Diagnosis Of Major Depressive Disorder Research Articles

US Medicaid program: An analysis of the spending and utilization patterns for antidepressants from 2017 to 2021

BackgroundMajor depressive disorder (MDD) is a serious mental health condition that contributes to health complications, financial burden and death. In 2020, about one in five US adults had a lifetime diagnosis of MDD. With Major Depressive Disorder (MDD) being a common mental health concern, it is important to understand treatment patterns within public health systems like Medicaid, as they play a crucial role in providing care to diverse populations. ObjectiveThe study investigated antidepressant usage and market distribution in the Medicaid Program. By doing so, the study aimed to provide insights into how these trends reflect broader changes in mental health treatment practices and policy implications within the Medicaid system during the study period. MethodsPublic Medicaid data from 2017 to 2021 were analyzed, focusing on 30 FDA-approved antidepressants. Spending and prescription data were aggregated using Excel and Python. ResultsThe total US Medicaid expenditure on antidepressants increased from about $1 billion dollars in 2017 to $1.12 billion dollars in 2021, an increase of about 10%. Consistently, SSRIs were the class of antidepressants that Medicaid spent the most on. The highest Medicaid spending on a single antidepressant in 2017 and 2018 was bupropion. During the remaining years of the study (2019, 2020, 2021) Medicaid appropriated most funds toward Vortioxetine. The total number of antidepressant prescriptions increased from 52 million scripts to 59 million scripts (an increase of about 14%). ConclusionsThe increase in Medicaid spending on antidepressants during the study period can be explained by an increase in utilization (a 14% increase in antidepressant prescriptions from 2017 to 2021), and a shift toward prescribing newer more costly antidepressants (like SSRIs and others) and away from prescribing older, less costly antidepressants like monoamine oxidase inhibitors (MAOIs) and tricyclic antidepressants (TCAs).”

P175: Cohort differences in depressive burden in old age: role of psychosocial, behavioral, and functional factors

Objective:Rapid societal changes occurred during the course of the 20th century. Previous literature has found an increase in depression over time for younger and middle- aged populations. Among older adults, the prevalence of major depression has been found to be stable over time, while for the milder forms, the findings are limited by the largely non-representative nature of analyzed samples. Given the dramatic secular changes in several factors linked to old-age depression, a careful examination of depressive symptom burden and prevalence of depression in representative cohorts of similarly-aged older adults separated in time is required.Methods:We will analyze data on 2,041 older adults from the Swedish National Study on Aging and Care in Kungsholmen. Separate individuals, aged 60 and 81 years were assessed with a Comprehensive Psychopathological Rating Scale (CPRS) during detailed clinical examinations, separated in time by 15 years (2001 vs. 2016). Information on 21 depressive symptoms, is subsequently combined into diagnoses of major depression (presence of at least one core symptom [low mood and/or loss of interest], and at least five out of the nine symptoms); minor depression (presence of at least one core symptom, and two to four symptoms in total), and subsyndromal depression (presence of at least two symptoms in the absence of any other depression diagnoses). Psychosocial (loneliness, bereavement), behavioral (alcohol consumption, smoking), and functional factors (impairments in activities of daily living) are used as potential explanatory factors for any observed cohort differences in symptom burden or prevalence of depression.Results:For the 60-year old age-group, comparison of symptom burden and diagnostic status will be done across 739 participants assessed in 2001 and 677 people assessed in 2013. For the 81-year old age- group, comparisons will involve 236 people assessed in 2001, 194 people assessed in 2010, and 195 people assessed in 2016.Conclusion:Preliminary results are expected by March, once data entry and cleaning are completed. We hypothesize that the burden of depressive symptoms and the prevalence of depression will be lower in later born cohorts and that explanatory factors may account for some of the cohort effect.

Enhancing Provider Mental Health Screening in Primary Care: A Quality Improvement Project.

