Genetic heterogeneity and subtypes of major depression

Inflammation-related abnormal dynamic brain activity correlates with cognitive impairment in first-episode, drug-naïve major depressive disorder

BACKGROUND

Cognitive impairment is common in major depressive disorder (MDD) and potentially linked to inflammation-induced alterations in brain function. However, the relationship between inflammation, dynamic brain activity, and cognitive impairment in MDD remains unclear.

METHODS

Fifty-seven first-episode, drug-naïve MDD patients and sixty healthy controls underwent fMRI scanning. Dynamic amplitude of low-frequency fluctuations (dALFF) and dynamic functional connectivity (dFC) were measured using the sliding window method. Plasma IL - 6 levels and cognitive function were assessed using enzyme-linked immunosorbent assay (ELISA) and the Repeated Battery for Assessment of Neuropsychological Status (RBANS), respectively.

RESULTS

MDD patients exhibited decreased dALFF in the bilateral inferior temporal gyrus (ITG), right inferior frontal gyrus, opercular part (IFGoperc), and bilateral middle occipital gyrus (MOG). Regions of dALFF associated with IL-6 included right ITG (r = -0.400/p = 0.003), left ITG (r = -0.381/p = 0.004), right IFGoperc (r = -0.342/p = 0.011), and right MOG (r = -0.327/p = 0.016). Furthermore, IL-6-related abnormal dALFF (including right ITG: r = 0.309/p = 0.023, left ITG: r = 0.276/p = 0.044) was associated with attention impairment. These associations were absent entirely in MDD patients without suicidal ideation. Additionally, IL-6 levels were correlated with dFC of specific brain regions.

LIMITATIONS

Small sample size and cross-sectional study design.

CONCLUSIONS

Inflammation-related dALFF was associated with attention impairment in MDD patients, with variations observed among MDD subgroups. These findings contribute to the understanding of the intricate relationship between inflammation, dynamic brain activity and cognitive impairments in MDD.

Individualized gray matter morphological abnormalities uncover two robust transdiagnostic biotypes

Psychiatric disorders exhibit a shared neuropathology, yet the diverse presentations among patients necessitate the identification of transdiagnostic subtypes to enhance diagnostic and treatment strategies. This study aims to unveil potential transdiagnostic subtypes based on personalized gray matter morphological abnormalities. A total of 496 patients with psychiatric disorders and 255 healthy controls (HCs) from three distinct datasets (one for discovery and two for validation) were enrolled. Individualized gray matter morphological abnormalities were determined using normative modeling to identify transdiagnostic subtypes. In the discovery dataset, two transdiagnostic subtypes with contrasting patterns of structural abnormalities compared to HCs were identified. Reproducibility and generalizability analyses demonstrated that these subtypes could be generalized to new patients and even to new disorders in the validation datasets. These subtypes were characterized by distinct disease epicenters. The gray matter abnormal pattern in subtype 1 was mainly linked to excitatory receptors, whereas subtype 2 showed a predominant association with inhibitory receptors. Furthermore, we observed that the gray matter abnormal pattern in subtype 2 was correlated with transcriptional profiles of inflammation-related genes, while subtype 1 did not show this association. Our findings reveal two robust transdiagnostic biotypes, offering novel insights into psychiatric nosology.

Polygenic liabilities and treatment trajectories in early-onset depression: a Danish register-based study

BACKGROUND

The clinical course of major depressive disorder (MDD) is heterogeneous, and early-onset MDD often has a more severe and complex clinical course. Our goal was to determine whether polygenic scores (PGSs) for psychiatric disorders are associated with treatment trajectories in early-onset MDD treated in secondary care.

METHODS

Data were drawn from the iPSYCH2015 sample, which includes all individuals born in Denmark between 1981 and 2008 who were treated in secondary care for depression between 1995 and 2015. We selected unrelated individuals of European ancestry with an MDD diagnosis between ages 10-25 (N = 10577). Seven-year trajectories of hospital contacts for depression were modeled using Latent Class Growth Analysis. Associations between PGS for MDD, bipolar disorder, schizophrenia, ADHD, and anorexia and trajectories of MDD contacts were modeled using multinomial logistic regressions.

RESULTS

We identified four trajectory patterns: brief contact (65%), prolonged initial contact (20%), later re-entry (8%), and persistent contact (7%). Relative to the brief contact trajectory, higher PGS for ADHD was associated with a decreased odds of membership in the prolonged initial contact (odds ratio = 1.06, 95% confidence interval = 1.01-1.11) and persistent contact (1.12, 1.03-1.21) trajectories, while PGS-AN was associated with increased odds of membership in the persistent contact trajectory (1.12, 1.03-1.21).

CONCLUSIONS

We found significant associations between polygenic liabilities for psychiatric disorders and treatment trajectories in patients with secondary-treated early-onset MDD. These findings help elucidate the relationship between a patient's genetics and their clinical course; however, the effect sizes are small and therefore unlikely to have predictive value in clinical settings.

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Evidence of symptom specificity for depression in multiple sclerosis: A two sample Mendelian randomization study

BACKGROUND

Depression is common and phenotypically heterogenous in multiple sclerosis (MS). MS may increase risk of some but not all affective symptoms or certain symptoms may predispose individuals to higher MS risk.

OBJECTIVE

To assess the existence and direction of causality between distinct depressive symptoms and MS using two-sample Mendelian randomization (MR).

METHODS

Using summary data from genome-wide association studies, we selected genetic instrument variables (IV) for MS (n = 115,776) and IVs for depressive symptoms (average n = 117,713): anhedonia, altered appetite, concentration, depressed mood, fatigue, inadequacy, psychomotor changes, sleeping problems and suicidality. We performed two-sample MR in either direction using inverse-variance models. Sensitivity analyses included weighted-median and MR-Egger regression. Obesity is a known risk factor for MS and depression; we adjusted for body mass index in multivariable-MR.

RESULTS

Genetic liability to MS was associated with anhedonia (IVW estimate per 102: 0.69; 95 % CI: 0.24-1.13; p = 0.002), concentration difficulty (0.66; 0.19-1.13; p = 0.006) and psychomotor changes (0.37; 0.08-0.65; p = 0.01). Results were similar in sensitivity analyses. In the opposite direction, we found no evidence of a causal relationship for any affective symptom on MS risk.

CONCLUSIONS

Genetic susceptibility to MS was associated with anhedonia, concentration, and psychomotor-related symptoms, suggesting a specific phenotype of depression in MS.

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.

A systematic review and network meta-analysis of psychological, psychosocial, pharmacological, physical and combined treatments for adults with a new episode of depression

Background

Various effective treatments for depression exist. We aimed to identify the most effective first-line treatments for new episodes of less and more severe depression (defined by depression scale cut-off scores), to update NICE guidance on the management of Depression in Adults in England.

Methods

Systematic review and network meta-analysis of randomised controlled trials (RCTs) published up to June 2020 (PROSPERO registration number CRD42019151328). We analysed interventions by class and individually. The primary efficacy outcome was depressive symptom change (expressed as standardised mean difference [SMD]). The review for this outcome was updated in November 2023.

Findings

We included 676 RCTs, 105,477 participants and 63 treatment classes. For less severe depression, group cognitive/cognitive behavioural therapy (CT/CBT) class was efficacious versus treatment as usual [TAU], the reference treatment for this population [SMD -1.01 (95% Credible Interval [CrI] -1.76; -0.06)]. For more severe depression, efficacious classes versus pill placebo (reference treatment for this population) included combined individual CT/CBT with antidepressants [-1.18 (-2.07; -0.44)], individual behavioural therapies [-0.86 (-1.65; -0.16)], combined light therapy with antidepressants [-0.86 (-1.59; -0.12)], combined acupuncture with antidepressants [-0.78 (-1.12; -0.44)], individual CT/CBT [-0.78 (-1.42; -0.33)], mirtazapine [-0.35 (-0.48; -0.22)], serotonin and norepinephrine reuptake inhibitors [-0.32 (-0.43; -0.22)], tricyclic antidepressants [-0.29 (-0.50; -0.05)], and selective serotonin reuptake inhibitors [-0.24 (-0.32; -0.16)]. Additional treatments showed evidence of efficacy at the intervention level. Evidence for less and more severe depression was of low and low-to-moderate quality, respectively. In the 2023 update, group yoga and self-help without support emerged as efficacious for less severe depression. For more severe depression, combined group exercise with antidepressants emerged as efficacious, whereas combined light therapy with antidepressants failed to remain efficacious.

Interpretation

Group CT/CBT (and possibly group yoga and self-help) appears efficacious in less severe depression, whereas antidepressants do not show evidence of effect. Combined antidepressants with individual CT/CBT, acupuncture and, possibly, group exercise, individual psychological therapies (behavioural therapies, CT/CBT) alone, and antidepressants alone appear efficacious in more severe depression. Quality of evidence, cost-effectiveness, applicability and implementation issues also need to be considered when formulating clinical practice recommendations.

Funding

National Institute for Health and Care Excellence.

Integrative molecular and structural neuroimaging analyses of the interaction between depression and age of onset: A multimodal magnetic resonance imaging study

Depression is a neurodevelopmental disorder that exhibits progressive gray matter volume (GMV) atrophy. Research indicates that brain development is influential in depression-induced GMV alterations. However, the interaction between depression and age of onset is not well understood by the underlying molecular and neuropathological mechanisms. Thus, 152 first-episode depression individuals and matched 130 healthy controls (HCs) were recruited to undergo T1-weighted high-resolution magnetic resonance imaging for this study. By two-way ANOVA, age and diagnosis were used as factors when analyzing the interaction of GMV in the participants. Then, spatial correlations between neurotransmitter maps and factor-related volume maps are established. Results illustrate a pronounced antagonistic interaction between depression and age of onset in the right insula, superior temporal gyrus, anterior cingulate gyrus, and orbitofrontal gyrus. Depression-caused reductions in GMV are mainly distributed in thalamic-limbic-cortical regions, regardless of age. For the main effect of age, adults exhibit brain atrophy in frontal, cerebellum, parietal, and temporal lobe structures. Cross-modal correlations showed that GMV changes in the interactive regions were linked with the serotonergic system and dopaminergic systems. Summarily, our results reveal the interaction between depression and age of onset in neurobiological mechanisms, which provide hints for future treatment of different ages of depression.

