Meta-review of depressive subtyping models

In perspective of specific symptoms of major depressive disorder: Functional connectivity analysis of electroencephalography and potential biomarkers of treatment response

BACKGROUND

The symptom variability in major depressive disorder (MDD) complicates treatment assessment, necessitating a thorough understanding of MDD symptoms and potential biomarkers.

METHODS

In this prospective study, we enrolled 54 MDD patients and 39 controls. Over the course of weeks 1, 2, and 4 participants underwent evaluations, with electroencephalograms (EEG) recorded at baseline and week 1. Our investigation considered five previously identified syndromal factors derived from the 17-item Hamilton Depression Rating Scale (17-item HAMD) for assessing depression: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. We assessed treatment response and EEG characteristics across all syndromal factors and total scores, all of which are based on the 17-item HAMD. To analyze the topology of brain networks, we employed functional connectivity (FC) and a graph theory-based method across various frequency bands.

RESULTS

The healthy control group had notably higher values in delta band EEG FC compared to the MDD patient group. Similar distinctions were observed between the responder and non-responder patient groups. Further exploration of baseline FC values across distinct syndromal factors revealed significant variations among the core, psychomotor-insight, and anorexia subgroups when using a specific graph theory-based approach, focusing on global efficiency and average clustering coefficient.

LIMITATIONS

Different antidepressants were included in this study. Therefore, the results should be interpreted with caution.

CONCLUSIONS

Our findings suggest that delta band EEG FC holds promise as a valuable predictor of antidepressant efficacy. It demonstrates an ability to adapt to individual variations in depressive symptomatology, offering insights into personalized treatment for patients with depression.

Baseline circulating biomarkers, their changes, and subsequent suicidal ideation and depression severity at 6 months: A prospective analysis in patients with mood disorders

BACKGROUND

Identifying circulating biomarkers associated with prospective suicidal ideation (SI) and depression could help better understand the dynamics of these phenomena and identify people in need of intense care. In this study, we investigated the associations between baseline peripheral biomarkers implicated in neuroplasticity, vascular homeostasis and inflammation, and prospective SI and depression severity during 6 months of follow-up in patients with mood disorders.

METHODS

149 patients underwent a psychiatric evaluation and gave blood to measure 32 plasma soluble proteins. At follow-up, SI incidence over six months was measured with the Columbia Suicide Severity Rating Scale, and depressive symptoms were assessed with the Inventory for Depressive Symptomatology. Ninety-six patients provided repeated blood samples. Statistical analyses included Spearman partial correlation and Elastic Net regression, followed by the covariate-adjusted regression models.

RESULTS

51.4 % (N = 71) of patients reported SI during follow-up. After adjustment for covariates, higher baseline levels of interferon-γ were associated with SI occurrence during follow-up. Higher baseline interferon-γ and lower orexin-A were associated with increased depression severity, and atypical and anxious, but not melancholic, symptoms. There was also a tendency for associations of elevated baseline levels of interferon-γ, interleukin-1β, and lower plasma serotonin levels with SI at the six-month follow-up time point. Meanwhile, reduction in transforming growth factor- β1 (TGF-β1) plasma concentration correlated with atypical symptoms reduction.

CONCLUSION

We identified interferon-γ and orexin-A as potential predictive biomarkers of SI and depression, whereas TGF-β1 was identified as a possible target of atypical symptoms.

Clinical associations with treatment resistance in depression: An electronic health record study

Treatment resistance is common in major depressive disorder (MDD), yet clinical risk factors are not well understood. Using a discovery-replication design, we conducted phenome-wide association studies (PheWASs) of MDD treatment resistance in two electronic health record (EHR)-linked biobanks. The PheWAS included participants with an MDD diagnosis in the EHR and at least one antidepressant (AD) prescription. Participant lifetime diagnoses were mapped to phecodes. PheWASs were conducted for three treatment resistance outcomes based on AD prescription data: number of unique ADs prescribed, ≥1 and ≥2 CE switches. Of the 180 phecodes significantly associated with these outcomes in the discovery cohort (n = 12,558), 71 replicated (n = 8,206). In addition to identifying known clinical factors for treatment resistance in MDD, the total unique AD prescriptions was associated with additional clinical variables including irritable bowel syndrome, gastroesophageal reflux disease, symptomatic menopause, and spondylosis. We calculated polygenic risk of specific-associated conditions and tested their association with AD outcomes revealing that genetic risk for many of these conditions is also associated with the total unique AD prescriptions. The number of unique ADs prescribed, which is easily assessed in EHRs, provides a more nuanced measure of treatment resistance, and may facilitate future research and clinical application in this area.

Common protein networks for various drug regimens of major depression are associated with complement and immunity

BACKGROUND

Major depressive disorder (MDD) can present a variety of clinical presentations and has high inter-individual heterogeneity. Multiple studies have suggested various subtype models related to symptoms, etiology, sex, and treatment response. Employing different regimens is common when treating MDD, and identifying effective therapeutics requires time. Frequent treatment attempts and failures can lead to a diagnosis of treatment resistance, and the heterogeneity of treatment responses among individuals makes it difficult to understand and interpret the biological mechanisms underlying MDD.

AIM

This study explored the differentially expressed proteins and commonly altered protein networks across drug treatments by comparing the serum proteomes of patients with MDD treated with drug regimens (T-MDD, n = 20) and untreated patients (NT-MDD, n = 20).

METHODS

Differentially expressed proteins were profiled in non-drug-treated and drug-treated patients with depression using liquid chromatography-mass spectrometry. The common protein networks affected by different medications were studied.

RESULTS

Of the proteins profiled, 12 were significantly differentially expressed between the T-MDD and NT-MDD groups. Commonly altered proteins and networks of various drug treatments for depression were related to the complement system and immunity.

CONCLUSIONS

Our results provide information on common biological changes across different pharmacological treatments employed for depression and provide an alternative perspective for improving our understanding of the biological mechanisms of drug response in MDD with great heterogeneity in the background of the disease.

Genome-wide meta-analysis of ascertainment and symptom structures of major depression in case-enriched and community cohorts

BACKGROUND

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

METHODS

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

RESULTS

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

CONCLUSION

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Functional Network Connectivity for Components of Depression-Related Psychological Fragility

Psychological resilience (PR) is known to be inversely associated with depression. While there is a growing body of research examining how depression alters activity across multiple functional neural networks, how differences in PR affect these networks is largely unexplored. This study examines the relationship between PR and functional connectivity in the alpha and beta bands within (and between) eighteen established cortical nodes in the default mode network, the central executive network, and the salience network. Resting-state EEG data from 99 adult participants (32 depressed, 67 non-depressed) were used to measure the correlation between the five factors of PR sourced from the Connor-Davidson Resilience Scale and eLORETA-based measures of coherence and phase synchronisation. Distinct functional connectivity patterns were seen across each resilience factor, with a notable absence of overlapping positive results across the depressed and non-depressed samples. These results indicate that depression may modulate how resilience is expressed in terms of fundamental neural activity.

The clinical utility and relevance in clinical practice of DSM-5 specifiers for major depressive disorder: A Delphi expert consensus study

Major depressive disorder (MDD) is a heterogeneous syndrome, associated with different levels of severity and impairment on the personal functioning for each patient. Classification systems in psychiatry, including ICD-11 and DSM-5, are used by clinicians in order to simplify the complexity of clinical manifestations. In particular, the DSM-5 introduced specifiers, subtypes, severity ratings, and cross-cutting symptom assessments allowing clinicians to better describe the specific clinical features of each patient. However, the use of DSM-5 specifiers for major depressive disorder in ordinary clinical practice is quite heterogeneous. The present study, using a Delphi method, aims to evaluate the consensus of a representative group of expert psychiatrists on a series of statements regarding the clinical utility and relevance of DSM-5 specifiers for major depressive disorder in ordinary clinical practice. Experts reached an almost perfect agreement on statements related to the use and clinical utility of DSM-5 specifiers in ordinary clinical practice. In particular, a complete consensus was found regarding the clinical utility for ordinary clinical practice of using DSM-5 specifiers. The use of specifiers is considered a first step toward a "dimensional" approach to the diagnosis of mental disorders.

