Diagnosis Of Major Depressive Disorder Research Articles

A Quasi-Experimental Clinical Trial of the Detached Mindfulness Group Therapy for Married Women with Major Depressive Disorder

Background: In an effort to make psychotherapy more widely available, group therapy sessions have recently emerged as a type of treatment. Although there is a lack of evidence about the effectiveness of psychotherapy groups in treating Major Depressive Disorder (MDD) in adults, literature shows that married women with a diagnosis of MDD could benefit from these treatments. Objective: This research aims to shed light on a quasi-experimental trial of the Detached Mindfulness Group Therapy (DMGT) for married women with a diagnosis of MDD. Methods: This study examined the effect of a twelve-week intervention in a quasi-experimental trial by using a pre- and post-intervention design. Results. Based on the results, the DMGT treatment is an effective strategy for treating major depressive disorder. It was found to have a large significant pre-post effect sizes on self-reported depressive symptoms, level of depression, and knowledge development. The level of satisfaction with the intervention was quite high. Conclusions: The strong evidence on treatment protocol acceptability, effect magnitude, outcome variance, and group members variables strongly support a future study to test the effectiveness of the DMGT among married women. Quasi-experimental trials should be used in future studies to determine the effectiveness of this DMGT treatment.

Burden and Predictors of Depression in Populations With Coronary Heart Disease.

Depression significantly impacts the quality of life and medical care in patients with coronary heart disease (CHD). This study assesses the burden of depression in adults aged 40 years and above with CHD and evaluates predictors of depression in this population. It has been reported that approximately 17-44% of persons with CHD have a major depression diagnosis and that nearly 27% of individuals undergoing coronary artery bypass graft operation suffer depression following the procedure. Methods: Data from the 2022 National Health Interview Survey was used. The sample was made up of adults 40 years and above with CHD. A chi-square analysis was used to identify differences between those who were depressed and those who were not. Logistic and ordinal regression analyses were used to identify predictors of depression and severe depression, respectively. The proportion of adults 40 years and above with CHD who reported having depression was 863/1700 (50.5%). Among those who were ≥65, the proportion of those who reported depression and those who did not were similar (49.3% vs. 50.7%). Most women reported having depression (57.4% vs. 42.6%), while fewer men reported having depression (46.3% vs. 53.7%). The positive predictors of depression include being insured (odds ratio (OR) 1.26 (1.05-1.53), p = 0.016), college degree (OR 1.09 (1.01-1.18), p = 0.040), diabetes mellitus (OR 1.28 (1.15-1.42), p < 0.001), and hypertension (OR 1.34 (1.24-1.44), p < 0.001). The negative predictors of being depressed were age ≥65 (OR 0.74 (0.69-0.80), p < 0.001), male sex (OR 0.54 (0.50-0.58), p < 0.001), and ratio of family income (RFI) ≥1 (OR 0.68 (0.61-0.77), p < 0.001). The positive predictors of severe depression include diabetes mellitus (OR 1.38 (1.06-1.81), p = 0.019) and current cigarette use (OR 2.10 (1.44-3.07), p < 0.001). A significant proportion of adults 40 years and above with CHD have depression, and socioeconomic and cardiovascular risk factors are associated with a high likelihood of depression. Cardiovascular risk factors alone predict the likelihood of severe depression. Interventions to address depression in CHD should target specifically these high-risk individuals.

Association between fatigue and depressive symptoms in persons with post-COVID-19 condition: a post hoc analysis

