Dataset Information

Are psychiatric disorders risk factors for COVID-19 susceptibility and severity? a two-sample, bidirectional, univariable, and multivariable Mendelian Randomization study.

ABSTRACT: Observational studies have suggested bidirectional associations between psychiatric disorders and COVID-19 phenotypes, but results of such studies are inconsistent. Mendelian Randomization (MR) may overcome the limitations of observational studies, e.g., unmeasured confounding and uncertainties about cause and effect. We aimed to elucidate associations between neuropsychiatric disorders and COVID-19 susceptibility and severity. To that end, we applied a two-sample, bidirectional, univariable, and multivariable MR design to genetic data from genome-wide association studies (GWASs) of neuropsychiatric disorders and COVID-19 phenotypes (released in January 2021). In single-variable Generalized Summary MR analysis, the most significant and only Bonferroni-corrected significant result was found for genetic liability to BIP-SCZ (a combined GWAS of bipolar disorder and schizophrenia as cases vs. controls) increasing risk of COVID-19 (OR = 1.17, 95% CI, 1.06-1.28). However, we found a significant, positive genetic correlation between BIP-SCZ and COVID-19 of 0.295 and could not confirm causal or horizontally pleiotropic effects using another method. No genetic liabilities to COVID-19 phenotypes increased the risk of (neuro)psychiatric disorders. In multivariable MR using both neuropsychiatric and a range of other phenotypes, only genetic instruments of BMI remained causally associated with COVID-19. All sensitivity analyses confirmed the results. In conclusion, while genetic liability to bipolar disorder and schizophrenia combined slightly increased COVID-19 susceptibility in one univariable analysis, other MR and multivariable analyses could only confirm genetic underpinnings of BMI to be causally implicated in COVID-19 susceptibility. Thus, using MR we found no consistent proof of genetic liabilities to (neuro)psychiatric disorders contributing to COVID-19 liability or vice versa, which is in line with at least two observational studies. Previously reported positive associations between psychiatric disorders and COVID-19 by others may have resulted from statistical models incompletely capturing BMI as a continuous covariate.

SUBMITTER: Luykx JJ

PROVIDER: S-EPMC8027711 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWhether the positive associations of blood lipids with psychiatric disorders are causal is uncertain. We conducted this two-sample Mendelian randomization (MR) analysis to comprehensively investigate associations of blood lipids with psychiatric disorders.MethodsUnivariable and multivariable models were established for MR analyses. Inverse variance-weighted (IVW) MR was employed as the main approach; weighted median and MR-Egger were used as sensitivity analysis methods. The possibility of violating MR assumptions was evaluated utilizing several sensitivity analyses, including heterogeneity statistics, horizontal pleiotropy statistics, single SNP analysis, leave-one-out analysis and MR-PRESSO analysis. As instrumental variables, we screened 362 independent single-nucleotide polymorphisms (SNP) related to blood lipids from a recent genome-wide association study involving 76,627 individuals of European ancestry, with a genome-wide significance level of p < 5 × 10- 8. Summary-level information for the six psychiatric disorders was extracted from Psychiatric Genomics Consortium and Alzheimer Disease Genetics Consortium.ResultsWe observed eight significant associations in univariable MR analysis, four of which were corroborated by multivariable MR (MVMR) analysis modified for the other three lipid traits: high-density lipoprotein cholesterol (HDL-C) level with the risk of PTSD (OR = 0.91, 95% CI = 0.85-0.97, p = 0.002) and AD (OR = 0.79, 95% CI = 0.71-0.88, p < 0.001) and triglycerides (TG) level with the risk of MDD (OR = 1.02, 95% CI = 1.003-1.03, p = 0.01) and panic disorder (OR = 0.83, 95% CI = 0.74-0.92, p < 0.001). In addition, four associations were not significant in MVMR analysis after adjustment for three lipid traits: total cholesterol (TC) level with the risk of PTSD, low-density lipoprotein cholesterol (LDL-C) level with the risk of MDD and AD and TG level with the risk of AD.ConclusionsOur results show that blood lipids and psychiatric disorders may be related in a causal manner. This shows that abnormal blood lipid levels may act as reliable biomarker of psychiatric disorders and as suitable targets for their prevention and treatment.

Project description:Background: Observational studies have identified impaired lung function accessed by forced expiratory volume in one second (FEV1), forced vital capacity (FVC) or the ratio of FEV1 over FVC (FEV1/FVC) as an independent risk factor for atrial fibrillation (AF). However, the result may be affected by confounders or reverse causality. Methods: We performed univariable MR (uvMR), multivariable MR (mvMR) and bidirectional two-sample MR to jointly estimate the causality of lung function with AF. Apart from the inverse variance weighted (IVW) approach as the main MR analysis, three complementary sensitive analyses approaches including MR-Egger regression, weighted median (WM) MR and Pleiotropy Residual Sum and Outlier (MR-PRESSO) in uvMR as well as mvMR-Egger and mvMR-PRESSO in mvMR were applied to control for pleiotropy. Linkage disequilibrium score (LDSC) regression was applied to estimate genetic correlation between lung function and AF. Results: All forward and reverse uvMR analyses consistently suggested absent causal relations between lung function and AF risk [forward IVW: odds ratio (OR)FEV1 = 1.031, 95% CI = 0.909-1.169, P = 0.630; ORFVC = 1.002, 95% CI = 0.834-1.204, P = 0.982; ORFEV1/FVC = 1.076, 95% CI = 0.966-1.199, P = 0.182; reverse IVW: ORFEV1 = 0.986, 95% CI = 0.966-1.007, P = 0.187; ORFVC = 0.985, 95% CI = 0.965-1.006, P = 0.158; ORFEV1/FVC = 0.994, 95% CI = 0.973-1.015, P = 0.545]. The forward MR-Egger showed that each standard deviation (SD) increase in FEV1/FVC was related to a higher AF risk (OR = 1.502, 95% CI = 1.178-1.915, P = 0.006) without heterogeneity (Q_pval = 0.064), but pleiotropy effect exist (intercept = -0.017, P = 0.012). However, this significant effect disappeared after adjustment of FEV1 and FVC (OR = 1.523, 95% CI = 0.445-5.217, P = 0.503) in mvMR. No evidence was found for independent causal effects of FEV1 and FVC on AF in mvMR analysis by using mvIVW method (ORFEV1 = 0.501, 95% CI = 0.056-4.457, P = 0.496; ORFVC = 1.969, 95% CI = 0.288-13.474, P = 0.490). Notably, the association between lung function and AF were replicated using the FinnGen cohort data. Conclusions: Our findings reported no coheritability between lung function and AF, and failed to find substantial causal relation between decreased lung function and risk of AF. However, lung function and AF were both associated with inflammation, which may be potential pathway, warranting further study.

Project description:BackgroundSubstantial evidence suggests an association between psychiatric disorders and chronic heart failure. However, further investigation is needed to confirm the causal relationship between these psychiatric disorders and chronic heart failure. To address this, we evaluated the potential effects of five psychiatric disorders on chronic heart failure using two-sample Mendelian Randomization (MR).MethodsWe selected single nucleotide polymorphisms (SNPs) associated with chronic heart failure and five psychiatric disorders (Attention-Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Major Depression, Bipolar Disorder and Schizophrenia (SCZ)). Univariable (UVMR) and multivariable two-sample Mendelian Randomization (MVMR) were employed to assess causality between these conditions. Ever smoked and alcohol consumption were controlled for mediating effects in the multivariable MR. The inverse variance weighting (IVW) and Wald ratio estimator methods served as the primary analytical methods for estimating potential causal effects. MR-Egger and weighted median analyses were also conducted to validate the results. Sensitivity analyses included the funnel plot, leave-one-out, and MR-Egger intercept tests. Additionally, potential mediators were investigated through risk factor analyses.ResultsGenetically predicted heart failure was significantly associated with ADHD (odds ratio (OR), 1.12; 95% CI, 1.04-1.20; p = 0.001), ASD (OR, 1.29; 95% CI, 1.07-1.56; p = 0.008), bipolar disorder (OR, 0.89; 95% CI, 0.83-0.96; p = 0.001), major depression (OR, 1.15; 95% CI, 1.03-1.29; p = 0.015), SCZ (OR, 1.04; 95% CI, 1.00-1.07; p = 0.024). Several risk factors for heart failure are implicated in the above cause-and-effect relationship, including ever smoked and alcohol consumption.ConclusionOur study demonstrated ADHD, ASD, SCZ and major depression may have a causal relationship with an increased risk of heart failure. In contrast, bipolar disorder was associated with a reduced risk of heart failure, which could potentially be mediated by ever smoked and alcohol consumption. Therefore, prevention strategies for heart failure should also incorporate mental health considerations, and vice versa.

Project description:BackgroundObservational studies have suggested the potential associations between atopic dermatitis (AD) and psychiatric disorders. However, the causal relationship between them remains uncertain. This study aimed to evaluate the potential bidirectional causal relationship between AD and psychiatric disorders, including autism spectrum disorder (ASD), major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), anorexia nervosa (AN), Tourette syndrome (TS), schizophrenia, and anxiety.MethodsBidirectional two-sample Mendelian randomization (MR) was employed to elucidate the causality between AD and psychiatric disorders, using summary statistics from the most comprehensive genome-wide association studies conducted on AD (Ncases = 60,653, Ncontrols = 804,329). Psychiatric disorders were derived from the Psychiatric Genomics Consortium and were independent of AD data sources. The MR analysis entailed the implementation of multiple methods, including the inverse variance weighted method, MR-Egger regression method, weighted median method, simple mode method, and weighted mode method.ResultsBidirectional two-sample MR analysis uncovered significant causal associations between AD and severe psychiatric disorders. Specifically, liability to AD was associated with increased risk of ADHD (OR = 1.116; 95% CI: [1.009, 1.234]; P = 0.033) and ASD (OR = 1.131; 95% CI: [1.023, 1.251]; P = 0.016). Additionally, evidence suggested that liability to ADHD (OR = 1.112; 95% CI: [1.094, 1.130]; P = 9.20e-40), liability to AN (OR = 1.1; 95% CI: [1.068, 1.134]; P = 4.45e-10) and liability to BD (OR = 1.067; 95% CI: [1.009, 1.128]; P = 0.023) were associated with an increased risk of AD. Only the causal association between AD and ASD was independent of the reverse effect bias. These causal associations were robust and not affected by biases of heterogeneity and horizontal pleiotropy.ConclusionsOur study emphasizes the significant causal association between AD and an increased risk of ASD, and also identifying BD and AN as risk factors for AD.