Background: Depression and anxiety occur in 7.8% and 19.1% of the US population, respectively. About half of those patients are diagnosed in primary care. Objective: The purpose of this quality improvement project was to improve the screening and diagnosing of anxiety and/or depression among adult patients at a primary care clinic by implementing an evidence-based mental health screening interview technique. Methods: The team implemented a mental health screening interview technique that incorporates background, affect, trouble, handling, and empathy and motivational interviewing techniques in addition to self-report surveys. Pre- and post-intervention surveys were conducted to assess providers' perceptions of the new interview technique. ICD-10 code data were gathered to assess the effectiveness of the new mental health screening interview technique. Results: The number of documented diagnoses of single-episode major depressive disorder decreased by 18%, recurrent major depressive disorder increased by 34%, and anxiety disorders increased by 3%. There were more favorable provider perceptions of the new screening interview technique versus the traditional screening method. Conclusions: The results show an improvement in provider comfort and providers preferred the new mental health screening when using an evidence-based mental health screening interview technique. Implications for nursing: The results show the potential benefits of using a structured mental health interview with self-report screening tools when diagnosing anxiety and depression in primary care.

Diagnosis of bipolar and major depressive disorders: The appropriateness of MINI compared to the clinical interview in a sample of patients with mood disorders in tertiary mental health care

ObjectiveThe Mini International Neuropsychiatric Interview (MINI) is one of the most used instruments for the assessment of Mental Disorders, playing an essential role in psychiatric research and in clinical and hospital practice. Despite this, the accuracy of the MINI, when used by a psychiatrist, is poorly studied, particularly in relation to Bipolar Disorder (BD). The early diagnosis of BD and Major Depressive Disorder (MDD) is extremely important, as it provides an opportunity for intervention that can reduce the impact on the patient's daily life and functionality. As such, this study assesses the suitability of MINI for diagnosing BD or MDD in a sample of patients with mood disorders. MethodAgreement between the MINI and the clinical interview was assessed in a sample of 347 outpatients by calculating Cohen's kappa, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve (AUC). ResultsThe sample consisted of 347 patients with mood disorders. 279 were women (80.40%), 105 (30.3%) were diagnosed with MDD and 242 (69.7%) with BD from the assessment performed in the clinical interview. In the MINI assessment, 97 individuals (28%) were classified with a diagnosis of MDD and 250 (72%) with BD. We found a sensitivity of 87.2% and specificity of 62.8% for the MINI in the diagnosis of BD and a Cohen's kappa between the MINI and the clinical interview of 0.51. The AUC was 0.75. ConclusionsMINI has greater sensitivity (87.2%) for the diagnosis of BD and greater specificity (87.2%) for the diagnosis of MDD. In addition, the moderate Cohen kappa (0.51) and AUC (0.75) values between the MINI and the clinical interview are acceptable when considering most available psychiatric diagnostic tools.

Moral injury and its mental health consequences among protesters: findings from Israel’s civil protest against the government's judicial reform

ABSTRACT Background: Government actions and participating in protracted-duration protests against it affect protesters' mental health, leading to high distress levels, such as posttraumatic and depressive symptoms. Aside from exposure to violence and other issues, protest participation can pose unique challenges to the protesters as they may be exposed to potentially morally injurious events (PMIEs), such as the betrayal of leaders they once trusted. This study's primary objective was to examine the extent of psychological difficulties among civilians participating in long-duration protests in Israel. More specifically, the study aimed to understand the contribution of exposure to protest-related PMIEs to psychological difficulties such as posttraumatic and depressive symptoms. Method: Participants comprised 4036 Israelis who were actively involved in the unfolding civil protest movement against the government-led judicial overhaul between January 2023 and August 2023. The protesters completed validated self-report questionnaires that included measures of PMIE exposure, PTSD and depressive symptoms. Results: About half (44.3%) of the sample met the criteria for self-report diagnosis of major depression and 10.6% for PTSD. Most of the protesters indicated their exposure to at least one moral injury event, with 63.9% reporting exposure to PMIE-Betrayal. Protesters exposed to PMIEs reported significantly higher levels of both PTSD and depression than non-PMIE-exposed protesters. Hierarchical regression analyses revealed that, beyond demographics and protest-related characteristics such as exposure to violence, PMIE dimensions significantly contributed to both PTSD and depression levels. Conclusions: The findings highlight the mental burden of protesters during the civil protests against the judicial overhaul in Israel. More central to the present research, the findings highlight the critical contribution of PMIEs exposure to this burden. Clinicians treating protesters coping with depression and PTSD following the civil actions should attend to their exposure to PMIEs, which may relate to the deleterious psychological effects among protesters.

Relevance of pharmacogenetic analyses and therapeutic drug monitoring of antidepressants for an individualized treatment of peripartum psychopathology.

Psychiatric disorders burden the peripartum period, often requiring psychopharmacological treatment, including antidepressants. Efficacy and tolerability of antidepressants are influenced by the physiological changes of the peripartum and individual metabolic profiles, which in turn can be modified by pregnancy. The objective of this study is to assess the relationship between antidepressants' pharmacokinetic profiles during pregnancy and individual metabolic profiles, along with the efficacy of the treatment. In total 87 outpatients with diagnoses of bipolar disorder, major depression, anxiety, obsessive-compulsive disorder and post-traumatic stress disorder who required antidepressant treatment during pregnancy were recruited. Genotyping analysis of hepatic cytochrome P450 (CYPs) individual isoforms was performed. Antidepressants' blood concentrations and psychometric assessments were collected at five time points. Antidepressants' cord blood concentrations were assessed at birth. Sertraline showed greater stability in plasma concentrations and a lower placental penetrance index. Most of the antidepressants' concentrations below the therapeutic range were found in women with an extensive/ultrarapid metabolic profile. Antidepressants mainly metabolized by CYP2C19 were less frequently below the therapeutic range compared with antidepressants metabolized by CYP2D6. Pregnancy modulates cytochrome activity and drugs' pharmacokinetics. Genotyping analysis of CYPs isoforms and therapeutic drug monitoring might be used to guide clinicians in a well-tolerated treatment of psychiatric symptoms in pregnant women.

Premorbid intelligence quotient and school failure as risk markers for bipolar disorder and major depressive disorder

Mood disorders significantly impact global health, with MDD ranking as the second leading cause of disability in the United States and BD ranking 18th. Despite their prevalence and impact, the relationship between premorbid intelligence and the subsequent development of BD and MDD remains inconclusive.This study investigates the potential of premorbid Intelligence Quotient (IQ) and school failure frequency as risk factors for Bipolar Disorder (BD) and Major Depressive Disorder (MDD) in a birth cohort setting. We analyze data from the Pelotas population-based birth cohort study, comprising 3580 participants aged 22, who had no prior mood disorder diagnoses. Utilizing regression models and accounting for potential confounders, we assess the impact of IQ and school failure, measured at age 18, on the emergence of BD and MDD diagnoses at age 22, using individuals without mood disorders as comparators. Results reveal that lower IQ (below 70) at 18 is associated with an increased risk of BD (Adjusted Odds Ratio [AOR] 1.75, 95%CI: 1.00–3.09, p < 0.05), while higher IQ (above 120) is linked to MDD (AOR 2.16, 95%CI: 1.24–3.75, p < 0.001). Moreover, an elevated number of school failures is associated with increased BD risk (AOR 1.23, 95%CI: 1.11–1.41, p < 0.001), particularly for BD type 1 (AOR 1.36, 95% CI: 1.17–1.58, p < 0.001). These findings offer insights into the distinct premorbid intellectual characteristics of BD and MDD and contribute to a deeper understanding of their developmental trajectories, potentially informing the development of risk assessment tools for mood disorders.

Response to ketamine treatment for major depressive disorder not associated with number of psychiatric comorbidities

BackgroundKetamine has demonstrated efficacy in treating depression unresponsive to traditional interventions and is increasingly utilized in psychiatric practice. However, limited information is available regarding effectiveness in patients with psychiatric comorbidity. The aim of this study is to compare the response of ketamine treatment for depression in patients in a community sample with and without one or more coexisting psychiatric diagnoses. MethodsA retrospective review of medical records from a community ketamine clinic was done. Statistical analyses compared response to intravenous ketamine treatment, as defined by a 50% reduction in Patient Health Questionnaire (PHQ-9) score or final score of <10, in patients with an isolated diagnosis of major depressive disorder (MDD) to those with comorbid generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD) and/or attention-deficit/hyperactivity disorder (ADHD). ResultsData from 129 patient were analyzed (55.8 % female). It was found that the number of psychiatric comorbidities was not associated with ketamine treatment response rate nor mean change in PHQ-9 scores. LimitationsLevel of previous treatment resistance was not assessed systematically, and psychiatric interview and some elements of the patients’ care were not standardized, such as treatment schedule and dosing. ConclusionsThese findings suggest ketamine may be an effective treatment for depression in appropriate patients with psychiatric comorbidities including GAD, OCD, PTSD and/or ADHD. Additional investigation is warranted to evaluate various patient-level and other factors that may impact ketamine's ability to attenuate depressive symptoms.

Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework.

Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative tool for the assessment of MDD diagnosis. In the domain of computer-aided magnetic resonance imaging diagnosis, current research predominantly focuses on isolated local or global information, often neglecting the synergistic integration of multiple data sources, thus potentially overlooking valuable details. To address this issue, we proposed a diagnostic model for MDD that integrates high-frequency and low-frequency information using data from diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and functional magnetic resonance imaging (fMRI). First, we designed a meta-low-frequency encoder (MLFE) and a meta-high-frequency encoder (MHFE) to extract the low-frequency and high-frequency feature information from DTI and sMRI, respectively. Then, we utilized a multilayer perceptron (MLP) to extract features from fMRI data. Following the feature cross-fusion, we designed the ensemble learning threshold voting method to determine the ultimate diagnosis for MDD. The model achieved accuracy, precision, specificity, F1-score, MCC, and AUC values of 0.724, 0.750, 0.882, 0.600, 0.421, and 0.667, respectively. This approach provides new research ideas for the diagnosis of MDD.

An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis

Objective. Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD. Approach. In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis. Main results. This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD. Significance. The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.

Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder.

Major depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis. This study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine learning. Forty first-episode and drug-naïve patients with MDD and forty healthy controls (HCs) were recruited. EEG data were collected from all subjects in the resting state with eyes closed for 10min. The severity of MDD was assessed by the Hamilton Depression Rating Scale (HAMD-17). Machine learning analysis was used to identify the patients with MDD. Compared to the HC group, the relative power of the low delta and theta bands was significantly higher in the right occipital region, and the relative power of the alpha band in the entire posterior occipital region was significantly lower in the MDD group. In the MDD group, the alpha band scalp functional connectivity was overall lower, while the scalp functional connectivity in the gamma band was significantly higher than that in the HC group. In the feature set of the relative power of the ROI in each band, the highest accuracy of 88.2% was achieved using the KNN classifier while using PCA feature selection. In the explanatory model using SHAP values, the top-ranking influence feature is the relative power of the alpha band in the left parietal region. Our findings reveal that the abnormal EEG neural oscillations may reflect an imbalance of excitation, inhibition and hyperactivity in the cerebral cortex in first-episode and drug-naïve patients with MDD. The relative power of the alpha band in the left parietal region is expected to be an objective electrophysiological indicator of MDD.

Family Functioning, Illness-Related Self-Regulation Processes, and Clinical Outcomes in Major Depression: A Prospective Study in Greece.

Major depressive disorder (MDD) is a common, seriously impairing, and often recurrent mental disorder. Based on the predictions of the Circumplex Model of Marital and Family Systems and the Common-Sense Self-Regulation Model, the aim of the present prospective study is to examine the predictive value of clinical outcomes of a process model in which associations between perceived family functioning and patient's clinical outcomes (i.e., symptom severity and suicide risk) are mediated by illness representations and coping strategies. A total of 113 patients with a clinical diagnosis of MDD (16.8% males and 83.2% females) aged 47.25 ± 13.98 years and recruited from the outpatient department and the mobile mental health unit of the Psychiatric Clinic of the University Hospital of Heraklion in Crete, Greece, and from a Greek online depression peer-support group participated in the study. Family functioning was assessed in terms of cohesion and flexibility (Family Adaptability and Cohesion Evaluation Scales IV) at baseline. Illness representations (Illness Perception Questionnaire-Mental Health) and coping strategies (Brief Cope Orientation to Problems Experienced) were measured about five months later (5.04 ± 1.16 months). Symptom severity (Beck Depression Inventory) and suicidality (Risk Assessment Suicidality Scale) were measured about 10 months after the baseline assessment (9.56 ± 2.52 months). The results indicated that representations about MDD impact and symptom severity serially mediated the association between family cohesion and suicide risk in MDD. Furthermore, family cohesion was found to be linked with maladaptive coping through MDD impact representations. Family-based psychotherapeutic interventions specifically designed to target unhealthy family functioning, along with negative illness perceptions and dysfunctional coping, could be further developed and explored as adjunctive therapy to standard treatment in MDD.

51 The Minimal Effect of Depression on Cognitive Functioning when Accounting for Performance Validity

Objective:While much research has demonstrated a relationship between depression and cognitive deficits, most studies have neglected to include measurements of performance validity. The very small number of studies that have examined this relationship when accounting for performance validity have found that the relationship between depression and cognition is small or nonsignificant. The current study examined the relationship between depression (assessed through both clinical interview and self-report symptom measures) and multiple domains of cognition after accounting for noncredible performance on neuropsychological testing.Participants and Methods:Participants were veterans referred for outpatient clinical evaluation. Among other tests that varied across patients, the neuropsychological battery included: California Verbal Learning Test - second edition (CVLT-II) total immediate recall across trials 1–5, short delay free recall, and long delay free recall; Trail Making Test; FAS and Animal Fluency; Rey–Osterrieth Complex Figure Test (ROCF) copy and 3-minute delay recall; and Wisconsin Card Sorting Test (WCST) categories completed, total errors, and percent perseverative errors. These tests represent domains that have previously been examined in relation to depression (e.g., memory, processing speed, executive functioning). Evaluations were conducted for clinical purposes, so that some individuals who were not administered certain tests have missing data. The first set of regression analyses (N=206) included age, sex, and education at Step 1, Beck Depression Inventory-2 (BDI-2) total score at Step 2, and pass or failure of Trial 1 of the Test of Memory Malingering (TOMM) at Step 3 as predictors of performance on the 12 test indices. The second set of regression analyses (N=559) mirrored the first but with Major Depressive Disorder (MDD) diagnosis at Step 2 instead.Results:In the first set of analyses, after including TOMM in the model, only the relationship between BDI-2 and verbal fluency remained significant, but did not survive Bonferroni correction (p&lt;.004). In the second set of analyses, before including the TOMM, MDD diagnosis was significantly related only to worse performance on Trails A and CVLT-II Short and Long Delay Free Recall, with small effect sizes (rp=.06–.15). When TOMM Trial 1 was included in the model, MDD diagnosis became a nonsignificant predictor of CVLT-II Long Delay Free Recall but remained a significant predictor for Trails A and CVLT-II Short Delay Free Recall (p&lt;.05). After Bonferroni correction (p&lt;.004), with TOMM Trial 1 included in the model, MDD diagnosis remained a significant predictor only of CVLT-II Short Delay Free Recall, with a small effect size (rp=.17).Conclusions:After accounting for noncredible performance, there was little evidence for a relationship between depression diagnosis or symptoms and many cognitive domains. These results suggest that previously reported effects of depression on cognition are not mainly due to underlying neurological mechanisms, but rather to motivational factors. Future research could focus on the potential psychological mechanisms (e.g., negative attitudes, expectancy bias, low motivation, etc.) driving the relationship between depression and low effort on cognitive testing. If replicated, the current findings could be valuable to clinicians treating depressed individuals who have concerns about their cognitive functioning, by indicating psychoeducation and reassurance.

49 A Preliminary Neurocognitive Profile Characterization of Treatment Resistant Depression

Objective:Major Depressive Disorder (MDD) subtypes have been shown to differentially impact psychiatric symptom presentation, clinical features, and functional abilities. While there is extensive research regarding MDD subtypes and clinical characteristics, there has been limited information regarding the relationship between MDD subtypes and neurocognitive functioning. In particular, the neurocognitive impact of the subtype of treatment resistant depression (TRD), defined as MDD that is unresponsive to treatment, is unknown. The aim of this preliminary study was to address this gap by characterizing the neurocognitive profile of TRD. We characterized the performance of older adults with TRD on measures across multiple neurocognitive domains, and explored whether performance varied based on age and education.Participants and Methods:Data utilized were drawn from a broader NIMH-funded, randomized, controlled study conducted at the University of New Mexico that investigated the clinical and cognitive outcomes of varying pulse amplitudes during acute electroconvulsive therapy (ECT) in adults with MDD. Participants in the study were age 50+ with a diagnosis of MDD, and further delineated by subtype as TRD. For this analysis, we utilized demographic and baseline neurocognitive data collected prior to start of treatment for those diagnosed with MDD, recurrent, severe (TRD). Neurocognitive measures included the Delis Kaplan Executive Function System (D-KEFS) Verbal Fluency and Color-Word Interference Subtests, Hopkins Verbal Learning Test-Revised (HVLT-R), and the Wechsler Adult Intelligence Scale 4th Edition (WAIS-IV) Digit Spans. Demographic-adjusted scaled scores were computed, and descriptive statistics were used to characterize the demographic and neurocognitive features of the sample. Multiple Analysis of Variance (MANOVA) was used to investigate difference in performance across neurocognitive measures based on level of education, with age as a covariate.Results:The sample (n = 42) had a mean age of 65 (SD=8), education level between12 and 14 years, 66.6% were female and 93% were Caucasian. DKEFS Verbal Fluency Category Switching Total Switching Accuracy fell in the Average range (Mean SS=9.5, SD=3.1), and Color Word Inhibition Total Completion Time fell in the Average range (Mean SS=8.5, SD=3.3). HVLT-R Total Recall Correct fell in the Mildly Impaired range (Mean T=35.5, SD=9.9) and Delayed Recall Correct fell in the Mildly to Moderately Impaired range (Mean T=32.9, SD=11.0). WAIS-IV Digit Span fell in the Average range (Mean SS=9.5, SD=2.2). Results indicated that age did not adjust outcomes on the neurocognitive variables, Wilks's λ=0.63, F(6, 23)=2.13, p=0.08. We found no evidence for significant effect of level of education on neurocognitive functioning when controlling for the covariate of age, Wilks's λ=-0.16, F(36, 103.7)=1.47, p=0.07.Conclusions:To our knowledge, this is one of two studies to examine neurocognitive functioning in patients with TRD. The analysis indicated generally intact performance in the neurocognitive domains of executive function (inclusive of verbal fluency, cognitive flexibility, and inhibition), auditory attention and working memory, and Impaired performance on indices of verbal learning and memory. Age did not impact performance on neurocognitive measures, and there was no significant effect for level of education. Further research is warranted to confirm these findings and further explicate the neurocognitive profile of TRD.

