Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome

2022 ◽  
Author(s):  
Xiaomin Liu ◽  
Xin Tong ◽  
Yuanqiang Zou ◽  
Xiaoqian Lin ◽  
Hui Zhao ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Mendelian Randomization ◽  
Blood Metabolites ◽  
Causal Relationships

scholarly journals Using a Two-Sample Mendelian Randomization Method in Assessing the Causal Relationships Between Human Blood Metabolites and Heart Failure

2021 ◽  
Vol 8 ◽  
Author(s):  
Zixian Wang ◽  
Shiyu Chen ◽  
Qian Zhu ◽  
Yonglin Wu ◽  
Guifeng Xu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Human Blood ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Blood Metabolites ◽  
Causal Relationships

Background: Heart failure (HF) is the main cause of morbidity and mortality worldwide, and metabolic dysfunction is an important factor related to HF pathogenesis and development. However, the causal effect of blood metabolites on HF remains unclear.Objectives: Our chief aim is to investigate the causal relationships between human blood metabolites and HF risk.Methods: We used an unbiased two-sample Mendelian randomization (MR) approach to assess the causal relationships between 486 human blood metabolites and HF risk. Exposure information was obtained from Sample 1, which is the largest metabolome-based genome-wide association study (mGWAS) data containing 7,824 Europeans. Outcome information was obtained from Sample 2, which is based on the results of a large-scale GWAS meta-analysis of HF and contains 47,309 cases and 930,014 controls of Europeans. The inverse variance weighted (IVW) model was used as the primary two-sample MR analysis method and followed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis.Results: We observed that 11 known metabolites were potentially related to the risk of HF after using the IVW method (P < 0.05). After adding another four MR models and performing sensitivity analyses, we found a 1-SD increase in the xenobiotics 4-vinylphenol sulfate was associated with ~22% higher risk of HF (OR [95%CI], 1.22 [1.07–1.38]).Conclusions: We revealed that the 4-vinylphenol sulfate may nominally increase the risk of HF by 22% after using a two-sample MR approach. Our findings may provide novel insights into the pathogenesis underlying HF and novel strategies for HF prevention.

scholarly journals Inter-determination of blood metabolite levels and gut microbiome supported by Mendelian randomization

2020 ◽  
Author(s):  
Xiaomin Liu ◽  
Xin Tong ◽  
Yuanqiang Zou ◽  
Xiaoqian Lin ◽  
Hui Zhao ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Genetic Information ◽  
Mendelian Randomization ◽  
The Other ◽  
Whole Body ◽  
Blood Metabolites ◽  
Whole Genome ◽  
Sequencing Data ◽  
Pectin Degradation

AbstractThe gut microbiome has been implicated in a variety of physiological states. Controversy over causality, however, has always haunted microbiome studies. Here, we utilized the bidirectional Mendelian randomization (MR) approach to address questions that are not yet mature for more costly randomized interventions. From a total of 3,432 Chinese individuals with shotgun sequencing data for whole genome and whole metagenome, as well as anthropometric and blood metabolic traits, we identified 58 causal relationships between the gut microbiome and blood metabolites, and replicated 43 out of the 58. Gut microbiome could determine features in the blood. For example, increased fecal relative abundances of Oscillibacter and Alistipes were causally linked to decreased triglyceride concentration, and fecal microbial module pectin degradation might increase serum uric acid. On the other hand, blood features may determine gut microbial features, e.g. glutamic acid appeared to decrease Oxalobacter, and a few members of Proteobacteria were unidirectionally influenced by cardiometabolically important metabolites such as 5-methyltetrahydrofolic acid, alanine, as well as selenium. This study illustrates the value of human genetic information to help prioritize gut microbial features for mechanistic and clinical studies. The results are consistent with whole-body cross-talks of the microbiome and the circulating molecules.

Assessing the Causal Relationships between Insulin Resistance and Hyperuricemia and Gout Using Bidirectional Mendelian Randomization

2021 ◽  
Author(s):  
Natalie McCormick ◽  
Mark J. O’Connor ◽  
Chio Yokose ◽  
Tony R. Merriman ◽  
David B. Mount ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Mendelian Randomization ◽  
Causal Relationships

scholarly journals Disentangling independent and mediated causal relationships between blood metabolites, cognitive factors, and Alzheimer’s Disease

2021 ◽  
Author(s):  
Jodie Lord ◽  
Rebecca Green ◽  
Shing Wan Choi ◽  
Christopher Hübel ◽  
Dag Aarsland ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Factors ◽  
Blood Metabolites ◽  
Causal Relationships

scholarly journals Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

2019 ◽  
Author(s):  
Simon Haworth ◽  
Pik Fang Kho ◽  
Pernilla Lif Holgerson ◽  
Liang-Dar Hwang ◽  
Nicholas J. Timpson ◽  
...  
Keyword(s):  
Dental Caries ◽  
Causal Inference ◽  
Mendelian Randomization ◽  
Association Studies ◽  
List Type ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Link Type ◽  
Alternative Method ◽  
Causal Variable

AbstractBackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments.MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference.ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health.ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome.Key messagesThe latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met.We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

scholarly journals Mendelian randomization reveals potential causal candidates for COVID-19 in 123 blood metabolites

2021 ◽  
Author(s):  
Shizheng Qiu ◽  
Donghua Wang ◽  
Yu Zhang ◽  
Yang Hu
Keyword(s):  
Mendelian Randomization ◽  
Blood Metabolites

scholarly journals Causal Association Between Heart Failure and Alzheimer’s Disease: A Two-Sample Bidirectional Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenglin Duan ◽  
Jingjing Shi ◽  
Guozhen Yuan ◽  
Xintian Shou ◽  
Ting Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sensitivity Analysis ◽  
Causal Relationship ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Relationships ◽  
Simple Mode

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer’s disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations.Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK’s Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results.Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis.Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis.

Mendelian Randomization Analysis as a Tool to Gain Insights into Causes of Diseases: A Primer

2021 ◽  
pp. ASN.2020121760
Author(s):  
Adrienne Tin ◽  
Anna Köttgen
Keyword(s):  
Risk Factors ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Mendelian Randomization ◽  
Mr Studies ◽  
Causal Relationships ◽  
Omics Technologies ◽  
Basic Concepts ◽  
Traditional Risk Factors ◽  
Randomization Analysis

Many Mendelian randomization (MR) studies have recently been published, with inferences on the causal relationships between risk factors and diseases that have potential implications for clinical research. In nephrology, MR methods have been applied to investigate potential causal relationships of traditional risk factors, lifestyle factors, and biomarkers from omics technologies with kidney function or chronic kidney disease. This primer summarizes the basic concepts of MR studies, highlighting methods employed in recent applications, and emphasizes key elements in conducting and reporting of MR studies that are important for interpreting the results.

scholarly journals The bidirectional causal relationships of insomnia with five major psychiatric disorders: A Mendelian randomization study

2019 ◽  
Vol 60 ◽  
pp. 79-85 ◽  
Author(s):  
Xue Gao ◽  
Ling-Xian Meng ◽  
Kai-Li Ma ◽  
Jie Liang ◽  
Hui Wang ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Confidence Interval ◽  
Psychiatric Disorders ◽  
Mendelian Randomization ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Outcome Variable ◽  
Genome Wide Association Studies ◽  
Causal Relationships

AbstractBackground:Several observational studies have investigated the association of insomnia with psychiatric disorders. Such studies yielded mixed results, and whether these associations are causal remains unclear. Thus, we aimed to identify the causal relationships between insomnia and five major psychiatric disorders.Methods:The analysis was implemented with six genome-wide association studies; one for insomnia and five for psychiatric disorders (attention-deficit/hyperactivity disorder, autism spectrum disorder, major depressive disorder, schizophrenia, and bipolar disorder). A heterogeneity in dependent instrument (HEIDI) approach was used to remove the pleiotropic instruments, Mendelian randomization (MR)-Egger regression was adopted to test the validity of the screened instruments, and bidirectional generalized summary data-based MR was performed to estimate the causal relationships between insomnia and these major psychiatric disorders.Results:We observed significant causal effects of insomnia on the risk of autism spectrum disorder and bipolar disorder, with odds ratios of 1.739 (95% confidence interval: 1.217–2.486, p = 0.002) and 1.786 (95% confidence interval: 1.396–2.285, p = 4.02 × 10−6), respectively. There was no convincing evidence of reverse causality for insomnia with these two disorders (p = 0.945 and 0.546, respectively). When insomnia was considered as either the exposure or outcome variable, causal estimates for the remaining three psychiatric disorders were not significant.Conclusions:Our results suggest a causal role of insomnia in autism spectrum disorder and bipolar disorder. Future disease models should include insomnia as a factor for these two disorders to develop effective interventions. More detailed mechanism studies may also be inspired by this causal inference.

scholarly journals Commentary: Orienting causal relationships between two phenotypes using bidirectional Mendelian randomization

2019 ◽  
Vol 48 (3) ◽  
pp. 907-911 ◽  
Author(s):  
Rebecca C Richmond ◽  
George Davey Smith
Keyword(s):  
Mendelian Randomization ◽  
Causal Relationships
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