Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases

Role of metabolites in mediating the effect of triacylglycerol on aplastic anemia

BACKGROUND

Observational studies have found a link between lipid metabolism disorders and aplastic anemia (AA). However, due to confounding variables and reverse causation, it is difficult to conclude such a causal link. The precise mechanism and potential implications of lipid metabolism disorder in AA remain unclear, necessitating further studies in this area.

METHOD

This study aimed to examine the causal relationship between 38 different subtypes of triacylglycerols and AA using two-sample Mendelian randomization (MR). Additionally, two-step MR analyses were conducted to investigate the mediating effects of vitamin A to oleoyl-linoleoyl-glycerol (18:1-18:2) ratio.

RESULTS

MR analysis showed that triacylglycerol (53:3) levels were positively associated with the risk of AA [inverse variance weighting (IVW): odds ratio (OR) = 1.131,95% confidence interval (CI):1.029-1.243, P = 0.011; Bayesian weighted MR (BWMR): OR = 1.137,95% CI:1.031-1.254, P = 0.010]. Triacylglycerol (53:3) level showed no inverse causality with AA (IVW:P = 0.834; BWMR:P = 0.349). Mediation analyses showed that increasing the vitamin A to oleoyl-linoleoyl-glycerol (18:1-18:2) ratio can decrease the risk of AA.

CONCLUSION

This study revealed the association between vitamin A to oleoyl-linoleoyl-glycerol (18:1-18:2) ratio, triacylglycerol (53:3) levels and AA, and indicated that lowering triacylglycerol (53:3) levels can reduce the risk of AA.

Causal association between metabolites and upper gastrointestinal tumors: A Mendelian randomization study

Upper gastrointestinal (UGI) tumors, notably gastric cancer (GC) and esophageal cancer (EC), are significant global health concerns due to their high morbidity and mortality rates. However, only a limited number of metabolites have been identified as biomarkers for these cancers. To explore the association between metabolites and UGI tumors, the present study conducted a comprehensive two‑sample Mendelian randomization (MR) analysis using publicly available genetic data. In the present study, the causal relationships were examined between 1,400 metabolites and UGI cancer using methods such as inverse variance weighting and weighted medians, along with sensitivity analyses for heterogeneity and pleiotropy. Functional experiments were conducted to validate the MR results. The analysis identified 57 metabolites associated with EC and 58 with GC. Key metabolites included fructosyllysine [EC: Odds ratio (OR)=1.450, 95% confidence interval (CI)=1.087‑1.934, P=0.011; GC: OR=1.728, 95% CI=1.202‑2.483, P=0.003], 2'‑deoxyuridine to cytidine ratio (EC: OR=1.464, 95% CI=1.111‑1.929, P=0.007; GC: OR=1.464, 95% CI=1.094‑1.957, P=0.010) and carnitine to protonylcarnitine (C3) ratio (EC: OR=0.655, 95% CI=0.499‑0.861, P=0.002; GC: OR=0.664, 95% CI=0.486‑0.906, P=0.010). Notably, fructosyllysine levels and the 2'‑deoxyuridine to cytidine ratio were identified as risk factors for both EC and GC, while the C3 ratio served as a protective factor. Functional experiments demonstrated that fructosyllysine and the 2'‑deoxyuridine to cytidine ratio promoted the proliferation of EC and GC cells, whereas carnitine inhibited their proliferation. In conclusion, the present findings provide insights into the causal factors and biomarkers associated with UGI tumors, which may be instrumental in guiding targeted dietary and pharmacological interventions, thereby contributing to the prevention and treatment of UGI cancer.

Dissecting causal relationships between immune cells, blood metabolites, and aortic dissection: A mediation Mendelian randomization study

Background

There exists a robust correlation between the infiltration of immune cells and the pathogenesis of aortic dissection (AD). Moreover, blood metabolites serve as immunomodulatory agents within the organism, influencing the immune system's response and potentially playing a role in the development of AD. Nevertheless, the intricate genetic causal nexus between specific immune cells, blood metabolites, and AD remains partially elucidated.

Objectives

This study aims to elucidate the causal relationships between specific immune cell types and the risk of developing AD, mediated by blood metabolites, using Mendelian Randomization (MR) methods.

Methods

We undertook a comprehensive investigation of 731 immune cell types through the analysis of published genome-wide association studies (GWAS). Our methodology hinged on the application of two-sample Mendelian randomization (MR) and mediator MR analyses, prioritizing blood metabolites as potential intermediary factors and AD as the principal outcome of interest. The primary statistical method employed was inverse variance-weighted estimation, complemented by a variety of sensitivity analyses to reinforce our conclusions. The entirety of our statistical analyses was executed on the R software platform.

Results

Our analyses elucidated that three immune cell types exhibited a positive correlation with the incidence of AD, whereas two immune cell types were inversely associated with AD risk. Significantly, our mediation Mendelian randomization (MR) findings identified Benzoate as a pivotal mediator in the influence of CD19 on IgD - CD38br cells on AD, with a mediation proportion of 5.38 %. Additionally, N-acetylproline was determined to mediate the effect of CD24 on IgD- CD38- cells on AD, accounting for a mediation proportion of 13.70 %. Furthermore, Carnitine C5:1 was found to mediate the effect of CD28 on secreting T regulatory (Treg) cells on AD, with a mediation proportion of 17.80 %.

