Serum metabolites and hypercholesterolemia: insights from a two-sample Mendelian randomization study
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ABSTRACT:
SUBMITTER: Wang W
PROVIDER: S-EPMC11337230 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is an irreversible lung disease with unclear pathological mechanisms. In this study, we utilized bidirectional Mendelian randomization (MR) to analyze the relationship between serum metabolites and IPF, and conducted metabolic pathway analysis.AimTo determine the causal relationship between serum metabolites and IPF using MR analysis.MethodsA two-sample MR analysis was conducted to evaluate the causal relationship between 824 serum metabolites and IPF. The inverse variance weighted (IVW) method was used to estimate the causal relationship between exposure and results. Sensitivity analysis was conducted using MR Egger, weighted median, and maximum likelihood to eliminate pleiotropy. Additionally, metabolic pathway analysis was conducted to identify potential metabolic pathways.ResultsWe identified 12 serum metabolites (6 risks and 6 protective) associated with IPF from 824 metabolites. Among them, 11 were known and 1 was unknown. 1-Eicosatrienoylglycophorophospholine and 1-myristoylglycophorophospholine were bidirectional MR positive factors, with 1-myristoylglycophorophospholine being a risk factor (1.0013, 1.0097) and 1-eicosatrienoylglycophorine being a protective factor (0.9914, 0.9990). The four lipids (1-linoleoylglycerophoethanolamine*, total cholesterol in large high-density lipoprotein [HDL], cholesterol esters in very large HDL, and phospholipids in very large HDL) and one NA metabolite (degree of unsaturation) were included in the known hazardous metabolites. The known protective metabolites included three types of lipids (carnitine, 1-linoleoylglycerophoethanolamine*, and 1-eicosatrienoylglycerophophophorine), one amino acid (hypoxanthine), and two unknown metabolites (the ratio of omega-6 fatty acids to omega-3 fatty acids, and the ratio of photoshopids to total lipids ratio in chylomicrons and extremely large very low-density lipoprotein [VLDL]). Moreover, sn-Glycerol 3-phosphate and 1-Acyl-sn-glycero-3-phosphocline were found to be involved in the pathogenesis of IPF through metabolic pathways such as Glycerolide metabolism and Glycerophospholipid metabolism.ConclusionOur study identified 6 causal risks and 6 protective serum metabolites associated with IPF. Additionally, 2 metabolites were found to be involved in the pathogenesis of IPF through metabolic pathways, providing a new perspective for further understanding the metabolic pathway and the pathogenesis of IPF.
| S-EPMC11369467 | biostudies-literature
Project description:Background and Aim: Previous observational studies indicated that the serum albumin levels and circulating metabolites are associated with a high risk of venous thromboembolism (VTE). However, whether these observations reflect causality remained unclear. Hence, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal associations of serum albumin and circulating metabolites with the risk of VTE. Methods and Results: Summary statistics of genetic instruments proxying serum albumin, total protein, and common circulating metabolites were extracted from genome-wide association studies in the European ancestry. Summary-level results of age- and sex-adjusted estimates for associations of the instruments with VTE were derived from the FinnGen consortium. We used the inverse-variance weighted (IVW) method as the primary analysis for univariable MR. Sensitivity analyses were performed to detect horizontal pleiotropy and outliers. Genetically proxied high-serum albumin and total protein levels were suggestive protective factor of VTE, with odds ratio (OR) = 0.69 (CI 0.54-0.89, p = 4.7 × 10-3) and 0.76 (CI 0.61-0.95, p = 0.015), respectively. Genetically proxied low-monounsaturated fatty acids and the ratio of monounsaturated fatty acid to total fatty acid are causally associated with increased risk of VTE, with ORs = 0.89 (CI 0.80-0.99, p = 0.031) and 0.85 (CI 0.78-0.94, p = 9.92 × 10-4), respectively. There is no indication of causal associations between other circulating metabolites and the risk of VTE. Conclusions: Genetically liability to low-serum albumin and total protein levels, low proxied monounsaturated fatty acids (MUFAs) and the ratio of MUFAs to total fatty acids are associated with the higher risk of VTE.
| S-EPMC8631825 | biostudies-literature
Project description:BackgroundObservational studies and clinical trials suggest associations between immune cells, inflammatory factors, serum metabolites, and hepatic cancer. However, the causal relationships between these factors and hepatic cancer remain to be established.ObjectiveTo explore the causal relationships between immune cells, inflammatory factors, serum metabolites, and hepatic cancer.MethodsThis study employed comprehensive two-sample Mendelian randomization (MR) utilizing publicly available genetic data (GWAS) to analyze causal relationships between 731 immune cell traits, 91 inflammatory factors, 1400 serum metabolites, and hepatic cancer. The primary analysis used inverse variance-weighted (IVW) MR, with additional sensitivity tests to assess the validity of causal relationships.ResultsAfter correction for heterogeneity and horizontal pleiotropy, in exploring the causal relationships between immune cell groups and hepatic cancer, we found that Terminally Differentiated CD4-CD8- T cell %T cell was negatively associated with hepatic cancer, serving as a protective factor, while Effector Memory CD4-CD8- T cell %CD4-CD8- T cell was positively associated with hepatic cancer, acting as a risk factor. In investigating the causal relationships between inflammatory factors and hepatic cancer, C-C motif chemokine 19 levels were positively associated with hepatic cancer, representing a risk factor, while Interleukin-10 levels were negatively associated with hepatic cancer, acting as a protective factor. Regarding the causal relationships between serum metabolites and hepatic cancer, (N(1) + N(8))-acetylspermidine levels were negatively associated with hepatic cancer, serving as a protective factor, while 1-(1-enyl-palmitoyl)-GPC (p-16:0) levels were positively associated with hepatic cancer, acting as a risk factor.ConclusionOur MR analysis indicates causal relationships between immune cells, inflammatory factors, serum metabolites, and hepatic cancer. However, further validation is needed to assess the potential of these immune cells, inflammatory factors, and serum metabolites as preventive or therapeutic targets for hepatic cancer.
| S-EPMC11336366 | biostudies-literature
Project description:BackgroundPathologically, metabolic disorder plays a crucial role in polycystic ovarian syndrome (PCOS). However, there is no conclusive evidence lipid metabolite levels to PCOS risk.MethodsIn this study, genome-wide association study (GWAS) genetic data for 122 lipid metabolites were used to assign instrumental variables (IVs). PCOS GWAS were derived from a large-scale meta-analysis of 10,074 PCOS cases and 103,164 controls. An inverse variance weighted (IVW) analysis was the primary methodology used for Mendelian randomization (MR). For sensitivity analyses, Cochran Q test, MR-Egger intercept, MR-PRESSO, leave-one-out analysis,and Steiger test were performed. Furthermore, we conducted replication analysis, meta-analysis, and metabolic pathway analysis. Lastly, reverse MR analysis was used to determine whether the onset of PCOS affected lipid metabolites.ResultsThis study detected the blood lipid metabolites and potential metabolic pathways that have a genetic association with PCOS onset. After IVW, sensitivity analyses, replication and meta-analysis, two pathogenic lipid metabolites of PCOS were finally identified: Hexadecanedioate (OR=1.85,95%CI=1.27-2.70, P=0.001) and Dihomo-linolenate (OR=2.45,95%CI=1.30-4.59, P=0.005). Besides, It was found that PCOS may be mediated by unsaturated fatty acid biosynthesis and primary bile acid biosynthesis metabolic pathways. Reverse MR analysis showed the causal association between PCOS and 2-tetradecenoyl carnitine at the genetic level (OR=1.025, 95% CI=1.003-1.048, P=0.026).ConclusionGenetic evidence suggests a causal relationship between hexadecanedioate and dihomo-linolenate and the risk of PCOS. These compounds could potentially serve as metabolic biomarkers for screening PCOS and selecting drug targets. The identification of these metabolic pathways is valuable in guiding the exploration of the pathological mechanisms of PCOS, although further studies are necessary for confirmation.
| S-EPMC11263216 | biostudies-literature
Project description: Not available
| S-EPMC10681133 | biostudies-literature
Project description:Background: Lipid metabolism disorders were observationally associated with chalazion, but the causality of the related circulating metabolites on chalazion remained unknown. Here, we investigated the potential causal relationship between circulating metabolites and chalazion using two-sample Mendelian randomization (MR) analysis. Methods: For the primary analysis, 249 metabolic biomarkers were obtained from the UK Biobank, and 123 circulating metabolites were obtained from the publication by Kuttunen et al. for the secondary analysis. Chalazion summary data were obtained from the FinnGen database. Inverse variance weighted (IVW) is the main MR analysis method, and the MR assumptions were evaluated in sensitivity and colocalization analyses. Results: Two MR analyses results showed that the common metabolite, alanine, exhibited a genetic protective effect against chalazion (primary analysis: odds ratio [OR] = 0.680; 95% confidence interval [CI], 0.507-0.912; p = 0.010; secondary analysis: OR = 0.578; 95% CI, 0.439-0.759; p = 0.00008). The robustness of the findings was supported by heterogeneity and horizontal pleiotropy analysis. Two colocalization analyses showed that alanine did not share a region of genetic variation with chalazion (primary analysis: PPH4 = 1.95%; secondary analysis: PPH4 = 25.3%). Moreover, previous studies have suggested that an increase in the degree of unsaturation is associated with an elevated risk of chalazion (OR = 1.216; 95% CI, 1.055-1.401; p = 0.007), with omega-3 fatty acids (OR = 1.204; 95% CI, 1.054-1.377; p = 0.006) appearing to be the major contributing factor, as opposed to omega-6 fatty acids (OR = 0.850; 95% CI, 0.735-0.982; p = 0.027). Conclusion: This study suggests that alanine and several unsaturated fatty acids are candidate molecules for mechanistic exploration and drug target selection in chalazion.
| S-EPMC10991826 | biostudies-literature
Project description:BackgroundAlthough anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms.MethodsA two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features (P < 1.03 × 10-4). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study.ResultsIn MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (ORfixed-effect IVW = 1.04; 95% CI 1.021-1.06; Pfixed-effect IVW = 4.3 × 10-5) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a "leave-one-out" analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = - 0.0013, SE = 0.0006, P = 0.06). No heterogeneity was detected by Cochran's Q test (MR-Egger. Q = 7.68, P = 0.742; IVW. Q = 12.12, P = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < 0.001). In addition, two significant pathways, the "primary bile acid biosynthesis" pathway (P = 0.008) and the "valine, leucine, and isoleucine biosynthesis" pathway (P = 0.03), were identified through metabolic pathway analysis.ConclusionThis study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.
| S-EPMC9583573 | biostudies-literature
Project description:BackgroundWhile several traditional observational studies have suggested associations between gut microbiota and asthma, these studies are limited by factors such as participant selection bias, confounders, and reverse causality. Therefore, the causal relationship between gut microbiota and asthma remains uncertain.MethodsWe performed two-sample bi-directional Mendelian randomization (MR) analysis to investigate the potential causal relationships between gut microbiota and asthma as well as its phenotypes. We also conducted MR analysis to evaluate the causal effect of gut metabolites on asthma. Genetic variants for gut microbiota were obtained from the MiBioGen consortium, GWAS summary statistics for metabolites from the TwinsUK study and KORA study, and GWAS summary statistics for asthma from the FinnGen consortium. The causal associations between gut microbiota, gut metabolites and asthma were examined using inverse variance weighted, maximum likelihood, MR-Egger, weighted median, and weighted model and further validated by MR-Egger intercept test, Cochran's Q test, and "leave-one-out" sensitivity analysis.ResultsWe identified nine gut microbes whose genetically predicted relative abundance causally impacted asthma risk. After FDR correction, significant causal relationships were observed for two of these microbes, namely the class Bacilli (OR = 0.84, 95%CI = 0.76-0.94, p = 1.98 × 10-3) and the order Lactobacillales (OR = 0.83, 95%CI = 0.74-0.94, p = 1.92 × 10-3). Additionally, in a reverse MR analysis, we observed a causal effect of genetically predicted asthma risk on the abundance of nine gut microbes, but these associations were no longer significant after FDR correction. No significant causal effect of gut metabolites was found on asthma.ConclusionsOur study provides insights into the development mechanism of microbiota-mediated asthma, as well as into the prevention and treatment of asthma through targeting specific gut microbiota.
