Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Wang Z

PROVIDER: S-EPMC8476837 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description: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.

Project description:BackgroundHeart Failure (HF) is a widespread condition that affects millions of people, and it is caused by issues with the heart and blood vessels. Even though we know hypertension, coronary artery disease, obesity, diabetes, and genetics can increase the risk of HF and Chronic Kidney Disease (CKD), the exact cause of these conditions remains a mystery. To bridge this gap, we adopted Mendelian Randomization (MR), which relies on genetic variants as proxies.MethodsWe used data from European populations for our Bidirectional Two-Sample MR Study, which included 930,014 controls and 47,309 cases of HF from the HERMES consortium, as well as 736,396 controls and 51,256 cases of CKD. We also employed several MR variations, including MR-Egger, Inverse Variance Weighted (IVW), and Weighted Median Estimator (WME), to guarantee the results were accurate and comprehensive.).ResultsIn this study, the MR analysis found that individuals with a genetic predisposition for HF have an elevated risk of CKD. Our study revealed a significant association between the genetic prediction of HF and the risk of CKD, as evidenced by the IVW method [with an odds ratio (OR) of 1.12 (95% CI, 1.03-1.21), p = 0.009] and the WME [with an OR of 1.14 (95% CI, 1.03-1.26), p = 0.008]. This causal relationship remained robust even after conducting MR analysis while adjusting for the effects of diabetes and hypertension, yielding ORs of 1.13 (IVW:95% CI, 1.03-1.23), 1.12 (MR-Egger: 95% CI, 0.85-1.48), and 1.15 (WME:95% CI, 1.04-1.27) (p = 0.008). However, in the reverse analysis aiming to explore CKD and renal function as exposures and HF as the outcome, we did not observe a statistically significant causal link between CKD and HF.ConclusionOur study demonstrates the significance of HF in CKD progression, thus having meaningful implications for treatment and the potential for discovering new therapies. To better understand the relationship between HF and CKD, we need to conduct research in a variety of populations.

Project description:Background and purposeWe investigated the causal relationships between the gut microbiota (GM), stroke, and potential metabolite mediators using Mendelian randomization (MR).MethodsWe leveraged the summary statistics of GM (n=18,340 in the MiBioGen consortium), blood metabolites (n=115,078 in the UK Biobank), and stroke (cases n=60,176 and controls n=1,310,725 in the Global Biobank Meta-Analysis Initiative) from the largest genome-wide association studies to date. We performed bidirectional MR analyses to explore the causal relationships between the GM and stroke, and two mediation analyses, two-step MR and multivariable MR, to discover potential mediating metabolites.ResultsTen taxa were causally associated with stroke, and stroke led to changes in 27 taxa. In the two-step MR, Bifidobacteriales order, Bifidobacteriaceae family, Desulfovibrio genus, apolipoprotein A1 (ApoA1), phospholipids in high-density lipoprotein (HDL_PL), and the ratio of apolipoprotein B to ApoA1 (ApoB/ApoA1) were causally associated with stroke (all P<0.044). The causal associations between Bifidobacteriales order, Bifidobacteriaceae family and stroke were validated using the weighted median method in an independent cohort. The three GM taxa were all positively associated with ApoA1 and HDL_PL, whereas Desulfovibrio genus was negatively associated with ApoB/ApoA1 (all P<0.010). Additionally, the causal associations between the three GM taxa and ApoA1 remained significant after correcting for the false discovery rate (all q-values <0.027). Multivariable MR showed that the associations between Bifidobacteriales order, Bifidobacteriaceae family and stroke were mediated by ApoA1 and HDL_PL, each accounting for 6.5% (P=0.028) and 4.6% (P=0.033); the association between Desulfovibrio genus and stroke was mediated by ApoA1, HDL_PL, and ApoB/ApoA1, with mediated proportions of 7.6% (P=0.019), 4.2% (P=0.035), and 9.1% (P=0.013), respectively.ConclusionThe current MR study provides evidence supporting the causal relationships between several specific GM taxa and stroke and potential mediating metabolites.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets