Polygenic dissection of major depression clinical heterogeneity

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.

Causal relationship between psychiatric disorders and sensorineural hearing loss: A bidirectional two-sample mendelian randomization analysis

OBJECTIVE

This study employed bidirectional two-sample Mendelian randomization (MR) to investigate the causal links between psychiatric disorders and sensorineural hearing loss (SNHL).

METHODS

Instrumental variables were chosen from genome-wide association studies of schizophrenia (SCH, N = 127,906), bipolar disorder (BD, N = 51,710), major depressive disorder (MDD, N = 500,199), and SNHL (N = 212,544). In the univariable MR analysis, the inverse-variance weighted method (IVW) was conducted as the primary analysis, complemented by various sensitivity analyses to ensure result robustness.

RESULTS

SCH exhibited a decreased the risk of SNHL (OR = 0.949, P = 0.005), whereas BD showed an increased incidence of SNHL (OR = 1.145, P = 0.005). No causal association was found for MDD on SNHL (OR = 1.088, P = 0.246). Multivariable MR validated these results. In the reverse direction, genetically predicted SNHL was linked to a decreased risk of SCH with suggestive significance (OR = 0.912, P = 0.023). No reverse causal relationships were observed for SNHL influencing BD or MDD. These findings remained consistent across various MR methods and sensitivity analyses.

CONCLUSION

This study demonstrated that the causal relationships between diverse psychiatric disorders with SNHL were heterogeneous. Specifically, SCH was inversely associated with SNHL susceptibility, and similarly, a reduced risk of SNHL was observed in schizophrenia patients. In contrast, BD exhibited an increased incidence of SNHL, although SNHL did not influence the prevalence of BD. No causal association between MDD and SNHL was found.

Adenylate cyclase 3: a potential genetic link between obesity and major depressive disorder

Obesity and major depressive disorder (MDD) are both significant health issues that have been increasing in prevalence and are associated with multiple comorbidities. Obesity and MDD have been shown to be bidirectionally associated, and they are both influenced by genetics and environmental factors. However, the molecular mechanisms that link these two diseases are not yet fully understood. It is possible that these diseases are connected through the actions of the cAMP/protein kinase A (PKA) pathway. Within this pathway, adenylate cyclase 3 (Adcy3) has emerged as a key player in both obesity and MDD. Numerous genetic variants in Adcy3 have been identified in humans in association with obesity. Rodent knockout studies have also validated the importance of this gene for energy homeostasis. Furthermore, Adcy3 has been identified as a top candidate gene and even a potential blood biomarker for MDD. Adcy3 and the cAMP/PKA pathway may therefore serve as an important genetic and functional link between these two diseases. In this mini-review, we discuss the role of both Adcy3 and the cAMP/PKA pathway, including specific genetic mutations, in both diseases. Understanding the role that Adcy3 mutations play in obesity and MDD could open the door for precision medicine approaches and treatments for both diseases that target this gene.

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up

BACKGROUND

The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level.

METHODS

Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82).

RESULTS

Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%).

CONCLUSIONS

This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements.

Mendelian randomization studies of depression: evidence, opportunities, and challenges

BACKGROUND

Major depressive disorder (MDD) poses a significant social and economic burden worldwide. Identifying exposures, risk factors, and biological mechanisms that are causally connected to MDD can help build a scientific basis for disease prevention and development of novel therapeutic approaches.

METHODS

In this systematic review, we assessed the evidence for causal relationships between putative causal risk factors and MDD from Mendelian randomization (MR) studies, following PRISMA. We assessed methodological quality based on key elements of the MR design: use of a full instrumental variable analysis and validation of the three key MR assumptions.

RESULTS

We included methodological details and results from 52 articles. A causal link between lifestyle, metabolic, inflammatory biomarkers, particular pathological states and MDD is supported by MR investigations, although results for each category varied substantially.

CONCLUSIONS

While this review shows how MR can offer useful information for examining prospective treatment targets and better understanding the pathophysiology of MDD, some methodological flaws in the existing literature limit reliability of results and probably underlie their heterogeneity. We highlight perspectives and recommendations for future works on MR in psychiatry.

The interplay of personality traits, anxiety, and depression in Chinese college students: a network analysis

Background

Anxiety and depression are among the greatest contributors to the global burden of diseases. The close associations of personality traits with anxiety and depression have been widely described. However, the common practice of sum scores in previous studies limits the understanding of the fine-grained connections between different personality traits and anxiety and depression symptoms and cannot explore and compare the risk or protective effects of personality traits on anxiety and depression symptoms.

Objective

We aimed to determine the fine-grained connections between different personality traits and anxiety and depression symptoms and identify the detrimental or protective effects of different personality traits on anxiety and depression symptoms.

Methods

A total of 536 college students from China were recruited online, and the average age was 19.98 ± 1.11. The Chinese version of the Ten-Item Personality Inventory, Generalized Anxiety Disorder-7, and Patient Health Questionnaire-9 was used to investigate the personality traits and symptoms of anxiety and depression of participants after they understood the purpose and filling method of the survey and signed the informed consent. The demographic characteristics were summarized, and the scale scores were calculated. The network model of personality traits and symptoms of anxiety and depression was constructed, and bridge expected influence (BEI) was measured to evaluate the effect of personality traits on anxiety and depression. The edge accuracy and BEI stability were estimated, and the BEI difference and the edge weight difference were tested.

Results

In the network, 29 edges (indicating partial correlations between variables) bridged the personality community and the anxiety and depression community, among which the strongest correlations were extraversion-fatigue, agreeableness-suicidal ideation, conscientiousness-uncontrollable worry, neuroticism-excessive worry, neuroticism-irritability, and openness-feelings of worthlessness. Neuroticism had the highest positive BEI value (0.32), agreeableness had the highest negative BEI value (-0.27), and the BEI values of neuroticism and agreeableness were significantly different from those of most other nodes (p < 0.05).

Conclusion

There are intricate correlations between personality traits and the symptoms of anxiety and depression in college students. Neuroticism was identified as the most crucial risk trait for depression and anxiety symptoms, while agreeableness was the most central protective trait.

Sodium-Glucose Cotransporter-2 Inhibitors in Depression

ABSTRACT

Novel treatment strategies that refract existing treatment algorithms for depressive disorders are being sought. Abnormal brain bioenergetic metabolism may represent an alternative, therapeutically targetable neurobiological basis for depression. A growing body of research points to endogenous ketones as candidate neuroprotective metabolites with the potential to enhance brain bioenergetics and improve mood. Sodium-glucose cotransporter-2 (SGLT2) inhibitors, originally approved for the treatment of diabetes, induce ketogenesis and are associated with mood improvement in population-based studies. In this column, we highlight the rationale for the hypothesis that ketogenesis induced by SGLT2 inhibitors may be an effective treatment for depressive disorders.

A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank (N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74-2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.

Associations between individual depressive symptoms and immunometabolic characteristics in major depression

Inflammation and metabolic dysregulations are likely to underlie atypical, energy-related depressive symptoms such as appetite and sleep alterations. Indeed, increased appetite was previously identified as a core symptom of an immunometabolic subtype of depression. The aim of this study was 1) to replicate the associations between individual depressive symptoms and immunometabolic markers, 2) to extend previous findings with additional markers, and 3) to evaluate the relative contribution of these markers to depressive symptoms. We analyzed data from 266 persons with major depressive disorder (MDD) in the last 12 months from the German Health Interview and Examination Survey for Adults and its mental health module. Diagnosis of MDD and individual depressive symptoms were determined by the Composite International Diagnostic Interview. Associations were analyzed using multivariable regression models, adjusting for depression severity, sociodemographic/behavioral variables, and medication use. Increased appetite was associated with higher body mass index (BMI), waist circumference (WC), insulin, and lower high-density lipoprotein. In contrast, decreased appetite was associated with lower BMI, WC, and fewer metabolic syndrome (MetS) components. Insomnia was associated with higher BMI, WC, number of MetS components, triglycerides, insulin, and lower albumin, while hypersomnia was associated with higher insulin. Suicidal ideation was associated with higher number of MetS components, glucose, and insulin. None of the symptoms were associated with C-reactive protein after adjustment. Appetite alterations and insomnia were most important symptoms associated with metabolic markers. Longitudinal studies should investigate whether the candidate symptoms identified here are predicted by or predict the development of metabolic pathology in MDD.

Dissection of depression heterogeneity using proteomic clusters

BACKGROUND

The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms.

METHODS

The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA).

RESULTS

Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster.

CONCLUSIONS

Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.

Characteristics and symptomatology of major depressive disorder with atypical features from symptom to syndromal level

OBJECTIVE

To explore clinical characteristics and symptomatology of major depressive disorder (MDD) with atypical features based on DSM criteria or only reversed vegetative symptoms.

METHOD

A total of 3187 patients who met DSM-IV TR criteria for MDD were enrolled. Demographics and symptomatology covering multiple symptom domains were assessed and compared between three groups of cases: those who met DSM criteria for atypical specifier (the DAD group), those who had at least one reversed vegetative symptoms (hypersomnia or hyperphagia) (the SAD group) without meeting DSM atypical specifier criteria, and those without any reversed vegetative symptoms (the NAD group).

RESULTS

The DAD and SAD group accounted for 4.4 % and 14.4 % of the participants, respectively. The DAD cases were characterized by a highest proportion of hospitalizations, longest duration of current episode and worst quality of life. The DAD and SAD cases were more likely to adopt unhealthy behaviors (smoking and alcohol drinking). Most depressive symptoms related to higher illness severity and treatment resistance were more frequent in the DAD cases, followed by the SAD cases, and least frequent in the NAD cases.

LIMITATIONS

A cross-sectional design and a non-validated questionnaire were used.

CONCLUSIONS

The findings support the role of DSM defined atypical depression as a valid MDD subtype and provide evidence for clinical utility of the simplified approach of defining atypical features based on only reversed vegetative symptoms. This has implications for illness screening, public health, suicide prevention and better treatment planning for depressed individuals with atypical features even below syndromal level.

The interactive association of adverse childhood experiences and polygenic susceptibility with depressive symptoms and chronic inflammation in older adults: a prospective cohort study

BACKGROUND

Adverse childhood experiences (ACEs) and genetic liability are important risk factors for depression and inflammation. However, little is known about the gene-environment (G × E) mechanisms underlying their aetiology. For the first time, we tested the independent and interactive associations of ACEs and polygenic scores of major depressive disorder (MDD-PGS) and C-reactive protein (CRP-PGS) with longitudinal trajectories of depression and chronic inflammation in older adults.

METHODS

Data were drawn from the English longitudinal study of ageing (N~3400). Retrospective information on ACEs was collected in wave3 (2006/07). We calculated a cumulative risk score of ACEs and also assessed distinct dimensions separately. Depressive symptoms were ascertained on eight occasions, from wave1 (2002/03) to wave8 (2016/17). CRP was measured in wave2 (2004/05), wave4 (2008/09), and wave6 (2012/13). The associations of the risk factors with group-based depressive-symptom trajectories and repeated exposure to high CRP (i.e. ⩾3 mg/L) were tested using multinomial and ordinal logistic regression.

RESULTS

All types of ACEs were independently associated with high depressive-symptom trajectories (OR 1.44, 95% CI 1.30-1.60) and inflammation (OR 1.08, 95% CI 1.07-1.09). The risk of high depressive-symptom trajectories (OR 1.47, 95% CI 1.28-1.70) and inflammation (OR 1.03, 95% CI 1.01-1.04) was also higher for participants with higher MDD-PGS. G×E analyses revealed that the associations between ACEs and depressive symptoms were larger among participants with higher MDD-PGS (OR 1.13, 95% CI 1.04-1.23). ACEs were also more strongly related to inflammation in participants with higher CRP-PGS (OR 1.02, 95% CI 1.01-1.03).

CONCLUSIONS

ACEs and polygenic susceptibility were independently and interactively associated with elevated depressive symptoms and chronic inflammation, highlighting the clinical importance of assessing both ACEs and genetic risk factors to design more targeted interventions.

The association between trauma exposure, polygenic risk and individual depression symptoms

BACKGROUND

Traumatic experiences are associated with increased risk for major depressive disorder (MDD). This study sought to determine the extent that trauma exposure, depression polygenic risk scores (PRS), and their interaction are associated with MDD and individual depression symptoms.

METHODS

Data from 102,182 individuals from the large-scale UK Biobank population cohort was analysed. A series of regression analyses were conducted to estimate the association between trauma, depression PRS and 1) current depression, 2) lifetime MDD case-control status, 3) nine individual current depressive symptoms, and 4) thirteen individual symptoms experienced during a major depressive episode. Additive and multiplicative PRS-by-trauma interactions were also assessed.

RESULTS

Trauma and depression PRS were significantly associated with both current depression and lifetime MDD. A positive, additive interaction effect was observed on depression, but multiplicative interactions were not significant. Trauma exposure and depression PRS were associated with specific patterns of depression symptoms; Trauma was associated with low self-esteem, suicidal ideation, and atypical (but not typical) neurovegetative symptoms. Additive interaction effects were observed on six out of nine current depressive symptoms.

CONCLUSIONS

Trauma exposure and genetic predisposition to depression may lead to particular symptomatology, which may contribute to the extreme clinical heterogeneity observed in individuals with major depression.

The association between adiposity and atypical energy-related symptoms of depression: A role for metabolic dysregulations

BACKGROUND

Adiposity has been shown to be linked with atypical energy-related symptoms (AES) of depression. We used genomics to separate the effect of adiposity from that of metabolic dysregulations to examine whether the link between obesity and AES is dependent on the presence of metabolic dysregulations.

METHOD

Data were from NEO (n = 5734 individuals) and NESDA (n = 2238 individuals) cohorts, in which the Inventory of Depressive Symptomatology (IDS-SR30) was assessed. AES profile was based on four symptoms: increased appetite, increased weight, low energy level, and leaden paralysis. We estimated associations between AES and two genetic risk scores (GRS) indexing increasing total body fat with (metabolically unhealthy adiposity, GRS-MUA) and without (metabolically healthy adiposity, GRS-MHA) metabolic dysregulations.

RESULTS

We validated that both GRS-MUA and GRS-MHA were associated with higher total body fat in NEO study, but divergently associated with biomarkers of metabolic health (e.g., fasting glucose and HDL-cholesterol) in both cohorts. In the pooled results, per standard deviation, GRS-MUA was specifically associated with a higher AES score (β = 0.03, 95%CI: 0.01; 0.05), while there was no association between GRS-MHA and AES (β = -0.01, 95%CI: -0.03; 0.01).

CONCLUSION

These results suggest that the established link between adiposity and AES profile emerges in the presence of metabolic dysregulations, which may represent the connecting substrate between the two conditions.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

The gut microbiota and depressive symptoms across ethnic groups

The gut microbiome is thought to play a role in depressive disorders, which makes it an attractive target for interventions. Both the microbiome and depressive symptom levels vary substantially across ethnic groups. Thus, any intervention for depression targeting the microbiome requires understanding of microbiome-depression associations across ethnicities. Analysing data from the HELIUS cohort, we characterize the gut microbiota and its associations with depressive symptoms in 6 ethnic groups (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, Moroccan; N = 3211), living in the same urban area. Diversity of the gut microbiota, both within (α-diversity) and between individuals (β-diversity), predicts depressive symptom levels, taking into account demographic, behavioural, and medical differences. These associations do not differ between ethnic groups. Further, β-diversity explains 29%-18% of the ethnic differences in depressive symptoms. Bacterial genera associated with depressive symptoms belong to mulitple families, prominently including the families Christensenellaceae, Lachnospiraceae, and Ruminococcaceae. In summary, the results show that the gut microbiota are linked to depressive symptom levels and that this association generalizes across ethnic groups. Moreover, the results suggest that ethnic differences in the gut microbiota may partly explain parallel disparities in depression.

Evolutionarily conserved gene expression patterns for affective disorders revealed using cross-species brain transcriptomic analyses in humans, rats and zebrafish

Widespread, debilitating and often treatment-resistant, depression and other stress-related neuropsychiatric disorders represent an urgent unmet biomedical and societal problem. Although animal models of these disorders are commonly used to study stress pathogenesis, they are often difficult to translate across species into valuable and meaningful clinically relevant data. To address this problem, here we utilized several cross-species/cross-taxon approaches to identify potential evolutionarily conserved differentially expressed genes and their sets. We also assessed enrichment of these genes for transcription factors DNA-binding sites down- and up- stream from their genetic sequences. For this, we compared our own RNA-seq brain transcriptomic data obtained from chronically stressed rats and zebrafish with publicly available human transcriptomic data for patients with major depression and their respective healthy control groups. Utilizing these data from the three species, we next analyzed their differential gene expression, gene set enrichment and protein-protein interaction networks, combined with validated tools for data pooling. This approach allowed us to identify several key brain proteins (GRIA1, DLG1, CDH1, THRB, PLCG2, NGEF, IKZF1 and FEZF2) as promising, evolutionarily conserved and shared affective 'hub' protein targets, as well as to propose a novel gene set that may be used to further study affective pathogenesis. Overall, these approaches may advance cross-species brain transcriptomic analyses, and call for further cross-species studies into putative shared molecular mechanisms of affective pathogenesis.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank

BACKGROUND

Considerable uncertainty remains regarding associations of multiple risk factors with Alzheimer's disease (AD). We aimed to systematically screen and validate a wide range of potential risk factors for AD.

METHODS

Among 502,493 participants from the UK Biobank, baseline data were extracted for 4171 factors spanning 10 different categories. Phenome-wide association analyses and time-to-event analyses were conducted to identify factors associated with both polygenic risk scores for AD and AD diagnosis at follow-up. We performed two-sample Mendelian randomization analysis to further assess their potential causal relationships with AD and imaging association analysis to discover underlying mechanisms.

RESULTS

We identified 39 factors significantly associated with both AD polygenic risk scores and risk of incident AD, where higher levels of education, body size, basal metabolic rate, fat-free mass, computer use, and cognitive functions were associated with a decreased risk of developing AD, and selective food intake and more outdoor exposures were associated with an increased risk of developing AD. The identified factors were also associated with AD-related brain structures, including the hippocampus, entorhinal cortex, and inferior/middle temporal cortex, and 21 of these factors were further supported by Mendelian randomization evidence.

CONCLUSIONS

To our knowledge, this is the first study to comprehensively and rigorously assess the effects of wide-ranging risk factors on AD. Strong evidence was found for fat-free body mass, basal metabolic rate, computer use, selective food intake, and outdoor exposures as new risk factors for AD. Integration of genetic, clinical, and neuroimaging information may help prioritize risk factors and prevention targets for AD.

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.

Metabolomic and inflammatory signatures of symptom dimensions in major depression

BACKGROUND

Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.

METHODS

Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.

RESULTS

The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.

CONCLUSION

The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Genetic heterogeneity and subtypes of major depression

Major depression (MD) is a heterogeneous disorder; however, the extent to which genetic factors distinguish MD patient subgroups (genetic heterogeneity) remains uncertain. This study sought evidence for genetic heterogeneity in MD. Using UK Biobank cohort, the authors defined 16 MD subtypes within eight comparison groups (vegetative symptoms, symptom severity, comorbid anxiety disorder, age at onset, recurrence, suicidality, impairment, and postpartum depression; N ~ 3000-47000). To compare genetic component of these subtypes, subtype-specific genome-wide association studies were performed to estimate SNP-heritability, and genetic correlations within subtype comparison and with other related disorders/traits. The findings indicated that MD subtypes were divergent in their SNP-heritability, and genetic correlations both within subtype comparisons and with other related disorders/traits. Three subtype comparisons (vegetative symptoms, age at onset, and impairment) showed significant differences in SNP-heritability; while genetic correlations within subtype comparisons ranged from 0.55 to 0.86, suggesting genetic profiles are only partially shared among MD subtypes. Furthermore, subtypes that are more clinically challenging, e.g., early-onset, recurrent, suicidal, more severely impaired, had stronger genetic correlations with other psychiatric disorders. MD with atypical-like features showed a positive genetic correlation (+0.40) with BMI while a negative correlation (-0.09) was found in those without atypical-like features. Novel genomic loci with subtype-specific effects were identified. These results provide the most comprehensive evidence to date for genetic heterogeneity within MD, and suggest that the phenotypic complexity of MD can be effectively reduced by studying the subtypes which share partially distinct etiologies.

Depression with atypical neurovegetative symptoms shares genetic predisposition with immuno-metabolic traits and alcohol consumption

BACKGROUND

Depression is a highly prevalent and heterogeneous disorder. This study aims to determine whether depression with atypical features shows different heritability and different degree of overlap with polygenic risk for psychiatric and immuno-metabolic traits than other depression subgroups.

METHODS

Data included 30 069 European ancestry individuals from the UK Biobank who met criteria for lifetime major depression. Participants reporting both weight gain and hypersomnia were classified as ↑WS depression (N = 1854) and the others as non-↑WS depression (N = 28 215). Cases with non-↑WS depression were further classified as ↓WS depression (i.e. weight loss and insomnia; N = 10 142). Polygenic risk scores (PRS) for 22 traits were generated using genome-wide summary statistics (Bonferroni corrected p = 2.1 × 10-4). Single-nucleotide polymorphism (SNP)-based heritability of depression subgroups was estimated.

RESULTS

↑WS depression had a higher polygenic risk for BMI [OR = 1.20 (1.15-1.26), p = 2.37 × 10-14] and C-reactive protein [OR = 1.11 (1.06-1.17), p = 8.86 × 10-06] v. non-↑WS depression and ↓WS depression. Leptin PRS was close to the significance threshold (p = 2.99 × 10-04), but the effect disappeared when considering GWAS summary statistics of leptin adjusted for BMI. PRS for daily alcohol use was inversely associated with ↑WS depression [OR = 0.88 (0.83-0.93), p = 1.04 × 10-05] v. non-↑WS depression. SNP-based heritability was not significantly different between ↑WS depression and ↓WS depression (14.3% and 12.2%, respectively).

CONCLUSIONS

↑WS depression shows evidence of distinct genetic predisposition to immune-metabolic traits and alcohol consumption. These genetic signals suggest that biological targets including immune-cardio-metabolic pathways may be relevant to therapies in individuals with ↑WS depression.

Mood and anxiety profiles differentially associate with physical conditions in US adolescents

BACKGROUND

Mood and anxiety are widely associated with physical conditions, but research and treatment are complicated by their overlap, clinical heterogeneity, and manifestation on a spectrum rather than as discrete disorders. In contrast to previous work relying on threshold-level disorders, we examined the association between empirically-derived profiles of mood and anxiety syndromes with physical conditions in a nationally-representative sample of US adolescents.

METHODS

Participants were 2,911 adolescents (aged 13-18) from the National Comorbidity Survey-Adolescent Supplement who provided information on physical conditions and reported at least one lifetime mood-anxiety 'syndrome' based on direct interviews with the Composite International Diagnostic Interview Version 3.0. Mood-anxiety syndromes reflected 3-level ratings from subthreshold to severe distress/impairment, and subtyped mood episodes. Stepwise latent profile analysis identified mood-anxiety profiles and tested associations with physical conditions.

RESULTS

Three mood-anxiety profiles were identified: "Mood-GAD" (25.6%)-non-atypical depression, mania, generalized anxiety; "Atypical-Panic" (11.3%)-atypical depression, panic; and "Reference" (63.1%)-lower mood and anxiety except specific phobia. Headaches were more prevalent in Mood-GAD and Atypical-Panic than Reference (47.9%, 50.1%, and 37.7%, respectively; p=0.011). Heart problems were more common in Mood-GAD than Atypical-Panic (7.4% v 2.2%, p=0.004) and Reference, with back/neck pain more prevalent in Mood-GAD than Reference (22.5% v 15.3%, p=0.016).

LIMITATIONS

Broad categories of physical conditions without information on specific diagnoses; replication regarding specificity is recommended.

CONCLUSIONS

Heart problems and pain-related conditions were differentially associated with specific mood-anxiety profiles. Subtyping depression and anxiety-inclusive of subthreshold syndromes-and their patterns of clustering may facilitate etiologic and intervention work in multimorbidity.

Major depressive disorder: a possible typisation according to serotonin, inflammation, and metabolic syndrome

OBJECTIVE

Major depressive disorder (MDD) is closely related to obesity, inflammation, and insulin resistance, all together being etiologically linked to metabolic syndrome (MetS) development. The depressive disorder has a neuroendocrinological component, co-influencing the MetS, while MetS is characterised by increased cytokine levels, which are known to cause a depressed mood. This study aimed to establish biological subtypes of the depressive disorder based on researched clinical, laboratory, and anthropometric variables.

METHODS

We performed a cross-sectional study on a sample of 293 subjects (145 suffering from a depressive disorder and 148 healthy controls). Results were analysed with multivariate statistical methods as well as with cluster and discriminant analysis. In order to classify depressive disorder on the grounds of laboratory, anthropometric, and clinical parameters, we performed cluster analysis, which resulted in three clusters.

RESULTS

The first cluster is characterised by low platelet serotonin, high cortisol levels, high blood glucose levels, high triglycerides levels, high Hamilton Depression Rating Scale score, high waist circumference, high C-Reactive Protein values, and a high number of previous depressive episodes, was named Combined (Metabolic) depression. The inflammatory depression cluster is defined with average platelet serotonin values, normal cortisol, and all other parameter levels, except for increased IL-6 levels. The serotoninergic depression cluster is characterised by markedly low platelet serotonin, and all other parameters are within the normal range.

CONCLUSIONS

From a biological point of view, depressive disorder is not uniform, and as such, these findings suggest potential clinically useful and generalisable biological subtypes of depressive disorder.

Cytokine and Reward Circuitry Relationships in Treatment-Resistant Depression

BACKGROUND

Depressive disorders are linked to dysfunction in reward-related behaviors and corticostriatal reward circuitry. Low-grade dysregulation of the immune system, e.g., elevations in plasma interleukin 6 (IL-6) and tumor necrosis factor α, have been thought to affect corticostriatal reward circuitry. Little is presently known about the degree to which these relationships generalize to patients with treatment-resistant depression (TRD) and/or childhood trauma history.

METHODS

Resting-state functional connectivity between the ventral striatum (VS) and ventromedial prefrontal cortex (vmPFC) regions and plasma inflammatory marker levels (IL-6, tumor necrosis factor α) were measured in 74 adults with TRD. Regression analyses examined associations of inflammatory markers with VS-vmPFC connectivity and the moderating effects of self-reported childhood trauma on these associations, with exploratory analyses examining trauma subtypes.

RESULTS

IL-6 was negatively associated with VS-vmPFC connectivity (specifically for the left VS). Childhood trauma moderated the relationships between tumor necrosis factor α and VS-vmPFC connectivity (specifically for the right VS) such that greater childhood trauma severity (particularly emotional neglect) was associated with stronger cytokine-connectivity associations.

CONCLUSIONS

This study independently extends previously reported associations between IL-6 and reductions in corticostriatal connectivity to a high-priority clinical population of treatment-seeking patients with TRD and further suggests that childhood trauma moderates specific associations between cytokines and corticostriatal connectivity. These findings suggest that associations between elevated plasma cytokine levels and reduced corticostriatal connectivity are a potential pathophysiological mechanism generalizable to patients with TRD and that such associations may be affected by trauma severity.

Exploring the genetic heterogeneity in major depression across diagnostic criteria

Major depressive disorder (MDD) is defined differently across genetic research studies and this may be a key source of heterogeneity. While previous literature highlights differences between minimal and strict phenotypes, the components contributing to this heterogeneity have not been identified. Using the cardinal symptoms (depressed mood/anhedonia) as a baseline, we build MDD phenotypes using five components-(1) five or more symptoms, (2) episode duration, (3) functional impairment, (4) episode persistence, and (5) episode recurrence-to determine the contributors to such heterogeneity. Thirty-two depression phenotypes which systematically incorporate different combinations of MDD components were created using the mental health questionnaire data within the UK Biobank. SNP-based heritabilities and genetic correlations with three previously defined major depression phenotypes were calculated (Psychiatric Genomics Consortium (PGC) defined depression, 23andMe self-reported depression and broad depression) and differences between estimates analysed. All phenotypes were heritable (h2SNP range: 0.102-0.162) and showed substantial genetic correlations with other major depression phenotypes (Rg range: 0.651-0.895 (PGC); 0.652-0.837 (23andMe); 0.699-0.900 (broad depression)). The strongest effect on SNP-based heritability was from the requirement for five or more symptoms (1.4% average increase) and for a long episode duration (2.7% average decrease). No significant differences were noted between genetic correlations. While there is some variation, the two cardinal symptoms largely reflect the genetic aetiology of phenotypes incorporating more MDD components. These components may index severity, however, their impact on heterogeneity in genetic results is likely to be limited.

Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts

We examined whether inflammation is uniformly associated with all depressive and anxiety symptoms, and whether these associations are potentially causal. Data was from 147,478 individuals from the UK Biobank (UKB) and 2,905 from the Netherlands Study of Depression and Anxiety (NESDA). Circulating C-reactive protein (CRP) was measured in both cohorts and interleukin-6 (IL-6) in NESDA. Genetic instruments for these proteins were obtained from published GWAS and UKB. Depressive and anxiety symptoms were assessed with self-report questionnaires. In NESDA, neurovegetative (appetite, sleep, psychomotor) symptoms were disaggregated as increased vs. decreased. In joint analyses, higher CRP was associated with depressive symptoms of depressed mood (OR = 1.06, 95% CI = 1.05-1.08), altered appetite (OR = 1.25, 95%CI = 1.23-1.28), sleep problems (OR = 1.05, 95%CI = 1.04-1.06), and fatigue (OR = 1.12, 95% CI = 1.11-1.14), and with anxiety symptoms of irritability (OR = 1.06, 95% CI = 1.05-1.08) and worrying control (OR = 1.03, 95% CI = 1.02-1.04). In NESDA, higher IL-6 was additionally associated with anhedonia (OR = 1.30, 95% CI = 1.12-1.52). Higher levels of both CRP (OR = 1.27, 95% CI = 1.13-1.43) and IL-6 (OR = 1.26, 95% CI = 1.07-1.49) were associated with increased sleep. Higher CRP was associated with increased appetite (OR = 1.21, 95% CI = 1.08-1.35) while higher IL-6 with decreased appetite (OR = 1.45, 95% CI = 1.18-1.79). In Mendelian Randomisation analyses, genetically predicted higher IL-6 activity was associated with increased risk of fatigue (estimate = 0.25, SE = 0.08) and sleep problems (estimate = 0.19, SE = 0.07). Inflammation was associated with core depressive symptoms of low mood and anhedonia and somatic/neurovegetative symptoms of fatigue, altered sleep and appetite changes. Less consistent associations were found for anxiety. The IL-6/IL-6R pathway could be causally linked to depression. Experimental studies are required to further evaluate causality, mechanisms, and usefulness of immunotherapies for depressive symptoms.

Comparison of depression and anxiety symptom networks in reporters and non-reporters of lifetime trauma in two samples of differing severity

BACKGROUND

Reported trauma is associated with differences in the course and outcomes of depression and anxiety. However, no research has explored the association between reported trauma and patterns of clinically relevant symptoms of both depression and anxiety.

METHODS

We used network analysis to investigate associations between reported trauma and depression and anxiety symptom interactions in affected individuals from the Genetic Links to Anxiety and Depression (GLAD) Study (n = 17720), and population volunteers from the UK Biobank (n = 11120). Participants with current moderate symptoms of depression or anxiety were grouped into reporters and non-reporters of lifetime trauma. Networks of 16 depression and anxiety symptoms in the two groups were compared using the network comparison test.

RESULTS

In the GLAD Study, networks of reporters and non-reporters of lifetime trauma did not differ on any metric. In the UK Biobank, the symptom network of reporters had significantly greater density (7.80) than the network of non-reporters (7.05).

LIMITATIONS

The data collected in the GLAD Study and the UK Biobank are self-reported with validated or semi-validated questionnaires.

CONCLUSIONS

Reported lifetime trauma was associated with stronger interactions between symptoms of depression and anxiety in population volunteers. Differences between reporters and non-reporters may not be observed in individuals with severe depression and/or anxiety due to limited variance in the presentation of disorder.

The association between plasma tryptophan catabolites and depression: The role of symptom profiles and inflammation

BACKGROUND

Tryptophan catabolites ("TRYCATs") produced by the kynurenine pathway (KP) may play a role in depression pathophysiology. Studies comparing TRYCATs levels in depressed subjects and controls provided mixed findings. We examined the association of TRYCATs levels with 1) the presence of Major Depressive Disorder (MDD), 2) depressive symptom profiles and 3) inflammatory markers.

METHODS

The sample from the Netherlands Study of Depression and Anxiety included participants with current (n = 1100) or remitted (n = 753) MDD DSM-IV diagnosis and healthy controls (n = 642). Plasma levels of tryptophan (TRP), kynurenine (KYN), kynurenic acid (KynA), quinolinic acid (QA), C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor (TNF) were measured. Atypical/energy-related symptom (AES), melancholic symptom (MS) and anxious-distress symptom (ADS) profiles were derived from questionnaires.

RESULTS

After adjustment for age, sex, education, smoking status, alcohol consumption and chronic diseases, no significant differences in TRYCATs were found comparing MDD cases versus controls. The MS profile was associated (q < 0.05) with lower KynA (β = -0.05), while AES was associated with higher KYN (β = 0.05), QA (β = 0.06) and TRP (β = 0.06). Inflammatory markers were associated with higher KYN (CRP β = 0.12, IL-6 β = 0.08, TNF β = 0.10) and QA (CRP β = 0.21, IL-6 β = 0.12, TNF β = 0.18). Significant differences against controls emerged after selecting MDD cases with high (top 30%) CRP (KYN d = 0.20, QA d = 0.33) and high TNF (KYN d = 0.24; QA d = 0.39).

CONCLUSIONS

TRYCATs levels were related to specific clinical and biological features, such as atypical symptoms or a proinflammatory status. Modulation of KP may potentially benefit a specific subset of depressed patients. Clinical studies should focus on patients with clear evidence of KP dysregulations.

Causal relationships between blood lipids and depression phenotypes: a Mendelian randomisation analysis

BACKGROUND

The etiology of depression remains poorly understood. Changes in blood lipid levels were reported to be associated with depression and suicide, however study findings were mixed.

METHODS

We performed a two-sample Mendelian randomisation (MR) analysis to investigate the causal relationship between blood lipids and depression phenotypes, based on large-scale GWAS summary statistics (N = 188 577/480 359 for lipid/depression traits respectively). Five depression-related phenotypes were included, namely major depression (MD; from PGC), depressive symptoms (DS; from SSGAC), longest duration and number of episodes of low mood, and history of deliberate self-harm (DSH)/suicide (from UK Biobank). MR was conducted with inverse-variance weighted (MR-IVW), Egger and Generalised Summary-data-based MR (GSMR) methods.

RESULTS

There was consistent evidence that triglyceride (TG) is causally associated with DS (MR-IVW β for one-s.d. increase in TG = 0.0346, 95% CI 0.0114-0.0578), supported by MR-IVW and GSMR and multiple r2 clumping thresholds. We also observed relatively consistent associations of TG with DSH/suicide (MR-Egger OR = 2.514, CI 1.579-4.003). There was moderate evidence for positive associations of TG with MD and the number of episodes of low mood. For HDL-c, we observed moderate evidence for causal associations with DS and MD. LDL-c and TC did not show robust causal relationships with depression phenotypes, except for weak evidence that LDL-c is inversely related to DSH/suicide. We did not detect significant associations when depression phenotypes were treated as exposures.

CONCLUSIONS

This study provides evidence to a causal relationship between TG, and to a lesser extent, altered cholesterol levels with depression phenotypes. Further studies on its mechanistic basis and the effects of lipid-lowering therapies are warranted.

Genetic Overlap Between Alzheimer's Disease and Depression Mapped Onto the Brain

Background: Alzheimer's disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterising their genetic overlap may provide aetiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects. Methods: We applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n = 79,145) and depression (n = 450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (UKB) (mean age 57.21, 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data. Results: MiXer estimated 98 causal genetic variants overlapping between the 2 disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B = -0.002, p = 9.1 × 10-4) and depression (B = 0.007, p = 3.2 × 10-9) in the UKB. This SNP was also associated with several regions of the corpus callosum volume anterior (B > 0.024, p < 8.6 × 10-4), third ventricle volume ventricle (B = -0.025, p = 5.0 × 10-6), and inferior temporal gyrus surface area (B = 0.017, p = 5.3 × 10-4). Discussion: Our results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

BACKGROUND

About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis.

METHODS

This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n = 110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n = 1058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n = 1143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples.

RESULTS

Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods.