Objective Post-COVID-19 Condition (PCC) is a prevalent, persistent and debilitating phenomenon occurring three or more months after resolution of acute COVID-19 infection. Fatigue and depressive symptoms are commonly reported in PCC. We aimed to further characterize PCC by assessing the relationship between fatigue and depressive symptom severity in adults with PCC. Methods A post hoc analysis was conducted on data retrieved from a randomized, double-blinded, placebo-controlled study evaluating vortioxetine for cognitive deficits in persons with PCC. We sought to determine the relationship between baseline fatigue [i.e. Fatigue Severity Scale (FSS) total score] and baseline depressive symptom severity [i.e. 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR-16) total score] in adults with PCC. Results The statistical analysis included baseline data from 142 participants. After adjusting for age, sex, education, employment status, history of major depressive disorder (MDD) diagnosis, self-reported physical activity, history of documented acute SARS-CoV-2 infection and body mass index (BMI), baseline FSS was significantly correlated with baseline QIDS-SR-16 (β = 0.825, p = .001) Conclusion In our sample, baseline measures of fatigue and depressive symptoms are correlated in persons living with PCC. Individuals presenting with PCC and fatigue should be screened for the presence and severity of depressive symptoms. Guideline-concordant care should be prescribed for individuals experiencing clinically significant depressive symptoms. Fatigue and depressive symptom severity scores were not pre-specified as primary objectives of the study. Multiple confounding factors (i.e. disturbance in sleep, anthropometrics and cognitive impairment) were not collected nor adjusted for in the analysis herein. Trial registration Unrestricted Research Grant from H. Lundbeck A/S, Copenhagen, Denmark. ClinicalTrials.gov Identifier: NCT05047952

The metabolome-wide signature of major depressive disorder.

Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e-07) and saturated (P = 3.2e-05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e-4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

Prevalence and Co-Occurrence of Psychiatric Conditions Among Bereaved Adults

Rates of grief-related psychiatric conditions, such as prolonged grief disorder (PGD), posttraumatic stress disorder (PTSD), and major depressive disorder (MDD), among bereaved adults in the US are largely unknown due to limited studies that leverage national samples. To assess risk factors for and prevalence rates and co-occurrence of PGD, PTSD, and MDD among bereaved adults in the US. Data for this survey study were collected from a large US panel sample between October 10 and 28, 2022, using a web-based survey. Participants were aged 18 years or older and were proficient in English. Data analysis was conducted between March and June 2023. Probable psychiatric conditions were assessed with validated measures that used conservative cutoff scores, duration, and impairment criteria. These measures included the revised Prolonged Grief Disorder scale for PGD, the PTSD Checklist for DSM-5 for PTSD, and the Patient Health Questionnaire-9 for MDD. Data were analyzed using basic descriptives and logistic regression. A total of 2034 adults (n = 1529 and 505 in the bereaved and comparison groups, respectively) completed the study. Respondents had a mean (SD) age of 40.7 (15.9) years; the majority were women (1314 [64.6%]) and had at least some college experience (1394 [68.5%]). With regard to race and ethnicity, 392 respondents (19.3%) were Black, 138 (6.8%) were Hispanic, and 1357 (66.7%) were White. Among bereaved adults, 312 (20.4%), 518 (33.9%), and 461 (30.2%) met criteria for a presumptive diagnosis of PGD, PTSD, and MDD, respectively. Comorbidities were common, with 441 participants (28.8%) meeting criteria for at least 2 co-occurring disorders. Comorbid PGD, PTSD, and MDD were more common than any 2 co-occurring or isolated disorders; the presence of co-occurring conditions was more likely among respondents who reported a traumatic loss. Age and educational attainment were associated with the risk of psychiatric conditions; less time since the index death, loss of a psychologically close other, and a traumatic loss experience were associated with increased risk of PGD, PTSD, and MDD or their co-occurrence. In this study, the majority of bereaved adults did not meet presumptive criteria for PGD, PTSD, or MDD. Nevertheless, PGD, PTSD, and MDD were highly prevalent and comorbid, particularly among those who experienced traumatic loss. These findings underscore the need for integrated psychological care that leverages transdiagnostic mechanisms of evidence-based practice.

Sleep disturbances and incident risk of major depressive disorder in a population-based cohort

Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general population-based cohort is not well known. This study investigated associations between both polysomnography (PSG)-based or subjective sleep features and incident MDD. Participants representative of the general population who had never had MDD completed sleep questionnaires (n = 2000) and/or underwent PSG (n = 717). Over 8 years’ follow-up, participants completed psychiatric interviews enabling the diagnosis of MDD. Survival Cox models were used to analyze associations between sleep features and MDD incidence. A higher Epworth Sleepiness Scale and presence of insomnia symptoms were significantly associated with a higher incidence of MDD (hazard ratio [HR] [95 % confidence interval (CI)]: 1.062 [1.022–1.103], p = 0.002 and 1.437 [1.064–1.940], p = 0.018, respectively). Higher density of rapid eye movements in rapid eye movement (REM) sleep was associated with a higher incidence of MDD in men (HR 1.270 [95 % CI 1.064–1.516], p = 0.008). In women, higher delta power spectral density was associated with a lower MDD incidence (HR 0.674 [95 % CI 0.463–0.981], p = 0.039). This study confirmed the associations between subjective and objective sleep features and the incidence of MDD in a large community dwelling cohort.