Conclusions

These findings offer a nuanced understanding of the pathophysiological mechanisms underlying AD, thereby advancing the precision medicine paradigm in the clinical management of AD.Abbreviations: AD: aortic dissection; AA: aortic aneurysm; GWAS: genome-wide association study; MR: Mendelian randomization; TSMR: two-step Mendelian randomization; Treg: secreting T regulatory cell; VSMC: vascular smooth muscle cell; MMP: matrix metalloproteinase; ROS: reactive oxygen species; IV: instrumental variable; SNP: single-nucleotide polymorphism; IVW: inverse variance weighted; LDSC: linkage disequilibrium score regression; OR: odds ratio; CI: confidence interval; LD: linkage disequilibrium; AC: absolute cell; MFI: median fluorescence intensity; MP: morphological parameter; RC: relative cell; CLSA: Canadian Longitudinal Study of Aging; Lp(a): Lipoprotein a; OxPL: oxidised phospholipid; NMDAR: N-methyl-d-aspartate glutamate receptor; STROBE-MR: Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization.

Association of SOGPI in mediating the effect of Phosphatidylcholine on polycystic Ovary Syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women of reproductive age, marked by hormonal imbalances and disruptions in glucose and lipid metabolism. Emerging research has indicated a correlation between lipids and PCOS, yet the specific lipid profiles or associated genes identified in various studies vary, and observational data alone cannot establish causation. Therefore, our study seeks to establish a causal association between lipidome and PCOS.

METHODS

Data from genome-wide association studies, liposomes, metabolites, and PCOS-related information were collected. Four rounds of double-sample bidirectional intermediate Mendelian Randomization analyses including liposomes to disease, liposomes to metabolites, metabolites to disease, and reverse Mendelian Randomization analysis of lipids, total effect values and intermediary effect values were calculated. The proportion mediated by the intermediary effect was determined by dividing the intermediary effect value by the total effect value.

RESULTS

The analyses revealed that three liposomes and nine metabolites were causally associated with PCOS. Specifically, phosphatidylcholine and 1-Stearoyl-2-Oleoyl-Glycosylphosphatidylinositol were identified as independent risk factors for PCOS through further Mendelian Randomization analysis. The risk of developing PCOS increased by 32% for every one standard deviation increase in phosphatidylcholine and by 17% for every one standard deviation increase in 1-Stearoyl-2-Oleoyl-Glycosylphosphatidylinositol. Furthermore, the study revealed that phosphatidylcholine can influence the development of PCOS with 1-Stearoyl-2-Oleoyl-Glycosylphosphatidylinositol acting as a mediator, explaining 4.97% of the effect.

CONCLUSIONS

This study confirmed a causal relationship between phosphatidylcholine and 1-Stearoyl-2-Oleoyl-Glycosylphosphatidylinositol with PCOS, where phosphatidylcholine can influence the occurrence of PCOS with 1-Stearoyl-2-Oleoyl-Glycosylphosphatidylinositol as a mediator.

Exploring the bidirectional causal association between Sleep Apnea Syndrome and Depression: A Mendelian randomization study involving gut microbiota, serum metabolites, and inflammatory factors

OBJECTIVE

This study aimed to investigate the potential causal association between Sleep Apnea Syndrome (SAS) and Depression, focusing on the roles of gut microbiota, serum metabolites, and inflammatory factors in these conditions.

METHODS

Mendelian Randomization (MR) analysis was performed using data from genome-wide association studies to assess 211 types of gut microbiota, 1400 serum metabolites, and 91 inflammatory factors as potential contributing factors. Causal inference was conducted using the Inverse Variance Weighted (IVW) method, with additional robustness checks through Cochran's Q test, MR-Egger regression intercept test, MR-PRESSO global test, and leave-one-out analysis.

RESULTS

The MR analysis indicated a positive correlation between the risk of SAS and Depression (OR = 1.12, 95 % CI: 1.05-1.19, P < 0.001), with a reciprocal analysis showing a similar positive correlation between Depression and the risk of SAS (OR = 1.19, 95 % CI: 1.07-1.31, P = 0.001). Additionally, causal associations were identified between 15 types of gut microbiota, 36 serum metabolites, and 2 inflammatory factors with SAS, and between 11 types of gut microbiota, 23 serum metabolites, and 3 inflammatory factors with Depression (IVW, all P < 0.05). The robustness of these findings was confirmed through the MR-Egger regression intercept test and MR-PRESSO global test.

CONCLUSION

This study provides epidemiological evidence of a bidirectional causal association between SAS and Depression, emphasizing the potential roles of gut microbiota, serum metabolites, and inflammatory factors in the pathogenesis of these disorders. These findings may inform the development of new therapeutic strategies.

Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks

The exploration of the interactions between diseases and metabolites holds significant implications for the diagnosis and treatment of diseases. However, traditional experimental methods are time-consuming and costly, and current computational methods often overlook the influence of other biological entities on both. In light of these limitations, we proposed a novel deep learning model based on metapath aggregation of tripartite heterogeneous networks (MAHN) to explore disease-related metabolites. Specifically, we introduced microbes to construct a tripartite heterogeneous network and employed graph convolutional network and enhanced GraphSAGE to learn node features with metapath length 3. Additionally, we utilized node-level and semantic-level attention mechanisms, a more granular approach, to aggregate node features with metapath length 2. Finally, the reconstructed association probability is obtained by fusing features from different metapaths into the bilinear decoder. The experiments demonstrate that the proposed MAHN model achieved superior performance in five-fold cross-validation with Acc (91.85%), Pre (90.48%), Recall (93.53%), F1 (91.94%), AUC (97.39%), and AUPR (97.47%), outperforming four state-of-the-art algorithms. Case studies on two complex diseases, irritable bowel syndrome and obesity, further validate the predictive results, and the MAHN model is a trustworthy prediction tool for discovering potential metabolites. Moreover, deep learning models integrating multi-omics data represent the future mainstream direction for predicting disease-related biological entities.

Explore key genes of Crohn's disease based on glycerophospholipid metabolism: A comprehensive analysis Utilizing Mendelian Randomization, Multi-Omics integration, Machine Learning, and SHAP methodology

BACKGROUND AND AIMS

Crohn's disease (CD) is a chronic, complex inflammatory condition with increasing incidence and prevalence worldwide. However, the causes of CD remain incompletely understood. We identified CD-related metabolites, inflammatory factors, and key genes by Mendelian randomization (MR), multi-omics integration, machine learning (ML), and SHAP.

METHODS

We first performed a mediation MR analysis on 1400 serum metabolites, 91 inflammatory factors, and CD. We found that certain phospholipids are causally related to CD. In the scRNA-seq data, monocytes were categorized into high and low metabolism groups based on their glycerophospholipid metabolism scores. The differentially expressed genes of these two groups of cells were extracted, and transcription factor prediction, cell communication analysis, and GSEA analysis were performed. After further screening of differentially expressed genes (FDR<0.05, log2FC>1), least absolute shrinkage and selection operator (LASSO) regression was performed to obtain hub genes. Models for hub genes were built using the Catboost, XGboost, and NGboost methods. Further, we used the SHAP method to interpret the models and obtain the gene with the highest contribution to each model. Finally, qRT-PCR was used to verify the expression of these genes in the peripheral blood mononuclear cells (PBMC) of CD patients and healthy subjects.

RESULT

MR results showed 1-palmitoyl-2-stearoyl-gpc (16:0/18:0) levels, 1-stearoyl-2-arachidonoyl-GPI (18:0/20:4) levels, 1-arachidonoyl-gpc (20:4n6) levels, 1-palmitoyl-2-arachidonoyl-gpc (16:0/20:4n6) levels, and 1-arachidonoyl-GPE (20:4n6) levels were significantly associated with CD risk reduction (FDR<0.05), with CXCL9 acting as a mediation between these phospholipids and CD. The analysis identified 19 hub genes, with Catboost, XGboost, and NGboost achieving AUC of 0.91, 0.88, and 0.85, respectively. The SHAP methodology obtained the three genes with the highest model contribution: G0S2, S100A8, and PLAUR. The qRT-PCR results showed that the expression levels of S100A8 (p = 0.0003), G0S2 (p < 0.0001), and PLAUR (p = 0.0141) in the PBMC of CD patients were higher than healthy subjects.

CONCLUSION

MR findings suggest that certain phospholipids may lower CD risk. G0S2, S100A8, and PLAUR may be potential pathogenic genes in CD. These phospholipids and genes could serve as novel diagnostic and therapeutic targets for CD.

Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study

BACKGROUND

Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes.

METHODS

Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies.

RESULTS

After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression.

CONCLUSIONS

The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

The genetically predicted causal associations between circulating 3-hydroxybutyrate levels and malignant neoplasms: A pan-cancer Mendelian randomization study

OBJECTIVE

The ketogenic diet or exogenous supplementation with 3-hydroxybutyrate (3HB) is progressively gaining recognition as a valuable therapeutic or health intervention strategy. However, the effects of 3HB on cancers have been inconsistent in previous studies. This study aimed to comprehensively investigate the causal effects of circulating 3HB levels on 120 cancer phenotypes, and explore the 3HB mediation effect between liver fat accumulation and cancers.

METHODS

Univariate Mendelian randomization (UVMR) was used in this study to investigate the causal impact of circulating 3HB levels on cancers. We conducted meta-analyses for 3HB-cancer associations sourced from different exposure data. In multivariate MR(MVMR), the body mass index, alcohol frequency and diabetes were included as covariates to investigate the independent effect of 3HB on cancer risk. Additionally, utilizing mediation MR analysis, we checked the potential mediating role of 3HB in the association between liver fat and cancer.

RESULTS

Integrating findings from UVMR and MVMR, we observed that elevated circulating 3HB levels were associated with reduced risk of developing diffuse large B-cell lymphoma(DLBCL) (OR[95%CI] = 0.28[0.14-0.57] p = 3.92e-04), biliary malignancies (OR[95%CI] = 0.30[0.15-0.60], p = 7.67e-04), hepatocellular carcinoma(HCC) (OR[95%CI] = 0.25[0.09-0.71], p = 9.33e-03), primary lymphoid and hematopoietic malignancies (OR[95%CI] = 0.76[0.58-0.99], p = 0.045). Further UVMR analysis revealed that an increase in the percent liver fat was associated with reduced 3HB levels (Beta[95%CI] = -0.073[-0.122∼-0.024], p = 0.0034) and enhanced susceptibility to HCC (OR[95%CI] = 13.9[9.76-19.79], p = 3.14e-48), biliary malignancies (OR[95%CI] = 4.04[3.22-5.07], p = 1.64e-33), nasopharyngeal cancer (OR[95%CI] = 3.26[1.10-9.67], p = 0.03), and primary lymphoid and hematopoietic malignancies (OR[95%CI] = 1.27[1.13-1.44], p = 1.04e-4). Furthermore, 3HB fully mediated the effect of liver fat on susceptibility to DLBCL (OR[95%CI] = 1.076[1.01-1.15], p = 0.034).

CONCLUSIONS

Circulating 3HB is associated with a reduced susceptibility to developing DLBCL, HCC, biliary malignancies, and primary lymphoid and hematopoietic malignancies. The impaired ketogenesis induced by metabolic-dysfunction associated fatty liver disease (MAFLD) contributes to risk of DLBCL.

The relationship between immune cells and prostate cancer, and the mediating role of metabolites: a Mendelian randomization study

Research has demonstrated the significant involvement of immune cells in the development and progression of prostate cancer (PCa). However, the precise causal relationship between immune cells and PCa remains unclear. This study utilized bidirectional Mendelian randomization (MR) analysis to investigate the causal link between immune cells and PCa. Additionally, employed mediation MR design to ascertain the potential mediating role of metabolites in the connection between immune cells and PCa outcomes. Unswitched memory B cell % lymphocyte and CD24 + CD27 + B cell % lymphocyte were positively related to PCa risk, while CD62L - monocyte absolute count and CD62L - monocyte % monocyte were negatively associated with PCa risk. Sensitivity analysis was conducted to validate these results. The mediation MR results indicate that 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) levels may be an independent risk factor for PCa, while the succinate to acetoacetate ratio (SA ratio) was found to be a mediator for the effect of CD62L - monocyte % monocyte on PCa, with a mediation proportion of 16.6% (mediation percentage: 16.6%, 95%CI - 163% - 196%). The research validates the genetic causality between particular immune cells and PCa, and has emphasized the potential intermediary function of SA ratio. These noteworthy discoveries provide fresh perspectives for the clinical management of PCa.

Plasma metabolites as mediators in the relationship between inflammation-related proteins and benign prostatic hyperplasia: insights from mendelian randomization

Benign prostatic hyperplasia (BPH) is a condition commonly observed in aging males. Inflammatory and metabolic factors are pivotal in the development and progression of BPH. The degree to which the effects of 91 inflammation-related proteins on BPH are mediated by 1400 plasma metabolites remains ambiguous. Our research analyzed the impact of these traits utilizing genetic evidence.Two-sample Mendelian randomization (MR) and multivariable MR (MVMR) were utilized in our study to infer the genetic causal effect of inflammation-related proteins on BPH, with metabolites serving as mediators. Increased levels of IL-2 were linked to a heightened incidence of BPH (β = 0.071, OR:1.074, 95% CI [1.002-1.152], p = 0.045), whereas lower concentrations of N6,N6-dimethyllysine were associated with decreased risk (β1=-0.127, p = 0.02; β2=-0.039, p = 0.008). The mediation effect was 0.005 (95% CI [0.0004, 0.012], OR: 1.005, 95% CI [1.000, 1.012]), accounting for 7.04% of the total effect. subsequently, we examined the phenotypic co-localization of the two pairings independently, revealing that the posterior probability of rs145516501 associated with IL-2 and BPH was 80.7%, whereas the posterior likelihood of rs4917820 linked to N6,N6-dimethyllysine levels and BPH was 95.9%. The research indicated that N6,N6-dimethyllysine levels seem to influence the causative relationship between IL-2 and BPH. These results elucidate the complex interplay between inflammation-related proteins and metabolism in the context of BPH, offering novel diagnostic and therapeutic avenues and enhancing our comprehension of the disease's etiology for prospective research.

Risk of Alzheimer's disease and genetically predicted levels of 1400 plasma metabolites: a Mendelian randomization study

Alzheimer's disease (AD) is a metabolic disorder. Discovering the metabolic products involved in the development of AD may help not only in the early detection and prevention of AD but also in understanding its pathogenesis and treatment. This study investigated the causal association between the latest large-scale plasma metabolites (1091 metabolites and 309 metabolite ratios) and AD. Through the application of Mendelian randomization analysis methods such as inverse-variance weighted (IVW), MR-Egger, and weighted median models, 66 metabolites and metabolite ratios were identified as potentially having a causal association with AD, with 13 showing significant causal associations. During the replication validation phase, six metabolites and metabolite ratios were confirmed for their roles in AD: N-lactoyl tyrosine, argininate, and the adenosine 5'-monophosphate to flavin adenine dinucleotide ratio were found to exhibit protective effects against AD. In contrast, ergothioneine, piperine, and 1,7-dimethyluric acid were identified as contributing to an increased risk of AD. Among them, argininate showed a significant effect against AD. Replication and sensitivity analyses confirmed the robustness of these findings. Metabolic pathway analysis linked "Vitamin B6 metabolism" to AD risk. No genetic correlations were found, but colocalization analysis indicated potential AD risk elevation through top SNPs in APOE and PSEN2 genes. This provides novel insights into AD's etiology from a metabolomic viewpoint, suggesting both protective and risk metabolites.

