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

Data mining algorithm predicts a range of adverse outcomes in major depression

Background: Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data. Methods: We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1–9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness. Results: Our model consistently predicted future episodes of MD in both test samples (AUC 0.68–0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65–0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas. Limitations: Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background. Conclusions: Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice

Metabolomics Profile in Depression:A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls

BACKGROUND: Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons. METHODS: Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses. RESULTS: Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q < .05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein A1 were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms. CONCLUSIONS: This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity

Metabolomics signatures of depression:the role of symptom profiles

Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an "atypical/energy-related" symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An 'AES profile' score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (β = 0.13, p = 1.35*10 -12), isoleucine (β = 0.13, p = 1.45*10 -10), very-low-density lipoproteins cholesterol (β = 0.11, p = 6.19*10 -9) and saturated fatty acid levels (β = 0.09, p = 3.68*10 -10), and lower high-density lipoproteins cholesterol (β = -0.07, p = 1.14*10 -4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were internally replicated using data from the same subjects (N = 2015) collected at 6-year follow-up. We identified a specific metabolomic signature-commonly linked to cardiometabolic disorders-associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile identifies a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and may represent a valuable target for interventions aiming at reducing depression's detrimental impact on health. </p

Lipid Peroxidation and Depressed Mood in Community-Dwelling Older Men and Women

It has been hypothesized that cellular damage caused by oxidative stress is associated with late-life depression but\ud epidemiological evidence is limited. In the present study we evaluated the association between urinary 8-iso-prostaglandin\ud F2a (8-iso-PGF2a), a biomarker of lipid peroxidation, and depressed mood in a large sample of community-dwelling older\ud adults. Participants were selected from the Health, Aging and Body Composition study, a community-based longitudinal\ud study of older persons (aged 70–79 years). The present analyses was based on a subsample of 1027 men and 948 women\ud free of mobility disability. Urinary concentration of 8-iso-PGF2a was measured by radioimmunoassay methods and adjusted\ud for urinary creatinine. Depressed mood was defined as a score greater than 5 on the 15-item Geriatric Depression Scale and/\ud or use of antidepressant medications. Depressed mood was present in 3.0% of men and 5.5% of women. Depressed men\ud presented higher urinary concentrations of 8-iso-PGF2a than non-depressed men even after adjustment for multiple\ud sociodemographic, lifestyle and health factors (p=0.03, Cohen’s d = 0.30). This association was not present in women\ud (depressed status-by-sex interaction p = 0.04). Our study showed that oxidative damage may be linked to depression in\ud older men from a large sample of the general population. Further studies are needed to explore whether the modulation of\ud oxidative stress may break down the link between late-life depression and its deleterious health consequences

Metabolomic profiles discriminating anxiety from depression

Objective: Depression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations. Methods: Data were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites. Results: As compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration. Conclusion: While substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders

Weighing poor immunometabolic health in relatives for severity of affective symptoms:A study of patients with depressive and anxiety disorders and their siblings

BACKGROUND: Affective (i.e. depressive and anxiety) disorders often co-occur with immunometabolic diseases and related biological pathways. Although many large population-based and meta-analytic studies have confirmed this link in community and clinical samples, studies in at-risk samples of siblings of persons with affective disorders are lacking. Furthermore, this somatic-mental co-occurrence may be partially explained by familial clustering of the conditions. First, we examined whether the association between a wide range of immunometabolic diseases and related biomarker based risk-profiles with psychological symptoms replicates in at-risk siblings of probands with affective disorders. Second, leveraging on a sibling-pair design, we disentangled and quantified the effect of probands' immunometabolic health on siblings' psychological symptoms and on the association between immunometabolic health and these symptoms in siblings.METHODS: The sample included 636 participants (M age = 49.7; 62.4% female) from 256 families, each including a proband with lifetime depressive and/or anxiety disorders and at least one of their sibling(s) (N = 380 proband-sibling pairs). Immunometabolic health included cardiometabolic and inflammatory diseases, body mass index (BMI), and composite metabolic (based on the five metabolic syndrome components) and inflammatory (based on interleukin-6 and C-reactive protein) biomarker indices. Overall affective symptoms and specific atypical, energy-related depressive symptoms were derived from self-report questionnaires. Mixed-effects analyses were used to model familial clustering. RESULTS: In siblings, inflammatory disease (γ = 0.25, p = 0.013), higher BMI (γ = 0.10, p = 0.033) and metabolic index (γ = 0.28, p &lt; 0.001) were associated with higher affective symptoms, with stronger associations for atypical, energy-related depressive symptoms (additionally associated with cardiometabolic disease; γ = 0.56, p = 0.048). Immunometabolic health in probands was not independently associated with psychological symptoms in siblings nor did it moderate the association between immunometabolic health and psychological symptoms estimated in siblings.CONCLUSIONS: Our findings demonstrate that the link between later life immunometabolic health and psychological symptoms is consistently present also in adult siblings at high risk for affective disorders. Familial clustering did not appear to have a substantial impact on this association. Instead, individual lifestyle, rather than familial factors, may have a relatively higher impact in the clustering of later life immunometabolic conditions with psychological symptoms in at-risk adult individuals. Furthermore, results highlighted the importance of focusing on specific depression profiles when investigating the overlap with immunometabolic health.</p

Associations between depressive symptom profiles and immunometabolic characteristics in individuals with depression and their siblings

Objectives: The present study examined associations between immunometabolic characteristics (IMCs) and depressive symptom profiles (DSPs) in probands with lifetime diagnoses of depression and/or anxiety disorders and their siblings.Methods: Data were from the Netherlands Study of Depression and Anxiety, comprising 256 probands with lifetime diagnoses of depression and/or anxiety and their 380 siblings. Measured IMCs included blood pressure, waist circumference, and levels of glucose, triglycerides, HDL cholesterol, CRP, TNF-α and IL-6. DSPs included mood, cognitive, somatic and atypical-like profiles. We cross-sectionally examined whether DSPs were associated with IMCs within probands and within siblings, and whether DSPs were associated with IMCs between probands and siblings.Results: Within probands and within siblings, higher BMI and waist circumference were associated with higher somatic and atypical-like profiles. Other IMCs (IL-6, glucose and HDL cholesterol) were significantly related to DSPs either within probands or within siblings. DSPs and IMCs were not associated between probands and siblings.Conclusions: The results suggest that there is a familial component for each trait, but no common familial factors for the association between DSPs and IMCs. Alternative mechanisms, such as direct causal effects or non-shared environmental risk factors, may better fit these results

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.</p

Genetic liability for depression, social factors and their interaction effect in depressive symptoms and depression over time in older adults

Objectives The objectives of this study were to investigate the effect of genetic and social factors on depressive symptoms and depression over time and to test whether social factors moderate the relationship between depressive symptoms and its underlying genetics in later life. Methods The study included 2,279 participants with a mean follow-up of 15 years from the Longitudinal Aging Study Amsterdam with genotyping data. The personal genetic loading for depression was estimated for each participant by calculating a polygenic risk scores (PRS-D), based on 23,032 single nucleotide polymorphisms associated with major depression in a large genome-wide association study. Partner status, network size, received and given emotional support were assessed via questionnaires and depressive symptoms were assessed using the CES-D Scale. A CES-D Scale of 16 and higher was considered as clinically relevant depression. Results Higher PRS-D was associated with more depressive symptoms whereas having a partner and having a larger network size were independently associated with less depressive symptoms. After extra adjustment for education, cognitive function and functional limitations, giving more emotional support was also associated with less depressive symptoms. No evidence for gene-environment interaction between PRS-D and social factors was found. Similar results were found for clinically relevant depression. Conclusion Genetic and social factors are independently associated with depressive symptoms over time in older adults. Strategies that boost social functioning should be encouraged in the general population of older adults regardless of the genetic liability for depression