59 The Impact of Anxiety on Memory Performance in Older Adults with Depression

Objective:Late life depression (LLD) refers to a diagnosis of major depressive disorder in people older than 60, and has been linked to significant cognitive impairment and increased risk of Alzheimer's disease. Although anxiety and depression are highly comorbid, the impact of anxiety on cognition in LLD is far less researched. This is important given that over 20% of middle aged and older adults endorse clinically significant chronic worry. Generalized anxiety disorder in older adults with major depression is associated with poorer cognition and worse treatment outcomes compared with those without anxiety. Therefore, the purpose of the study is to examine the role of anxiety on memory in LLD. We hypothesized that presence of anxiety among older depressed adults would be associated with worse cognitive performance over time.Participants and Methods:Participants included 124 individuals (69.4% female, 90.3% Caucasian) aged 60 or above (M = 71.5, SD = 7.4) who met criteria for major depression, single episode or recurrent. They completed the State Trait Anxiety Inventory, Montgomery Asberg Depression Rating Scale, and a measure of verbal episodic memory (WMS-IV Logical Memory) as part of a larger neuropsychological battery. Data were collected from baseline to three years as part of a larger NIMH-supported longitudinal study. Two-level linear mixed-effect models were fitted to predict memory. State and trait anxiety were used as time-varying predictors. The between-person (level 2) and within-person (level 1) effects of anxiety on memory were assessed controlling for the time trend, age, education, gender, race, and change in depression over time.Results:Plot trajectories across variables revealed a negative correlation such that as anxiety decreased, memory improved over time. Hierarchical linear mixed-effect models revealed that average state anxiety was a marginally significant between-person (level2) predictor for memory [B=-0.041, t(128)=-1.8, p=0.083]. Individuals with greater average state anxiety were more likely to experience memory decline compared to those with lower average state anxiety. In addition, the within-person effect (level 1) of state anxiety was significant [B=-0.096, t(253)=-2.7, p=0.007]. As an individual's anxiety increased over time, their memory declined. Trait anxiety showed a significant within-person effect on memory [B=-0.087, t(254)=-2.0, p=0.048], but a non-significant between-person effect [B=-0.005, t(124)=-0.06, p=0.95].Conclusions:Anxiety appears to increase the risk of memory decline in older adults with major depression, a cohort who are already at risk of cognitive decline. Changes in anxiety increased risk of memory decline even when accounting for changes in depression over time. Although the causal link between anxiety and cognitive impairment remains unclear, it is possible that anxiety and worry may compete for cognitive resources necessary for demanding tasks and situations, detracting from abilities, such as attention and working memory. Older adults with depression may also have difficulty coping adaptively with anxiety, which may negatively affect cognition. Finally, presence of anxiety may represent a form of mild behavioral impairment, a prodrome of cognitive decline leading to dementia. Overall, the present study highlights the negative impact of anxiety on memory performance, indicating that treatment interventions targeting anxiety in older adults are essential to help prevent cognitive decline.

73 Sleep Onset Latency and Duration in rTMS Treatment in Veterans with Treatment-Resistant Major Depressive Disorder

Objective:This study builds on the work by Rehman et al (2022) who argued that transcranial magnetic stimulation (TMS) treatment not only helps treat depression but also decreases sleep problems such as difficulty falling asleep,staying asleep, and waking too early. The present study further explores differences in sleep onset latency, meaning the time it takes to fall asleep, and duration of sleep per night in the pre and post treatment phases of rTMS. The information regarding major attributes of sleep is critical because recent research shows that about 90% of patients with major depressive disorder (MDD) also struggle with sleep disorders (Li et al., 2022), and sleeping for less than seven hours may eventually lead to sleep deprivation (Hirshkowitz et al., 2015), with increased risk of physical and mental health problems (Sheehan et al, 2019). Sleep onset latency estimates vary from individual to individual but typical sleep latency is considered between 10 to 20 minutes (Jung et al, 2013). As it has been shown that overall sleep problems improve with rTMS, we hypothesized that self-reported sleep onset latency will decrease, and sleep duration will increase.Participants and Methods:All participants met inclusion criteria for MDD diagnosis and completed a full course of TMS treatment (N=470; Mean age=53.45, SD=13.73). The sample was mostly male (81%) and ethnically diverse: 77.7% non-Hispanic White, 13.3% Black Americans, 1.9% Asian, 0.2 % Asian Indian, and 1.9% other ethnicities. Sleep problems were assessed using the following questions at the pre and post treatment stages: the number of minutes it takes to fall asleep and duration of sleep each night.Results:A Wilcoxon matched-pairs signed-rank test was conducted to determine whether there was a difference in sleep onset latency and hours of sleep per night between pre and post intervention. The results indicated a significant difference in time to fall asleep between pre and post treatment (pre-treatment M = 1.19, SD = 0.99, post-treatment M = 0.93, SD = 0.91; z = -5.01, p &lt; .001. In addition, there was a significant increase in the minutes of sleep per night in pre (M = 6.11, SD = 2.07) compared to the post treatment (M = 6.32, SD = 1.77), z = -2.56, p = .010.Conclusions:Reduced sleep is known to negatively impact mood, cognitive ability, work performance, and immune function (Besedovsky et al., 2012; Killgore, 2010; Massar et al, 2019; Vandekerckhove &amp; Wang, 2018). Similarly, longer sleep onset latency can cause an individual to enter the first sleep stage later than expected and complete fewer sleep cycles. The results of the present study show the effectiveness of rTMS in decreasing sleep onset latency and increasing the duration of sleep. Given the comorbidity and bidirectionality between sleep disturbances and mood disorders (Fang et al., 2019; Palagini et al., 2019), further researching treatments such as rTMS to improve sleep as a means to also improve mood is crucial. We propose acquiring knowledge about sleep attributes as an essential part of clinicians’ work early on in the rTMS treatment in order to monitor an individual’s global functioning level in light of improved sleep.

Serendipitous neurotransmitter findings in a frontline defender participating in a magnetic resonance spectroscopy study of first responders and military personnel with PTSD

We report a case study of a patient with a clinical diagnosis of Post-traumatic Stress Disorder (PTSD) referred to a neurochemistry Magnetic Resonance Spectroscopy (MRS) study. The clinical evaluation prior to scanning suggested the presence of PTSD, Major Depressive Disorder (MDD) accompanied by suicidal ideation. MRS prior to and at the completion treatment and 12-weeks post treatment identified neurotransmitters consistent with a diagnosis of PTSD and MDD. The neurochemistry evaluation immediately post-treatment identified levels that compared to healthy subjects while at 12-weeks post-treatment the neurochemistry reflected a recurrence of depressive symptoms that were described by the patient. These results offer the potential for neurochemical markers to monitor the response of an individual to therapy.

TCF4 and RBFOX1 as peripheral biomarkers for the differential diagnosis and treatment of major depressive disorder

BackgroundRecent genome-wide association studies on major depressive disorder (MDD) have indicated the involvement of LRFN5 and OLFM4; however, the expression levels and roles of these molecules in MDD remain unclear. The present study aimed to determine the serum levels of TCF4 and RBFOX1 in patients with MDD and to investigate whether these molecules could be used as biomarkers for MDD diagnosis. MethodsThe study included 99 drug-naïve MDD patients, 90 drug-treated MDD patients, and 81 healthy controls (HCs). Serum TCF4 and RBFOX1 levels were measured by ELISA. Pearson's correlation analysis was conducted to determine the association between TCF4/RBFOX1 and clinical variables. Linear support vector machine classifier was used to evaluate the diagnostic capabilities of TCF4 and RBFOX1. ResultsSerum TCF4 and RBFOX1 levels were substantially higher in MDD patients than in HCs and significantly lower in drug-treated MDD patients than in drug-naïve MDD patients. Moreover, serum TCF4 and RBFOX1 levels were associated with the Hamilton Depression Scale score, duration of illness, serum lipids levels, and hepatic function. Thus, both these molecules showed potential as biomarkers for MDD. TCF4 and RBFOX1 combination exhibited a higher diagnostic performance, with the mean area under the curve values of 0.9861 and 0.9936 in the training and testing sets, respectively. LimitationsSmall sample size and investigation of only the peripheral nervous system. ConclusionsTCF4 and RBFOX1 may be involved in the pathogenesis of MDD, and their combination may serve as a diagnostic biomarker panel for MDD.

Metacognition and Mentalization in Patients with Major Depressive Disorder

Social cognition is necessary to comprehend and interpret the behavioural intentions of oneself and others in social contexts. Despite an increasing understanding of social cognition deficits in mental illness, the relationship between Major Depressive Disorder (MDD) and high-order social cognition deficits (i.e., mentalization and metacognition) remains under-investigated. This paper aims to provide a holistic review of the existing empirical evidence on mentalization and metacognition in patients with MDD and suggest areas in need of further research. While a close relationship between MDD and metacognition has been consistently reported, some disagreement was raised regarding the association between mentalization capacity and MDD. Besides, the direction of the association between MDD, mentalization and metacognition was also underlined by studies examining the outcomes of high-order social cognition therapies. Mentalization and metacognition were both found to be predictors or at least moderators, of MDD diagnosis and symptom severity. Future evidence from developmental and neuroimaging studies may provide valuable insights into the field of study by uncovering the varying degrees of the impact caused by social cognition deficits and their associated neural mechanisms.

Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression.

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools. To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information. This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023. Patient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview. Of 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001). This study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.