CONCLUSIONS

Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

Is an irritable ADHD profile traceable using personality dimensions? Replicability, stability, and predictive value over time of data-driven profiles

Pediatric attention deficit/hyperactivity disorder (ADHD) is a heterogeneous condition. In particular, children with ADHD display varying profiles of dispositional traits, as assessed through temperament and personality questionnaires. Previous data-driven community detection analyses based on temperament dimensions identified an irritable profile of patients with ADHD, uniquely characterized by elevated emotional dysregulation symptoms. Belonging to this profile increased the risk of developing comorbid disorders. Here, we investigated whether we could replicate this profile in a sample of 178 children with ADHD, using community detection based on personality dimensions. Stability of the identified profiles, of individual classifications, and clinical prediction were longitudinally assessed over a 1-year interval. Three personality profiles were detected: The first two profiles had high levels of neuroticism, with the first displaying higher ADHD severity and lower openness to experience (profile 1; N = 38), and the second lower agreeableness (profile 2; N = 73). The third profile displayed scores closer to the normative range on all five factors (profile 3; N = 67). The identified profiles did only partially replicate the temperament-based profiles previously reported, as higher levels of neuroticism were found in two of the three detected profiles. Nonetheless, despite changes in individual classifications, the profiles themselves were highly stable over time and of clinical predictive value. Whereas children belonging to profiles 1 and 2 benefited from starting medication, children in profile 3 did not. Hence, belonging to an emotionally dysregulated profile at baseline predicted the effect of medication at follow-up over and above initial ADHD symptom severity. This finding suggests that personality profiles could play a role in predicting treatment response in ADHD.

Factors related to age at depression onset: the role of SLC6A4 methylation, sex, exposure to stressful life events and personality in a sample of inpatients suffering from major depression

BACKGROUND

An early onset of depression is associated with higher chronicity and disability, more stressful life events (SLEs), higher negative emotionality as described by the primary emotion SADNESS and more severe depressive symptomatology compared to depression onset later in life. Additionally, methylation of the serotonin transporter gene (SLC6A4) is associated with SLEs and depressive symptoms.

METHODS

We investigated the relation of SLEs, SLC6A4 methylation in peripheral blood, the primary emotions SADNESS and SEEKING (measured by the Affective Neuroscience Personality Scales) as well as depressive symptom severity to age at depression onset in a sample of N = 146 inpatients suffering from major depression.

RESULTS

Depressed women showed higher SADNESS (t (91.05) = - 3.17, p = 0.028, d = - 0.57) and higher SLC6A4 methylation (t (88.79) = - 2.95, p = 0.02, d = - 0.55) compared to men. There were associations between SLEs, primary emotions and depression severity, which partly differed between women and men. The Akaike information criterion (AIC) indicated the selection of a model including sex, SLEs, SEEKING and SADNESS for the prediction of age at depression onset. SLC6A4 methylation was not related to depression severity, age at depression onset or SLEs in the entire group, but positively related to depression severity in women.

CONCLUSIONS

Taken together, we provide further evidence that age at depression onset is associated with SLEs, personality and depression severity. However, we found no associations between age at onset and SLC6A4 methylation. The joint investigation of variables originating in biology, psychology and psychiatry could make an important contribution to understanding the development of depressive disorders by elucidating potential subtypes of depression.

Linking atypical depression and insulin resistance-related disorders via low-grade chronic inflammation: Integrating the phenotypic, molecular and neuroanatomical dimensions

Insulin resistance (IR) and related disorders, such as T2DM, increase the risk of major depressive disorder (MDD) and vice versa. Current evidence indicates that psychological stress and overeating can induce chronic low-grade inflammation that can interfere with glutamate metabolism in MDD as well as insulin signaling, particularly in the atypical subtype. Here we first review the interactive role of inflammatory processes in the development of MDD, IR and related metabolic disorders. Next, we describe the role of the anterior cingulate cortex in the pathophysiology of MDD and IR-related disorders. Furthermore, we outline how specific clinical features of atypical depression, such as hyperphagia, are more associated with inflammation and IR-related disorders. Finally, we examine the regional specificity of the effects of inflammation on the brain that show an overlap with the functional and morphometric brain patterns activated in MDD and IR-related disorders.

GABAergic polygenic risk for cocaine use disorder is negatively correlated with precuneus activity during cognitive control in African American individuals

Impaired cognitive control has been implicated in cocaine use disorder (CUD). GABAergic treatments have been proposed for CUD. Here we examined relationships between GABAergic genes and neural correlates of cognitive control in CUD. We analyzed two independent African American cohorts: one of >3000 genomewide-genotyped subjects with substance dependence and another of 40 CUD and 22 healthy control (HC) subjects who were exome-array genotyped and completed an fMRI Stroop task. We used five association thresholds to select variants of GABAergic genes in the reference cohort, yielding five polygenic risk scores (i.e., CUD-GABA-PRSs) for the fMRI cohort. At p < 0.005, the CUD-GABA-PRSs, which aggregated relative risks of CUD from 89 variants harboring in 16 genes, differed between CUD and HC individuals in the fMRI sample (p = 0.013). This CUD-GABA-PRS correlated inversely with Stroop-related activity in the left precuneus in CUD (r = -80.58, p_FWE < 0.05) but not HC participants. Post-hoc seed-based connectivity analysis of the left precuneus identified reduced functional connectivity to the posterior cingulate cortex (PCC) in CUD compared to HC subjects (p = 0.0062) and the degree of connectivity correlated with CUD-GABA-PRSs in CUD individuals (r = 0.287, p = 0.036). Our findings suggest that the GABAergic genetic risk of CUD in African Americans relates to precuneus/PCC functional connectivity during cognitive control. Identification of these GABAergic processes may be relevant targets in CUD treatment. The novel identification of 16 GABAergic genes may be investigated further to inform treatment development efforts for this condition that currently has no medication with a formal indication for its treatment.

Associations Between Common Forms of Psychopathology and Fecundity: Evidence From a Prospective, Longitudinal Twin Study

We examined associations between common psychiatric disorders and fecundity in a population-based cohort of 1252 twins prospectively assessed from adolescence into adulthood. Major depressive, anxiety, and alcohol use disorders were associated with lower likelihood of having children and having fewer children. Survival analyses yielded similar results accounting for timing/recurrence. Although both early- and adult-onset psychiatric disorders were associated with decreased fecundity, early-onset major depressive, anxiety (among boys), and alcohol use disorders (among girls) were associated with greater likelihood of having a child during adolescence. Among twin pairs discordant for psychiatric disorders, twins affected by anxiety and alcohol use, but not major depressive, disorders were less likely to have children than unaffected co-twins. However, unaffected twins with an affected co-twin were no more likely to have children than twins from unaffected twin pairs, inconsistent with the balancing selection hypothesis that increased fecundity in unaffected relatives accounts for persistence of psychiatric disorders.

Reconsidering the reasons for heightened inflammation in major depressive disorder

BACKGROUND

Increased circulating pro-inflammatory markers have repeatedly been associated with major depressive disorder (MDD). However, it remains unclear whether inflammation represents a causal mechanism for MDD, or whether the association is influenced by confounding factors such as body mass index (BMI).

METHODS

To better understand this complex relationship, we generated polygenic risk scores (PRS) for MDD and BMI in a population cohort and attempted to isolate the impact these potential risk factors have on adulthood inflammation. Peripheral blood samples were collected as part of the South East London Community Health study, where we generated individualized PRS for MDD and BMI and quantified inflammatory markers using multiplex ELISA-based technology. We performed linear regressions to investigate the effects of PRS for MDD and BMI on inflammatory marker levels.