A multi-site 99mTc-HMPAO SPECT study of cerebral blood flow in a community sample of patients with major depression

Prior regional Cerebral Blood Flow (rCBF) studies in Major Depressive Disorder (MDD) have been limited by small, highly selective, non-representative samples that have yielded variable and poorly replicated findings. The aim of this study was to compare rCBF measures in a large, more representative community sample of adults with MDD and healthy control participants. This is a cross-sectional, retrospective multi-site cohort study in which clinical data from 338 patients 18–65 years of age with a primary diagnosis of MDD were retrieved from a central database for 8 privately owned, private-pay outpatient psychiatric centers across the United States. Two 99mTc-HMPAO SPECT brain scans, one at rest and one during performance of a continuous performance task, were acquired as a routine component of their initial clinical evaluation. In total, 103 healthy controls, 18–65 years old and recruited from the community were also assessed and scanned. Depressed patients had significantly higher rCBF in frontal, anterior cingulate, and association cortices, and in basal ganglia, thalamus, and cerebellum, after accounting for significantly higher overall CBF. Depression severity associated positively with rCBF in the basal ganglia, hippocampus, cerebellum, and posterior white matter. Elevated rCBF was especially prominent in women and older patients. Elevated rCBF likely represents pathogenic hypermetabolism in MDD, with its magnitude in direct proportion to depression severity. It is brain-wide, with disproportionate increases in cortical and subcortical attentional networks. Hypermetabolism may be a reasonable target for novel therapeutics in MDD.

Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy.

Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.

Staging of psychiatric disorders in patients with limited and advanced/metastatic solid cancer: A pilot study.

e22545 Background: 38% of cancer patients (pts) receive diagnosis of major depressive disorder. According with the clinimetric approach of the staging of psychiatric disease, the 5-step model of unipolar depression allows for the evaluation of the extent of the disorder, its severity, and characteristics at a specific time in the course of the disease. This study was aimed: a) to assess mental health and quality of life of cancer patients with limited (LS) and advanced/metastatic (AS) solid cancer through the join evaluation of the staging of psychiatric disorders; b) to explore the potential role of severity of oncological disease in anxious/depressive disease development. Methods: A cross-sectional study was conducted at the Oncology Unit of Luigi Sacco Hospital of Milan during the first session of a psychological support path. Patients’ cancer diagnosis and stage, as well as regular engagement in work, leisure and social activities were explored. Hospital and Depression Anxiety Scale (HADS), the Revised Illness Perception Questionnaire (IPQ-R) and Mental Health Continuum Short-Form (MHC-SF) were also administered. Results: 104 pts were included: F 86, M 18, median age 57,5 (27-84), 30% AS, and 70% LS. Pts’ anxious/depressive symptoms were assessed by HADS scoring and they were divided into 3 groups according to the model of unipolar depression: Group 1a (prodromal phase – no symptoms), Group 1b (prodromal phase – mood symptoms) and Group 2 (anxious/depressive disorder). Pts in group 2 were significantly more frequently AS pts, while in group 1a pts in LS stage (χ² = 7.78, p &lt; .05). Based on MHC-SF scores, flourishing and languishing were significantly more frequently reported by pts in group 1a (55,5% and 46% of pts, respectively), and moderate mental health in group 2 (45,9% of pts, χ² = 18.1, p &lt; .001). Regression analysis highlighted non-hobbyist vs. hobbyists, AS vs. LS cancer pts, asthenic vs. non asthenic pts, and moderate vs flourishing metal health were more likely to receive a provisional anxious depressive disorder diagnosis (Stage 2). No association emerged for other physical symptoms, work, and socialization. Conclusions: AS pts with asthenia, no engagement in structured activities and moderate mental health had a higher risk of developing anxious/depressive disorders. Screening psychiatric disorders in cancer pts may improve the physician's ability to recognize earlier phase of psychopathological disease and guide pts to tailored treatment. These may prevent progression of psychiatric disease and promote mental health and positive quality of life.

Disparities in the prevalence of anxiety and major depressive disorder in patients with lung cancer and the associated risk factors: A retrospective analysis.

e22544 Background: Cancer is a leading cause of mortality and has been associated with an increasing prevalence of psychological disorders. Previous studies indicate that approximately 1 in 8 individuals diagnosed with lung cancer also experience symptoms of depression. Our study aimed to assess the disparities in the prevalence and association of Anxiety and Major depressive disorder (MDD) in lung cancer patients. Methods: Using the National Inpatient Sample 2017-2020, we identified lung cancer patients with regional and advanced metastatic disease. They were divided into two groups - with and without concomitant Anxiety and MDD. Baseline demographic characteristics were analyzed to assess disparities in prevalence, and multivariate logistic regression was used to evaluate the association of Anxiety and MDD in these patients. Results: A total of 461975 patients had lung cancer, and 25780(5%) had concurrent diagnoses of Anxiety and MDD. The anxiety + MDD group had 70% females and 30% males (p &lt; 0.001).Females had an increased likelihood of MDD + Anxiety (OR = 2.36 (2.21-2.52); p &lt; 0.001). Compared to younger patients, older patients were less likely to have MDD + Anxiety ( &gt; 65: OR = 0.45(0.25-0.81); p = 0.008). Compared to the white population, patients of other races had a decreased likelihood of MDD + Anxiety (Black: OR = 0.41( 0.36-0.47), p &lt; 0.001; Hispanic: OR = 0.65( 0.55-0.77), p &lt; 0.001; Other races OR = 0.60(0.48-0.76), p &lt; 0.001). Private insurance and patients without insurance were less likely to have MDD + Anxiety (OR = 0.74(0.67-0.81) and OR = 0.56(0.44-0.71), p &lt; 0.001, respectively). Patients admitted to teaching and large hospitals had an increased likelihood of MDD + Anxiety compared to smaller and nonteaching hospitals (OR = 1.13(1.03-1.24), p = 0.006 and OR = 1.14(1.03-1.25), p = 0.007, respectively). Patients with Hyperlipidemia, Hypertension, COPD, and Obesity were more likely to have MDD + Anxiety (OR = 1.39(1.30-1.48), p &lt; 0.001; OR = 1.11(1.03-1.18), p = 0.002; OR = 1.29(1.21-1.38), p &lt; 0.001 and OR = 1.33(1.22-1.45), p &lt; 0.001 respectively). In contrast, patients with Diabetes had a decreased likelihood of concomitant Anxiety + Depression (OR = 0.92(0.85-0.99), p = 0.04). Lung cancer patients with concurrent Anxiety + MDD had a higher total cost of hospitalization. (+$ 2611(66-5156); p = 0.04). Conclusions: Female lung cancer patients and white individuals had an increased risk of anxiety and depression. Patients with private insurance had decreased risk, and those admitted to larger and teaching hospitals had increased association with these psychological disorders. Devising comprehensive strategies to effectively control underlying psychiatric conditions is crucial for improving outcomes in lung cancer patients, aiming to enhance quality of life and prolong life expectancy.

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data

BackgroundThe absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presentations. Despite extensive machine learning studies in psychiatric diagnosis, a reliable tool integrating multi-modality data is still lacking. MethodsIn this study, blood samples from 100 MDD and 100 HC were analyzed, along with MRI images from 46 MDD and 49 HC. Here, we devised a novel algorithm, integrating graph neural networks and attention modules, for MDD diagnosis based on inflammatory cytokines, neurotrophic factors, and Orexin A levels in the blood samples. Model performance was assessed via accuracy and F1 value in 3-fold cross-validation, comparing with 9 traditional algorithms. We then applied our algorithm to a dataset containing both the aforementioned protein quantifications and neuroimages, evaluating if integrating neuroimages into the model improves performance. ResultsCompared to HC, MDD showed significant alterations in plasma protein levels and gray matter volume revealed by MRI. Our new algorithm exhibited superior performance, achieving an F1 value and accuracy of 0.9436 and 94.08 %, respectively. Integration of neuroimaging data enhanced our novel algorithm's performance, resulting in an improved F1 value and accuracy, reaching 0.9543 and 95.06 %. LimitationsThis single-center study with a small sample size requires future evaluations on a larger test set for improved reliability. ConclusionsIn comparison to traditional machine learning models, our newly developed MDD diagnostic model exhibited superior performance and showed promising potential for inclusion in routine clinical diagnosis for MDD.

The impact of major depressive disorder on glycaemic control in type 2 diabetes: a longitudinal cohort study using UK Biobank primary care records

BackgroundThis study evaluates longitudinal associations between glycaemic control, measured by mean and within-patient variability of glycated haemaglobin (HbA1c) levels, and major depressive disorder (MDD) in individuals with type 2 diabetes (T2D), focusing on the timings of these diagnoses.MethodsIn UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered.ResultsT2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 years median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis (β=0.51\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta =0.51$$\\end{document}, 95% CI: [0.17, 0.86]). Retrospectively, across study follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13–1.19) times higher in post-T2D MDD individuals.ConclusionsThe timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

Adolescent and Adult Transitions From Major Depressive Disorder to Bipolar Disorder

Bipolar disorder (BD) often first appears in adolescence after onset of major depressive disorder (MDD), but diagnosis and treatment are commonly delayed. This delay is a concern because untreated BD is associated with adverse long-term outcomes, a more recurrent disease course and difficult-to-treat illness, and suicide attempts and deaths. To examine the association of age at MDD onset with early transition to BD and the subsequent use of psychiatric inpatient services as a severity indicator. This retrospective cohort study analyzed comprehensive data sourced from the Stockholm MDD Cohort data from 1997 to 2018, which encompass both outpatient and inpatient care. Individuals with an initial MDD episode from January 1, 2010, to December 31, 2013, who transitioned to BD by December 31, 2018, were identified. Data were analyzed between September 5 and December 28, 2023. Post MDD assessments included a depression severity index, comorbidities, psychotherapy, psychotropic drugs, and electroconvulsive therapy. The main outcome was the transition from MDD to BD, dichotomized as occurring early (within 3 years of MDD onset) or late (3 years after MDD onset). Secondary outcomes encompassed the use of psychiatric inpatient services post transition and patterns of medication usage. A robust propensity score matching framework was used to estimate outcomes. The final balanced cohort included 228 individuals, with an equal distribution between adults (n = 114; mean [SD] age, 24.5 [6.3] years; 96 female [84.2%]; 20 experiencing an early transition to BD [17.5%]) and youths (n = 114; mean [SD] age, 15.3 [1.6] years; 93 female [81.6%]; 8 experiencing an early transition to BD [7.0%]). Youths were substantially less likely to transition early (odds ratio, 0.42; 95% CI, 0.20-0.88; P = .02), despite having more outpatient visits (mean [SD] visits per month, 1.21 [1.07] vs 0.97 [0.98] for adults; P = .01). Both groups experienced substantially reduced inpatient care following a BD diagnosis, concurring with a marked decline in antidepressant use without increased lithium use. These findings suggest that adolescents may experience delayed BD progression and that diagnosis substantially reduced inpatient care in all age groups, which coincided with a reduction in the use of antidepressants. These findings may inform pharmacologic strategies in patients with first-episode MDD at risk for BD.

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach

BackgroundMajor depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.e. too many miRNAs for consideration) and stability. MethodsThis study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n = 60) from healthy controls (HCs, n = 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application. ResultsPatients with MDD could be differentiated from HCs with the area under the receiver operating characteristic curve (AUC) of 0.81 on testing data when all available miRNAs were considered (which served as a benchmark). Our LR model selected miRNAs up to 5 (known as LR-5 model) emerged as the best model because it achieved a moderate classification ability (AUC = 0.75), relatively high interpretability (feature number = 5) and stability (ϕ̂Z=0.55) compared to the benchmark. The top-ranking miRNAs identified by our model have demonstrated associations with MDD pathways involving cytokine signalling in the immune system, the reelin signalling pathway, programmed cell death and cellular responses to stress. ConclusionThe LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.