Tissue-specific gene expression of genome-wide significant loci associated with major depressive disorder subtypes

Major depressive disorder (MDD) is a clinically and genetically heterogeneous disorder. To reduce heterogeneity, large-scale genome-wide association studies have recently identified genome-wide significant loci associated with seven MDD subtypes. However, it was unclear in which tissues the genes near those loci are specifically expressed. We investigated whether genes related to specific MDD subtypes would be preferably expressed in a specific tissue. At 14 novel subtype-specific loci related to seven MDD subtypes-(1) non-atypical-like features MDD, (2) early-onset MDD, (3) recurrent MDD, (4) MDD with suicidal thoughts, (5) MDD without suicidal thoughts, (6) MDD with moderate impairment, and (7) postpartum depression, we investigated whether 22 genome-wide significant genetic variant-mapped genes were tissue-specifically expressed in brain, female reproductive, male specific, cardiovascular, gastrointestinal, or urinary tissues in the Genotype-Tissue Expression (GTEx) subjects (n ≤ 948). To confirm the tissue-specific expression in the GTEx, we used independent Human Protein Atlas (HPA) RNA-seq subjects (n ≤ 95). Of 22 genes, nine and five genes were tissue-specifically expressed in brain and female reproductive tissues, respectively (p < 2.27 × 10-3). RTN1, ERBB4, and AMIGO1 related to early-onset MDD, recurrent MDD, or MDD with suicidal thoughts were highly expressed in brain tissues (d = 1.19-2.71), while OAS1, LRRC9, DHRS7, PSMA5, SYPL2, and GULP1 related to non-atypical-like features MDD, early-onset MDD, MDD with suicidal thoughts, or postpartum depression were expressed at low levels in brain tissues (d = -0.17--1.48). DFNA5, CTBP2, PCNX4, SDCCAG8, and GULP1, which are related to early-onset MDD, MDD with moderate impairment, or postpartum depression, were highly expressed in female reproductive tissues (d = 0.80-2.08). Brain and female reproductive tissue-specific expression was confirmed in the HPA RNA-seq subjects. Our findings suggest that brain and female reproductive tissue-specific expression might contribute to the pathogenesis of MDD subtypes.

Latent profile analysis of depressive symptoms in college students and its relationship with physical activity

OBJECTIVE

To explore the classification of depressive symptoms in college students and the relationship between those symptoms and physical activity.

METHODS

A convenience sampling method was employed to enlist college students from Songjiang University Town in Shanghai to complete in the General Information Questionnaire, Patient Health Questionnaire-9, and Physical Activity Scale-3.

RESULTS

A total of 3541 students were analyzed, averaging 19.34 years of age with a male composition of 53 %. The participants can be classified into four categories, namely: Group 1, which exhibits the most severe depressive symptoms, including suicidal ideation and cognitive symptoms (11.07 %); Group 2, which only manifests cognitive symptoms without suicidal ideation (14.35 %); Group 3, which presents mild depressive symptoms (23.61 %); and Group 4, consisting of normal college students (50.97 %). Students with strained family and interpersonal relationships, high levels of academic stress, and low frequency of social activities were at higher risk for detecting suicidal intention and cognitive symptoms. The level of physical activity was significantly higher in the normal group than in the other groups (all P < 0.001), and only the frequency of exercise was significantly different among the remaining three groups (χ2 = 14.716, P = 0.005). The detection rate of cognitive symptoms was significantly lower when exercising >3 times per week for 30-59 min (OR = 0.740, 95 % CI: 0.590-0.928; OR = 0.596, 95 % CI: 0.427-0.831).The detection rate of suicidal thoughts was significantly lower when exercising 2 times per month to 2 times per week or >3 times per week (OR = 0.585, 95 % CI: 0.404-0.847; OR = 0.392, 95 % CI: 0.258-0.595).

CONCLUSION

Suicidal ideation and cognitive symptoms can differentiate between various categories of depressive symptoms among college students. Engaging in physical activity serves as a protective factor against depressive symptoms among college students.

Identifying two distinct neuroanatomical subtypes of first-episode depression using heterogeneity through discriminative analysis

BACKGROUND

Neurobiological heterogeneity in depression remains largely unknown, leading to inconsistent neuroimaging findings.

METHODS

Here, we adopted a novel proposed machine learning method ground on gray matter volumes (GMVs) to investigate neuroanatomical subtypes of first-episode treatment-naïve depression. GMVs were obtained from high-resolution T1-weighted images of 195 patients with first-episode, treatment-naïve depression and 78 matched healthy controls (HCs). Then we explored distinct subtypes of depression by employing heterogeneity through discriminative analysis (HYDRA) with regional GMVs as features.

RESULTS

Two prominently divergent subtypes of first-episode depression were identified, exhibiting opposite structural alterations compared with HCs but no different demographic features. Subtype 1 presented widespread increased GMVs mainly located in frontal, parietal, temporal cortex and partially located in limbic system. Subtype 2 presented widespread decreased GMVs mainly located in thalamus, cerebellum, limbic system and partially located in frontal, parietal, temporal cortex. Subtype 2 had smaller TIV and longer illness duration than Subtype 1. And TIV in Subtype 1 was positively correlated with age of onset while not in Subtype 2, probably implying the different potential neuropathological mechanisms.

LIMITATIONS

Despite results obtained in this study were validated by employing another brain atlas, the conclusions were acquired from a single dataset.

CONCLUSIONS

This study revealed two distinguishing neuroanatomical subtypes of first-episode depression, which provides new insights into underlying biological mechanisms of the heterogeneity in depression and might be helpful for accurate clinical diagnosis and future treatment.

Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts

The Patient Health Questionnaire 9 (PHQ-9) is the current standard outpatient screening tool for measuring and tracking the nine symptoms of major depressive disorder (MDD). While the PHQ-9 was originally conceptualized as a unidimensional measure, it has become clear that MDD is not a monolithic construct, as evidenced by high comorbidities with other theoretically distinct diagnoses and common symptom overlap between depression and other diagnoses. Therefore, identifying reliable and temporally stable subfactors of depressive symptoms could allow research and care to be tailored to different depression phenotypes. This study improved on previous factor analysis studies of the PHQ-9 by leveraging samples that were clinical (participants with depression only), large (N = 1483 depressed individuals in total), longitudinal (up to 5 years), and from three diverse (matching racial distribution of the United States) datasets. By refraining from assuming the number of factors or item loadings a priori, and thus utilizing a solely data-driven approach, we identified a ranked list of best-fitting models, with the parsimonious one achieving good model fit across studies at most timepoints (average TLI >= 0.90). This model categorizes the PHQ-9 items into four factors: (1) Affective (Anhedonia + Depressed Mood), (2) Somatic (Sleep + Fatigue + Appetite), (3) Internalizing (Worth/Guilt + Suicidality), (4) Sensorimotor (Concentration + Psychomotor), which may be used to further precision psychiatry by testing factor-specific interventions in research and clinical settings.