Systems biology approaches identify metabolic signatures of dietary lifespan and healthspan across species

Dietary restriction (DR) is a potent method to enhance lifespan and healthspan, but individual responses are influenced by genetic variations. Understanding how metabolism-related genetic differences impact longevity and healthspan are unclear. To investigate this, we used metabolites as markers to reveal how different genotypes respond to diet to influence longevity and healthspan traits. We analyzed data from Drosophila Genetic Reference Panel (DGRP) strains raised under AL and DR conditions, combining metabolomic, phenotypic, and genome-wide information. We employed two computational and complementary methods across species-random forest modeling within the DGRP as our primary analysis and Mendelian randomization in human cohorts as a secondary analysis. We pinpointed key traits with cross-species relevance as well as underlying heterogeneity and pleiotropy that influence lifespan and healthspan. Notably, orotate was linked to parental age at death in humans and blocked the DR lifespan extension in flies, while threonine supplementation extended lifespan, in a strain- and sex-specific manner. Thus, utilizing natural genetic variation data from flies and humans, we employed a systems biology approach to elucidate potential therapeutic pathways and metabolomic targets for diet-dependent changes in lifespan and healthspan.

Esophageal cancer risk is influenced by genetically determined blood metabolites

It remains unclear what causes esophageal cancer (EC), but blood metabolites have been connected to it. Our study performed a Mendelian randomization (MR) analysis to assess the causality from genetically proxied 1400 blood metabolites to EC level. A two-sample MR analysis was employed to evaluate the causal relationship between 1400 blood metabolites and EC. Initially, the EC genome-wide association study (GWAS) data (from Jiang L et al) were examined, leading to the identification of certain metabolites. Subsequently, another set of EC GWAS data from FINNGEN was utilized to validate the findings. Causality was primarily determined through inverse variance weighting, with additional support from the MR-Egger, weighted median, and MR-PRESSO models. Heterogeneity was assessed using the MR Cochran Q test. The MR-Egger intercept and MR-PRESSO global methods were employed to detect multicollinearity. In this study, Bonferroni corrected P value was used for significance threshold. We found 2 metabolites with overlaps, which are lipids. Docosatrienoate (22:3n3) was found to be causally associated with a decreased risk of EC, as evidenced by the EC GWAS data (from Jiang et al) (odds ratio [OR] = 0.620, 95% confidence interval [CI] = 0.390-0.986, P = .044) and the EC GWAS data (from FINNGEN) (OR = 0.77, 95% CI = 0.6-0.99, P = .042), these results were consistent across both data sets. Another overlapping metabolite, glycosyl-N-(2-hydroxyneuramoyl)-sphingosine, was associated with the risk of ES, with EC GWAS data (from Jiang L et al) (OR = 1.536, 95% CI = 1.000-2.360, P = .049), while EC GWAS data (from FINNGEN) (OR = 0.733, 95% CI = 0.574-0.937, P = .013), the 2 data had opposite conclusions. The findings of this study indicate a potential association between lipid metabolites (Docosatrienoate (22:3n3) and glycosyl-N-(2-hydroxynervonoyl)-sphingosine (d18:1/24:1 (2OH))) and the risk of esophageal carcinogenesis.

Association of serum lipidomic profiles with risk of intracranial aneurysm: A Mendelian randomization study

A two-sample Mendelian randomization (MR) analysis was utilized to assess the causal relationship between lipidomic profiles and the risk of intracranial aneurysms (IAs). Genetic variants related to lipidomic profiles (227 components) and IA [IA, aneurysmal subarachnoid hemorrhage (aSAH) only, unruptured IA (uIA) only] were obtained from published genome-wide association studies (GWASs) or the IEU Open GWAS project and used as instrumental variables for MR analysis. The inverse-variance weighted method was used in the primary analyses to derive causality estimates and was expressed as odds ratio (OR) with 95% confidence interval (CI). Of these 227 lipidomic profiles, only genetically predicted high levels of cholesterol to total lipids ratio in very small very-low-density lipoproteins (VLDL) [OR = 0.629 (95% CI, 0.504-0.786)], cholesteryl esters to total lipids ratio in very small VLDL [OR = 0.637 (95% CI, 0.509-0.797)], ratio of docosahexaenoic acid to total fatty acids [OR = 0.691 (95% CI, 0.582-0.820)], and ratio of polyunsaturated fatty acids to monounsaturated fatty acids [OR = 0.630 (95% CI, 0.522-0.760)] reduced the risk of aSAH, whereas genetically predicted high ratio of monounsaturated fatty acids to total fatty acids [OR = 1.471 (95% CI, 1.215-1.781)] increased the risk of aSAH. Moreover, genetically predicted high levels of cholesterol to total lipids ratio in very small VLDL [OR = 0.657 (95% CI, 0.542-0.798)], cholesteryl esters to total lipids ratio in very small VLDL [OR = 0.663 (95% CI, 0.548-0.803)], free cholesterol to total lipids ratio in small VLDL [OR = 0.682 (95% CI, 0.560-0.832)], phospholipids to total lipids ratio in small VLDL [OR = 0.674 (95% CI, 0.548-0.830)], and ratio of polyunsaturated fatty acids to monounsaturated fatty acids [OR = 0.678 (95% CI, 0.569-0.808)] reduced the risk of IA. The results of multivariable MR demonstrated that these causal associations persisted after adjusting for systolic blood pressure and cigarettes smoked per day. The effect of serum lipids on IA and aSAH may be mainly caused by subclasses of lipids such as VLDL.

