Polygenic dissection of major depression clinical heterogeneity

Associations of mindful eating domains with depressive symptoms and depression in three European countries

OBJECTIVE

To examine associations of mindful eating domains with depressive symptoms and depression in three European countries. Moderation by change in appetite-with increased appetite as marker for depression with atypical features - was also tested.

METHODS

Data were collected in Denmark (n = 1522), Spain (n = 1512) and the Netherlands (n = 1439). Multiple linear and logistic regression analyses segregated by country were used to test associations of four mindful eating domains (Mindful Eating Behaviour Scale; MEBS) with depressive symptoms (continuous score on the Center for Epidemiologic Studies Depression Scale; CES-D) and depression (score above the CES-D cut-off value, and/or use of antidepressants, and/or psychological treatment). Moderation by change in appetite was tested with bias-corrected bootstrap confidence intervals.

RESULTS

The domains Focused Eating, Eating with Awareness and Eating without Distraction were significantly negatively associated with depressive symptoms and depression in all three countries (e.g. Focused Eating Denmark: B = - 0.71, 95% CI: - 0.87, - 0.54; OR = 0.89, 95% CI: 0.86, 0.93). The domain Hunger and Satiety Cues (only measured in the Netherlands) was significantly positively associated with depressive symptoms in the adjusted models (B = 0.09, 95% CI: 0.02, 0.16), but not with depression (OR = 1.02, 95% CI: 0.98, 1.05). These associations were found for both people with and without increased appetite.

LIMITATIONS

The cross-sectional design, which makes it impossible to draw causal conclusions.

CONCLUSIONS

The present study indicates that higher scores on three mindful eating domains are consistently associated with a lower level of depressive symptoms and a lower likelihood of having depression in three European countries.

Post-GWAS in Psychiatric Genetics: A Developmental Perspective on the "Other" Next Steps

As psychiatric genetics enters an era where gene identification is finally yielding robust, replicable genetic associations and polygenic risk scores, it is important to consider next steps and delineate how that knowledge will be applied to ultimately ameliorate suffering associated with substance use and psychiatric disorders. Much of the post-genome-wide association study discussion has focused on the potential of genetic information to elucidate the underlying biology and use this information for the development of more effective pharmaceutical treatments. In this review we focus on additional areas of research that should follow gene identification. By taking genetic findings into longitudinal, developmental studies, we can map the pathways by which genetic risk manifests across development, elucidating the early behavioral manifestations of risk, and studying how various environments and interventions moderate that risk across developmental stages. The delineation of risk across development will advance our understanding of mechanism, sex differences and risk and resilience processes in different racial/ethnic groups. Here, we review how the extant twin study literature can be used to guide these efforts. Together, these new lines of research will enable us to develop more informed, tailored prevention and intervention efforts.

Eating styles in major depressive disorder: Results from a large-scale study

Depressed persons have been found to present disturbances in eating styles, but it is unclear whether eating styles are different in subgroups of depressed patients. We studied the association between depressive disorder, severity, course and specific depressive symptom profiles and unhealthy eating styles. Cross-sectional and course data from 1060 remitted depressed patients, 309 currently depressed patients and 381 healthy controls from the Netherlands Study of Depression and Anxiety were used. Depressive disorders (DSM-IV based psychiatric interview) and self-reported depressive symptoms (Inventory of Depressive Symptomatology) were related to emotional, external and restrained eating (Dutch Eating Behavior Questionnaire) using analyses of covariance and linear regression. Remitted and current depressive disorders were significantly associated with higher emotional eating (Cohen's d = 0.40 and 0.60 respectively, p < 0.001) and higher external eating (Cohen's d = 0.20, p = 0.001 and Cohen's d = 0.32, p < 0.001 respectively). Little differences in eating styles between depression course groups were observed. Associations followed a dose-response association, with more emotional and external eating when depression was more severe (both p-values <0.001). Longer symptom duration was also associated to more emotional and external eating (p < 0.001 and p = 0.001 respectively). When examining individual depressive symptoms, neuro-vegetative depressive symptoms contributed relatively more to emotional and external eating, while mood and anxious symptoms contributed relatively less to emotional and external eating. No depression associations were found with restrained eating. Intervention programs for depression should examine whether treating disordered eating specifically in those with neuro-vegetative, atypical depressive symptoms may help prevent or minimize adverse health consequences.

Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders

IMPORTANCE

The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices.

OBJECTIVE

To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017.

MAIN OUTCOMES AND MEASURES

We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference.

RESULTS

Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded β power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded β power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171).

CONCLUSIONS AND RELEVANCE

These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.

Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk

BACKGROUND

Genetic risk and environmental adversity-both important risk factors for major depression (MD)-are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD?

METHODS

A clinical sample of 5784 Han Chinese women with recurrent MD were interviewed about their depressive symptoms during their lifetime worst episode of MD. The cases were classified into subgroups based on their genetic risk for MD (family history, polygenic risk score, early age at onset) and severe adversity (childhood sexual abuse, stressful life events). Differences in MD symptom network structure were statistically examined for these subgroups using permutation-based network comparison tests.

RESULTS

Although significant differences in symptom endorsement rates were seen in 18.8% of group comparisons, associations between depressive symptoms were similar across the different subgroups of genetic and environmental risk. Network comparison tests showed no significant differences in network strength, structure, or specific edges (P-value > 0.05) and correlations between edges were strong (0.60-0.71).

LIMITATIONS

This study analyzed depressive symptoms retrospectively reported by severely depressed women using novel statistical methods. Future studies are warranted to investigate whether similar findings hold in prospective longitudinal data, less severely depressed patients, and men.

CONCLUSIONS

Similar depressive symptom networks for MD patients with a higher or lower genetic or environmental risk suggest that differences in these etiological influences may produce similar symptom networks downstream for severely depressed women.

Genome-wide meta-analyses of stratified depression in Generation Scotland and UK Biobank

Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. Recent studies of MDD have identified common risk variants by using a broader phenotype definition in very large samples, or by reducing phenotypic and ancestral heterogeneity. We sought to ascertain whether it is more informative to maximize the sample size using data from all available cases and controls, or to use a sex or recurrent stratified subset of affected individuals. To test this, we compared heritability estimates, genetic correlation with other traits, variance explained by MDD polygenic score, and variants identified by genome-wide meta-analysis for broad and narrow MDD classifications in two large British cohorts - Generation Scotland and UK Biobank. Genome-wide meta-analysis of MDD in males yielded one genome-wide significant locus on 3p22.3, with three genes in this region (CRTAP, GLB1, and TMPPE) demonstrating a significant association in gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD yielded equivalent heritability estimates, showed no detectable difference in association with polygenic scores, and were each genetically correlated with six health-correlated traits (neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder). Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, and the consistent pattern of results in other MDD classifications suggests that phenotypic stratification using recurrence or sex in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain.

Common genes associated with antidepressant response in mouse and man identify key role of glucocorticoid receptor sensitivity

Response to antidepressant treatment in major depressive disorder (MDD) cannot be predicted currently, leading to uncertainty in medication selection, increasing costs, and prolonged suffering for many patients. Despite tremendous efforts in identifying response-associated genes in large genome-wide association studies, the results have been fairly modest, underlining the need to establish conceptually novel strategies. For the identification of transcriptome signatures that can distinguish between treatment responders and nonresponders, we herein submit a novel animal experimental approach focusing on extreme phenotypes. We utilized the large variance in response to antidepressant treatment occurring in DBA/2J mice, enabling sample stratification into subpopulations of good and poor treatment responders to delineate response-associated signature transcript profiles in peripheral blood samples. As a proof of concept, we translated our murine data to the transcriptome data of a clinically relevant human cohort. A cluster of 259 differentially regulated genes was identified when peripheral transcriptome profiles of good and poor treatment responders were compared in the murine model. Differences in expression profiles from baseline to week 12 of the human orthologues selected on the basis of the murine transcript signature allowed prediction of response status with an accuracy of 76% in the patient population. Finally, we show that glucocorticoid receptor (GR)-regulated genes are significantly enriched in this cluster of antidepressant-response genes. Our findings point to the involvement of GR sensitivity as a potential key mechanism shaping response to antidepressant treatment and support the hypothesis that antidepressants could stimulate resilience-promoting molecular mechanisms. Our data highlight the suitability of an appropriate animal experimental approach for the discovery of treatment response-associated pathways across species.

Major depression is the second leading cause of disability worldwide. However, only one-third of patients with depression benefit from the first antidepressant compound they are prescribed. It is a fundamental problem that the outcomes of individual antidepressant treatments are still highly unpredictable. In clinical studies, discovery of biomarkers for antidepressant response is hampered by confounding factors such as the heterogeneity of the disease phenotype and additional environmental factors, e.g., previous life events and different schedules of psychopharmacological treatment, which reduce the power to detect true response biomarkers. To overcome some of these limitations, we have established a conceptually novel approach that allows the selection of extreme phenotypes in an antidepressant-responsive mouse strain. In the first step, we identify signatures in the transcriptome of peripheral blood associated with responses following stratification into good and poor treatment responders. As proof of concept, we translate the murine data to a population of depressed patients. We show that differences in expression profiles from baseline to week 12 of the human orthologues predict response status in patients. We finally provide evidence that sensitivity of the glucocorticoid receptor could be a potential key mechanism shaping response to antidepressant treatment.

Common variants at 2q11.2, 8q21.3, and 11q13.2 are associated with major mood disorders

Bipolar disorder (BPD) and major depressive disorder (MDD) are primary major mood disorders. Recent studies suggest that they share certain psychopathological features and common risk genes, but unraveling the full genetic architecture underlying the risk of major mood disorders remains an important scientific task. The public genome-wide association study (GWAS) data sets offer the opportunity to examine this topic by utilizing large amounts of combined genetic data, which should ultimately allow a better understanding of the onset and development of these illnesses. Genome-wide meta-analysis was performed by combining two GWAS data sets on BPD and MDD (19,637 cases and 18,083 controls), followed by replication analyses for the loci of interest in independent 12,364 cases and 76,633 controls from additional samples that were not included in the two GWAS data sets. The single-nucleotide polymorphism (SNP) rs10791889 at 11q13.2 was significant in both discovery and replication samples. When combining all samples, this SNP and multiple other SNPs at 2q11.2 (rs717454), 8q21.3 (rs10103191), and 11q13.2 (rs2167457) exhibited genome-wide significant association with major mood disorders. The SNPs in 2q11.2 and 8q21.3 were novel risk SNPs that were not previously reported, and SNPs at 11q13.2 were in high LD with potential BPD risk SNPs implicated in a previous GWAS. The genome-wide significant loci at 2q11.2 and 11q13.2 exhibited strong effects on the mRNA expression of certain nearby genes in cerebellum. In conclusion, we have identified several novel loci associated with major mood disorders, adding further support for shared genetic risk between BPD and MDD. Our study highlights the necessity and importance of mining public data sets to explore risk genes for complex diseases such as mood disorders.

Genetic Association of Major Depression With Atypical Features and Obesity-Related Immunometabolic Dysregulations

IMPORTANCE

The association between major depressive disorder (MDD) and obesity may stem from shared immunometabolic mechanisms particularly evident in MDD with atypical features, characterized by increased appetite and/or weight (A/W) during an active episode.

OBJECTIVE

To determine whether subgroups of patients with MDD stratified according to the A/W criterion had a different degree of genetic overlap with obesity-related traits (body mass index [BMI] and levels of C-reactive protein [CRP] and leptin).

DESIGN, SETTING, AND PATIENTS

This multicenter study assembled genome-wide genotypic and phenotypic measures from 14 data sets of the Psychiatric Genomics Consortium. Data sets were drawn from case-control, cohort, and population-based studies, including 26 628 participants with established psychiatric diagnoses and genome-wide genotype data. Data on BMI were available for 15 237 participants. Data were retrieved and analyzed from September 28, 2015, through May 20, 2017.

MAIN OUTCOMES AND MEASURES

Lifetime DSM-IV MDD was diagnosed using structured diagnostic instruments. Patients with MDD were stratified into subgroups according to change in the DSM-IV A/W symptoms as decreased or increased.

RESULTS

Data included 11 837 participants with MDD and 14 791 control individuals, for a total of 26 628 participants (59.1% female and 40.9% male). Among participants with MDD, 5347 (45.2%) were classified in the decreased A/W and 1871 (15.8%) in the increased A/W subgroups. Common genetic variants explained approximately 10% of the heritability in the 2 subgroups. The increased A/W subgroup showed a strong and positive genetic correlation (SE) with BMI (0.53 [0.15]; P = 6.3 × 10-4), whereas the decreased A/W subgroup showed an inverse correlation (-0.28 [0.14]; P = .06). Furthermore, the decreased A/W subgroup had a higher polygenic risk for increased BMI (odds ratio [OR], 1.18; 95% CI, 1.12-1.25; P = 1.6 × 10-10) and levels of CRP (OR, 1.08; 95% CI, 1.02-1.13; P = 7.3 × 10-3) and leptin (OR, 1.09; 95% CI, 1.06-1.12; P = 1.7 × 10-3).

CONCLUSIONS AND RELEVANCE

The phenotypic associations between atypical depressive symptoms and obesity-related traits may arise from shared pathophysiologic mechanisms in patients with MDD. Development of treatments effectively targeting immunometabolic dysregulations may benefit patients with depression and obesity, both syndromes with important disability.

The association of childhood maltreatment with depression and anxiety is not moderated by the oxytocin receptor gene

BACKGROUND

The oxytocin receptor (OXTR) gene may be involved in resilience or vulnerability towards stress, and hence in the development of stress-related disorders. There are indications that OXTR single nucleotide polymorphisms (SNPs) interact with early life stressors in predicting levels of depression and anxiety. To replicate and extend these findings, we examined whether three literature-based OXTR SNPs (rs2254298, rs53576, rs2268498) interact with childhood maltreatment in the development of clinically diagnosed depression and anxiety disorders.

METHODS

We included 2567 individuals from the Netherlands Study of Depression and Anxiety. This sample consisted of 387 healthy controls, 428 people with a current or past depressive disorder, 243 people with a current or past anxiety disorder, and 1509 people with both lifetime depression and anxiety diagnoses. Childhood maltreatment was measured with both an interview and via self-report. Additional questionnaires measured depression and anxiety sensitivity.

RESULTS

Childhood maltreatment was strongly associated with both lifetime depression and anxiety diagnoses, as well as with depression and anxiety sensitivity. However, the OXTR SNPs did not moderate these associations nor had main effects on outcomes.

CONCLUSIONS

The three OXTR gene SNPs did not interact with childhood maltreatment in predicting lifetime depression and anxiety diagnoses or sensitivity. This stresses the importance of replication studies with regard to OXTR gene variants in general populations as well as in clearly established clinical samples.

Genome-wide Regional Heritability Mapping Identifies a Locus Within the TOX2 Gene Associated With Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) is the second largest cause of global disease burden. It has an estimated heritability of 37%, but published genome-wide association studies have so far identified few risk loci. Haplotype-block-based regional heritability mapping (HRHM) estimates the localized genetic variance explained by common variants within haplotype blocks, integrating the effects of multiple variants, and may be more powerful for identifying MDD-associated genomic regions.

METHODS

We applied HRHM to Generation Scotland: The Scottish Family Health Study, a large family- and population-based Scottish cohort (N = 19,896). Single-single nucleotide polymorphism (SNP) and haplotype-based association tests were used to localize the association signal within the regions identified by HRHM. Functional prediction was used to investigate the effect of MDD-associated SNPs within the regions.

RESULTS

A haplotype block across a 24-kb region within the TOX2 gene reached genome-wide significance in HRHM. Single-SNP- and haplotype-based association tests demonstrated that five of nine genotyped SNPs and two haplotypes within this block were significantly associated with MDD. The expression of TOX2 and a brain-specific long noncoding RNA RP1-269M15.3 in frontal cortex and nucleus accumbens basal ganglia, respectively, were significantly regulated by MDD-associated SNPs within this region. Both the regional heritability and single-SNP associations within this block were replicated in the UK-Ireland group of the most recent release of the Psychiatric Genomics Consortium (PGC), the PGC2-MDD (Major Depression Dataset). The SNP association was also replicated in a depressive symptom sample that shares some individuals with the PGC2-MDD.

CONCLUSIONS

This study highlights the value of HRHM for MDD and provides an important target within TOX2 for further functional studies.

Depressive subtypes in an elderly cohort identified using latent class analysis

BACKGROUND

Clinical findings indicate heterogeneity of depressive disorders, stressing the importance of subtyping depression for research and clinical care. Subtypes of the common late life depression are however seldom studied. Data-driven methods may help provide a more empirically-based classification of late-life depression.

METHODS

Data were used from the Netherlands Study of Depression in Older People (NESDO) derived from 359 persons, aged 60 years or older, with a current diagnosis of major depressive disorder. Latent class analysis (LCA) was used to identify subtypes of depression, using ten CIDI-based depression items. Classes were then characterized using various sociodemographic and clinical characteristics.

RESULTS

The most prevalent class, as identified by LCA, was a moderate-severe class (prevalence 46.5%), followed by a severe melancholic class (prevalence 38.4%), and a severe atypical class (prevalence 15.0%). The strongest distinguishing features between the three classes were appetite and weight and, to a lesser extent, psychomotor symptoms and loss of interest. Compared with the melancholic class, the severe atypical class had the highest prevalence of females, the lowest mean age, the highest BMI, and highest prevalence of both cardiovascular disease, and metabolic syndrome.

LIMITATIONS

The strongest distinguishing symptoms, appetite and weight, could be correlated. Further, only longitudinal studies could demonstrate whether the identified classes are stable on the long term.

DISCUSSION

In older persons with depressive disorders, three distinct subtypes were identified, similar to subtypes found in younger adults. The strongest distinguishing features were appetite and weight; moreover, classes differed strongly on prevalence of metabolic syndrome and cardiovascular disease. These findings suggest differences in the involvement of metabolic pathways across classes, which should be considered when investigating the pathogenesis and (eventually) treatment of depression in older persons.

Genetics of Depression: Progress at Last

PURPOSE OF REVIEW

We will describe the success of recent genome-wide association studies that identify genetic variants associated with depression and outline the strategies used to reduce heterogeneity and increase sample size.

RECENT FINDINGS

The CONVERGE consortium identified two genetic associations by focusing on a sample of Chinese women with recurrent severe depression. Three other loci have been found in Europeans by combining cohorts with clinical diagnosis and measures of depressive symptoms to increase sample size. 23andMe identified 15 loci associated with depression using self-report of clinical diagnosis in a study of over 300,000 individuals. The first genetic associations with depression have been identified, and this number is now expected to increase linearly with sample size, as seen in other polygenic disorders. These loci provide invaluable insights into the biology of depression and exciting opportunities to develop new biomarkers and therapeutic targets.

Leptin Dysregulation Is Specifically Associated With Major Depression With Atypical Features: Evidence for a Mechanism Connecting Obesity and Depression

BACKGROUND

Obesity-related dysregulation of leptin signaling (e.g., hyperleptinemia due to central functional resistance) may affect mood. However, evidence for leptin dysregulation in major depressive disorder (MDD) is conflicting. Inconclusive findings may be attributable to heterogeneity of MDD, aggregating biologically different subtypes. We examined the relationship of leptin with MDD, its common subtypes (typical and atypical), and clinical features.

METHODS

The sample consisted of participants (aged 18 to 65 years) from the Netherlands Study of Depression and Anxiety with current (n = 1062) or remitted (n = 711) MDD and healthy control subjects (n = 497). Diagnoses of MDD and subtypes were based on DSM-IV symptoms. Additional symptoms were measured with the Inventory of Depressive Symptomatology. Blood levels of leptin and adiposity indexes (body mass index and waist circumference) were assessed.

RESULTS

As compared to control subjects, higher leptin was associated with the atypical MDD subtype both for remitted (n = 144, odds ratio = 1.53, 95% confidence interval = 1.16-2.03, p = .003) and current (n = 270, odds ratio = 1.90, 95% confidence interval = 1.51-2.93, p = 5.3e-8) cases. This association was stronger for increasing adiposity levels (leptin by body mass index interaction, p < .02), strengthening the hypothesis of the involvement of leptin resistance. No association with leptin was found for overall MDD or the typical subtype. Among currently depressed patients, higher leptin was associated with key symptoms identifying the atypical subtype, such as hyperphagia, increased weight, and leaden paralysis.

CONCLUSIONS

Leptin dysregulation (resistance) may represent an underlying mechanism connecting obesity and MDD with atypical features. Development of treatment effectively targeting leptin resistance may benefit patients with atypical depression characterized by obesity-related metabolic alterations.

Assessing the presence of shared genetic architecture between Alzheimer's disease and major depressive disorder using genome-wide association data

Major depressive disorder (MDD) and Alzheimer's disease (AD) are both common in older age and frequently co-occur. Numerous phenotypic studies based on clinical diagnoses suggest that a history of depression increases risk of subsequent AD, although the basis of this relationship is uncertain. Both illnesses are polygenic, and shared genetic risk factors could explain some of the observed association. We used genotype data to test whether MDD and AD have an overlapping polygenic architecture in two large population-based cohorts, Generation Scotland's Scottish Family Health Study (GS:SFHS; N=19 889) and UK Biobank (N=25 118), and whether age of depression onset influences any relationship. Using two complementary techniques, we found no evidence that the disorders are influenced by common genetic variants. Using linkage disequilibrium score regression with genome-wide association study (GWAS) summary statistics from the International Genomics of Alzheimer's Project, we report no significant genetic correlation between AD and MDD (r_G=-0.103, P=0.59). Polygenic risk scores (PRS) generated using summary data from International Genomics of Alzheimer's Project (IGAP) and the Psychiatric Genomics Consortium were used to assess potential pleiotropy between the disorders. PRS for MDD were nominally associated with participant-recalled AD family history in GS:SFHS, although this association did not survive multiple comparison testing. AD PRS were not associated with depression status or late-onset depression, and a survival analysis showed no association between age of depression onset and genetic risk for AD. This study found no evidence to support a common polygenic structure for AD and MDD, suggesting that the comorbidity of these disorders is not explained by common genetic variants.

Using Clinical Characteristics to Identify Which Patients With Major Depressive Disorder Have a Higher Genetic Load for Three Psychiatric Disorders

BACKGROUND

Limited successes of gene finding for major depressive disorder (MDD) may be partly due to phenotypic heterogeneity. We tested whether the genetic load for MDD, bipolar disorder, and schizophrenia (SCZ) is increased in phenotypically more homogenous MDD patients identified by specific clinical characteristics.

METHODS

Patients (n = 1539) with a DSM-IV MDD diagnosis and control subjects (n = 1792) were from two large cohort studies (Netherlands Study of Depression and Anxiety and Netherlands Twin Register). Genomic profile risk scores (GPRSs) for MDD, bipolar disorder, and SCZ were based on meta-analysis results of the Psychiatric Genomics Consortium. Regression analyses (adjusted for year of birth, sex, three principal components) examined the association between GPRSs with characteristics and GPRSs with MDD subgroups stratified according to the most relevant characteristics. The proportion of liability variance explained by GPRSs for each MDD subgroup was estimated.

RESULTS

GPRS-MDD explained 1.0% (p = 4.19e^-09) of MDD variance, and 1.5% (p = 4.23e^-09) for MDD endorsing nine DSM symptoms. GPRS-bipolar disorder explained 0.6% (p = 2.97e^-05) of MDD variance and 1.1% (p = 1.30e^-05) for MDD with age at onset <18 years. GPRS-SCZ explained 2.0% (p = 6.15e^-16) of MDD variance, 2.6% (p = 2.88e^-10) for MDD with higher symptom severity, and 2.3% (p = 2.26e^-13) for MDD endorsing nine DSM symptoms. An independent sample replicated the same pattern of stronger associations between cases with more DSM symptoms, as compared to overall MDD, and GPRS-SCZ.

CONCLUSIONS

MDD patients with early age at onset and higher symptom severity have an increased genetic risk for three major psychiatric disorders, suggesting that it is useful to create phenotypically more homogenous groups when searching for genes associated with MDD.

Validity of LIDAS (LIfetime Depression Assessment Self-report): a self-report online assessment of lifetime major depressive disorder

BACKGROUND

There is a paucity of valid, brief instruments for the assessment of lifetime major depressive disorder (MDD) that can be used in, for example, large-scale genomics, imaging or biomarker studies on depression. We developed the LIfetime Depression Assessment Self-report (LIDAS), which assesses lifetime MDD diagnosis according to DSM criteria, and is largely based on the widely used Composite International Diagnostic Interview (CIDI). Here, we tested the feasibility and determined the sensitivity and specificity for measuring lifetime MDD with this new questionnaire, with a regular CIDI as reference.

METHOD

Sensitivity and specificity analyses of the online lifetime MDD questionnaire were performed in adults with (n = 177) and without (n = 87) lifetime MDD according to regular index CIDIs, selected from the Netherlands Study of Depression and Anxiety (NESDA) and Netherlands Twin Register (NTR). Feasibility was tested in an additional non-selective, population-based sample of NTR participants (n = 245).

RESULTS

Of the 753 invited persons, 509 (68%) completed the LIDAS, of which 419 (82%) did this online. User-friendliness of the instrument was rated high. Median completion time was 6.2 min. Sensitivity and specificity for lifetime MDD were 85% [95% confidence interval (CI) 80-91%] and 80% (95% CI 72-89%), respectively. This LIDAS instrument gave a lifetime MDD prevalence of 20.8% in the population-based sample.

CONCLUSIONS

Measuring lifetime MDD with an online instrument was feasible. Sensitivity and specificity were adequate. The instrument gave a prevalence of lifetime MDD in line with reported population prevalences. LIDAS is a promising tool for rapid determination of lifetime MDD status in large samples, such as needed for genomics studies.

Dissection of major depressive disorder using polygenic risk scores for schizophrenia in two independent cohorts

Major depressive disorder (MDD) is known for its substantial clinical and suspected causal heterogeneity. It is characterized by low mood, psychomotor slowing and increased levels of the personality trait neuroticism; factors also associated with schizophrenia (SCZ). It is possible that some cases of MDD may have a substantial genetic loading for SCZ. The presence of SCZ-like MDD subgroups would be indicated by an interaction between MDD status and polygenic risk of SCZ on cognitive, personality and mood measures. Here, we hypothesized that higher SCZ polygenic risk would define larger MDD case-control differences in cognitive ability, and smaller differences in distress and neuroticism. Polygenic risk scores (PRSs) for SCZ and their association with cognitive variables, neuroticism, mood and psychological distress were estimated in a large population-based cohort (Generation Scotland: Scottish Family Health Study, GS:SFHS). The individuals were divided into those with, and without, depression (n=2587 and n=16 764, respectively) to test for the interactions between MDD status and schizophrenia risk. Replication was sought in UK Biobank (UKB; n=6049 and n=27 476 cases and controls, respectively). In both the cohorts, we found significant interactions between SCZ-PRS and MDD status for measures of psychological distress (βGS=-0.04, PGS=0.014 and βUKB=-0.09, PUKB⩽0.001 for GS:SFHS and UKB, respectively) and neuroticism (βGS=-0.04, PGS=0.002 and βUKB=-0.06, PUKB=0.023). In both the cohorts, there was a reduction of case-control differences on a background of higher genetic risk of SCZ. These findings suggest that depression on a background of high genetic risk for SCZ may show attenuated associations with distress and neuroticism. This may represent a causally distinct form of MDD more closely related to SCZ.

Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders

Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders, aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the European Roadmap for Mental Health Research, both of which emphasize finding stratifications that are based on biological systems and that cut across current classifications. We first introduce the basic concepts for stratifying psychiatric disorders and then provide a methodologically oriented and critical review of the existing literature. This shows that the predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders we review, but for most disorders it has not converged on a consistent set of subgroups. We highlight problems with current approaches that are not widely recognized and discuss the importance of validation to ensure that the derived subgroups index clinically relevant variation. Finally, we review emerging techniques-such as those that estimate normative models for mappings between biology and behavior-that provide new ways to parse the heterogeneity underlying psychiatric disorders and evaluate all methods to meeting the objectives of such as the National Institute of Mental Health Research Domain Criteria and Roadmap for Mental Health Research.

Depression and cardiovascular disease: Epidemiological evidence on their linking mechanisms

Depression's burden of disease goes beyond functioning and quality of life and extends to somatic health. Results from longitudinal cohort studies converge in illustrating that major depressive disorder (MDD) subsequently increases the risk of cardiovascular morbidity and mortality with about 80%. The impact of MDD on cardiovascular health may be partly explained by mediating mechanisms such as unhealthy lifestyle (smoking, excessive alcohol use, physical inactivity, unhealthy diet, therapy non-compliance) and unfavorable pathophysiological disturbances (autonomic, HPA-axis, metabolic and immuno-inflammatory dysregulations). A summary of the literature findings as well as relevant results from the large-scale Netherlands Study of Depression and Anxiety (N=2981) are presented. Persons with MDD have significantly worse lifestyles as well as more pathophysiological disturbances as compared to healthy controls. Some of these differences seem to be specific for (typical versus 'atypical', or antidepressant treated versus drug-naive) subgroups of MDD patients. Alternative explanations are also present, namely undetected confounding, iatrogenic effects or 'third factors' such as genetics.

Serum proteomic profiles of depressive subtypes

Depression is a highly heterogeneous disorder. Accumulating evidence suggests biological and genetic differences between subtypes of depression that are homogeneous in symptom presentation. We aimed to evaluate differences in serum protein profiles between persons with atypical and melancholic depressive subtypes, and compare these profiles with serum protein levels of healthy controls. We used the baseline data from the Netherlands Study of Depression and Anxiety on 414 controls, 231 persons with a melancholic depressive subtype and 128 persons with an atypical depressive subtype for whom the proteomic data were available. Depressive subtypes were previously established using a data-driven analysis, and 171 serum proteins were measured on a multi-analyte profiling platform. Linear regression models were adjusted for several covariates and corrected for multiple testing using false discovery rate q-values. We observed differences in analytes between the atypical and melancholic subtypes (9 analytes, q<0.05) and between atypical depression and controls (23 analytes, q<0.05). Eight of the nine markers differing between the atypical and melancholic subtype overlapped with markers from the comparison between atypical subtype and controls (mesothelin, leptin, IGFBP1, IGFBP2, FABPa, insulin, C3 and B2M), and were mainly involved in cellular communication and signal transduction, and immune response. No markers differed significantly between the melancholic subtype and controls. To conclude, although some uncertainties exist in our results as a result of missing data imputation and lack of proteomic replication samples, many of the identified analytes are inflammatory or metabolic markers, which supports the notion of atypical depression as a syndrome characterized by metabolic disturbances and inflammation, and underline the importance and relevance of subtypes of depression in biological and genetic research, and potentially in the treatment of depression.

Genetic Epidemiology and Public Health: The Evolution From Theory to Technology

Genetic epidemiology represents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic and environmental risk factors for disease. It is a relatively new field in public health; the term was first coined only 35 years ago. In this short time, the field has been through a major evolution, changing from a field driven by theory, without the technology for genetic measurement or computational capacity to apply much of the designs and methods developed, to a field driven by rapidly expanding technology in genomic measurement and computational analyses while epidemiologic theory struggles to keep up. In this commentary, we describe 4 different eras of genetic epidemiology, spanning this evolution from theory to technology, what we have learned, what we have added to the broader field of public health, and what remains to be done.

Role of Neuro-Immunological Factors in the Pathophysiology of Mood Disorders: Implications for Novel Therapeutics for Treatment Resistant Depression

Mood disorders are associated with persistently high rates of morbidity and mortality, despite the widespread availability of antidepressant treatments. One limitation to extant therapeutic options has been that nearly all approved antidepressant pharmacotherapies exert a similar primary action of blocking monoamine transporters, and few options exist for transitioning treatment resistant patients to alternatives with distinct mechanisms. An emerging area of science that promises novel pathways to antidepressant and mood-stabilizing therapies has followed from evidence that immunological factors play major roles in the pathophysiology of at least some mood disorder subtypes. Here we review evidence that the compounds that reduce the release or signaling of neuroactive cytokines, particularly IL-1β, IL-6, and TNF-α, can exert antidepressant effects in subgroups of depressed patients who are identified by blood-based biomarkers associated with inflammation. Within this context we discuss the role of microglia in central neuroinflammation, and the interaction between the peripheral immune system and the central synaptic microenvironment during and after neuroinflammation. Finally we review data using preclinical neuroinflammation models that produce depression-like behaviors in experimental animals to guide the discovery of novel neuro-immune drug targets.