Heterogeneity Of Major Depressive Disorder Research Articles

Genetic factors and symptom dimensions associated with antidepressant treatment outcomes: clues for new potential therapeutic targets?

Treatment response and resistance in major depressive disorder (MDD) show a significant genetic component, but previous studies had limited power also due to MDD heterogeneity. This literature review focuses on the genetic factors associated with treatment outcomes in MDD, exploring their overlap with those associated with clinically relevant symptom dimensions. We searched PubMed for: (1) genome-wide association studies (GWASs) or whole exome sequencing studies (WESs) that investigated efficacy outcomes in MDD; (2) studies examining the association between MDD treatment outcomes and specific depressive symptom dimensions; and (3) GWASs of the identified symptom dimensions. We identified 13 GWASs and one WES of treatment outcomes in MDD, reporting several significant loci, genes, and gene sets involved in gene expression, immune system regulation, synaptic transmission and plasticity, neurogenesis and differentiation. Nine symptom dimensions were associated with poor treatment outcomes and studied by previous GWASs (anxiety, neuroticism, anhedonia, cognitive functioning, melancholia, suicide attempt, psychosis, sleep, sociability). Four genes were associated with both treatment outcomes and these symptom dimensions: CGREF1 (anxiety); MCHR1 (neuroticism); FTO and NRXN3 (sleep). Other overlapping signals were found when considering genes suggestively associated with treatment outcomes. Genetic studies of treatment outcomes showed convergence at the level of biological processes, despite no replication at gene or variant level. The genetic signals overlapping with symptom dimensions of interest may point to shared biological mechanisms and potential targets for new treatments tailored to the individual patient's clinical profile.

Major depression symptom severity associations with willingness to exert effort and patch foraging strategy.

Individuals with major depressive disorder (MDD) can experience reduced motivation and cognitive function, leading to challenges with goal-directed behavior. When selecting goals, people maximize 'expected value' by selecting actions that maximize potential reward while minimizing associated costs, including effort 'costs' and the opportunity cost of time. In MDD, differential weighing of costs and benefits are theorized mechanisms underlying changes in goal-directed cognition and may contribute to symptom heterogeneity. We used the Effort Foraging Task to quantify cognitive and physical effort costs, and patch leaving thresholds in low effort conditions (hypothesized to reflect perceived opportunity cost of time) and investigated their shared versus distinct relationships to clinical features in participants with MDD (N=52, 43 in-episode) and comparisons (N=27). Contrary to our predictions, none of the decision-making measures differed with MDD diagnosis. However, each of the measures were related to symptom severity, over and above effects of ability (i.e., performance). Greater anxiety symptoms were selectively associated with lower cognitive effort cost (i.e. greater willingness to exert effort). Anhedonia symptoms were associated with increased physical effort costs. Finally, greater physical anergia was related to decreased patch leaving thresholds. Markers of effort-based decision-making may inform understanding of MDD heterogeneity. Increased willingness to exert cognitive effort may contribute to anxiety symptoms such as rumination and worry. The association of decreased leaving thresholds with symptom severity is consistent with reward rate-based accounts of reduced vigor in MDD. Future research should address subtypes of depression with or without anxiety, which may relate differentially to cognitive effort decisions.

The relationship of peripheral blood cell inflammatory biomarkers and psychological stress in unmedicated major depressive disorder

BackgroundRecent research has explored the linkage between major depressive disorder (MDD) and inflammation, especially via altered peripheral blood immune markers. However, the relationship between several novel leukocyte-derived ratios (LDR) and psychological stress in MDD remains uncertain. This study aimed to explore the relationship between LDR, clinical characteristics, recent life events, and childhood maltreatment in MDD patients. MethodsA cross-sectional case-control study was conducted involving 59 healthy controls (HC) and 50 unmedicated MDD patients. Subjects underwent psychological assessments and peripheral blood measurements. LDR assessed in this study included neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), white blood cell-to-mean platelet volume ratio (WMR), systemic immune inflammation index (SII), multiplication of neutrophil and monocyte counts (MNM), and systemic inflammation response index (SIRI). ResultsMDD patients displayed significant alterations in WMR, PLR, and MNM compared to HC, as well as correlations between several LDR and various clinical features (duration of untreated psychosis and dNLR, the nine-item Patient Health Questionnaire and PLR, the 7-item Generalized Anxiety Disorder Questionnaire and SIRI (NLR and dNLR). There was a significant difference in the comparison of WMR in first-episode patients than in recurrent patients. Analyses further revealed an association between Life Event Scale total scores and NLR (dNLR). No correlation was found between Childhood Trauma Questionnaire total (or subscale) scores and LDR. Additionally, WMR and dNLR presented potential predictive value for distinguishing between MDD and HC. ConclusionThe study concludes that MDD and some clinical features are associated with alterations in some peripheral blood LDR. These findings emphasize the potential role of peripheral blood LDR in the pathogenesis and clinical heterogeneity of MDD.