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Aberrant resting-state voxel-mirrored homotopic connectivity in major depressive disorder with and without anxiety

OBJECTIVE

Prior researchers have identified distinct differences in functional connectivity neuroimaging characteristics among MDD patients. However, the auxiliary diagnosis and subtype differentiation roles of VMHC values in MDD patients have yet to be fully understood. We aim to explore the separating ability of VMHC values in patients with anxious MDD or with non-anxious MDD and HCs.

METHODS

We recruited 90 patients with anxious MDD, 69 patients with non-anxious MDD and 84 HCs. We collected a set of clinical variables included HAMD-17 scores, HAMA scores and rs-fMRI data. The data were analyzed combining difference analysis, SVM, correlation analysis and ROC analysis.

RESULTS

Relative to HCs, non-anxious MDD patients displayed significant lower VMHC values in the insula and PCG, and anxious MDD patients displayed a significant decrease in VMHC values in the cerebellum_crus2, STG, postCG, MFG and IFG. Compared with non-anxious MDD patients, the anxious MDD showed significant enhanced VMHC values in the PCG. The VMHC values in the insula and cerebellum_crus2 regions showed a better ability to discriminate HCs from patients with non-anxious MDD or with anxious MDD. The VMHC values in PCG showed a better ability to discriminate patients with anxious MDD and non-anxious MDD patients.

CONCLUSION

The VMHC values in the insula and cerebellum_crus2 regions could be served as imaging markers to differentiate HCs from patients with non-anxious MDD or with anxious MDD respectively. And the VMHC values in the PCG could be used to discriminate patients with anxious MDD from the non-anxious MDD patients.

Factors influencing the tendency of residual symptoms in patients with depressive disorders: a longitudinal study

BACKGROUND

Residual symptoms of depressive disorders are serious health problems. However, the progression process is hardly predictable due to high heterogeneity of the disease. This study aims to: (1) classify the patterns of changes in residual symptoms based on homogeneous data, and (2) identify potential predictors for these patterns.

METHODS

In this study, we conducted a data-driven Latent Class Growth Analysis (LCGA) to identify distinct tendencies of changes in residual symptoms, which were longitudinally quantified using the QIDS-SR16 at baseline and 1/3/6 months post-baseline for depressed patients. The association between baseline characteristics (e.g. clinical features and cognitive functions) and different progression tendencies were also identified.

RESULTS

The tendency of changes in residual symptoms was categorized into four classes: "light residual symptom decline (15.4%)", "residual symptom disappears (39.3%)", "steady residual symptom (6.3%)" and "severe residual symptom decline (39.0%)". We observed that the second class displayed more favorable recuperation outcomes than the rest of patients. The severity, recurrence, polypharmacy, and medication adherence of symptoms are intricately linked to the duration of residual symptoms' persistence. Additionally, clinical characteristics including sleep disturbances, depressive moods, alterations in appetite or weight, and difficulties with concentration have been identified as significant factors in the recovery process.

CONCLUSIONS

Our research findings indicate that certain clinical characteristics in patients with depressive disorders are associated with poor recovery from residual symptoms following acute treatment. This revelation holds significant value in the targeted attention to specific patients and the development of early intervention strategies for residual symptoms accordingly.

Genetic Architectures of Adolescent Depression Trajectories in 2 Longitudinal Population Cohorts

Importance

Adolescent depression is characterized by diverse symptom trajectories over time and has a strong genetic influence. Research has determined genetic overlap between depression and other psychiatric conditions; investigating the shared genetic architecture of heterogeneous depression trajectories is crucial for understanding disease etiology, prediction, and early intervention.

Objective

To investigate univariate and multivariate genetic risk for adolescent depression trajectories and assess generalizability across ancestries.

Design, Setting, and Participants

This cohort study entailed longitudinal growth modeling followed by polygenic risk score (PRS) association testing for individual and multitrait genetic models. Two longitudinal cohorts from the US and UK were used: the Adolescent Brain and Cognitive Development (ABCD; N = 11 876) study and the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 8787) study. Included were adolescents with genetic information and depression measures at up to 8 and 4 occasions, respectively. Study data were analyzed January to July 2023.

Main Outcomes and Measures

Trajectories were derived from growth mixture modeling of longitudinal depression symptoms. PRSs were computed for depression, anxiety, neuroticism, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, and autism in European ancestry. Genomic structural equation modeling was used to build multitrait genetic models of psychopathology followed by multitrait PRS. Depression PRSs were computed in African, East Asian, and Hispanic ancestries in the ABCD cohort only. Association testing was performed between all PRSs and trajectories for both cohorts.

Results

A total sample size of 14 112 adolescents (at baseline: mean [SD] age, 10.5 [0.5] years; 7269 male sex [52%]) from both cohorts were included in this analysis. Distinct depression trajectories (stable low, adolescent persistent, increasing, and decreasing) were replicated in the ALSPAC cohort (6096 participants; 3091 female [51%]) and ABCD cohort (8016 participants; 4274 male [53%]) between ages 10 and 17 years. Most univariate PRSs showed significant uniform associations with persistent trajectories, but fewer were significantly associated with intermediate (increasing and decreasing) trajectories. Multitrait PRSs-derived from a hierarchical factor model-showed the strongest associations for persistent trajectories (ABCD cohort: OR, 1.46; 95% CI, 1.26-1.68; ALSPAC cohort: OR, 1.34; 95% CI, 1.20-1.49), surpassing the effect size of univariate PRS in both cohorts. Multitrait PRSs were associated with intermediate trajectories but to a lesser extent (ABCD cohort: hierarchical increasing, OR, 1.27; 95% CI, 1.13-1.43; decreasing, OR, 1.23; 95% CI, 1.09-1.40; ALSPAC cohort: hierarchical increasing, OR, 1.16; 95% CI, 1.04-1.28; decreasing, OR, 1.32; 95% CI, 1.18-1.47). Transancestral genetic risk for depression showed no evidence for association with trajectories.

Conclusions and Relevance

Results of this cohort study revealed a high multitrait genetic loading of persistent symptom trajectories, consistent across traits and cohorts. Variability in univariate genetic association with intermediate trajectories may stem from environmental factors. Multitrait genetics may strengthen depression prediction models, but more diverse data are needed for generalizability.

Sex Influences Genetic Susceptibility to Depression-Like Behaviors in Chronic Unpredictable Mild Stress-Exposed Wistar Rats

Depression is one of the most common mood disorders among psychiatric diseases. It affects about 10% of the adult population. However, its etiopathogenesis remains poorly understood. Exploring the dynamics of stress-susceptibility and resilience will help in understanding the molecular and biological mechanisms underlying the etiopathogenesis of depression. This study aimed to determine the differences and/or similarities in factors responsible for susceptibility to depression-like behaviors in male and female Wistar rats subjected to chronic unpredictable mild stress (CUMS). Sixty Wistar rats (30 male and 30 female) weighing between 120 and 150 g were used for this study. The rats were divided into two sub-groups: control (10) and test (20) groups. Rats in the test groups were subjected to CUMS. Depression-like behaviors were assessed using light-dark box, sucrose preference, and tail suspension tests. Rats that showed depression-like behaviors following the behavioral tests (CUMS-susceptible group) were sacrificed, and their hippocampi were excised. Genomic deoxyribonucleic acid (gDNA) was purified from the hippocampal samples. Purified gDNA was subjected to whole genome sequencing (WGS). Base-calling of sequence reads from raw sequencing signal (FAST5) files was carried out, and variants were called from alignment BAM files. The corresponding VCF files generated from the variant calling experiment were filtered. Genes were identified, their impacts estimated, and variants annotated. Functional enrichment analysis was then carried out. Approximately 41% of the male and 49% of the female rats subjected to CUMS showed significant (p < 0.05) depression-like behaviors following assessment on behavioral tests. WGS of the hippocampal DNA revealed 289,839 single nucleotide polymorphisms variant types, 7002 insertions, and 34,459 deletions in males, and 1,570,186 single nucleotide polymorphisms variant types, 109,860 insertions, and 597,241 deletions in female Wistar rats. Three genes with high-impact variants were identified in male and 22 in female Wistar rats, respectively. In conclusion, female Wistar rats are more susceptible to depression-like behaviors after exposure to CUMS than males. They also have more gene variants (especially high-impact variants) than male Wistar rats.

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.

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling

MAIN PROBLEM

Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia.

METHODS

A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients.

RESULTS

CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method.

CONCLUSIONS

This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine

Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males (n = 149,879) and females (n = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression.

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.

A phenomenologically grounded specification of varieties of adolescent depression

Researchers are increasingly acknowledging that psychopathological conditions usually grouped together under the generic label "depression" are highly diverse. However, no differential therapeutic approach currently exists that is sensitive to the varieties of depression afflicting young people. In fact, the discussion is missing something much more fundamental: a specification of the types of adolescent depression. Recent research that has aimed to classify different kinds of depression has mainly studied adult populations and predominantly used technically complicated measurements of biological markers. The neglect of the potential particularities of dysphoric disorders affecting youths is unfortunate, and the exclusive focus on biological parameters unnecessarily restrictive. Moreover, this one-sidedness obfuscates more directly available sources of clinically relevant data that could orient conceptualization efforts in child and adolescent psychiatry. Particularly, clues for discriminating different types of adolescent depression may be obtained by analyzing personally articulated accounts of how affected young people experience changes in their relation to the world and to themselves. Thus, here we present and discuss the findings of a study that explored the possibility of specifying types of adolescent depression in a phenomenological way. The study investigated the association between these types and the vicissitudes of personality development. In accounts given by youths diagnosed with depression during semi-structured interviews, we identified themes and examined their phenomenological centrality. Specifically, our qualitative analyses aimed to determine the relative importance of certain themes with respect to the overall intelligibility of the described changes to the relational space. Based on the findings of these analyses, we differentiate three specifiers of adolescent depression and suggest an association between particular types of experiences and the trajectory of affected adolescents' personality development. To our knowledge, this is the first phenomenologically grounded specification of types of adolescent depression with potential therapeutic significance. Thus, based on this contribution, we propose that modes of scientific exploration that are close to phenomenological philosophy-which have been ignored in the context of developmental psychopathology-could offer a foundation to theories developed in the field of child and adolescent psychiatry.

Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice

Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.

Common and distinct functional brain network abnormalities in adolescent, early-middle adult, and late adult major depressive disorders

BACKGROUND

The age-related heterogeneity in major depressive disorder (MDD) has received significant attention. However, the neural mechanisms underlying such heterogeneity still need further investigation. This study aimed to explore the common and distinct functional brain abnormalities across different age groups of MDD patients from a large-sample, multicenter analysis.

METHODS

The analyzed sample consisted of a total of 1238 individuals including 617 MDD patients (108 adolescents, 12-17 years old; 411 early-middle adults, 18-54 years old; and 98 late adults, > = 55 years old) and 621 demographically matched healthy controls (60 adolescents, 449 early-middle adults, and 112 late adults). MDD-related abnormalities in brain functional connectivity (FC) patterns were investigated in each age group separately and using the whole pooled sample, respectively.

RESULTS

We found shared FC reductions among the sensorimotor, visual, and auditory networks across all three age groups of MDD patients. Furthermore, adolescent patients uniquely exhibited increased sensorimotor-subcortical FC; early-middle adult patients uniquely exhibited decreased visual-subcortical FC; and late adult patients uniquely exhibited wide FC reductions within the subcortical, default-mode, cingulo-opercular, and attention networks. Analysis of covariance models using the whole pooled sample further revealed: (1) significant main effects of age group on FCs within most brain networks, suggesting that they are decreased with aging; and (2) a significant age group × MDD diagnosis interaction on FC within the default-mode network, which may be reflective of an accelerated aging-related decline in default-mode FCs.

CONCLUSIONS

To summarize, these findings may deepen our understanding of the age-related biological and clinical heterogeneity in MDD.

Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.

Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity

Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.

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.

Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases

BACKGROUND

Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety.

METHODS

Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses.

RESULTS

Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10-27), sleep apnea (OR 1.71; p = 1 × 10-22), and type II diabetes (OR 1.74; p = 9 × 10-18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10-25), benign skin neoplasms (OR 1.61; p = 2 × 10-17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10-12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression.

CONCLUSIONS

While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression

OBJECTIVE

Postpartum depression (PPD) is a common subtype of major depressive disorder (MDD) that is more heritable, yet is understudied in psychiatric genetics. The authors conducted meta-analyses of genome-wide association studies (GWASs) to investigate the genetic architecture of PPD.

METHOD

Meta-analyses were conducted on 18 cohorts of European ancestry (17,339 PPD cases and 53,426 controls), one cohort of East Asian ancestry (975 cases and 3,780 controls), and one cohort of African ancestry (456 cases and 1,255 controls), totaling 18,770 PPD cases and 58,461 controls. Post-GWAS analyses included 1) single-nucleotide polymorphism (SNP)-based heritability ([Formula: see text]), 2) genetic correlations between PPD and other phenotypes, and 3) enrichment of the PPD GWAS findings in 27 human tissues and 265 cell types from the mouse central and peripheral nervous system.

RESULTS

No SNP achieved genome-wide significance in the European or the trans-ancestry meta-analyses. The [Formula: see text] of PPD was 0.14 (SE=0.02). Significant genetic correlations were estimated for PPD with MDD, bipolar disorder, anxiety disorders, posttraumatic stress disorder, insomnia, age at menarche, and polycystic ovary syndrome. Cell-type enrichment analyses implicate inhibitory neurons in the thalamus and cholinergic neurons within septal nuclei of the hypothalamus, a pattern that differs from MDD.

CONCLUSIONS

While more samples are needed to reach genome-wide levels of significance, the results presented confirm PPD as a polygenic and heritable phenotype. There is also evidence that despite a high correlation with MDD, PPD may have unique genetic components. Cell enrichment results suggest GABAergic neurons, which converge on a common mechanism with the only medication approved by the U.S. Food and Drug Administration for PPD (brexanolone).

Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies

Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.