High-Order line graphs of fMRI data in major depressive disorder.

Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy=92.42%, sensitivity=92.86%, specificity=92.11%, precision=89.66%, F1=91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders

BackgroundSex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics.MethodsWe here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV).ResultsLimbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male–female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction.ConclusionsOverall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Effect of l-arginine compared to placebo on sexual function in women with major depressive disorder: a randomized controlled trial

BackgroundWhile some evidence suggests that l-arginine may improve sexual function and alleviate depression, it has not been investigated in women with depression to assess both its effects on the depression and sexual function concurrently.MethodsPatients who had received a diagnosis of major depressive disorder, as determined by predetermined inclusion and exclusion criteria, were enrolled in this triple-blind clinical trial. Patients were divided into two groups: group A, received L-arginine 1 gram twice daily, and group B, received a placebo for four weeks. They were evaluated at baseline, after four and eight weeks with the Hamilton Depression Rating Scale (HDRS), and Rosen’s questionnaire or Female Sexual Function Index (FSFI).ResultsA decrease in the severity of depression was observed in all patients, which was determined due to Hamilton’s questionnaire (P-value < 0.001). During the time in group A, FSFI increased. Based on the FSFI questionnaire, they had improvement in some domains, including the lubrication index and orgasm index, which significantly changed in the eighth week compared to the baseline (P-value < 0.05). However, these two indicators did not change statistically significantly compared to the placebo group.ConclusionL-arginine supplementation can improve sexual function, particularly lubrication and orgasm, and mood in women with depression, with minimal side effects observed. Additional research is necessary to validate these results by examining the effects of higher dosages, extended durations, and larger populations of depressed patients.Trial registrationIranian Registry of Clinical Trial: IRCT20100127003210N26.

Algorithm-based modular psychotherapy vs. cognitive-behavioral therapy for patients with depression, psychiatric comorbidities and early trauma: a proof-of-concept randomized controlled trial.

Effect sizes of psychotherapies currently stagnate at a low-to-moderate level. Personalizing psychotherapy by algorithm-based modular procedures promisesimproved outcomes, greater flexibility, and a better fit between research and practice. However, evidence for the feasibility and efficacy of modular-based psychotherapy, using a personalized treatment algorithm, is lacking. This proof-of-concept randomized controlled trial was conducted in 70 adult outpatients with a primary DSM-5 diagnosis of major depressive disorder, a score higher than 18 on the 24-item Hamilton Rating Scale for Depression (HRSD-24), at least one comorbid psychiatric diagnosis according to the Structured Clinical Interview for DSM-5 (SCID-5), a history of at least "moderate to severe" childhood maltreatment on at least one domain of the Childhood Trauma Questionnaire (CTQ), and exceeding the cut-off value on at least one of three measures of early trauma-related transdiagnostic mechanisms: the Rejection Sensitivity Questionnaire (RSQ), the Interpersonal Reactivity Index (IRI), and the Difficulties in Emotion Regulation Scale-16 (DERS-16). Patients were randomized to 20 sessions of either standard cognitive-behavioral therapy alone (CBT) or CBT plus transdiagnostic modules according to a mechanism-based treatment algorithm (MoBa), over 16 weeks. We aimed to assess the feasibility of MoBa, and to compare MoBa vs. CBT with respect to participants' and therapists' overall satisfaction and ratings of therapeutic alliance (using the Working Alliance Inventory - Short Revised, WAI-SR), efficacy, impact on early trauma-related transdiagnostic mechanisms, and safety. The primary outcome for efficacy was the HRSD-24 score at post-treatment. Secondary outcomes included, among others, the rate of response (defined as a reduction of the HRSD-24 score by at least 50% from baseline and a score <16 at post-treatment), the rate of remission (defined as a HRSD-24 score ≤8 at post-treatment), and improvements in early trauma-related mechanisms of social threat response, hyperarousal, and social processes/empathy. We found no difficulties in the selection of the transdiagnostic modules in the individual patients, applying the above-mentioned cut-offs, and in the implementation of MoBa. Both participants and therapists reported higher overall satisfaction and had higher WAI-SR ratings with MoBa than CBT. Both approaches led to major reductions of depressive symptoms at post-treatment, with a non-significant superiority of MoBa over CBT. Patients randomized to MoBa were nearly three times as likely to experience remission at the end of therapy (29.4% vs. 11.4%; odds ratio, OR = 3.2, 95% CI: 0.9-11.6). Among mechanism-based outcomes, MoBa patients showed a significantly higher post-treatment effect on social processes/empathy (p<0.05) compared to CBT patients, who presented an exacerbation on this domain at post-treatment. Substantially less adverse events were reported for MoBa compared to CBT. These results suggest the feasibility and acceptability of an algorithm-based modular psychotherapy complementing CBT in depressed patients with psychiatric comorbidities and early trauma. While initial evidence of efficacy was observed, potential clinical advantages and interindividual heterogeneity in treatment outcomes will have to be investigated in fully powered confirmation trials.