Latent class of depressive symptoms of and its determinants: A cross-sectional study among Iranian University students

Background

According to the report of the World Health Organization, mental disorders are one of the 10 most important causes of disability in the world. This study was conducted with the aim of determining the number and frequency of latent classes of depression and its determinants in Isfahan university of medical students.

Materials and Methods

A total of 1408 medical students from Isfahan University of Medical Sciences, Iran, were enrolled in the study in 2017. The symptoms and severity of depression were assessed using the standard Hospital Anxiety and Depression scale questionnaire. Latent class analysis was applied to seven symptoms of depression, all of which had four levels. Latent class subgroups were compared using the Chi-square test and analysis of variance test. The regression model was used to check the relationship between identified classes and related factors. Analyzes were done using SPSS-21 and Mplus7 software.

Results

In this study, three latent classes were identified, that is, the group of healthy people, the group of borderline people, and the group of people suspected of depression. The prevalence of identified latent classes among medical students is 0.52, 0.32, and 0.16%, respectively. The regression results showed that compared to the healthy group, the factors affecting depression in the borderline and suspicious group were increasing age, female gender, interest in the field of study, physical activity, history of depression, and history of anxiety.

Conclusion

The three classes that were identified based on the students' answers to the depression symptoms questions differed only based on severity. The history of depression and anxiety were the strongest predictors of latent classes of depression.

Melancholic features and treatment outcome to selective serotonin reuptake inhibitors in major depressive disorder: A re-analysis of the STAR*D trial

BACKGROUND

Melancholia has been positioned as a qualitatively different form of Major Depressive Disorder (MDD). Some studies have suggested that melancholic MDD patients may show lower remission when receiving treatment with Selective Serotonin Reuptake Inhibitors, but this has not yet been explored in large, representative samples of MDD.

METHODS

We used data from the STAR*D, a multisite randomized controlled trial (n = 4041). We defined melancholia status through the BA Melancholia Empirical Index, constructed using items from the Inventory of Depressive Symptomatology (IDSC). The main outcome of interest was symptomatic remission defined as a Quick Inventory of Depressive Symptoms (Clinician version) (QIDS-C) below or equal to 5. Inverse probability weighting was used to control for confounding.

RESULTS

3827 patients were eligible for this study. Melancholic patients were more likely to be unemployed, never married, to self-report an African American race, and to have a higher depressive severity. The adjusted 4-month probability of remission was 26.9 % (22.0, 45.5) for melancholic and 53.8 % (53.2, 58.5), for nonmelancholic patients. Compared with nonmelancholic, the difference in 4-month probability of remission was -26.9 % (-37.0, -15.6). Results were consistent across sensitivity analyses.

LIMITATIONS

Items from IDSC were used as a surrogate measure of the BA Melancholia Index, and extrapolation of the results to agents other than citalopram and to psychotic MDD patients requires caution.

CONCLUSIONS

Melancholic MDD patients showed lower probabilities of remission at 4-months receiving treatment with citalopram. The results of this study show how validly subtyping episodes could contribute to the personalized treatment of depression.

Subtypes of major depressive disorders and objectively measured physical activity and sedentary behaviors in the community

BACKGROUND

Lack of physical activity (PA) and high sedentary behavior (SB) may enhance mental health problems, including depression, and are associated with increased mortality. Aside from a large body of research on major depressive disorder (MDD) assessed as an entity and either PA or SB, few studies have examined associations among subtypes of MDD and both PA and SB simultaneously derived from wrist-worn accelerometers. Accordingly, our aim was to explore the associations among MDD subtypes (atypical, melancholic, combined atypical-melancholic and unspecified) and four actigraphy-derived behaviors combining the levels of PA and SB.

METHODS

The sample stemmed from CoLaus|PsyCoLaus, a population-based cohort study, consisting of 2375 participants (55.1% women; mean age: 62.4 years) who wore an accelorometer for 14 days after a physical exam and subsequently completed a semi-structured psychiatric interview. Activity behaviors were defined according to the combination of the levels of moderate-to-vigorous intensity PA and SB. Associations of remitted MDD subtypes, current MDD and physical inactivity behaviors were assessed using multinomial logistic regression, adjusted for socio-demographic characteristics, a history of anxiety, alcohol and drug use disorders and cardiovascular risk factors.

RESULTS

In the fully adjusted model, participants with the remitted combined atypical-melancholic subtype had a higher risk of being more physically inactive.

CONCLUSIONS

Our findings suggest that low PA and high SB are not restricted to the duration of depressive episodes in people with atypical and melancholic episodes. The lack of PA and high SB in this group of depressive patients exposes them to an additional long-term cardiovascular risk and measures to increase PA may be particularly fruitful in this MDD subgroup.

Increased brain nucleus accumbens functional connectivity in melancholic depression

BACKGROUND

Melancholic depression, marked by typical symptoms of anhedonia, is regarded as a homogeneous subtype of major depressive disorder (MDD). However, little attention was paid to underlying mechanisms of melancholic depression. This study aims to examine functional connectivity of the reward circuit associated with anhedonia symptoms in melancholic depression.

METHODS

Fifty-nine patients with first-episode drug- naive MDD, including 31 melancholic patients and 28 non-melancholic patients, were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-two healthy volunteers were recruited as controls. Bilateral nucleus accumbens (NAc) were selected as seed points to form functional NAc network. Then support vector machine (SVM) was used to distinguish melancholic patients from non-melancholic patients.

RESULTS

Relative to non-melancholic patients, melancholic patients displayed increased functional connectivity (FC) between bilateral NAc and right middle frontal gyrus (MFG) and between right NAc and left cerebellum lobule VIII. Compared to healthy controls, melancholic patients showed increased FC between right NAc and right lingual gyrus and between left NAc and left postcentral gyrus; non-melancholic patients had increased FC between bilateral NAc and right lingual gyrus. No significant correlations were observed between altered FC and clinical variables in melancholic patients. SVM results showed that FC between left NAc and right MFG could accurately distinguish melancholic patients from non-melancholic patients.

CONCLUSION

Melancholic depression exhibited different patterns of functional connectivity of the reward circuit relative to non-melancholic patients. This study highlights the significance of the reward circuit in the neuropathology of melancholic depression.

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking.

METHODS

Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity.

RESULTS

Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes.

CONCLUSIONS

These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.

Investigating the relationship between negative symptoms and metacognitive functioning in psychosis: An individual participant data meta-analysis

PURPOSE

Negative symptoms are a persistent, yet under-explored problem in psychosis. Disturbances in metacognition are a potential causal factor in negative symptom development and maintenance. This meta-analysis uses individual participant data (IPD) from existing research to assess the relationship between negative symptoms and metacognition treated as summed scores and domains.

METHODS

Data sets containing individuals with negative symptoms and metacognition data, aged 16+ with psychosis, were identified according to pre-specific parameters. IPD integrity and completeness were checked and data were synthesized in two-stage meta-analyses of each negative symptoms cluster compared with metacognition in seemingly unrelated regression using restricted maximum likelihood estimation. Planned and exploratory sensitivity analyses were also conducted.

RESULTS

Thirty-three eligible data sets were identified with 21 with sufficient similarity and availability to be included in meta-analyses, corresponding to 1301 participants. The strongest relationships observed were between summed scores of negative symptoms and metacognition. Metacognitive domains of self-reflectivity and understanding others' minds, and expressive negative symptoms emerged as significant in some meta-analyses. The uncertainty of several effect estimates increased significantly when controlling for covariates.

CONCLUSIONS

This robust meta-analysis highlights the impact of using summed versus domain-specific scores of metacognition and negative symptoms, and relationships are not as clear-cut as once believed. Findings support arguments for further differentiation of negative symptom profiles and continued granular exploration of the relationship between metacognition and negative symptoms.