RESULTS

Out of 35 inflammatory markers, we found a nominal effect of PRS for MDD on interleukin-10. We also found a significant positive effect of BMI on nine inflammatory markers, of which the two most strongly affected markers, interleukin-6 (IL-6) and C-reactive protein (CRP), were also nominally predicted by BMI PRS.

LIMITATIONS

The study utilized a cross-sectional design with a moderately sized sample.

CONCLUSIONS

Our findings suggest there may not be a shared genetic mechanism contributing to MDD and higher inflammatory marker levels. However, there may be shared genetic etiology between BMI and adulthood levels of CRP and IL-6. Therefore, polygenic risk scores for BMI may represent a useful indicator for heightened levels of inflammation in adulthood.

Dietary Patterns are Differentially Associated with Atypical and Melancholic Subtypes of Depression

Diet has been associated with the risk of depression, whereas different subtypes of depression have been linked with different cardiovascular risk factors (CVRFs). In this study, our aims were to (1) identify dietary patterns with exploratory factor analysis, (2) assess cross-sectional associations between dietary patterns and depression subtypes, and (3) examine the potentially mediating effect of dietary patterns in the associations between CVRFs and depression subtypes. In the first follow-up of the population-based CoLaus|PsyCoLaus study (2009–2013, 3554 participants, 45.6% men, mean age 57.5 years), a food frequency questionnaire assessed dietary intake and a semi-structured interview allowed to characterize major depressive disorder into current or remitted atypical, melancholic, and unspecified subtypes. Three dietary patterns were identified: Western, Mediterranean, and Sweet-Dairy. Western diet was positively associated with current atypical depression, but negatively associated with current and remitted melancholic depression. Sweet-Dairy was positively associated with current melancholic depression. However, these dietary patterns did not mediate the associations between CVRFs and depression subtypes. Hence, although we could show that people with different subtypes of depression make different choices regarding their diet, it is unlikely that these differential dietary choices account for the well-established associations between depression subtypes and CVRFs.

Obesity and atypical depression symptoms: findings from Mendelian randomization in two European cohorts

Studies considering the causal role of body mass index (BMI) for the predisposition of major depressive disorder (MDD) based on a Mendelian Randomization (MR) approach have shown contradictory results. These inconsistent findings may be attributable to the heterogeneity of MDD; in fact, several studies have documented associations between BMI and mainly the atypical subtype of MDD. Using a MR approach, we investigated the potential causal role of obesity in both the atypical subtype and its five specific symptoms assessed according to the Statistical Manual of Mental Disorders (DSM), in two large European cohorts, CoLaus|PsyCoLaus (n = 3350, 1461 cases and 1889 controls) and NESDA|NTR (n = 4139, 1182 cases and 2957 controls). We first tested general obesity measured by BMI and then the body fat distribution measured by waist-to-hip ratio (WHR). Results suggested that BMI is potentially causally related to the symptom increase in appetite, for which inverse variance weighted, simple median and weighted median MR regression estimated slopes were 0.68 (SE = 0.23, p = 0.004), 0.77 (SE = 0.37, p = 0.036), and 1.11 (SE = 0.39, p = 0.004). No causal effect of BMI or WHR was found on the risk of the atypical subtype or for any of the other atypical symptoms. Our findings show that higher obesity is likely causal for the specific symptom of increase in appetite in depressed participants and reiterate the need to study depression at the granular level of its symptoms to further elucidate potential causal relationships and gain additional insight into its biological underpinnings.

Dissecting the Association Between Inflammation, Metabolic Dysregulation, and Specific Depressive Symptoms: A Genetic Correlation and 2-Sample Mendelian Randomization Study

IMPORTANCE

Observational studies highlight associations of C-reactive protein (CRP), a general marker of inflammation, and interleukin 6 (IL-6), a cytokine-stimulating CRP production, with individual depressive symptoms. However, it is unclear whether inflammatory activity is associated with individual depressive symptoms and to what extent metabolic dysregulation underlies the reported associations.

OBJECTIVE

To explore the genetic overlap and associations between inflammatory activity, metabolic dysregulation, and individual depressive symptoms.

GWAS DATA SOURCES

Genome-wide association study (GWAS) summary data of European individuals, including the following: CRP levels (204 402 individuals); 9 individual depressive symptoms (3 of which did not differentiate between underlying diametrically opposite symptoms [eg, insomnia and hypersomnia]) as measured with the Patient Health Questionnaire 9 (up to 117 907 individuals); summary statistics for major depression, including and excluding UK Biobank participants, resulting in sample sizes of 500 199 and up to 230 214 individuals, respectively; insomnia (up to 386 533 individuals); body mass index (BMI) (up to 322 154 individuals); and height (up to 253 280 individuals).

DESIGN

In this genetic correlation and 2-sample mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer single-nucleotide variant-based heritability and genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with CRP levels, IL-6 signaling, and BMI with depressive symptoms. The study dates were November 2019 to April 2020.

RESULTS

Based on large GWAS data sources, genetic correlation analyses revealed consistent false discovery rate (FDR)-controlled associations (genetic correlation range, 0.152-0.362; FDR P = .006 to P < .001) between CRP levels and depressive symptoms that were similar in size to genetic correlations of BMI with depressive symptoms. Two-sample MR analyses suggested that genetic upregulation of IL-6 signaling was associated with suicidality (estimate [SE], 0.035 [0.010]; FDR plus Bonferroni correction P = .01), a finding that remained stable across statistical models and sensitivity analyses using alternative instrument selection strategies. Mendelian randomization analyses did not consistently show associations of higher CRP levels or IL-6 signaling with other depressive symptoms, but higher BMI was associated with anhedonia, tiredness, changes in appetite, and feelings of inadequacy.

CONCLUSIONS AND RELEVANCE

This study reports coheritability between CRP levels and individual depressive symptoms, which may result from the potentially causal association of metabolic dysregulation with anhedonia, tiredness, changes in appetite, and feelings of inadequacy. The study also found that IL-6 signaling is associated with suicidality. These findings may have clinical implications, highlighting the potential of anti-inflammatory approaches, especially IL-6 blockade, as a putative strategy for suicide prevention.

Neuroendocrine Response to Psychosocial Stressors, Inflammation Mediators and Brain-periphery Pathways of Adaptation

Threats, challenging events, adverse experiences, predictable or unpredictable, namely stressors, characterize life, being unavoidable for humans. The hypothalamus-pituitary-adrenal axis (HPA) and the sympathetic nervous system (SNS) are well-known to underlie adaptation to psychosocial stress in the context of other interacting systems, signals and mediators. However, much more effort is necessary to elucidate these modulatory cues for a better understanding of how and why the "brain-body axis" acts for resilience or, on the contrary, cannot cope with stress from a biochemical and biological point of view. Indeed, failure to adapt increases the risk of developing and/or relapsing mental illnesses such as burnout, post-traumatic stress disorder (PTSD), and at least some types of depression, even favoring/worsening neurodegenerative and somatic comorbidities, especially in the elderly. We will review here the current knowledge on this area, focusing on works presenting the main brain centers responsible for stressor interpretation and processing, together with those underscoring the physiology/biochemistry of endogenous stress responses. Autonomic and HPA patterns, inflammatory cascades and energy/redox metabolic arrays will be presented as allostasis promoters, leading towards adaptation to psychosocial stress and homeostasis, but also as possible vulnerability factors for allostatic overload and non-adaptive reactions. Besides, the existence of allostasis buffering systems will be treated. Finally, we will suggest promising lines of future research, particularly the use of animal and cell culture models together with human studies by means of high-throughput multi-omics technologies, which could entangle the biochemical signature of resilience or stress-related illness, a considerably helpful facet for improving patients' treatment and monitoring.