Clinical distinctions in symptomatology and psychiatric comorbidities between misdiagnosed bipolar I and bipolar II disorder versus major depressive disorder

BackgroundTo explore the demographic and clinical features of current depressive episode that discriminate patients diagnosed with major depressive disorder (MDD) from those with bipolar I (BP-I) and bipolar II (BP-II) disorder who were misdiagnosed as having MDD .MethodsThe Mini-International Neuropsychiatric Interview (MINI) assessment was performed to establish DSM-IV diagnoses of MDD, and BP-I and BP-II, previously being misdiagnosed as MDD. Demographics, depressive symptoms and psychiatric comorbidities were compared between 1463 patients with BP-I, BP-II and MDD from 8 psychiatric settings in mainland China. A multinomial logistic regression model was performed to assess clinical correlates of diagnoses.ResultsA total of 14.5% of the enrolled patients initially diagnosed with MDD were eventually diagnosed with BP. Broad illness characteristics including younger age, higher prevalence of recurrence, concurrent dysthymia, suicidal attempts, agitation, psychotic features and psychiatric comorbidities, as well as lower prevalence of insomnia, weight loss and somatic symptoms were featured by patients with BP-I and/or BP-I, compared to those with MDD. Comparisons between BP-I and BP-II versus MDD indicated distinct symptom profiles and comorbidity patterns with more differences being observed between BP-II and MDD, than between BP-I and MDD .ConclusionThe results provide evidence of clinically distinguishing characteristics between misdiagnosed BP-I and BP- II versus MDD. The findings have implications for guiding more accurate diagnoses of bipolar disorders.

The prevalence of major depressive disorder in people with HIV: Results from the All of Us Research Program.

The All of Us (AoU) Research Program is a national-scale effort to build a dataset to help transform the future of health research by equipping researchers with comprehensive health data from diverse populations, especially those underrepresented in biomedical research. Our objectives were to evaluate the burden of HIV and major depressive disorder (MDD) in underrepresented groups and the frequency of the HIV/MDD comorbidity. We conducted a cross-sectional analysis combining collected survey and electronic health record (EHR) data. We ascertained HIV and MDD cases using Observational Medical Outcomes Partnership codes. We used multivariable logistic regression to obtain the odds ratio of HIV in AoU participants and MDD in AoU participants with HIV. The latest AoU data release includes 412 211 participants: 254 700 have at least one medical condition concept in their EHR, of whom 5193 (1.3%) had HIV, and 2238 (43%) of those with HIV had a diagnosis of MDD. Black AoU participants had approximately 4.58 times the odds of having an HIV diagnosis compared with the combined odds of all other racial groups. AoU participants with HIV were more likely to have MDD (p = 0.001) than were participants without HIV. Among AoU participants, Black individuals have a disproportionately high burden of HIV, pointing to underlying factors such as social determinants of health, limited access to healthcare or prevention resources, and potential systemic biases that contribute to these differences. In addition, HIV is a risk factor for mental health issues like MDD. Further data collection from people with HIV will elucidate contributing factors and the need for interventions.