Evaluating the causal relationship of Levo-carnitine and risk of schizophrenia: a bidirectional two-sample mendelian randomization study

BACKGROUND

Schizophrenia is a debilitating mental disorder affecting about 1% of the global population, characterized by significant cognitive impairments and a strong hereditary component. Carnitine, particularly Levo-carnitine and its derivatives, plays a crucial role in cellular metabolism and mitochondrial function, with evidence suggesting a link between levo-carnitine deficiency and schizophrenia pathology. This study aims to investigate the causal relationship between different subtypes of levo-carnitine and the susceptibility to schizophrenia using Mendelian randomization analysis.

METHODS

Forward Mendelian randomization analysis was conducted using levo-carnitine and its derivatives as exposure and schizophrenia as the outcome. Candidate data were obtained from the Open-GWAS database. Instrumental variables were identified as single nucleotide polymorphisms closely associated with exposure and harmonized with the outcome data after removing confounders and outliers. Mendelian randomization analysis was performed using inverse variance weighting as the primary approach, and sensitivity analysis was conducted to assess the reliability and robustness of the results. Finally, a reverse Mendelian randomization analysis was carried out using the same analytical procedures.

RESULTS

The Mendelian randomization results indicate a significant negative causal relationship between isovaleryl-levo-carnitine and schizophrenia (P < 0.05), but no significant associations in other groups (P > 0.05). Additionally, the reverse Mendelian randomization analysis did not identify any causal relationship between schizophrenia and levo-carnitine related exposures (P > 0.05). Sensitivity analyses, including pleiotropy and heterogeneity analysis, did not reveal any potential bias in the Mendelian randomization results (P > 0.05).

CONCLUSION

The results suggest that elevated levels of isovaleryl-levo-carnitine may potentially mitigate the risk of developing schizophrenia, highlighting the prospective therapeutic and preventive implications of isovaleryl-levo-carnitine in the clinical management of schizophrenia.

Integration of network pharmacology, metabolomics and lipidomics for clarifying the role of sphingolipid metabolism in the treatment of liver cancer by regorafenib

AIMS

Regorafenib, an FDA-approved drug for advanced primary liver cancer (PLC), could provide survival benefits for patients. However, markers for its therapeutic sensitivity are lacking. This study seeks to identify sensitive targets of regorafenib in PLC from the perspective of small molecular metabolites.

MATERIALS AND METHODS

Initiated with network pharmacology (NP) to map regorafenib's target landscape and metabolic regulatory network in liver cancer. Subsequently, regorafenib's impact on hepatoma cells was evaluated by flow cytometry, western blotting (WB) and cell viability assay. Advanced metabolomics and lipidomics were employed to elucidate regorafenib's metabolic reprogramming effects in liver cancer. Metabolic enzyme expression was assessed by WB, immunohistochemical and immunofluorescence assays. Ultimately, mendelian randomization (MR) analysis was utilized to investigate the potential causality of sphingolipid metabolism in hepatic cancer.

KEY FINDINGS

Regorafenib was observed to inhibit hepatoma cell proliferation and cell cycle progression at G0/G1 phase, resulting in significant alterations in sphingolipid levels. It promoted the significant accumulation of 16:0 dihydroceramide (16:0 dhCer) by upregulating ceramide synthase 6 (CERS6) expression and inhibiting dihydroceramide desaturase 1 (DEGS1) activity. The MR analysis revealed that DEGS1 was a risk factor for the development and progression of liver cancer, while cumulative 16:0 dhCer was a protective factor.

SIGNIFICANCE

Sphingolipids, particularly dhCer and regulatory enzymes, may be potential sensitive markers of regorafenib in the treatment of liver cancer, providing new insights for enhancing the treated efficacy of regorafenib in liver cancer.

A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms.

RESULTS

We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1).

CONCLUSIONS

This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.

GNLY as a novel cis-eQTL and cis-pQTL mediated susceptibility gene in suppressing prostatitis. Mendelian randomization study

BACKGROUND

Prostatitis is characterized by high prevalence, low cure rates, and frequent recurrences, and remains one of the most clinically challenging problems. Hence, in this article, we first integrated Mendelian randomization (MR) with expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data to identify novel therapeutic targets and their potential metabolic mechanisms for prostatitis.

METHODS

Prostatitis-related genetic data, eQTLs, pQTLs, and 1400 metabolites were downloaded from online databases. MR, or summary data-based MR (SMR) analyses were applied to assess the potential causal relationships between exposures and predicted outcomes. Sensitivity analysis was conducted using pleiotropy, heterogeneity, and leave-one-out analysis to evaluate the robustness of our results.

RESULTS

Based on our results, we first identified and validated GNLY as a novel cis-eQTL and cis-pQTL-mediated susceptibility gene for reducing prostatitis risk in five independent datasets (one discovery dataset and four validation datasets) (all p <0.05). Meanwhile, we also found that the GNLY eQTL could increase the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0) risks (p <0.05), and the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0) could reduce the risk of prostatitis (p <0.05). According to the above-mentioned relationships, we finally revealed the potential metabolic mechanism of GNLY eQTL in suppressing prostatitis via regulating the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0).