| S-EPMC10848467 | biostudies-literature
Project description:Growing evidence suggests a potential link between the gut microbiome and schizophrenia. However, it is unclear whether the gut microbiome is causally associated with schizophrenia. We performed two-sample bidirectional Mendelian randomization to detect bidirectional causal relationships between gut microbiome and schizophrenia. Summary genome-wide association study (GWAS) datasets of the gut microbiome from the MiBioGen consortium (n = 18,340) and schizophrenia (n = 130,644) were utilized in our study. Then a cohort of sensitive analyses was followed to validate the robustness of MR results. We identified nine taxa that exerted positive causal effects on schizophrenia (OR: 1.08-1.16) and six taxa that conferred negative causal effects on schizophrenia (OR: 0.88-0.94). On the other hand, the reverse MR analysis showed that schizophrenia may increase the abundance of nine taxa (OR: 1.03-1.08) and reduce the abundance of two taxa (OR: 0.94). Our study unveiled mutual causal relationships between gut microbiome and schizophrenia. The findings may provide evidence for the treatment potential of gut microbiomes in schizophrenia.
| S-EPMC11369294 | biostudies-literature
Project description:BackgroundChronic kidney disease (CKD) is often accompanied by alterations in the metabolic profile of the body, yet the causative role of these metabolic changes in the onset of CKD remains a subject of ongoing debate. This study investigates the causative links between metabolites and CKD by leveraging the results of genomewide association study (GWAS) from 486 blood metabolites, employing bulk two-sample Mendelian randomization (MR) analyses. Building on the metabolites that exhibit a causal relationship with CKD, we delve deeper using enrichment analysis to identify the metabolic pathways that may contribute to the development and progression of CKD.MethodsIn conducting the Mendelian randomization analysis, we treated the GWAS data for 486 metabolic traits as exposure variables while using GWAS data for estimated glomerular filtration rate based on serum creatinine (eGFRcrea), microalbuminuria, and the urinary albumin-to-creatinine ratio (UACR) sourced from the CKDGen consortium as the outcome variables. Inverse-variance weighting (IVW) analysis was used to identify metabolites with a causal relationship to outcome. Using Bonferroni correction, metabolites with more robust causal relationships are screened. Additionally, the IVW-positive results were supplemented with the weighted median, MR-Egger, weighted mode, and simple mode. Furthermore, we performed sensitivity analyses using the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out (LOO) test. Pathway enrichment analysis was conducted using two databases, KEGG and SMPDB, for eligible metabolites.ResultsDuring the batch Mendelian randomization (MR) analyses, upon completion of the inverse-variance weighted (IVW) approach, sensitivity analysis, and directional consistency checks, 78 metabolites were found to meet the criteria. The following four metabolites satisfy Bonferroni correction: mannose, N-acetylornithine, glycine, and bilirubin (Z, Z), and mannose is causally related to all outcomes of CKD. By pathway enrichment analysis, we identified eight metabolic pathways that contribute to CKD occurrence and progression.ConclusionBased on the present analysis, mannose met Bonferroni correction and had causal associations with CKD, eGFRcrea, microalbuminuria, and UACR. As a potential target for CKD diagnosis and treatment, mannose is believed to play an important role in the occurrence and development of CKD.
| S-EPMC10800733 | biostudies-literature