Genetic Contribution to the Heterogeneity of Major Depressive Disorder: Evidence From a Sibling-Based Design Using Swedish National Registers

OBJECTIVE

Major depressive disorder (MDD) is highly heterogeneous. Standard typology partly captures the disorder's symptomatic heterogeneity, although whether it adequately captures etiological heterogeneity remains elusive. The aim of this study was to investigate the genetic characterization of MDD heterogeneity.

METHODS

Using Swedish patient register data on 1.5 million individuals, the authors identified 46,255 individuals with specialist-diagnosed MDD. Eighteen subgroups were identified based on nine comparison groups defined by clinical and psychosocial features, including severity, recurrence, comorbidities, suicidality, impairment, disability, care unit, and age at diagnosis. A sibling-based design and classic quantitative genetic models were applied to estimate heritability of MDD subgroups and genetic correlations between subgroups.

RESULTS

Estimates of heritability ranged from 30.5% to 58.3% across subgroups. The disabled and youth-onset subgroups showed significantly higher heritability (55.1%-58.3%) than the overall MDD sample (45.3%, 95% CI=43.0-47.5), and the subgroups with single-episode MDD and without psychiatric comorbidity showed significantly lower estimates (30.5%-34.4%). Estimates of genetic correlations between the subgroups within comparison groups ranged from 0.33 to 0.90. Seven of nine genetic correlations were significantly smaller than 1, suggesting differences in underlying genetic architecture. These results were largely consistent with previous work using genomic data.

CONCLUSIONS

The findings of differential heritability and partially distinct genetic components in subgroups provide important insights into the genetic heterogeneity of MDD and a deeper etiological understanding of MDD clinical subgroups.

Symptom clustering of major depression in a national telehealth sample

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous disorder whose possible symptom combinations have not been well delineated. The aim of this study was to explore the heterogeneity of symptoms experienced by those with MDD to characterize phenotypic presentations.

METHODS

Cross-sectional data (N = 10,158) from a large telemental health platform were used to identify subtypes of MDD. Symptom data, gathered from both clinically-validated surveys and intake questions, were analyzed via polychoric correlations, principal component analysis, and cluster analysis.

RESULTS

Principal components analysis (PCA) of baseline symptom data revealed 5 components, including anxious distress, core emotional, agitation/irritability, insomnia, and anergic/apathy components. PCA-based cluster analysis resulted in four MDD phenotypes, the largest of which was characterized by a prominent elevation on the anergic/apathy component, but also core emotional. The four clusters differed on demographic and clinical characteristics.

LIMITATIONS

The primary limitation of this study is that the phenotypes uncovered are limited by the questions asked. These phenotypes will need to be cross validated with other samples, potentially expanded to include biological/genetic variables, and followed longitudinally.

CONCLUSIONS

The heterogeneity in MDD, as illustrated by the phenotypes in this sample, may explain the heterogeneity of treatment response in large-scale treatment trials. These phenotypes can be used to study varying rates of recovery following treatment and to develop clinical decision support tools and artificial intelligence algorithms. Strengths of this study include its size, breadth of included symptoms, and novel use of a telehealth platform.

Opportunities for digital health technology: identifying unmet needs for bipolar misdiagnosis and depression care management

Introduction

Digital health technologies (DHTs) driven by artificial intelligence applications, particularly those including predictive models derived with machine learning methods, have garnered substantial attention and financial investment in recent years. Yet, there is little evidence of widespread adoption and scant proof of gains in patient health outcomes. One factor of this paradox is the disconnect between DHT developers and digital health ecosystem stakeholders, which can result in developing technologies that are highly sophisticated but clinically irrelevant. Here, we aimed to uncover challenges faced by psychiatrists treating patients with major depressive disorder (MDD). Specifically, we focused on challenges psychiatrists raised about bipolar disorder (BD) misdiagnosis.

Methods

We conducted semi-structured interviews with 10 United States-based psychiatrists. We applied text and thematic analysis to the resulting interview transcripts.

Results

Three main themes emerged: (1) BD is often misdiagnosed, (2) information crucial to evaluating BD is often occluded from clinical observation, and (3) BD misdiagnosis has important treatment implications.

Discussion

Using upstream stakeholder engagement methods, we were able to identify a narrow, unforeseen, and clinically relevant problem. We propose an organizing framework for development of digital tools based upon clinician-identified unmet need.

Valence in the eyes: An emotion decoding profile of adults with major depressive disorder and a history of childhood maltreatment

BACKGROUND

Individuals with major depressive disorder (MDD) and childhood maltreatment have been proposed to constitute a subgroup with worse illness course and outcomes. To elucidate a potential social cognitive vulnerability in this subgroup, this study compared the emotion decoding abilities of MDD patients with and without a history of childhood maltreatment.

METHODS

Participants with a diagnosis of MDD were recruited from nationwide mental health organizations. Emotion decoding abilities were assessed using the Reading the Mind in the Eyes Test, while childhood maltreatment was measured with the Adverse Childhood Experiences Questionnaire.

RESULTS

The MDD patients with a history of childhood maltreatment exhibited poorer emotion decoding abilities than MDD patients without such past. This difference applied specifically to the decoding of positive and negative emotions, while no group differences emerged for the decoding of neutral emotions. When specific maltreatment types were considered as predictors only emotional neglect was associated with lower emotion decoding abilities. These associations remained when adjusting for demographic and clinical covariates.

CONCLUSIONS

By indicating that emotion decoding difficulties characterize the MDD subgroup with childhood maltreatment, the findings highlight a potential vulnerability that merits further examination in terms of its developmental antecedents and prognostic relevance.

Disruptive mutations in the serotonin transporter associate serotonin dysfunction with treatment-resistant affective disorder

Affective or mood disorders are a leading cause of disability worldwide. The serotonergic system has been heavily implicated in the complex etiology and serves as a therapeutic target. The serotonin transporter (SERT) is a major regulator of serotonin neurotransmission, yet the disease-relevance of impaired SERT function remains unknown. Here, we present the first identification and functional characterization of disruptive coding SERT variants found in patients with psychiatric diseases. In a unique cohort of 144 patients characterized by treatment-resistant chronic affective disorders with a lifetime history of electroconvulsive therapy, we identified two previously uncharacterized coding SERT variants: SERT-N217S and SERT-A500T. Both variants were significantly enriched in the patient cohort compared to GnomAD (SERT-N217S: OR = 151, P = 0.0001 and SERT-A500T: OR = 1348, P = 0.0022) and ethnicity-matched healthy controls (SERT-N217S: OR ≥ 17.7, P ≤ 0.013 and SERT-A500T: OR = ∞, P = 0.029). Functional investigations revealed that the mutations exert distinct perturbations to SERT function, but their overall effects converge on a partial loss-of-function molecular phenotype. Thus, the SERT-A500T variant compromises the catalytic activity, while SERT-N217S disrupts proper glycosylation of SERT with a resulting dominant-negative trafficking deficiency. Moreover, we demonstrate that the trafficking deficiency of SERT-N217S is amenable to pharmacochaperoning by noribogaine. Collectively, our findings describe the first disease-associated loss-of-function SERT variants and implicate serotonergic disturbances arising from SERT dysfunction as a risk factor for chronic affective disorders.

Resolving heterogeneity in depression using individualized structural covariance network analysis

BACKGROUND

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

RESULTS

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

CONCLUSIONS

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Genetic structure of major depression symptoms across clinical and community cohorts

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

Stability of the Subtypes of Major Depressive Disorder in Older Adults and the Influence of Mild Cognitive Impairment on the Stability

OBJECTIVES

To assess 1) the longitudinal stability of the atypical, melancholic, combined atypical-melancholic and the unspecified subtypes of major depressive disorder (MDD) according to the diagnostic and statistical manual of mental disorders (DSM -IV) specifiers in older adults, and 2) the effect of mild cognitive impairment (MCI) on the stability of these subtypes.

DESIGN

Prospective cohort study with a 5.1 year-follow-up.

SETTING

Population-based cohort from Lausanne, Switzerland.

PARTICIPANTS

A total of 1,888 participants (mean age: 61.7 years, women: 69.2%) with at least two psychiatric evaluations, one after the age of 65 years.

MEASUREMENTS

Semistructured diagnostic interview to assess lifetime and 12-month DSM-IV Axis-1 disorders at each investigation and neuro-cognitive tests to identify MCI in participants aged 65 years and over. Associations between lifetime MDD status before and 12-month depression status after the follow-up were assessed using multinomial logistic regression. The effect of MCI on these associations was assessed by testing interactions between MDD subtypes and MCI status.

RESULTS

1) Associations between depression status before and after the follow-up were observed for atypical (adjusted OR [95% CI] = 7.99 [3.13; 20.44]), combined (5.73 [1.50; 21.90]) and unspecified (2.14 [1.15; 3.98]), but not melancholic MDD (3.36 [0.89; 12.69]). However, there was a certain degree of overlap across the subtypes, particularly between melancholic MDD and the other subtypes. 2) No significant interactions were found between MCI and lifetime MDD subtypes regarding depression status after follow-up.

CONCLUSION

The strong stability of the atypical subtype in particular highlights the need for identifying this subtype in clinical and research settings, given its well-documented links to inflammatory and metabolic markers.

Network analysis of frontal lobe alpha asymmetry confirms the neurophysiological basis of four subtypes of depressive behavior

Introduction

Although depression is widespread carries a major disease burden, current treatments remain non-universally effective, arguably due to the heterogeneity of depression, and leading to the consideration of depressive "subtypes" or "depressive behavior subtypes." One such model of depressive behavior (DB) subtypes was investigated for its associations with frontal lobe asymmetry (FLA), using a different data analytic procedure than in previous research in this field.

Methods

100 community volunteers (54 males, 46 females) aged between 18 yr. and 75 years (M = 32.53 yr., SD = 14.13 yr) completed the Zung Self-rating Depression Scale (SDS) and underwent 15 min of eyes closed EEG resting data collection across 10 frontal lobe sites. DB subtypes were defined on the basis of previous research using the SDS, and alpha-wave (8-13 Hz) data produced an index of FLA. Data were examined via network analysis.

Results

Several network analyses were conducted, producing two models of the association between DB subtypes and FLA, confirming unique neurophysiological profiles for each of the four DB subtypes.

Discussion

As well as providing a firm basis for using these DB subtypes in clinical settings, these findings provide a reasonable explanation for the inconsistency in previous FLA-depression research.

The genetic basis of major depressive disorder

The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

Correlation of systemic immune-inflammation index and moderate/major depression in patients with depressive disorders: a large sample cross-sectional study

Objective

To evaluate the clinical value of systemic immune-inflammation index (SII) based on peripheral blood neutrophil, lymphocyte, and platelet count in evaluating the subtype and severity of depression in patients with depressive disorder.

Methods

This retrospective cohort study was conducted in the Third People's Hospital of Fuyang City from January 1, 2020 to December 31, 2022. The data included sociodemographic information at admission, clinical data, discharge diagnosis and inflammatory markers. Patients were divided into low SII group and high SII group according to the optimal threshold of SII determined by receiver operating characteristic curve (ROC curve). Binary logistic regression was used to analyze the correlation between moderate/major depression and SII level.

Results

Compared to the low SII group, the high SII group had a higher age level (χ² = 7.663, p = 0.006), more smokers (χ² = 9.458, p = 0.002), more moderate/major depression patients (χ² = 45.645, p < 0.001), and a higher proportion of patients with accompanying somatic symptoms (χ² = 14.867, p < 0.001). In the final logistic regression model, after controlling for confounding factors, SII at admission was significantly associated with moderate/major depression [β =1.285, p < 0.001; odds ratio (95% confidence intervals) = 3.614 (2.693-4.850)]. Patients with high SII scores were 3.614 times more likely to have moderate/severe depression than those with low SII scores. We propose a cut-off value of SII =540.78 (sensitivity = 36.4% and specificity = 80.3%) according to the maximum Youden index.

Conclusion

Our research indicates that SII may be a useful, repeatable, convenient, and affordable index to identify moderate/major depression in depressive disorder.

Biotypes of major depressive disorder identified by a multiview clustering framework

BACKGROUND

The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS

We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS

Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS

The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS

Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

Associations between specific depressive symptoms and psychosocial functioning in psychotherapy

BACKGROUND

Psychotherapy for depression aims to reduce symptoms and to improve psychosocial functioning. We examined whether some symptoms are more important than others in the association between depression and functioning over the course of psychotherapy treatment.

METHODS

We studied associations between specific symptoms of depression (PHQ-9) and change in social and occupational functioning (SOFAS), both with structural equation models (considering liabilities of depression and each specific symptom) and with logistic regression models (considering the risk for individual patients). The study sample consisted of adult patients (n = 771) from the Finnish Psychotherapy Quality Registry (FPQR) who completed psychotherapy treatment between September 2018 and September 2021.

RESULTS

Based on our results of logistic regression analyses and SEM models, the baseline measures of depression symptoms were not associated with changes in functioning. Changes in depressed mood or hopelessness, problems with sleep, feeling tired, and feeling little interest or pleasure were associated with improved functioning during psychotherapy. The strongest evidence for symptom-specific effects was found for the symptom of depressed mood or hopelessness.

LIMITATIONS

Due to our naturalistic study design containing only two measurement points, we were unable to study the causal relationship between symptoms and functioning.

CONCLUSIONS

Changes in certain symptoms during psychotherapy may affect functioning independently of underlying depression. Knowledge about the dynamics between symptoms and functioning could be used in treatment planning or implementation. Depressed mood or hopelessness appears to have a role in the dynamic relationship between depression and functioning.

Apathy in melancholic depression and abnormal neural activity within the reward-related circuit

Major depressive disorder is a heterogeneous syndrome, of which the most common subtype is melancholic depression (MEL). Previous studies have indicated that anhedonia is frequently a cardinal feature in MEL. As a common syndrome of motivational deficit, anhedonia is closely associated with dysfunction in reward-related networks. However, little is currently known about apathy, another syndrome of motivational deficits, and the underlying neural mechanisms in MEL and non-melancholic depression (NMEL). Herein, the Apathy Evaluation Scale (AES) was used to compare apathy between MEL and NMEL. On the basis of resting-state functional magnetic resonance imaging, functional connectivity strength (FCS) and seed-based functional connectivity (FC) were calculated within reward-related networks and compared among 43 patients with MEL, 30 patients with NMEL, and 35 healthy controls. Patients with MEL had higher AES scores than those with NMEL (t = -2.20, P = 0.03). Relative to NMEL, MEL was associated with greater FCS (t = 4.27, P < 0.001) in the left ventral striatum (VS), and greater FC of the VS with the ventral medial prefrontal cortex (t = 5.03, P < 0.001) and dorsolateral prefrontal cortex (t = 3.18, P = 0.005). Taken together the results indicate that reward-related networks may play diverse pathophysiological roles in MEL and NMEL, thus providing potential directions for future interventions in the treatment of various depression subtypes.

Conceptualizing anxiety and depression in children and adolescents: a latent factor and network analysis

The objective of this study is to gain insight into the inherent structure of anxiety and depressive symptoms by combining the strengths of latent factor analysis and network analysis. The sample comprised 743 children and adolescents aged 4–18 years (M = 11.64, SD = 3.66, 61% males) who sought routine care outpatient psychotherapy. Parents or primary caregivers rated anxiety and depressive symptoms of their children on a DSM-5-/ICD-10-based symptom checklist. First, we analyzed the factor structure of the internalizing symptoms using exploratory factor analysis (EFA). Next, we conducted a network analysis and identified central and bridge symptoms that may explain comorbidity between anxiety disorders and depression. We then employed exploratory graph analysis (EGA) as an alternative tool within the framework of network psychometrics to estimate the number of dimensions (i.e., communities within a network). Finally, we tested a model based on these results using confirmatory factor analysis. The results demonstrate a complex interplay between anxiety and depressive symptom domains. Four factors/communities were identified by EFA and EGA, but the item-community allocation differed, and the interpretation of factors/communities was unclear. A clear distinction between these domains could not be supported. However, associations within a domain were stronger than associations between the two domains. We identified pain, suicidal, irritable, and afraid of adults as bridge items between the symptom domains. In conclusion, our findings further advance the general understanding of the frequently reported co-occurrence of anxiety and depressive symptoms and diagnoses in clinical practice. Identifying bridge symptoms may inform intervention practices by targeting specific symptoms that contribute to the maintenance of anxious and depressive behaviors.

Multiple examinations indicated associations between abnormal regional homogeneity and cognitive dysfunction in major depressive disorder

Background

This study aimed to investigate the relationships between regional neural activity and multiple related indicators in patients with major depressive disorder (MDD).

Methods

Forty-two patients and 42 healthy controls (HCs) were enrolled. Pearson/Spearman correlation analyses were applied to examine the associations between abnormal regional homogeneity (ReHo) and different indicators in the patients.

Results

Compared with HCs, patients with MDD had increased ReHo in the left inferior temporal gyrus (ITG) and decreased ReHo values in the left putamen, anterior cingulate cortex (ACC), and precentral gyrus. The ReHo of the left putamen was positively correlated with the PR interval, Repeatable Battery for the Assessment of Neuropsychological Status 4A, and Discriminant analysis (D), and negatively correlated with Ae (block) and Ae (total) in the patients. The ReHo value of the left ACC was positively correlated with the severity of depression, Stroop Color Word Test of C - 2B + 100 in reaction time, and negatively correlated with Ce (Missay) and Perseverative Responses in the patients. The ReHo of the left ITG was positively correlated with the Neuroticism scores and negatively correlated with the Lie scores in the patients.

Conclusion

These results suggested that the decreased ReHo of the salience network might be the underpinning of cognitive impairments in patients with MDD.

The Immune System and Depression: From Epidemiological to Clinical Evidence

Depression is a frequent mental disorder with a substantial contribution to years lived with disability worldwide. In the search for new treatment targets, the immune system's contribution to the pathogenesis of depression has received increased attention as immune activation has been associated with depression in various epidemiological and case-control studies. Epidemiological studies have shown that immune exposures such as severe infections and autoimmune disorders increase the risk of depression. Furthermore, immune system activation has been indicated in case-control studies of depression revealing higher levels of key pro-inflammatory cytokines among patients with depression than healthy controls, particularly in blood and to some extent in the cerebrospinal fluid. Moreover, brain imaging studies indicate increased microglial activity during depression, and gut microbiota studies have documented alterations of gut microbiota composition to be associated with depression. Based on findings from animal and human studies, several immune-mediated molecular mechanisms have been suggested to underlie the association between increased immunological activity and depression. However, the research is challenged by the heterogeneity of the depression diagnosis and - to some extent - the precision of currently available technology for immune biomarker quantification, particularly regarding the assessment of low-grade neuroinflammation. Nonetheless, an enhanced understanding of the complex interactions between the immune system and the brain in the context of depression could pave the way for precision medicine approaches with immune-modulating treatment as a promising additional option in the treatment of depression.

Melancholic features and typical neurovegetative symptoms of major depressive disorder show specific polygenic patterns

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent psychiatric condition characterised by a heterogeneous clinical presentation and an estimated twin-based heritability of ~40-50 %. Different clinical MDD subtypes might partly reflect distinctive underlying genetics. This study aims to investigate if polygenic risk scores (PRSs) for different psychiatric disorders, personality traits, and substance use-related traits may be associated with different clinical subtypes of MDD (i.e., MDD with melancholic or psychotic features), higher symptom severity, or different clusters of depressive symptoms (i.e., sadness symptoms, typical neurovegetative symptoms, detachment symptoms, and negative thoughts).

METHODS

The target sample included 1149 patients with MDD, recruited by the European Group for the Study of Resistant Depression. PRSs for 25 psychiatric disorders and traits were computed based on the most recent publicly available summary statistics of the largest genome-wide association studies. PRSs were then used as predictors in regression models, adjusting for age, sex, population stratification, and recruitment sites.

RESULTS

Patients with MDD having higher PRS for MDD and loneliness were more likely to exhibit melancholic features of MDD (p = 0.0009 and p = 0.005, respectively). Moreover, patients with higher PRS for alcohol intake and post-traumatic stress disorder were more likely to experience greater typical neurovegetative symptoms (p = 0.0012 and p = 0.0045, respectively).

LIMITATIONS

The proportion of phenotypic variance explained by the PRSs was limited.

CONCLUSIONS

This study suggests that melancholic features and typical neurovegetative symptoms of MDD may show distinctive underlying genetics. Our findings provide a new contribution to the understanding of the genetic heterogeneity of MDD.

Abnormal long- and short-range functional connectivity in patients with first-episode drug-naïve melancholic and non-melancholic major depressive disorder

BACKGROUND

We attempted to explore the common and distinct long- and short-range functional connectivity (FC) patterns of melancholic and non-melancholic major depressive disorder (MDD) and their associations with clinical characteristics.

METHODS

Fifty-nine patients with first-episode drug-naïve MDD, including 31 patients with melancholic features and 28 patients with non-melancholic features, underwent resting-state functional magnetic resonance imaging (fMRI) scanning to examine long- and short-range FC. Thirty-two healthy volunteers were recruited as controls. The support vector machines (SVM) was applied to distinguish the melancholic patients from the non-melancholic patients by using the FC of abnormal brain regions.

RESULTS

Compared to healthy volunteers, patients with MDD showed increased long-range positive FC (lpFC) in the right insula/inferior frontal gyrus and left insula. Relative to non-melancholic patients, melancholic patients displayed decreased lpFC in the right lingual gyrus, decreased short-range positive FC (spFC) in the right middle temporal gyrus and right superior parietal lobule, increased lpFC in the left inferior parietal lobule, and increased spFC in the left middle occipital gyrus/inferior occipital gyrus, left cerebellum VII/IX, and bilateral cerebellum CrusII. Increased lpFC in the left inferior parietal lobule in melancholic patients was correlated with the TEPS abstract anticipatory scores. SVM results showed that FCs of five combinations within different brain regions could distinguish melancholic patients from non-melancholic patients.

CONCLUSIONS

FC abnormalities in the default mode network and parietal-occipital brain regions may underlie the neurobiology of melancholic MDD. An increased lpFC in the left inferior parietal lobule correlated with anhedonia may be a distinctive neurobiological feature of melancholic MDD.

Functional MRI study of feedback-based reinforcement learning in depression

Reinforcement learning depends upon the integrity of emotional circuitry to establish associations between environmental cues, decisions, and positive or negative outcomes in order to guide behavior through experience. The emotional dysregulation characteristic of major depressive disorder (MDD) may alter activity in frontal and limbic structures that are key to learning. Although reward and decision-making have been examined in MDD, the effects of depression on associative learning is less well studied. We investigated whether depressive symptoms would be related to abnormalities in learning-related brain activity as measured by functional magnetic resonance imaging (fMRI). Also, we explored whether melancholic and atypical features were associated with altered brain activity. We conducted MRI scans on a 4T Varian MRI system in 10 individuals with MDD and 10 healthy subjects. We examined event-related brain activation during feedback-based learning task using Analysis of Functional NeuroImages (AFNI) for image processing and statistical analysis. We observed that MDD patients exhibited reduced activation in visual cortex but increased activation in cingulate and insular regions compared to healthy participants. Also, in relation to features of depressive subtypes, we observed that levels of activation in striatal, thalamic, and precuneus regions were negatively correlated with atypical characteristics. These results suggest that the effects of MDD change the neural circuitry underlying associative learning, and these effects may depend upon subtype features of MDD.

Prevalence of major depressive disorder in 51,658 otherwise healthy adult Danes: Sex differences in symptomatology and prediction of future anti-depressive medication

Major Depressive Disorder (MDD) is a heterogeneous disease, which displays sex differences in symptomatology. This study aimed to assess point prevalence of MDD in undiagnosed, healthy adults as well as sex differences in symptomatology and clarify if specific symptoms increased the later need for anti-depressive medication. The study included 51,658 blood donors. Depressive symptoms were assessed according to ICD-10 using the Major Depression Inventory. Demographics, previous MDD, anti-depressive medication were collected from questionnaires and population registers. Descriptive, Logistic and Cox regression analyses were conducted. In total, 1.15% participants met the criteria for MDD. Women were significantly more likely to experience "increased appetite" and less likely to experience "a feeling of life not worth living", compared to men. MDD significantly associated with an increased hazard of later receiving a prescription for anti-depressive medication. The risk increased proportionally with increasing MDD severity. The two symptoms, "feeling that life is not worth living" and "trouble sleeping" were the strongest individual predictive symptoms of future anti-depressive medication in women and men, respectively. The results confirm findings in MDD patient groups. The diagnostic and prognostic value should be investigated further to address their potential as part of the clinical assessment.

The cortisol awakening response at admission to hospital predicts depression severity after discharge in major depressive disorder patients—A replication study

The cortisol awakening response (CAR) is a non-invasive biomarker for hypothalamic-pituitary-adrenal axis (HPA) dysregulation, reflecting accumulated stress over time. In a previous study we reported that a blunted CAR before an inpatient treatment predicted self-reported depressive symptoms six weeks and six months after discharge [Eikeseth, F. F., Denninghaus, S., Cropley, M., Witthöft, M., Pawelzik, M., &amp; Sütterlin, S. (2019). The cortisol awakening response at admission to hospital predicts depression severity after discharge in major depressive disorder (MDD) patients. Journal of Psychiatric Research, 111, 44-50)]. This replication study adopted an improved overall methodology with more stringent assessment protocols and monitoring. The longitudinal design included 122 inpatients from a psychosomatic hospital with a diagnosis of MDD displaying symptoms of moderate to severe major depression (n = 80 females). The CAR was measured at intake. Depression severity was assessed as Beck Depression Inventory II scores at intake, discharge, 6 weeks and 6 months following discharge. Results from the original study were replicated in terms of effect size but did not reach statistical significance (correlation between BDI-II 6 months after discharge and AUCg: r = −0.213; p = 0.054). The replication study yielded nearly identical correlation coefficients as in the original study (BDI-II 6 months and CAR, r = −0.223, p &lt; 0.05). The replication of previously reported effect sizes with a concurrent lack of statistical significance in the more restrictive, larger and better controlled replication study may well inform research on psycho-endocrinological predictors for treatment success, but suggests a rather limited practical relevance for cortisol awakening response measures in this clinical context.

Imaging the human brain on oral contraceptives: A review of structural imaging methods and implications for future research goals

Worldwide over 150 million women use oral contraceptives (OCs), which are the most prescribed form of contraception in both the United States and in European countries. Sex hormones, such as estradiol and progesterone, are important endogenous hormones known for shaping the brain across the life span. Synthetic hormones, which are present in OCs, interfere with the natural hormonal balance by reducing the endogenous hormone levels. Little is known how this affects the brain, especially during the most vulnerable times of brain maturation. Here, we review studies that investigate differences in brain gray and white matter in women using OCs in comparison to naturally cycling women. We focus on two neuroimaging methods used to quantify structural gray and white matter changes, namely structural MRI and diffusion MRI. Finally, we discuss the potential of these imaging techniques to advance knowledge about the effects of OCs on the brain and wellbeing in women.

Routinely accessible parameters of mineralocorticoid receptor function, depression subtypes and response prediction: a post-hoc analysis from the early medication change trial in major depressive disorder

OBJECTIVES

Previous studies indicated a relationship between aldosterone, the mineralocorticoid receptor (MR), and antidepressant treatment outcome. Physiological indicators of MR function (blood pressure and electrolytes) are easily accessible and may therefore serve as useful predictors. Thus, our aim was to investigate the predictive value of peripheral MR-related markers for antidepressant treatment outcomes.

METHODS

826 MDD patients who had participated in the randomised-controlled Early Medication Change (EMC) trial were analysed. Depression severity and MR-related markers were assessed weekly. In 562 patients, genetic variation of five MR-related genes was determined.

RESULTS

Patients with blood pressure <120mmHg showed higher depression severity (p = 0.005) than patients with blood pressure ≥120mmHg. Patients with a melancholic subtype had significantly lower blood pressures (p = 0.004). Na⁺/K⁺ ratio was positively and K⁺-concentration was negatively correlated to depression severity and to relative changes in HAMD from baseline to day 14, and 56 respectively (p < 0.001). For none of the MR-related genes, genetic variation was associated with treatment outcomes.

CONCLUSIONS

We confirmed early observations of an altered peripheral MR sensitivity, reflected by lower blood pressure, low K⁺ or high Na⁺/K⁺ ratio in patients with more severe depression. These routinely collected biomarkers may potentially be useful for risk stratification in an early stage of treatment. Trial Registration: clinicaltrials.gov Identifier: NCT00974155; https://www.clinicaltrials.gov/ct2/results?term=NCT00974155.

The Australian Genetics of Depression Study: New Risk Loci and Dissecting Heterogeneity Between Subtypes

BACKGROUND

Major depressive disorder (MDD) is a common and highly heterogeneous psychiatric disorder, but little is known about the genetic characterization of this heterogeneity. Understanding the genetic etiology of MDD can be challenging because large sample sizes are needed for gene discovery-often achieved with a trade-off in the depth of phenotyping.

METHODS

The Australian Genetics of Depression Study is the largest stand-alone depression cohort with both genetic data and in-depth phenotyping and comprises a total of 15,792 participants of European ancestry, 92% of whom met diagnostic criteria for MDD. We leveraged the unique nature of this cohort to conduct a meta-analysis with the largest publicly available depression genome-wide association study to date and subsequently used polygenic scores to investigate genetic heterogeneity across various clinical subtypes of MDD.

RESULTS

We increased the number of known genome-wide significant variants associated with depression from 103 to 126 and found evidence of association of novel genes implicated in neuronal development. We found that a polygenic score for depression explained 5.7% of variance in MDD liability in our sample. Finally, we found strong support for genetic heterogeneity in depression with differential associations of multiple psychiatric and comorbid traits with age of onset, longitudinal course, and various subtypes of MDD.