Identifying data-driven subtypes of major depressive disorder with electronic health records

BackgroundEfforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. MethodsWe utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. ResultsAmong 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LimitationsElectronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. ConclusionThese data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles.

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Linking inter-subject variability of cerebellar functional connectome to clinical symptoms in major depressive disorder

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder with remarkable inter-subject variability in clinical manifestations. Neuroimaging changes of the cerebellum have been recently proposed as a way to characterize MDD-related brain disruptions and might further explain various clinical symptoms. However, the cerebellar contributions to MDD clinical heterogeneity remain largely unknown.The analyzed data consisted of 251 MDD patients and 235 matching healthy controls (HC). The inter-subject variability of functional connectomes (IVFC) was estimated via Pearson's correlation analysis between each pair of the cerebellar and cerebral regions based on resting-state functional magnetic resonance imaging (rs-fMRI). A partial least squares (PLS) regression analysis was performed to determine the potential dimension linking the IVFC to clinical symptom measures.The results indicated that similar spatial distribution patterns of the cerebellar IVFC were observed between MDD and HC, but the MDD group exhibited abnormal IVFC alterations in the bilateral Cerebelum_4_5, bilateral Cerebelum_6, Vermis_1_2 and Vermis_8. The PLS model revealed that the IVFC pattern in the left Cerebelum_6 was significantly associated with three HAMD-17 items including the work and activities, psychomotor retardation, and depressed mood. These findings provided new evidence for the cerebellar changes in MDD. Specifically, we found that the altered inter-subject variability measurements correlated with clinical manifestations of this illness. Elucidating this variability could prove helpful for the evaluation of MDD heterogeneity as well as for understanding its pathophysiological mechanism.

Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments

BackgroundSymptoms of major depressive disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire-9 (PHQ-9) (current symptoms) and the Composite International Diagnostic Interview Short-Form (CIDI-SF) (worst-episode symptoms). We performed a systematic comparison between them for their genetic architecture and utility in investigating MDD heterogeneity. MethodsUsing data from the UK Biobank (n = 41,948–109,417), we assessed the single nucleotide polymorphism heritability and genetic correlation (rg) of both sets of MDD symptoms. We further compared their rg with non-MDD traits and used Mendelian randomization to assess whether either set of symptoms has more genetic sharing with non-MDD traits. We also assessed how specific each set of symptoms is to MDD using the metric polygenic risk score pleiotropy. Finally, we used genomic structural equation modeling to identify factors that explain the genetic covariance between each set of symptoms. ResultsCorresponding symptoms reported through the PHQ-9 and CIDI-SF have low to moderate genetic correlations (rg = 0.43–0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip structure in the CIDI-SF. Both Mendelian randomization and polygenic risk score pleiotropy analyses showed that PHQ-9 symptoms are more associated with traits that reflect general dysphoria, whereas the skip structure in the CIDI-SF allows for the identification of heterogeneity among likely MDD cases. Finally, the 2 sets of symptoms showed different factor structures in genomic structural equation modeling, reflective of their genetic differences. ConclusionsMDD symptoms assessed using the PHQ-9 and CIDI-SF are not interchangeable; the former better indexes general dysphoria, while the latter is more informative about within-MDD heterogeneity.

Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study

Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as “factors”), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.

Alpha wave asymmetry is associated with only one component of melancholia, and in different directions across brain regions.

Alpha wave asymmetry inconsistently correlates with Major Depressive Disorder (MDD). One possible reason for this inconsistency is the heterogeneity of MDD, leading to study of depressive 'subtypes', one of which is Melancholia. To investigate the correlation between Melancholia and alpha-wave asymmetry, 100 community participants (44 males, 56 females; aged at least 18yr) completed the Zung self-rated Depression Scale, and underwent 3min of eyes closed EEG recording from 24 scalp sites. There was no significant correlation between EEG data and Melancholia total score for the entire sample, but there was for those participants who had clinically significant depression (n=33). When examined at the level of individual Melancholia scale items, significant EEG data correlations were found for some of the items but not for others. Factor analysis revealed a two-factor structure for the Melancholia scale, only one of which exhibited significant correlations with EEG AA data. Further exploration of those data identified two subcomponents of that Melancholia factor, one which was inversely correlated with frontal alpha asymmetry, and another which was directly correlated with parietal-occipital alpha wave asymmetry. These findings suggest that Melancholia may itself be heterogeneous, similarly to MDD, and rely upon different aspects of cognitive function.

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

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. 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. 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. 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.

What does the immunometabolic status tell us about depression?

AbstractDespite being a clinical identifiable entity, major depressive disorder (MDD) is an heterogenous clinical syndrome, with a variety of clinical presentations which likely reflects different biological underpinnings. The identification of biologically-based depression symptoms profiles would be of great importance to unravel different pathophysiological pathways in MDD and therefore to achieve more precise and personalized therapeutical approaches as well as preventive strategies.Converging evidence from epidemiological and clinical studies, points to the importance of inflammation in MDD, shown by increased levels of pro-inflammatory proteins and increased inflammation-related comorbidities, including metabolic diseases. In fact, there exists a bidirectional relationship between inflammation and metabolic dysfunction that could be linked to multiple factors, including life style, stress and genetic predisposition. MDD patients exhibit several metabolic disturbances such as overweight, insuline resistance and dyslipidemia, among others, which are not always fully explained by life style factors. These findings have led to the formulation of an immunometabolic hypothesis, which could be present in a subgroup of MDD patients, associated to specific symptoms and clinical features.In this presentation, data reflecting the complex relationships and interactions between immune and metabolic disturbances in MDD will be shown. In particular, it will be shown how machine learning approaches can be useful to disentangle the clinical and biological heterogeneity of MDD, using immunometabolic biomarkers.Disclosure of InterestP. Lopez-Garcia Grant / Research support from: Grant from Carlos III Health Institute ref PI15/00204 and FEDER funding from the EU

Biotypes of major depressive disorder identified by a multiview clustering framework

BackgroundThe 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. MethodsWe 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). ResultsTwo 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. LimitationsThe power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes. ConclusionsOur 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.

Understanding the Role of Oxidative Stress, Neuroinflammation and Abnormal Myelination in Excessive Aggression Associated with Depression: Recent Input from Mechanistic Studies.

Aggression and deficient cognitive control problems are widespread in psychiatric disorders, including major depressive disorder (MDD). These abnormalities are known to contribute significantly to the accompanying functional impairment and the global burden of disease. Progress in the development of targeted treatments of excessive aggression and accompanying symptoms has been limited, and there exists a major unmet need to develop more efficacious treatments for depressed patients. Due to the complex nature and the clinical heterogeneity of MDD and the lack of precise knowledge regarding its pathophysiology, effective management is challenging. Nonetheless, the aetiology and pathophysiology of MDD has been the subject of extensive research and there is a vast body of the latest literature that points to new mechanisms for this disorder. Here, we overview the key mechanisms, which include neuroinflammation, oxidative stress, insulin receptor signalling and abnormal myelination. We discuss the hypotheses that have been proposed to unify these processes, as many of these pathways are integrated for the neurobiology of MDD. We also describe the current translational approaches in modelling depression, including the recent advances in stress models of MDD, and emerging novel therapies, including novel approaches to management of excessive aggression, such as anti-diabetic drugs, antioxidant treatment and herbal compositions.

Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

BackgroundTo address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes. MethodsPretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD17) and response/remission rates by treatment group. ResultsThree subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline. LimitationsDue to the exploratory nature of these analyses, there were no adjustments for multiple comparisons. ConclusionsBaseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.

Efficacy of eicosapentaenoic acid in inflammatory depression: study protocol for a match-mismatch trial

BackgroundMost antidepressant treatment studies have included patients strictly based on the Diagnostic and Statistical Manual of Mental Disorders definition of Major Depressive Disorder (MDD). Given the heterogeneity of MDD, this approach may have obscured inter-patient differences and hampered the development of novel and targeted treatment strategies. An alternative strategy is ​​to use biomarkers to delineate endophenotypes of depression and test if these can be targeted via mechanism-based interventions. Several lines of evidence suggest that “inflammatory depression” is a clinically meaningful subtype of depression. Preliminary data indicate that omega-3 fatty acids, with their anti-inflammatory and neuroprotective properties, may be efficacious in this subtype of depression, and this study aims to test this hypothesis.MethodWe conduct a match-mismatch-trial to test if add-on omega-3 fatty acid eicosapentaenoic acid (EPA) reduces depressive symptoms in patients with MDD and systemic low-grade inflammation. MDD patients on a stable antidepressant treatment are stratified at baseline on high sensitivity-C-reactive protein (hs-CRP) levels to a high-inflammation group (hs-CRP ≥ 3 mg/L) or a low-inflammation group (hs-CRP < 3 mg/L). Both groups receive add-on EPA (2 g per day) for 8 weeks with three study visits, all including blood draws. Patients and raters are blind to inflammation status. Primary outcome measure is change in Hamilton Depression Rating Scale score between baseline and week 8. We hypothesize that the inflammation group has a superior antidepressant response to EPA compared to the non-inflammation group. Secondary outcomes include a composite score of “inflammatory depressive symptoms”, quality of life, anxiety, anhedonia, sleep disturbances, fatigue, cognitive performance and change in biomarkers relating to inflammation, oxidative stress, metabolomics and cellular aging.DiscussionIn this study we will, for the first time using a match-mismatch trial design, test if omega-3 is an efficacious treatment for inflammatory depression. If our study is successful, it could add to the field of precision psychiatry.Trial registrationThis trial was registered May 8, 2017 on clinicaltrials.gov under the reference number NCT03143075

Differential expression of MDGA1 in major depressive disorder

The identification of gene expression-based biomarkers for major depressive disorder (MDD) continues to be an important challenge. In order to identify candidate biomarkers and mechanisms, we apply statistical and machine learning feature selection to an RNA-Seq gene expression dataset of 78 unmedicated individuals with MDD and 79 healthy controls. We identify 49 genes by LASSO penalized logistic regression and 45 genes at the false discovery rate threshold 0.188. The MDGA1 gene has the lowest P-value (4.9e-5) and is expressed in the developing brain, involved in axon guidance, and associated with related mood disorders in previous studies of bipolar disorder (BD) and schizophrenia (SCZ). The expression of MDGA1 is associated with age and sex, but its association with MDD remains significant when adjusted for covariates. MDGA1 is in a co-expression cluster with another top gene, ATXN7L2 (ataxin 7 like 2), which was associated with MDD in a recent GWAS. The LASSO classification model of MDD includes MDGA1, and the model has a cross-validation accuracy of 79%. Another noteworthy top gene, IRF2BPL, is in a close co-expression cluster with MDGA1 and may be related to microglial inflammatory states in MDD. Future exploration of MDGA1 and its gene interactions may provide insights into mechanisms and heterogeneity of MDD.

Age of onset for major depressive disorder and its association with symptomatology

BackgroundThe age of onset (AOO) is a key factor for heterogeneity in major depressive disorder (MDD). Looking at the effect of AOO on symptomatology may improve clinical outcomes. This study aims to examine whether and how AOO affects symptomatology using a machine learning approach and latent profile analysis (LPA). MethodsThe study enrolled 915 participants diagnosed with MDD from eight hospitals across China. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale. The relationship between symptom profiles and AOO was explored using Random Forest. The effect of AOO on symptom clusters and subtypes was investigated using multiple linear regression and LPA. A continuous AOO indicator was used to conduct the analyses. ResultsBased on the Random Forest, symptom profiles were closely associated with AOO. The regression model showed that the severity of neurovegetative symptoms was positively associated with AOO (β = 0.18, p < 0.001), and the severity of cognitive-behavioral symptoms was negatively associated with AOO (β = −0.12, p < 0.001). LPA demonstrated that the subgroups characterized by suicide and guilt had earlier onset of depression. The subgroup with the lowest global severity of depression had the latest onset. LimitationsAOO was recalled retrospectively. The relative scarcity of participants with childhood and adolescence onset depression. ConclusionsAOO has an important impact on symptomatology. The findings may enhance clinical evaluations for MDD and assist clinicians in promoting earlier detection and individualized care in vulnerable individuals.

Not Only Gene Discovery: Genome-wide Association Studies and Polygenic Risk Scores as Tools to Dissect the Heterogeneity of Major Depressive Disorder

Large-scale genome-wide association studies (GWASs) based on biobanks and meta-analyses of many cohorts have revolutionized our ability to investigate the polygenic architecture of psychiatric disorders and traits. This has been possible because of national and international collaborations such as the Psychiatric Genomics Consortium (PGC) ( 1 Sullivan P.F. Agrawal A. Bulik C.M. Andreassen O.A. Borglum A.D. Breen G. et al. Psychiatric genomics: An update and an agenda. Am J Psychiatry. 2018; 175: 15-27 Crossref PubMed Scopus (292) Google Scholar ). While combining cohorts with different characteristics and various phenotypic assessments led to uncovering hundreds of loci associated with major depressive disorder (MDD) ( 2 Levey D.F. Stein M.B. Wendt F.R. Pathak G.A. Zhou H. Aslan M. et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nat Neurosci. 2021; 24: 954-963 Crossref PubMed Scopus (47) Google Scholar ), this has limited our ability to investigate the genetic heterogeneity of MDD. Owing to the massive sample sizes required to identify the small individual effects of single risk loci, it is not useful to directly compare gene discovery of GWASs of different MDD subtypes because the sample size available for each subtype is a primary driver of a GWAS’s statistical power. Indeed, although minimal phenotyping may reduce the specificity of MDD loci identified by GWASs ( 3 Cai N. Revez J.A. Adams M.J. Andlauer T.F.M. Breen G. Byrne E.M. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat Genet. 2020; 52: 437-447 Crossref PubMed Scopus (92) Google Scholar ), sample size still trumps phenotyping when focusing only on gene discovery. SEE CORRESPONDING ARTICLE ON PAGE 227 The Australian Genetics of Depression Study: New Risk Loci and Dissecting Heterogeneity Between SubtypesBiological PsychiatryVol. 92Issue 3PreviewMajor 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. Full-Text PDF

Comparative analysis of gut microbiota and fecal metabolome features among multiple depressive animal models

BackgroundsEmerging studies reported that gut microbiota and fecal metabolites take part in major depressive disorder (MDD) pathogenesis. However, the conclusions based on a single depressive animal model seem inconsistent or even controversial. MethodsMultiple depression rat models, including chronic unpredictable mild stress, chronic restraint stress, social defeat, and learned helplessness, were used. Then, the 16S ribosomal RNA gene sequencing and liquid chromatography-mass spectrometry analysis determined the alteration of gut microbiota and fecal metabolites. ResultsThe results of sucrose preference test and forced swimming test suggested that each model successfully established depression-like behavior. A total of 179 discriminative amplicon sequence variants (ASVs) were identified among four models. The overall discriminative ASVs mainly belonged to the family Lachnospiraceae, Muribaculaceae, and Oscillospiraceae. Moreover, the fecal metabolomic analysis identified 468 differential expressed metabolites. Among all the differential metabolites, 11 specific pathways significantly altered, which were mainly belonged to lipid and amino acid metabolism. Finally, co-occurrence network analysis suggested that target differential metabolites were associated with discriminative ASVs mainly assigned to family taxon Lachnospiraceae, Muribaculaceae, and Oscillospiraceae. LimitationsThe heterogeneity of MDD in humans cannot be totally imitated by animal models. ConclusionsIn multiple depression models, the alterations of family Lachnospiraceae, Muribaculaceae, and Oscillospiraceae with the dysbiosis of lipid and amino acid metabolism were gut microbiota and fecal metabolome features. The findings of our research may help us to have a comprehensive understanding of gut microbiota and fecal metabolome in depression.