Project description:ObjectivesRecent studies suggested obesity to be a possible risk factor for COVID-19 disease in the wake of the coronavirus (SARS-CoV-2) infection. However, the causality and especially the role of body fat distribution in this context is still unclear. Thus, using a univariable as well as multivariable two-sample Mendelian randomization (MR) approach, we investigated for the first time the causal impact of body composition on the susceptibility and severity of COVID-19.MethodsAs indicators of overall and abdominal obesity we considered the measures body mass index (BMI), waist circumference (WC), and trunk fat ratio (TFR). Summary statistics of genome-wide association studies (GWASs) for these body composition measures were drawn from the GIANT consortium and UK Biobank, while for susceptibility and severity due to COVID-19 disease data from the COVID-19 Host Genetics Initiative was used. For the COVID-19 cohort neither age nor gender was available. Total and direct causal effect estimates were calculated using Single Nucleotide Polymorphisms (SNPs), sensitivity analyses were done applying several robust MR techniques and mediation effects of type 2 diabetes (T2D) and cardiovascular diseases (CVD) were investigated within multivariable MR analyses.ResultsGenetically predicted BMI was strongly associated with both, susceptibility (OR = 1.31 per 1 SD increase; 95% CI: 1.15-1.50; P-value = 7.3·10-5) and hospitalization (OR = 1.62 per 1 SD increase; 95% CI: 1.33-1.99; P-value = 2.8·10-6) even after adjustment for genetically predicted visceral obesity traits. These associations were neither mediated substantially by T2D nor by CVD. Finally, total but not direct effects of visceral body fat on outcomes could be detected.ConclusionsThis study provides strong evidence for a causal impact of overall obesity on the susceptibility and severity of COVID-19 disease. The impact of abdominal obesity was weaker and disappeared after adjustment for BMI. Therefore, obese people should be regarded as a high-risk group. Future research is necessary to investigate the underlying mechanisms linking obesity with COVID-19.

Project description:BackgroundPhosphodiesterases (PDEs) have been associated with psychiatric disorders in observational studies; however, the causality of associations remains unestablished.MethodsSpecifically, cyclic nucleotide PDEs were collected from genome-wide association studies (GWASs), including PDEs obtained by hydrolyzing both cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) (PDE1A, PDE2A, and PDE3A), specific to cGMP (PDE5A, PDE6D, and PDE9A) and cAMP (PDE4D and PDE7A). We performed a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the relationship between PDEs and nine psychiatric disorders. The inverse-variance-weighted (IVW) method, MR-Egger, and weighted median were used to estimate causal effects. The Cochran's Q test, MR-Egger intercept test, MR Steiger test, leave-one-out analyses, funnel plot, and MR pleiotropy residual sum and outlier (MR-PRESSO) were used for sensitivity analyses.ResultsThe PDEs specific to cAMP were associated with higher-odds psychiatric disorders. For example, PDE4D and schizophrenia (SCZ) (odds ratios (OR) = 1.0531, PIVW = 0.0414), as well as major depressive disorder (MDD) (OR = 1.0329, PIVW = 0.0011). Similarly, PDE7A was associated with higher odds of attention-deficit/hyperactivity disorder (ADHD) (OR = 1.0861, PIVW = 0.0038). Exploring specific PDE subtypes and increase intracellular cAMP levels can inform the development of targeted interventions. We also observed PDEs (which hydrolyzes both cAMP and cGMP) was associated with psychiatric disorders [OR of PDE1A was 1.0836 for autism spectrum disorder; OR of PDE2A was 0.8968 for Tourette syndrome (TS) and 0.9449 for SCZ; and OR of PDE3A was 0.9796 for MDD; P < 0.05]. Furthermore, psychiatric disorders also had some causal effects on PDEs [obsessive-compulsive disorder on increased PDE6D and decreased PDE2A and PDE4D; anorexia nervosa on decreased PDE9A]. The results of MR were found to be robust using multiple sensitivity analysis.ConclusionsIn this study, potential causal relationships between plasma PDE proteins and psychiatric disorders were established. Exploring other PDE subtypes not included in this study could provide a more comprehensive understanding of the role of PDEs in psychiatric disorders. The development of specific medications targeting PDE subtypes may be a promising therapeutic approach for treating psychiatric disorders.

Dataset Information

Are psychiatric disorders risk factors for COVID-19 susceptibility and severity? a two-sample, bidirectional, univariable, and multivariable Mendelian Randomization study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets