effect modifiers. The VA CDW, participant mailings and phone calls. Three hundred fifty-seven individuals who underwent an incident transtibial or transfemoral amputation due to diabetes and/or peripheral arterial disease. Not applicable. (1) Self-reported prosthesis abandonment. (2) Level of mobility assessed using the Locomotor Capabilities Index. Rurally located NHB individuals without a major depressive disorder (MDD) had increased odds of abandoning their prosthesis (adjusted odds ratios [aOR] = 5.3; 95% confidence interval [CI]: [1.3-21.1]). This disparity was nearly three times as large for rurally located NHB individuals with MDD diagnosis, compared with other races from rural areas and with MDD (aOR = 15.8; 95% CI, 2.5-97.6). NHB individuals living in an urban area were significantly less likely to achieve advanced mobility, both with MDD (aOR=0.16; 95% CI: [0.04-7.0]) and without MDD (aOR = 0.26; 95% CI: [0.09-0.73]). This study demonstrated that health care disparities persist for NHB veterans following a dysvascular LEA, with increased prosthesis abandonment and worse mobility outcomes.

The role of social determinants of health in mental health: An examination of the moderating effects of race, ethnicity, and gender on depression through the all of us research program dataset

We investigate how select identity characteristics moderate the role of several SDoH domains on major depressive disorder (MDD). Our study considers an analytical sample of 86,954 participants from the NIH-funded All of Us (AoU) Research Program in the USA. Our independent variables and moderators come from survey responses and our outcome is an EHR diagnostic code. We include race/ethnicity and gender/sexual identity to moderate the role of food insecurity, discrimination, neighborhood social cohesion, and loneliness in assessing risk for MDD diagnosis. We examine those moderating effects based on connections seen in the literature. Our findings illustrate the complexity of where and how people live their lives can have significant differential impact on MDD. Women (AOR = 1.60, 95% CI = [1.53, 1.68]) and LGBTQIA2+ individuals (AOR = 1.71, 95% CI = [1.60, 1.84]) exhibit a significantly higher likelihood of MDD diagnosis compared to cisgender heterosexual males. Our study also reveals a lower likelihood of MDD diagnosis among Asian/Asian American individuals (AOR = 0.41, 95% CI = [0.35, 0.49]) compared to White individuals. Our results align with previous research indicating that higher levels of food insecurity (AOR = 1.30, 95% CI = [1.17, 1.44]) and loneliness (AOR = 6.89, 95% CI = [6.04, 7.87]) are strongly associated with an increased likelihood of MDD. However, we also find that social cohesion (AOR = 0.92, 95% CI = [0.81, 1.05]) does not emerge as a significant predictor, contradicting some literature emphasizing the protective role of neighborhood cohesion. Similarly, our finding that transience (AOR = 0.95, 95% CI = [0.92, 0.98]) reduces the likelihood of MDD diagnosis contradicts conventional wisdom and warrants further exploration. Our study provides a reminder of the substantial challenges for research focused on marginalized community segments and that deliberate sampling plans are needed to examine those most marginalized and underserved.

Uric Acid as a Biomarker for Mood Disorders: A Comparative Study of Blood Uric Acid Levels Correlating With the Symptom Severity and Treatment Response.

Background Bipolar affective disorder (BPAD) and major depressive disorder (MDD) are two mood disorders whose pathophysiology may involve the purinergic system. Elevated uric acid levels, associated with this system, can impact various behaviors in individuals affected by these conditions. In addition to genetic predisposition, blood uric acid levels can be impacted by various factors, including metabolic syndrome, the consumption of psychoactive medications, and other underlying kidney conditions such as gout. Objective The study aims to investigate the relationship between blood uric acid levels and mental health conditions, specifically BPAD subtypes (manic and depressive) and MDD. The study also examines changes in blood uric acid levels following treatment and evaluates the effectiveness of different treatment approaches in reducing uric acid levels. Methodology To be eligible to participate, individuals must have a confirmed diagnosis of BPAD (manic or depressive type) or MDD, according to the International Classification of Diseases (ICD-10). Blood uric acid levels were measured at both baseline and follow-up assessments. Symptoms were assessed weekly using standardized rating scales (Young Mania Rating Scale (YMRS) and Hamilton Rating Scale for Depression (HAM-D)) until treatment response was achieved, which was defined as a 50% reduction in initial scores on both scales. We used ANOVA to examine the differences among the three patient groups and paired sample t-tests to examine the changes in means before and after treatment conditions. Results A significant positive correlation was found between the severity of illness and serum uric acid levels across all three patient groups: those with BPAD-mania, BPAD-depression, and MDD. Notably, patients with BPAD-mania patients had significantly higher serum uric acid levels (5.2±0.9 mg/dL) compared to those with BPAD-depression (4.8±1.0 mg/dL) and MDD (4.0±1.1 mg/dL). After treatment, all patient groups exhibited a decrease in serum uric acid levels. The reduction in serum uric acid levels was pronounced in all patient groups, with decreases of 3.1±0.8 mg/dL in patients with BPAD-mania, 3.1±0.9 mg/dL in those with BPAD-depression, and 3.5±1.1 mg/dL in those with MDD. The study showed that the reduction in serum uric acid levels was significantly correlated with the severity of illness in patients with BPAD-mania, but not in those with BPAD-depression or MDD. Furthermore, the study found that treatment with lithium carbonate, sodium valproate, or carbamazepine was equally effective in reducing serum uric acid levels, regardless of the mood stabilizer used. Conclusion The study supports that dysfunction in the purine system might play a significant role in the development and progression of BPAD, suggesting that this phenomenon is not solely due to chronicity or medication exposure. This study also introduces a fresh perspective on the underlying biological processes that contribute to the development of BPAD and also sheds light on new treatment regimens targeting uric acid reduction in treating patients with bipolar disorder.

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

Regional structural-functional connectivity coupling in major depressive disorder is associated with neurotransmitter and genetic profiles

BackgroundAbnormalities in structural–functional connectivity (SC–FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms underlying regional SC–FC coupling patterns are not well understood. MethodsWe enrolled 182 patients with MDD and 157 healthy control (HC) subjects, quantifying the intergroup differences in regional SC–FC coupling. The extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) models were constructed to assess the potential of SC–FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC–FC coupling in patients with MDD, neurotransmitter distributions, and gene expression. ResultsWe observed increased regional SC–FC coupling in default mode network (T = 3.233) and decreased coupling in frontoparietal network (T = −3.471) in MDD relative to HC. XGBoost (AUC = 0.853), SVM (AUC = 0.832) and RF (p < 0.05) models exhibited good prediction performance. The alterations in regional SC–FC coupling in patients with MDD were correlated with the distributions of four neurotransmitters (p < 0.05) and expression maps of specific genes. These genes were strongly enriched in genes implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on two brain atlases. ConclusionsThis work enhances our understanding of MDD and pave the way for the development of additional targeted therapeutic interventions.

Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center

BackgroundDepression can be associated with increased mortality and morbidity, but no studies have investigated the specific causes of death based on autopsy reports. Autopsy studies can yield valuable and detailed information on pathological ailments or underreported conditions. This study aimed to compare autopsy-confirmed causes of death (CoD) between individuals diagnosed with major depressive disorder (MDD) and matched controls. We also analyzed subgroups within our MDD sample, including late-life depression and recurrent depression. We further investigated whether machine learning (ML) algorithms could distinguish MDD and each subgroup from controls based on their CoD.MethodsWe conducted a comprehensive analysis of CoD in individuals who died from nontraumatic causes. The diagnosis of lifetime MDD was ascertained based on the DSM-5 criteria using information from a structured interview with a knowledgeable informant. Eleven established ML algorithms were used to differentiate MDD individuals from controls by simultaneously analyzing different disease category groups to account for multiple tests. The McNemar test was further used to compare paired nominal data.ResultsThe initial dataset included records of 1,102 individuals, among whom 232 (21.1%) had a lifetime diagnosis of MDD. Each MDD individual was strictly paired with a control non-psychiatric counterpart. In the MDD group, the most common CoD were circulatory (67.2%), respiratory (13.4%), digestive (6.0%), and cancer (5.6%). Despite employing a range of ML models, we could not find distinctive CoD patterns that could reliably distinguish individuals with MDD from individuals in the control group (average accuracy: 50.6%; accuracy range: 39-59%). These findings were consistent even when considering factors within the MDD group, such as late-life or recurrent MDD. When comparing groups with paired nominal tests, no differences were found for circulatory (p=0.450), respiratory (p=0.790), digestive (p=1.000), or cancer (p=0.855) CoD.ConclusionsOur analysis revealed that autopsy-confirmed CoD exhibited remarkable similarity between individuals with depression and their matched controls, underscoring the existing heterogeneity in the literature. Future research should prioritize more severe manifestations of depression and larger sample sizes, particularly in the context of CoD related to cancer.

Screening for depression in chronic haemodialysis patients as a part of care in dialysis setting: a cross-sectional study.

Depressive disorder is common among haemodialysis patients. The purpose of this study was to explore approaches to diagnosing depression in the context of a real-life setting, with the view of creating practical recommendations. It also aimed to evaluate the prevalence of depression and dementia. We conducted a cross-sectional study in two Dialysis Centres in Poland. Cognitive functions were evaluated using Mini-Mental State Examination (MMSE). The screening for depressive symptoms was assessed using Beck Depression Inventory II (BDI-II). The diagnosis of major depressive disorder was confirmed by a psychiatrist using Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5). Sociodemographic and clinical data were also collected. Initially, 136 patients agreed to participate in the study. Dementia was found in 13% of the study group. Sixty-two patients did not agree to perform all the proposed tests and were not included in the analysis, which eventually consisted of 70 patients. According to BDI-II, depressive symptoms were present in 35.7% of patients, while the diagnosis of major depressive disorder (MDD) was confirmed by the psychiatrist in 25.7%. According to the ROC analysis the optimal cut-off score for diagnosing MDD using BDI-II was ≥13 points. This study suggests that the regular screening for depressive symptoms, followed by a psychiatric consultation in selected patients, might improve diagnosing depression with the goal of achieving a higher quality of life and a lower mortality rate. It may also be a cost-effective model for the management of depression among the haemodialysis population.

Correlates of disability among primary care patients with common mental disorders and chronic medical conditions- a cross-sectional study from rural South India.

We examined the correlates of disability among people with non-communicable diseases (NCDs) and comorbid common mental disorders (CMDs) from rural India. The sample comprised 2,486 participants enrolled in a cluster randomized trial (cRCT), Healthier OPtions through Empowerment (HOPE). Participants were 30 years or older, with a diagnosis of major depressive disorder, dysthymia, generalized anxiety disorder, and/or panic disorder on the MINI-International Neuropsychiatric Interview, with hypertension, diabetes, dsylipidemia and/ or ischemic heart disease. Disability was measured with the 12-item version of WHODAS 2.0. The severity of depression and anxiety was measured using the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale (GAD-7), respectively. Multiple linear regression analyses were used to examine associations. The mean age was 59 ± 10.0 years, three quarters (1864) of the participants were female, and 64.0% were married. More than half of the participants had no formal education (57.9%). Most of the participants had two or more chronic medical conditions (73.0%). The mean disability score was 24.3. The mean depression score was 8.5, and the mean anxiety score was 6.7. Higher levels of disability were reported by participants ≥ 60 years of age, those with moderate and severe depression, and moderate anxiety. Among female participants, being unmarried was associated with greater disability. Male participants without formal education reported greater levels of disability. Higher severity of CMDs is significantly associated with higher levels of disability. For women, being unmarried and for men having no formal education was associated with higher levels of disability. ClinicalTrials.gov NCT02310932 [URL: https://clinicaltrials.gov/ct2/show/record/NCT02310932 ] registered on December 8, 2014, and Clinical Trials Registry India CTRI/2018/04/013001, registered on April 4, 2018. Retrospectively registered.

Real-world treatment patterns of patients with major depressive disorder treated with Auvelity in the United States

Aims Major depressive disorder (MDD) is a prevalent, chronic disorder. Auvelity (dextromethorphan-bupropion) is a novel, oral N-methyl-D-aspartate (NMDA) receptor antagonist and sigma-1 receptor agonist approved (August 2022) by the FDA for treating MDD in adults. This is the first analysis on real-world Auvelity usage in the United States. Methods Adult patients initiating Auvelity in the Symphony IDV databases by September 2023 were identified (index date: the first Auvelity claim). Patients had continuous eligibility over the 12-month pre-index period and ≥1 MDD diagnosis (ICD-10-CM codes: F32.*, F33.*) over the 5-year pre-index period. Demographic and clinical characteristics, comorbidities, prior MDD-related medications, and Auvelity initiation status were assessed. Results This analysis included 22,288 patients with MDD treated with Auvelity (mean age 45.1 years; 68.1% women); 40.0% lived in the South and 58.5% had commercial insurance. Comorbidities included mental health disorders (53.5%; 47.6% had anxiety disorders). Overall, 83.7% of the patients had received treatment with selective serotonin reuptake inhibitors (SSRIs; 54.9%), the norepinephrine-dopamine reuptake inhibitor (NDRI [bupropion]; 40.4%), and/or serotonin-norepinephrine reuptake inhibitors (SNRIs; 35.9%) over the 12-month pre-index period. The last MDD-related treatment prior to Auvelity comprised SSRI (22.4%), SNRI (13.2%), and NDRI (12.8%) monotherapies; 294 (1.3%) patients received esketamine. In total, 6,418 (28.8%) patients initiated Auvelity as monotherapy vs 15,870 (71.2%) as an add-on; Auvelity was most frequently added to an SSRI alone (10.7%) or SNRI alone (6.5%). A total of 2,254 (10.1%) patients initiated Auvelity without prior treatment in the 12-month pre-index period. Limitations Incomplete data due to reporting; diagnoses captured subject to coding error; and limited generalizability to other populations. Conclusions Using a large demographically distributed claims database, 22,288 patients with MDD initiated Auvelity within a year of its approval; 10.1% were treatment-naïve and 28.8% initiated Auvelity as monotherapy. Most patients had mental health-related comorbidities and attempted various MDD-related treatments prior to Auvelity.

Seasonality and sun exposure in incidence of major depression, bipolar disorder, and first-time use of antidepressant medication

Introduction Seasonality in depressive and bipolar disorders, are recognized in the ICD-10/11 and DSM-5 diagnostic systems. The existence of a seasonal pattern of hospital diagnosis of major depression, bipolar disorder and prescription of antidepressant medications has not been evaluated in the Danish population. Methods We retrieved date and year for all first-time hospital contacts with depression or bipolar disorder between 1999 and 2019, registered in the Danish National Patient Registry. Depression was defined using the ICD-10 F32-F33 codes, and for bipolar disorder the F30 or F31 codes. Date and year of all first-time purchases of antidepressant medications with ATC codes (N06A) between 1999 and 2021 were retrieved from the Danish National Prescription Registry, containing information on all prescribed drugs dispensed at pharmacies since 1995. Data on sunlight hours from 2012 to 2021 were retrieved from the Danish Metrological Institute. Results Incidences of hospital diagnoses as well as purchases of medication varied with month and season. The monthly variations were larger for antidepressant medication and smallest for bipolar disorder. The multiple linear regression analysis showed that number of first-time diagnoses of depression or bipolar disorder did not correlate with season. For antidepressant medication the number of first-time prescriptions was significantly lower in summer compared to the winter season. Conclusion This study found a seasonal variation of first-time prescriptions of antidepressant medication. We did not find a seasonal variation in first-time hospital diagnoses. Further research looking into depression severity, polarity of bipolar illness episodes, lag-time for sunlight exposure, and specific parts of the yearly photoperiods should be conducted.

Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study.

Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC). In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17). During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked. Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression. The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians

While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.

Argyria and Neuropsychiatric Disorders: When not only the Matter is Gray. A Literature Review and a Case Report.

Argyria is the chronic accumulation of silver in biological tissues such as skin, liver, kidneys, lungs, peripheral nerves, and brain. The presence of an actual pathophysiological and clinical correlate related to silver encephalic and peripheral nerve deposition is still much debated. In this paper, we reviewed and described case reports regarding argyria associated with neuropsychiatric symptoms in order to explain the underlying mechanism of the disease. We conducted a narrative review by searching for case reports that described subjects with chronic silver accumulation and who had associated neurological or psychiatric symptoms. Moreover, we report a case of a 50-year-old man admitted to our hospital with a diagnosis of major depression who presented with worsening psychiatric symptoms after abuse of silver-containing nasal spray. We found 15 cases of patients with argyria and neuropsychiatric manifestations such as epilepsy, neurodegenerative syndromes, multiple sclerosis, peripheral neuropathy, and psychiatric disorders. The knowledge of possible pathogenetic mechanisms and recognition of clinical features of argyria can help clinicians prevent brain deposition and its complications.