Postpartum and non-postpartum depression: a population-based matched case-control study comparing polygenic risk scores for severe mental disorders

It remains inconclusive whether postpartum depression (PPD) and depression with onset outside the postpartum period (MDD) are genetically distinct disorders. We aimed to investigate whether polygenic risk scores (PGSs) for major mental disorders differ between PPD cases and MDD cases in a nested case-control study of 50,057 women born from 1981 to 1997 in the iPSYCH2015 sample in Demark. We identified 333 women with first-onset postpartum depression (PPD group), who were matched with 993 women with first-onset depression diagnosed outside of postpartum (MDD group), and 999 female population controls. Data on genetics and depressive disorders were retrieved from neonatal biobanks and the Psychiatric Central Research Register. PGSs were calculated from both individual-level genetic data and meta-analysis summary statistics from the Psychiatric Genomics Consortium. Conditional logistic regression was used to calculate the odds ratio (OR), accounting for the selection-related reproductive behavior. After adjustment for covariates, higher PGSs for severe mental disorders were associated with increased ORs of both PPD and MDD. Compared with MDD cases, MDD PGS and attention-deficit/hyperactivity disorder PGS were marginally but not statistically higher for PPD cases, with the OR of PPD versus MDD being 1.12 (95% CI: 0 .97-1.29) and 1.11 (0.97-1.27) per-standard deviation increase, respectively. The ORs of PPD versus MDD did not statistically differ by PGSs of bipolar disorder, schizophrenia, or autism spectrum disorder. Our findings suggest that relying on PGS data, there was no clear evidence of distinct genetic make-up of women with depression occurring during or outside postpartum, after taking the selection-related reproductive behavior into account.

Sex differences in the symptom network structure of depression: Findings from a nationwide sample of the Spanish adult population

BACKGROUND

Sex differences in the prevalence and clinical features of depression have been widely described. However, some authors argue that categorical diagnostic systems do not adequately capture the complexity of depression. The aim of this study was to examine sex differences in the symptom network structure of depressive symptoms among individuals with a major depressive episode.

METHODS

The study sample consisted of 510 participants (age 62.17 ± 14.43, 71.96 % women) from a nationwide study of the Spanish non-institutionalised adult population (Edad con Salud). To estimate the presence of a 12-month major depressive episode according to DSM-IV criteria, participants were administered an adapted version of the Composite International Diagnostic Interview (CIDI 3.0). A network analysis was carried out to determine possible interrelationships between different depressive symptoms by sex.

RESULTS

Men and women showed a similar overall structure and network strength. However, sex-specific variations emerged in relation to individual symptom associations and symptom centrality. Specifically, for individual symptom associations "loss of confidence" and "suicide attempts" were more strongly related in women, and "suicidal ideation" and "impaired thinking" in men. For symptom centrality, "anxiety" played a central role in men's symptomatology, whereas "hopelessness", "loss of confidence", "distress" and "slowness of movement" were the most central symptoms in the women's group.

LIMITATIONS

Reliance on cross-sectional data precludes us from determining the direction and temporality of the association between different symptoms.

CONCLUSIONS

This study suggests that specific symptoms should be prioritised in the prevention, diagnosis assessment and treatment of depressed patients based on sex.

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.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis

BACKGROUND

Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM.

METHODS

313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM.

RESULTS

We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety.

CONCLUSION

Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.

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.

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.

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.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.

Biological factors influencing depression in later life: role of aging processes and treatment implications

Late-life depression occurring in older adults is common, recurrent, and malignant. It is characterized by affective symptoms, but also cognitive decline, medical comorbidity, and physical disability. This behavioral and cognitive presentation results from altered function of discrete functional brain networks and circuits. A wide range of factors across the lifespan contributes to fragility and vulnerability of those networks to dysfunction. In many cases, these factors occur earlier in life and contribute to adolescent or earlier adulthood depressive episodes, where the onset was related to adverse childhood events, maladaptive personality traits, reproductive events, or other factors. Other individuals exhibit a later-life onset characterized by medical comorbidity, pro-inflammatory processes, cerebrovascular disease, or developing neurodegenerative processes. These later-life processes may not only lead to vulnerability to the affective symptoms, but also contribute to the comorbid cognitive and physical symptoms. Importantly, repeated depressive episodes themselves may accelerate the aging process by shifting allostatic processes to dysfunctional states and increasing allostatic load through the hypothalamic-pituitary-adrenal axis and inflammatory processes. Over time, this may accelerate the path of biological aging, leading to greater brain atrophy, cognitive decline, and the development of physical decline and frailty. It is unclear whether successful treatment of depression and avoidance of recurrent episodes would shift biological aging processes back towards a more normative trajectory. However, current antidepressant treatments exhibit good efficacy for older adults, including pharmacotherapy, neuromodulation, and psychotherapy, with recent work in these areas providing new guidance on optimal treatment approaches. Moreover, there is a host of nonpharmacological treatment approaches being examined that take advantage of resiliency factors and decrease vulnerability to depression. Thus, while late-life depression is a recurrent yet highly heterogeneous disorder, better phenotypic characterization provides opportunities to better utilize a range of nonspecific and targeted interventions that can promote recovery, resilience, and maintenance of remission.

Dorsal raphe serotonergic neurons preferentially reactivate dorsal dentate gyrus cell ensembles associated with positive experience

Major depressive disorder (MDD) is among the most common mental illnesses. Serotonergic (5-HT) neurons are central to the pathophysiology and treatment of MDD. Repeatedly recalling positive episodes is effective for MDD. Stimulating 5-HT neurons of the dorsal raphe nucleus (DRN) or neuronal ensembles in the dorsal dentate gyrus (dDG) associated with positive memories reverses the stress-induced behavioral abnormalities. Despite this phenotypic similarity, their causal relationship is unclear. This study revealed that the DRN 5-HT neurons activate dDG neurons; surprisingly, this activation was specifically observed in positive memory ensembles rather than neutral or negative ensembles. Furthermore, we revealed that dopaminergic signaling induced by activation of DRN 5-HT neurons projecting to the ventral tegmental area mediates an increase in active coping behavior and positive dDG ensemble reactivation. Our study identifies a role of DRN 5-HT neurons as specific reactivators of positive memories and provides insights into how serotonin elicits antidepressive effects.

Disrupted interhemispheric coordination of sensory-motor networks and insula in major depressive disorder

Objective

Prior researches have identified distinct differences in neuroimaging characteristics between healthy controls (HCs) and patients with major depressive disorder (MDD). However, the correlations between homotopic connectivity and clinical characteristics in patients with MDD have yet to be fully understood. The present study aimed to investigate common and unique patterns of homotopic connectivity and their relationships with clinical characteristics in patients with MDD.

Methods

We recruited 42 patients diagnosed with MDD and 42 HCs. We collected a range of clinical variables, as well as exploratory eye movement (EEM), event-related potentials (ERPs) and resting-state functional magnetic resonance imaging (rs-fMRI) data. The data were analyzed using correlation analysis, support vector machine (SVM), and voxel-mirrored homotopic connectivity (VMHC).

Results

Compared with HCs, patients with MDD showed decreased VMHC in the insula, and increased VMHC in the cerebellum 8/vermis 8/vermis 9 and superior/middle occipital gyrus. SVM analysis using VMHC values in the cerebellum 8/vermis 8/vermis 9 and insula, or VMHC values in the superior/middle occipital gyrus and insula as inputs can distinguish HCs and patients with MDD with high accuracy, sensitivity, and specificity.

Conclusion

The study demonstrated that decreased VMHC in the insula and increased VMHC values in the sensory-motor networks may be a distinctive neurobiological feature for patients with MDD, which could potentially serve as imaging markers to discriminate HCs and patients with MDD.

Transcriptional profiles in the mouse amygdala after a cognitive judgment bias test largely depend on the genotype

Background: The amygdala is crucial for emotional cognitive processing. Affective or emotional states can bias cognitive processes, including attention, memory, and decision-making. This can result in optimistic or pessimistic behaviors that are partially driven by the activation of the amygdala. The resulting emotional cognitive bias is a common feature of anxiety and mood disorders, both of which are interactively influenced by genetic and environmental factors. It is also known that emotional cognitive biases can be influenced by environmental factors. However, little is known about the effects of genetics and/or gene-environment interactions on emotional cognitive biases. We investigated the effects of the genetic background and environmental enrichment on the transcriptional profiles of the mouse amygdala following a well-established cognitive bias test.

Methods: Twenty-four female C57BL/6J and B6D2F1N mice were housed either in standard (control) conditions or in an enriched environment. After appropriate training, the cognitive bias test was performed on 19 mice that satisfactorily completed the training scheme to assess their responses to ambiguous cues. This allowed us to calculate an “optimism score” for each mouse. Subsequently, we dissected the anterior and posterior portions of the amygdala to perform RNA-sequencing for differential expression and other statistical analyses.

Results: In general, we found only minor changes in the amygdala’s transcriptome associated with the levels of optimism in our mice. In contrast, we observed wide molecular effects of the genetic background in both housing environments. The C57BL/6J animals showed more transcriptional changes in response to enriched environments than the B6D2F1N mice. We also generally found more dysregulated genes in the posterior than in the anterior portion of the amygdala. Gene set overrepresentation analyses consistently implicated cellular metabolic responses and immune processes in the differences observed between mouse strains, while processes favoring neurogenesis and neurotransmission were implicated in the responses to environmental enrichment. In a correlation analysis, lipid metabolism in the anterior amygdala was suggested to influence the levels of optimism.

Conclusions: Our observations underscore the importance of selecting appropriate animal models when performing molecular studies of affective conditions or emotional states, and suggest an important role of immune and stress responses in the genetic component of emotion regulation.

Ten challenges for clinical translation in psychiatric genetics

Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.

Maternal pregnancy-related infections and autism spectrum disorder-the genetic perspective

Autism spectrum disorder (ASD) refers to a group of neurodevelopmental disorders which include deficits in behavior, social interaction and communication. ASD has a complex genetic architecture, and it is also influenced by certain environmental exposures. Both types of predisposing factors may be related to immunological mechanisms, involving, for example, immune system genes and infections. Past studies have shown an association between infections occurring during the pregnancy in the mother and increased risk of ASD in the child, an observation which has received recent support from experimental animal studies of ASD-like behavior. The aim of this study was to study the genetic contribution to this effect. We employed genetic correlation analyses across potential ASD subtypes stratified on the basis of maternal pregnancy-related infections within the iPSYCH ASD case-cohort sample, as well as a case-case GWAS. We validated the trends of the genetic correlation analyses observed in our sample using GWAS summary statistics from the PGC ASD study (excluding iPSYCH). The genetic correlation between ASD with a history of maternal pregnancy-related infections and ASD without a history of maternal infections in iPSYCH was r_g = 0.3811. We obtained a similar estimate between the former and the PGC ASD phenotype (r_g = 0.3997). Both estimates are lower compared to the genetic correlation between ASD without a history of maternal infections and the PGC ASD phenotype (r_g = 0.6735), and between ASD with a history of maternal infections occurring only more than 2 months following childbirth and the PGC ASD phenotype (r_g = 0.6293). Additionally, we observed genetic variance between the two main ASD phenotypes using summary statistics from the case-case GWAS in iPSYCH (h²_cc = 0.1059), indicating genome-wide differences between the phenotypes. Our results suggest potentially different etiologies of ASD based on a history of maternal pregnancy-related infections, which may, in part, be genetic. This highlights the relevance of maternal pregnancy-related infections to genetic studies of ASD and provides new insights into the molecular underpinnings of ASD.

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.

The interaction of early life factors and depression-associated loci affecting the age at onset of the depression

Multiple previous studies explored the associations between early life factors and the age at onset of the depression. However, they only focused on the influence of environmental or genetic factors, without considering the interactions between them. Based on previous genome-wide association study (GWAS) data, we first calculated polygenic risk score (PRS) for depression. Regression analyses were conducted to assess the interacting effects of depression PRS and 5 early life factors, including felt hated by family member (N = 40,112), physically abused by family (N = 40,464), felt loved (N = 35633), and sexually molested (N = 41,595) in childhood and maternal smoking during pregnancy (N = 38,309), on the age at onset of the depression. Genome-wide environment interaction studies (GWEIS) were then performed to identify the genes interacting with early life factors for the age at onset of the depression. In regression analyses, we observed significant interacting effects of felt loved as a child and depression PRS on the age at onset of depression in total sample (β = 0.708, P = 5.03 × 10^-3) and males (β = 1.421, P = 7.64 × 10^-4). GWEIS identified a novel candidate loci interacting with felt loved as a child at GSAP (rs2068031, P = 4.24 × 10^-8) and detected several genes with suggestive significance association, such as CMYA5 (rs7343, P = 2.03 × 10^-6) and KIRREL3 (rs535603, P = 4.84 × 10^-6) in males. Our results indicate emotional care in childhood may affect the age at onset of depression, especially in males, and GSAP plays an important role in their interaction.

Genetic variables of the glutamatergic system associated with treatment-resistant depression: A review of the literature

Depression is a common, recurrent mental disorder and one of the leading causes of disability and global burden of disease worldwide. Up to 15%-40% of cases do not respond to diverse pharmacological treatments and, thus, can be defined as treatment-resistant depression (TRD). The development of biomarkers predictive of drug response could guide us towards personalized and earlier treatment. Growing evidence points to the involvement of the glutamatergic system in the pathogenesis of TRD. Specifically, the N-methyl-D-aspartic acid receptor (NMDAR) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR), which are targeted by ketamine and esketamine, are proposed as promising pathways. A literature search was performed to identify studies on the genetics of the glutamatergic system in depression, focused on variables related to NMDARs and AMPARs. Our review highlights GRIN2B, which encodes the NR2B subunit of NMDAR, as a candidate gene in the pathogenesis of TRD. In addition, several studies have associated genes encoding AMPAR subunits with symptomatic severity and suicidal ideation. These genes encoding glutamatergic receptors could, therefore, be candidate genes for understanding the etiopathogenesis of TRD, as well as for understanding the pharmacodynamic mechanisms and response to ketamine and esketamine treatment.

A Possible Antidepressive Effect of Dietary Interventions: Emergent Findings and Research Challenges

Purpose

Despite recent advancements in the treatment of depression, the prevalence of affected individuals continues to grow. The development of new strategies has been required and emerging evidence has linked a possible antidepressant effect with dietary interventions. In this review, we discuss recent findings about the possible antidepressant effect of dietary interventions with an emphasis on the results of randomized controlled trials.

Recent findings

A high consumption of refined sugars and saturated fat and a low dietary content of fruits and vegetables has been associated with the development of depression. There is evidence supporting a small to moderate beneficial effect of a Mediterranean-type diet in depression. In addition, new dietary protocols are being studied for their use as possible interventions, such as the ketogenic diet, Nordic diet, and plant-based diet.

Summary

Lifestyle interventions surrounding diet and nutrition are a relatively affordable way to enhance response to treatment and to be employed as an adjunct in mental health care. Most studies, however, are limited by the difficulty in controlling for the placebo effect. Mediterranean-style diets seem to be the most promising as an adjunctive treatment for mood disorders. Larger randomized controlled trials that could assess predictors of response to dietary interventions are needed to establish a clear positive effect of diet and guide clinical care and nutritional recommendations concerning mental health care.