Risk Factors for Adult Depression: Adverse Childhood Experiences and Personality Functioning

Background: Depressive disorder is one of the main health problems worldwide. Many risk factors have been associated with this pathology. However, while the association between risks factors and adult depression is well established, the mechanisms behind its impact remains poorly understood. A possible, yet untested explanation is the mediating impact of levels of personality functioning, i.e., impairments with regard to self and interpersonal.

Method: Around 162 patients were assessed at the beginning of their therapy, with regard to risk factors, such as sociodemographic, physical, hereditary (Information Form), and adverse childhood experiences (ACE; CTQ). Depressive symptoms (Beck Depression Inventory, BDI) and personality functioning (OPD-SQ) were also measured. Associations between the related variables as well as other possible covariates were examined by means of zero-order correlations and bootstrapping-based mediation analysis.

Results: Of all the risk factors taken into account, level of education and physical illness were associated with depression. On the other hand, the most significant predictor of depressive symptomatology was ACE, and this relationship was mediated by personality functioning. This indicates that patients presenting adverse childhood experiences are more likely to develop deficiencies in personality functioning, which in turn increases their likelihood of developing depressive symptomatology.

Conclusion: These results reaffirm the importance of incorporating risk and vulnerability factors such as personality functioning in understanding depression.

How to Utilize Clinical and Genetic Information for Personalized Treatment of Major Depressive Disorder: Step by Step Strategic Approach

Depression is the single largest contributor to non-fatal health loss and affects 322 million people globally. The clinical heterogeneity of this disorder shows biological correlates and it makes the personalization of antidepressant prescription an important pillar of treatment. There is increasing evidence of genetic overlap between depression, other psychiatric and non-psychiatric disorders, which varies across depression subtypes. Therefore, the first step of clinical evaluation should include a careful assessment of psychopathology and physical health, not limited to previously diagnosed disorders. In part of the patients indeed the pathogenesis of depression may be strictly linked to inflammatory and metabolic abnormalities, and the treatment should target these as much as the depressive symptoms themselves. When the evaluation of the symptom and drug tolerability profile, the concomitant biochemical abnormalities and physical conditions is not enough and at least one pharmacotherapy failed, the genotyping of variants in CYP2D6/CYP2C19 (cytochromes responsible for antidepressant metabolism) should be considered. Individuals with altered metabolism through one of these enzymes may benefit from some antidepressants rather than others or need dose adjustments. Finally, if available, the polygenic predisposition towards cardio-metabolic disorders can be integrated with non-genetic risk factors to tune the identification of patients who should avoid medications associated with this type of side effects. A sufficient knowledge of the polygenic risk of complex medical and psychiatric conditions is becoming relevant as this information can be obtained through direct-to-consumer genetic tests and in the future it may provided by national health care systems.

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.

Leveraging correlations between variants in polygenic risk scores to detect heterogeneity in GWAS cohorts

Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases generated by a liability threshold model, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 3% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54 × 0⁻⁴), as well as in individual cohorts meta-analyzed using Fisher’s method (p-value 0.03), based on significantly associated variants. We also apply CLiP-Y to detect heterogeneity in neuroticism in over 10, 000 individuals from the UK Biobank and detect heterogeneity with a p-value of 1.68 × 10⁻⁹. Scores were not significantly reduced when partitioning by known subclusters (“Depression” and “Worry”), suggesting that these factors are not the primary source of observed heterogeneity.

Several traits, such as bipolar disease, are known to be heterogeneous and comprise distinct subtypes with unique genomic associations. For other traits such as schizophrenia, heterogeneity may be suspected, but specific subtypes are less well characterized. Furthermore, conventional mixture model-based methods to detect subtypes in genome-wide association data struggle with the high polygenicity of complex traits. We propose CLiP (Correlated Liability Predictors), a method that does not identify subtype-specific effects, but is very well-powered to detect heterogeneity of any kind within the very weak signals of GWAS. CLiP serves as a method to flag particular phenotypes for potential further study into the genomic factors driving heterogeneity, as well as a means to evaluate the transferability of polygenic risk scores. We also develop extensions of CLiP applicable to scoring heterogeneity in quantitative phenotypes and quantitative predictors such as gene expression. We apply CLiP to scoring heterogeneity in schizophrenia cohorts from the Psychiatric Genomics Consortium and neuroticism in individuals in the UK Biobank and find significant heterogeneity in both phenotypes, warranting further study.

Depression Heterogeneity and Its Biological Underpinnings: Toward Immunometabolic Depression

Epidemiological evidence indicates the presence of dysregulated homeostatic biological pathways in depressed patients, such as increased inflammation and disrupted energy-regulating neuroendocrine signaling (e.g., leptin, insulin). Alterations in these biological pathways may explain the considerable comorbidity between depression and cardiometabolic conditions (e.g., obesity, metabolic syndrome, diabetes) and represent a promising target for intervention. This review describes how immunometabolic dysregulations vary as a function of depression heterogeneity by illustrating that such biological dysregulations map more consistently to atypical behavioral symptoms reflecting altered energy intake/expenditure balance (hyperphagia, weight gain, hypersomnia, fatigue, and leaden paralysis) and may moderate the antidepressant effects of standard or novel (e.g., anti-inflammatory) therapeutic approaches. These lines of evidence are integrated in a conceptual model of immunometabolic depression emerging from the clustering of immunometabolic biological dysregulations and specific behavioral symptoms. The review finally elicits questions to be answered by future research and describes how the immunometabolic depression dimension could be used to dissect the heterogeneity of depression and potentially to match subgroups of patients to specific treatments with higher likelihood of clinical success.

Deconstructing major depressive episodes across unipolar and bipolar depression by severity and duration: a cross-diagnostic cluster analysis on a large, international, observational study

A cross-diagnostic, post-hoc analysis of the BRIDGE-II-MIX study was performed to investigate how unipolar and bipolar patients suffering from an acute major depressive episode (MDE) cluster according to severity and duration. Duration of index episode, Clinical Global Impression-Bipolar Version-Depression (CGI-BP-D) and Global Assessment of Functioning (GAF) were used as clustering variables. MANOVA and post-hoc ANOVAs examined between-group differences in clustering variables. A stepwise backward regression model explored the relationship with the 56 clinical-demographic variables available. Agglomerative hierarchical clustering with two clusters was shown as the best fit and separated the study population (n = 2314) into 65.73% (Cluster 1 (C1)) and 34.26% (Cluster 2 (C2)). MANOVA showed a significant main effect for cluster group (p < 0.001) but ANOVA revealed that significant between-group differences were restricted to CGI-BP-D (p < 0.001) and GAF (p < 0.001), showing greater severity in C2. Psychotic features and a minimum of three DSM-5 criteria for mixed features (DSM-5-3C) had the strongest association with C2, that with greater disease burden, while non-mixed depression in bipolar disorder (BD) type II had negative association. Mixed affect defined as DSM-5-3C associates with greater acute severity and overall impairment, independently of the diagnosis of bipolar or unipolar depression. In this study a pure, non-mixed depression in BD type II significantly associates with lesser burden of clinical and functional severity. The lack of association for less restrictive, researched-based definitions of mixed features underlines DSM-5-3C specificity. If confirmed in further prospective studies, these findings would warrant major revisions of treatment algorithms for both unipolar and bipolar depression.