Utilization and outcomes of transcranial magnetic stimulation and usual care for MDD in a large group psychiatric practice

BackgroundGeneral psychiatrists’ practice standards vary regarding when to implement transcranial magnetic stimulation (TMS) for care of patients with major depressive disorder (MDD). Furthermore, few studies have examined real-world utilization and clinical outcomes of TMS. This study analyzed data from a large, multi-site psychiatric practice to evaluate utilization and outcomes of TMS as well as usual care (UC) for patients with MDD.MethodsDepression outcomes for TMS and UC among adult patients at a multi-site psychiatric group practice were examined in this retrospective cohort analysis. Patients with a primary diagnosis of MDD, PHQ-9 ≥ 10, and a visit in November 2020 with 6-month follow-up were included and categorized into the TMS or UC cohorts.ResultsOf 1,011 patients with qualifying PHQ-9 at the baseline visit, 9% (89) received a full course of TMS, and 583 patients receiving UC met study inclusion criteria (339 patients were excluded due to lacking a 6-month follow-up visit or receiving esketamine during the study period). The TMS cohort had higher baseline PHQ-9 than UC (17.9 vs. 15.5, p < .001) and had failed more medication trials (≥ 4 vs. 3.1, p < .001). Mean PHQ-9 decreased by 5.7 points (SD = 6.7, p < .001) in the TMS cohort and by 4.2 points (SD = 6.4, p < .001) in the UC cohort over the study period. Among patients who had failed four or more antidepressant medications, PHQ-9 decreased by 5.8 points in the TMS cohort (SD = 6.7, p < .001) and by 3.2 points in the UC cohort (SD = 6.3, p < .001).ConclusionsTMS utilization was low, despite TMS showing significant real-world clinical benefits. Future research should examine and address barriers to wider adoption of TMS into routine patient care for patients with treatment-resistant MDD. Wider adoption including routine use of TMS in less treatment-resistant patients will allow statistical comparisons of outcomes between TMS and UC populations that are difficult to do when TMS is underutilized.

Comorbidity of depression and posttraumatic stress disorder: Outcomes from a randomized controlled trial of surf and hike therapies among service members.

Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are commonly comorbid mental health disorders. Exercise performed in the natural environment has shown promise in relieving symptoms of each disorder separately; however, the effectiveness has seldom been studied in comorbid populations. Data were derived from a randomized controlled trial of surf and hike therapy for active duty service members with MDD (N = 95). In this study, participants were grouped by comorbidity status (MDD, n = 37; MDD-PTSD, n = 58). Clinician-administered and self-reported measures were completed at preprogram, postprogram, and 3-month follow-up; a brief depression/anxiety measure was completed before and after each session. Multilevel modeling results showed clinically significant decreases in depression severity across participants from pre- to postprogram (p < .001) and within exercise sessions (p < .001), with no further change through follow-up. No significant differences emerged in depression severity change over time by comorbidity status, intervention condition, or their three-way interaction. Those with PTSD showed reductions in posttraumatic stress symptoms from pre- to postprogram (p < .001), which did not differ by intervention condition; gains were maintained at follow-up. Remission rates from MDD and PTSD diagnoses (if applicable) were significant from pre- to postprogram for both MDD-only and MDD-PTSD groups (p < .001). These improvements were maintained at 3 months. Both surf and hike therapies can improve MDD and PTSD symptoms, regardless of comorbidity status, suggesting utility of these interventions among service members with one or both disorders. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Genetic Correlates of Treatment-Resistant Depression: Insights from Polygenic Scores Across Cognitive, Temperamental, and Sleep Traits in the All of US cohort.

Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable economic and social burdens. The etiological factors contributing to TRD are complex and not fully understood. To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits, and to explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us Research Program (AoU). Data from 292,663 participants in the AoU were analyzed using a case-cohort design. Treatment resistant depression (TRD), treatment responsive Major Depressive Disorder (trMDD), and all others who have no formal diagnosis of Major Depressive Disorder (non-MDD) were identified through diagnostic codes and prescription patterns. Polygenic scores (PGS) for 61 unique traits from seven domains were used and logistic regressions were conducted to assess associations between PGS and TRD. Finally, Cox proportional hazard models were used to explore the predictive value of PGS for progression rate from the diagnostic event of Major Depressive Disorder (MDD) to TRD. In the discovery set (104128 non-MDD, 16640 trMDD, and 4177 TRD), 44 of 61 selected PGS were found to be significantly associated with MDD, regardless of treatment responsiveness. Eleven of them were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia and specific neuroticism traits were associated with increased TRD risk (OR range from 1.05 to 1.15), while higher education and intelligence scores were protective (ORs 0.88 and 0.91, respectively). These associations are consistent across two other independent sets within AoU (n = 104,388 and 63,330). Among 28,964 individuals tracked over time, 3,854 developed TRD within an average of 944 days (95% CI: 883 ~ 992 days) after MDD diagnosis. All eleven previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD. Thus, those having higher education PGS would experiencing slower conversion rates than those who have lower education PGS with hazard ratios in 0.79 (80th versus 20th percentile, 95% CI: 0.74 ~ 0.85). Those who had higher insomnia PGS experience faster conversion rates than those who had lower insomnia PGS, with hazard ratios in 1.21 (80th versus 20th percentile, 95% CI: 1.13 ~ 1.30). Our results indicate that genetic predisposition related to neuroticism, cognitive function, and sleep patterns play a significant role in the development of TRD. These findings underscore the importance of considering genetic and psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance our understanding of pathways leading to treatment resistance.

Screening of Secretory Proteins Linking Major Depressive Disorder with Heart Failure Based on Comprehensive Bioinformatics Analysis and Machine Learning.

Major depressive disorder (MDD) plays a crucial role in the occurrence of heart failure (HF). This investigation was undertaken to explore the possible mechanism of MDD's involvement in HF pathogenesis and identify candidate biomarkers for the diagnosis of MDD with HF. GWAS data for MDD and HF were collected, and Mendelian randomization (MR) analysis was performed to investigate the causal relationship between MDD and HF. Differential expression analysis (DEA) and WGCNA were used to detect HF key genes and MDD-associated secretory proteins. Protein-protein interaction (PPI), functional enrichment, and cMAP analysis were used to reveal potential mechanisms and drugs for MDD-related HF. Then, four machine learning (ML) algorithms (including GLM, RF, SVM, and XGB) were used to screen candidate biomarkers, construct diagnostic nomograms, and predict MDD-related HF. Furthermore, the MCPcounter algorithm was used to explore immune cell infiltration in HF, and MR analysis was performed to explore the causal effect of immunophenotypes on HF. Finally, the validation of the association of MDD with reduced left ventricular ejection fraction (LVEF) and the performance assessment of diagnostic biomarkers was accomplished based on animal models mimicking MDD. The MR analysis showed that the MDD was linked to an increased risk of HF (OR = 1.129, p < 0.001). DEA combined with WGCNA and secretory protein gene set identified 315 HF key genes and 332 MDD-associated secretory proteins, respectively. Through PPI and MCODE analysis, 78 genes were pinpointed as MDD-related pathogenic genes for HF. The enrichment analysis revealed that these genes were predominantly enriched in immune and inflammatory regulation. Through four ML algorithms, two hub genes (ISLR/SFRP4) were identified as candidate HF biomarkers, and a nomogram was developed. ROC analysis showed that the AUC of the nomogram was higher than 0.90 in both the HF combined dataset and two external cohorts. In addition, an immune cell infiltration analysis revealed the immune dysregulation in HF, with ISLR/SFRP4 displaying notable associations with the infiltration of B cells, CD8 T cells, and fibroblasts. More importantly, animal experiments showed that the expression levels of ISLR (r = -0.653, p < 0.001) and SFRP4 (r = -0.476, p = 0.008) were significantly negatively correlated with LVEF. The MR analysis indicated that MDD is a risk factor for HF at the genetic level. Bioinformatics analysis and the ML results suggest that ISLR and SFRP4 have the potential to serve as diagnostic biomarkers for HF. Animal experiments showed a negative correlation between the serum levels of ISLR/SFRP4 and LVEF, emphasizing the need for additional clinical studies to elucidate their diagnostic value.