Major depressive disorder and cardiometabolic diseases: a bidirectional Mendelian randomisation study.
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SUBMITTER: Tang B
PROVIDER: S-EPMC7286869 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
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Major depressive disorder and cardiometabolic diseases: a bidirectional Mendelian randomisation study.
Tang Bowen B Yuan Shuai S Xiong Ying Y He Qiqiang Q Larsson Susanna C SC
Diabetologia 20200408 7
<h4>Aims/hypothesis</h4>Observational studies have shown a bidirectional association between major depressive disorder (MDD) and cardiometabolic diseases. We conducted a two-sample bidirectional Mendelian randomisation (MR) study to assess the causal associations of MDD with type 2 diabetes, coronary artery disease (CAD) and heart failure and vice versa.<h4>Methods</h4>We extracted summary-level data for MDD, type 2 diabetes, CAD and heart failure from corresponding published large genome-wide a ...[more]
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Project description:PurposeMajor depressive disorder (MDD) and venous thromboembolism (VTE) may be linked in observational studies. However, the causal association remains ambiguous. Therefore, this study investigates the causal associations between them.MethodsWe performed a two-sample univariable and multivariable bidirectional Mendelian randomization (MR) analysis to evaluate the associations between MDD and VTE. The summary genetic associations of MDD statistics were obtained from the Psychiatric Genomics Consortium and UK Biobank. Information on VTE, deep vein thrombosis (DVT), and pulmonary embolism (PE) were obtained from the FinnGen Biobank. Inverse-variance weighting was used as the main analysis method. Other methods include weighted median, MR-Egger, Simple mode, and Weighted mode.ResultsUnivariable MR analysis revealed no significant associations between MDD and VTE risk (odds ratio (OR): 0.936, 95% confidence interval (CI): 0.736-1.190, p = 0.590); however, after adjusting the potential relevant polymorphisms of body mass index and education, the multivariable MR analysis showed suggestive evidence of association between them (OR: 1.163, 95% CI: 1.004-1.346, p = 0.044). Univariable MR analysis also revealed significant associations between MDD and PE risk (OR: 1.310, 95% CI: 1.073-1.598, p = 0.008), but the association between them was no longer significant in MVMR analysis (p = 0.072). We found no significant causal effects between MDD and DVT risk in univariable or multivariable MR analyses. There was also no clear evidence showing the causal effects between VTE, PE, or DVT and MDD risk.ConclusionWe provide suggestive genetic evidence to support the causal association between MDD and VTE risk. No causal associations were observed between VTE, PE, or DVT and MDD risk. Further validation of these associations and investigations of potential mechanisms are required.
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Project description:BackgroundThe association between psychiatric disorders and dentofacial deformities has attracted widespread attention. However, their relationship is currently unclear and controversial.MethodsA two-sample bidirectional MR analysis was performed to study the causal relationship between dentofacial deformity and eight psychiatric disorders, including major depressive disorder, panic disorder, schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, Alzheimer's disease, autism spectrum disorder, and neuroticism. Inverse variance weighted, weighted median, MR-Egger regression, weighted mode four methods, and further sensitivity analyses were conducted.ResultsThe major depressive disorder affected dentofacial deformity, with an OR = 1.387 (95% CI = 1.181-1.629, P = 6.77×10-5). No other psychiatric disorders were found to be associated with dentofacial deformity. In turn, dentofacial deformity were associated with neuroticism, with an OR = 1.050 (95% CI = 1.008-1.093, P = 0.018). And there was no evidence that dentofacial deformity would increase the risk of other psychiatric disorders.ConclusionsMajor depressive disorder might elevate the risk of dentofacial deformities, and dentofacial deformity conditions would increase the risk of the incidence of neuroticism.
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Project description:Major depressive disorder (MDD) exhibits numerous clinical, epidemiological, and molecular features that are consistent with partially inherited and partially acquired epigenetic misregulation. We performed microarray based DNA modification study of MDD, utilizing affected and unaffected samples from white blood cells from monozygotic twins discordant for MDD, post-mortem prefrontal cortex tissues, and sperm samples. We performed DNA methylome analysis on white blood cells from monozygotic twins discordant for depression (n=200), pre-frontal cortex (n=71), and germline samples (n=33) from affected individuals and controls (total n=304). DNA samples were enriched for unmodified fraction of the genome using DNA-modification sensitive restriction enzyme digestion followed by adaptor-mediated PCR. The enriched fractions were labelled with a fluorescent dye (Cy5) and hybridized onto the array with a common reference pool (Cy3) generated from individuals unrelated to this study.
2012-04-27 | E-GEOD-37579 | biostudies-arrayexpress
Project description:BackgroundMajor depressive disorder (MDD) is a highly heterogeneous mental illness and a major public health problem worldwide. A large number of observational studies have demonstrated a clear association between MDD and coronary heart disease (CHD), and some studies have even suggested that the relationship is bidirectional. However, it was unknown whether any causal relationship existed between them and whether causality was bidirectional in such an instance. Thus, we aimed to determine whether there is a bidirectional causal relationship between major depressive disorders and coronary heart disease.MethodsOur two-sample Bidirectional Mendelian Randomization Study consisted of two parts: forward MR analysis regarded MDD as exposure and CHD as the outcome, and reverse MR analysis considered CHD as exposure and MDD as the outcome. Summary data on MDD and CHD were obtained from the IEU Open GWAS database. After screening criteria(P < [Formula: see text]), 47 MDD-associated SNPs and 39 CHD-associated SNPs were identified. The inverse-variance weighted (IVW) method, ME-Egger regression, and weighted median method were used to estimate causality. In addition, sensitivity methods, including the heterogeneity test, horizontal pleiotropy test, and leave-one-out method, were applied to ensure the robustness of causal estimation.ResultsBased on the MR-Egger regression intercept test results, there did not appear to be any horizontal pleiotropy in this study (MDD: intercept = -0.0000376, P = 0.9996; CHD: intercept = -0.0002698, P = 0.920). Accordingly, IVW results suggested consistent estimates of causal effect values. The results showed that people with MDD increased the risk of CHD by 14.7% compared with those without MDD (OR = 1.147, 95%CI: 1.045-1.249, P = 0.009). But there was no direct evidence that CHD would increase the risk of MDD(OR = 1.008, 95%CI: 0.985-1.031, P = 0.490). The heterogeneity test and funnel plot showed no heterogeneity in 47 SNPs of MDD (Q = 42.28, [Formula: see text]=0, P = 0.629), but there was heterogeneity in 39 SNPs of CHD (Q = 62.48, [Formula: see text]=39.18%, P = 0.007). The leave-one-out method failed to identify instances where a single SNP was either biased toward or dependent on the causation.ConclusionOur study supports a one-way causal relationship between MDD and CHD, but there is no bidirectional causal relationship. MDD increases the risk of CHD, but there is no evidence that CHD increases the risk of MDD. Therefore, the influence of psychological factors should also be considered in the prevention and treatment of CHD. For MDD patients, it is necessary to prevent cardiovascular diseases.
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Project description:Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher's ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites.We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers.The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets.
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