CONCLUSIONS

Until now, this degree of detailed phenotyping in such a large sample of MDD cases has not been possible. Along with the discovery of novel loci, we provide support for differential pathways to illness models that recognize the overlap with other common psychiatric disorders as well as pathophysiological differences.

Revisiting the theoretical and methodological foundations of depression measurement

Depressive disorders are among the leading causes of global disease burden, but there has been limited progress in understanding the causes and treatments for these disorders. In this Perspective, we suggest that such progress crucially depends on our ability to measure depression. We review the many problems with depression measurement, including limited evidence of validity and reliability. These issues raise grave concerns about common uses of depression measures, such as diagnosis or tracking treatment progress. We argue that shortcomings arise because depression measurement rests on shaky methodological and theoretical foundations. Moving forward, we need to break with the field's tradition that has, for decades, divorced theories about depression from how we measure it. Instead, we suggest that epistemic iteration, an iterative exchange between theory and measurement, provides a crucial avenue for depression measurement to progress.

Sex Differences in Depression and Anxiety Symptoms: Measurement Invariance, Prevalence, and Symptom Heterogeneity Among University Students in South Africa

Adequate measurement is an essential component of the assessment of mental health disorders and symptoms such as depression and anxiety. The present study investigated sex-specific differences in the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7). This comprehensive cross-sectional design study pursued four objectives: measurement invariance of PHQ-9 and GAD-7 between male and female; depression and anxiety prevalence differences; cross-sex differences in the relationship between depression and anxiety; and a comparison of symptom heterogeneity. A sample of 1966 (male = 592; female = 1374; mean age = 21 years) students from South Africa completed the PHQ-9 and the GAD-7. Data analyses for measurement invariance, latent class analysis, inter-variable correlations and group comparisons were conducted in Mplus. The two-dimensional PHQ-9 achieved scalar invariance, while the GAD-7 yielded metric invariance. The somatic and non-somatic latent dimensions of depression were compared and showed no significant difference between male and female groups. The positive relationship between depression and anxiety was also not significantly different between the two groups. While the PHQ-9 symptoms formed three classes in the male group, and four classes in the female group, the GAD-7 had the same number of classes (three) and a similar pattern between the two groups. These findings hold implications for the measurement, assessment and understanding of symptom manifestation and distribution, as well as the treatment of depression and anxiety in South Africa.

A Comparative Study of Regional Homogeneity of Resting-State fMRI Between the Early-Onset and Late-Onset Recurrent Depression in Adults

Background

Neurobiological mechanisms underlying the recurrence of major depressive disorder (MDD) at different ages are unclear, and this study used the regional homogeneity (ReHo) index to compare whether there are differences between early onset recurrent depression (EORD) and late onset recurrent depression (LORD).

Methods

Eighteen EORD patients, 18 LORD patients, 18 young healthy controls (HCs), and 18 older HCs were included in the rs-fMRI scans. ReHo observational metrics were used for image analysis and further correlation of differential brain regions with clinical symptoms was analyzed.

Results

ANOVA analysis revealed significant differences between the four groups in ReHo values in the prefrontal, parietal, temporal lobes, and insula. Compared with EORD, the LORD had higher ReHo in the right fusiform gyrus/right middle temporal gyrus, left middle temporal gyrus/left angular gyrus, and right middle temporal gyrus/right angular gyrus, and lower ReHo in the right inferior frontal gyrus/right insula and left superior temporal gyrus/left insula. Compared with young HCs, the EORD had higher ReHo in the right inferior frontal gyrus/right insula, left superior temporal gyrus/left insula, and left rolandic operculum gyrus/left superior temporal gyrus, and lower ReHo in the left inferior parietal lobule, right inferior parietal lobule, and left middle temporal gyrus/left angular gyrus. Compared with old HCs, the LORD had higher ReHo in the right fusiform gyrus/right middle temporal gyrus, right middle temporal gyrus/right angular gyrus, and left rolandic operculum gyrus/left superior temporal gyrus, and lower ReHo in the right inferior frontal gyrus/right insula. ReHo in the right inferior frontal gyrus/right insula of patients with LORD was negatively correlated with the severity of 17-item Hamilton Rating Scale for Depression (HAMD-17) scores (r = −0.5778, p = 0.0120).

Conclusion

Adult EORD and LORD patients of different ages have abnormal neuronal functional activity in some brain regions, with differences closely related to the default mode network (DMN) and the salience network (SN), and patients of each age group exhibit ReHo abnormalities relative to matched HCs.

Clinical Trial Registration

[http://www.chictr.org.cn/], [ChiCTR1800014277].

Age-related heterogeneity revealed by disruption of white matter structural networks in patients with first-episode untreated major depressive disorder: WM Network In OA-MDD

The clinical treatment and prognosis of major depressive disorder (MDD) are limited by the high degree of disease heterogeneity. It is unclear whether there is a potential network mechanism for age-related heterogeneity. We aimed to uncover the heterogeneity of the white matter (WM) network at different ages of onset and its correlation with different symptom characteristics. 85 first-episode MDD patients and 84 corresponding healthy controls (HCs) were recruited and underwent diffusion tensor imaging scans. Structural network characteristics were analyzed using graph theory methods. We observed an accelerated age-related decline of the WM network in MDD patients compared with HCs. Distinct symptom-related networks were identified in three MDD groups with different onset-age. For early-onset MDD (18-29 years; EOD), higher guilt and loss of interest were correlated with the insula, and inferior parietal lobe which in default mode network and salience network. For mid-term-onset MDD (30-44 years; MOD), higher somatic symptoms were correlated with thalamus which in cortico-striatal-thalamic-cortical circuit. For later-onset MDD (45-60 years; LOD), poor sleep symptoms were correlated with the caudate in the basal ganglia, which suggests the cingulate operculum network in the control of sleep. These results supported a circuit-based heterogeneity associated with the age of onset in MDD. Understanding this circuit-based heterogeneity might help to develop a new target for clinical treatment strategies.

Genetics of age-at-onset in major depression

Major depression (MD) is a complex, heterogeneous neuropsychiatric disorder. An early age at onset of major depression (AAO-MD) has been associated with more severe illness, psychosis, and suicidality. However, not much is known about what contributes to individual variation in this important clinical characteristic. This study sought to investigate the genetic components underlying AAO-MD. To investigate the genetics of AAO-MD, we conducted a genome-wide association meta-analysis of AAO-MD based on self-reported age of symptoms onset and self-reported age at first diagnosis from the UK Biobank cohort (total N = 94,154). We examined the genetic relationship between AAO-MD and five other psychiatric disorders. Polygenic risk scores were derived to examine their association with five psychiatric outcomes and AAO-MD in independent sub-samples. We found a small but significant SNP-heritability (~6%) for the AAO-MD phenotype. No SNP or gene reached SNP or gene-level significance. We found evidence that AAO-MD has genetic overlap with MD risk ([Formula: see text] = -0.49). Similarly, we found shared genetic risks between AAO-MD and autism-spectrum disorder, schizophrenia, bipolar disorder, and anorexia nervosa ([Formula: see text] range: -0.3 to -0.5). Polygenic risk scores for AAO-MD were associated with MD, schizophrenia, and bipolar disorder, and AAO-MD was in turn associated with polygenic risk scores derived from these disorders. Overall, our results indicate that AAO-MD is heritable, and there is an inverse genetic relationship between AAO-MD and both major depression and other psychiatric disorders, meaning that SNPs associated with earlier age at onset tend to increase the risk for psychiatric disorders. These findings suggest that the genetics of AAO-MD contribute to the shared genetic architecture observed between psychiatric disorders.