CONCLUSIONS

We successfully identified GNLY as a novel cis-eQTL and cis-pQTL-mediated susceptibility gene in suppressing prostatitis and its potential metabolic mechanism via regulating sphingomyelin (d18:0/20:0, d16:0/22:0) levels, providing a novel therapeutic target and paving the way for future GNLY-related studies in prostatitis.

Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n = 227,469 UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies.

Causal Metabolomic and Lipidomic Analysis of Circulating Plasma Metabolites in Autism: A Comprehensive Mendelian Randomization Study with Independent Cohort Validation

BACKGROUND

The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear.

METHODS

Bidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets-comprising 1091 metabolites, 309 ratios, and 179 lipids-and three European autism datasets (PGC 2015: n = 10,610 and 10,263; 2017: n = 46,351). Inverse-variance weighted (IVW) and weighted median methods were employed, along with robust sensitivity and power analyses followed by independent cohort validation.

RESULTS

Higher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024-1.245; p = 0.015) were causally linked to increased ASD risk. Additionally, ASD children had higher plasma creatine/carnitine ratios. These MR findings were validated in an independent US autism cohort using machine learning analysis.

CONCLUSION

Utilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology.

Mediation effect of plasma metabolites on the relationship between immune cells and the risk of prostatitis: A study by bidirectional 2-sample and Bayesian-weighted Mendelian randomization

According to the findings of multiple observational studies, immune disorder was a risk factor for prostatitis. However, it remained unknown whether there was a direct causal relationship between immune cells and prostatitis or whether this relationship was mediated by plasma metabolites. Based on the pooled data of a genome-wide association study (GWAS), a genetic variant was used to predict the effects of 731 immunophenotypes on the risk of prostatitis and determine whether the effects were mediated by 1400 metabolites. The bidirectional 2-sample Mendelian randomization (MR) method was adopted to uncover the causal relationship between immunophenotypes and prostatitis. Subsequently, a 2-step MR method was employed to evaluate whether the metabolites mediated this causal relationship and quantify the mediating effects and the corresponding ratios. In addition, the Bayesian-weighted Mendelian randomization (BWMR) method was employed to verify the results. Among the 731 immunophenotypes analyzed, 16 had causal relationships with the risk of prostatitis, including 11 with positive correlations (P < .05, beta > 0) and 5 with negative correlations (P < .05, beta < 0). The MR analysis screened out 9 metabolites related to the risk of prostatitis. The X - 24344 levels mediated the causal relationship between CD3 on CD39+ activated Treg and prostatitis (mediation effect: 0.01; ratio: 9.82%). Both histidine betaine (hercynine) levels and the proline-to-glutamate ratio mediated the causal relationship between CD14-CD16+ monocyte absolute count and prostatitis, with the mediation effects of -0.016 (14.20%) and -0.008 (7.24%), respectively. The glutamine degradant levels mediated the causal relationship between HLA DR+ CD4+ %T cells and prostatitis, with a mediation effect of -0.012, accounting for 8.07% of the total. The present study indicated that the immune cell subsets predicted based on gene expression profiles were potentially beneficial or harmful risk factors of prostatitis, and plasma metabolites may serve as the mediating factors of the relationship. The study thus shed light on deciphering the immunologic mechanism of prostatitis.

Causal relationship and mediation effects of immune cells and plasma metabolites in atopic dermatitis: A Mendelian randomization study

Atopic dermatitis (AD) is a chronic inflammatory skin condition with complex etiology involving genetic, environmental, and immunological factors. This study employs Mendelian randomization to explore the causal relationships between immune cell phenotypes and AD, and the mediating effects of plasma metabolites. Using data from European cohorts, we identified 7 immune cell phenotypes significantly associated with AD. Mediation analysis revealed that the alpha-ketobutyrate to 4-methyl-2-oxopentanoate ratio negatively regulates CCR2 on monocytes, while the glycerol to carnitine ratio positively regulates HLA-DR on CD14- CD16- cells. These findings underscore the critical role of metabolic pathways in modulating immune responses and suggest potential dietary and therapeutic interventions for AD management. Further research should consider more diverse populations to validate these findings.

Plasma metabolome mediates the causal relationship between immune cells and heart failure: a two-step bidirectional Mendelian randomization study

Background

Prior research has established a correlation between immune cell activity and heart failure (HF), but the causal nature of this relationship remains unclear. Furthermore, the potential influence of metabolite levels on this interaction has not been comprehensively explored. To address these gaps, we employed a bidirectional Mendelian randomization (MR) approach in two stages to examine whether metabolite levels can mediate the causal relationship between immune cells and HF.

Methods

Genetic information was extracted from summary data of genome-wide association studies. By applying a two-sample, two-step MR approach, we investigated the causal relationships among immune cells, metabolite levels, and HF, with a specific focus on the mediating effects of metabolites. Sensitivity analysis techniques were implemented to ensure the robustness of our findings.

Results

MR analysis revealed significant causal associations between HF and eight specific immune cells and five metabolites. Mediation analysis further identified three mediated relationships. Particularly, hexadecenedioate (C16:1-DC) mediated the influence of both the CD28- CD127- CD25++ CD8br%CD8br (mediation proportion: 19.2%) and CD28+ CD45RA + CD8br%T cells (mediation proportion: 11.9%) on HF. Additionally, the relationship between IgD + CD38br AC cells and HF appeared to be mediated by the phosphate to alanine ratio (mediation proportion: 16.3%). Sensitivity analyses validated that the used instrumental variables were free from pleiotropy and heterogeneity.

Conclusion

This study provides evidence that certain immune cell levels are associated with the risk of HF and that metabolite levels may mediate these relationships. However, to strengthen these findings, further validation using MR analyses with larger sample sizes is essential.

The levels of amino acid metabolites in serum induce the pathogenesis of atopic dermatitis by mediating the inflammatory protein S100A12

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting tens of millions of people globally. The causal relationship between metabolites and AD pathology has not yet been formally indicated, and the mediating mechanism by which metabolites affect AD has not yet been explored. This study aimed to determine the genetic relationship between metabolites and AD and to determine the pathways through which amino acid metabolites affect AD. Meta-analysis integrates the results of multiple GWAS analyses using METAL software. Using bidirectional two-sample Mendelian randomization (MR), we analyzed the causal relationships between metabolites and AD. The principal MR test of causal effects was conducted using inverse-variance weighted regression, and we used reverse MR analysis to exclude reverse causality. We also performed the MR-PRESSO test to detect and correct for possible pleiotropic effects, and used the Cochran Q test to assess heterogeneity. Two-step MR was utilized to analyze the mediating factors between amino acid metabolites and the onset of AD. The correlation between mediating factors (inflammatory protein S100A12) and immune cell infiltration was analyzed using the edgeR and GSVA software packages. Using single-cell sequencing data from skin tissues of patients with AD, we studied the regulatory role of the S100A12 gene in immune cells. Multiple drug databases and macromolecular docking were used to search for S100A12-targeting drugs. Bidirectional two-sample MR analyses indicated that twenty-two metabolites and one inflammatory protein (S100A12) were significantly associated with AD pathogenesis. S100A12 is a mediator of amino acid metabolites (N6-methyllysine; N2-acetyl,N6,N6-dimethyllysine and N6,N6-dimethyllysine) that are genetically associated with AD. S100A12 was positively correlated with the infiltration of multiple immune cell types in lesional AD skin. The amino acid metabolites N6-methyllysine; N2-acetyl,N6,N6-dimethyllysine and N6,N6-dimethyllysine influence AD pathogenesis by mediating S100A12 expression.

A genetic study to identify pathogenic mechanisms and drug targets for benign prostatic hyperplasia: a multi-omics Mendelian randomization study

Benign prostatic hyperplasia (BPH) as a common geriatric disease in urology, the incidence and prevalence are rapidly increasing with the aging society, prompting an urgent need for effective prevention and treatment of BPH. However, limited therapeutic efficacy and higher risk of complications result in the treatment of BPH remaining challenging. The unclear pathogenic mechanism also hampers further exploration of therapeutic approaches for BPH. In this study, we used multi-omics methods to integrate genomics, transcriptomics, immunomics, and metabolomics data and identify biomolecules associated with BPH. We performed transcriptomic imputation, summary data-based Mendelian randomization (SMR), joint/conditional analysis, colocalization analysis, and FOCUS to explore high-confidence genes associated with BPH in blood and prostate tissue. Subsequently, three-step SMR was used to identify the DNA methylation sites regulating high-confidence genes to improve the pathogenic pathways of BPH. We also used cis-instruments of druggable genes to conduct SMR analysis to find potential drug targets for BPH. Finally, we used MR analysis to explore the immune pathways and metabolomics related to BPH. Multiple analytical methods identified BTN3A2 (Blood: TWAS Z score = 5.02912, TWAS P = 4.93 × 10-7; Prostate: TWAS Z score = 4.89, TWAS P = 1.01 × 10-6) and C4A (Blood: TWAS Z score = 4.90754, TWAS P = 9.22 × 10-7; Prostate: TWAS Z score = 5.084, TWAS P = 3.70 × 10-7) as high-confidence genes for BPH and identified the cg14345882-BTN3A2-BPH pathogenic pathway. We also used druggable gene data to identify 30 promising therapeutic target genes, including BTN3A2 and C4A. For MR analysis of immune pathways, we identified immune cell surface molecules as well as the inflammatory factor IL-17 (OR = 1.25, 95% CI = 1.09-1.43, PFDR = 0.12, Maximum likelihood) as risk factors for BPH. In addition, we found that disulfide levels of cysteinylglycine (OR = 1.11, 95% CI = 1.05-1.18, P = 5.18 × 10-4, Weighted median), oxidation levels of cysteinylglycine (OR = 1.09, 95% CI = 1.04-1.14, P = 3.87 × 10-4, Weighted median), and sebacate levels (OR = 1.05, 95% CI = 1.02-1.08, P = 3.0 × 10-4, Maximum likelihood) increase the risk of BPH. This multi-omics study explored biomolecules associated with BPH, improved the pathogenic pathways of BPH, and identified promising therapeutic targets. Our results provide evidence for future studies aimed at developing appropriate therapeutic interventions.

Causal relationship between plasma metabolites and carpal tunnel syndrome risk: evidence from a mendelian randomization study

Background

Carpal tunnel syndrome (CTS) is a common symptom of nerve compression and a leading cause of pain and hand dysfunction. However, the underlying biological mechanisms are not fully understood. The aim of this study was to reveal the causal effect of circulating metabolites on susceptibility to CTS.

Methods

We employed various Mendelian randomization (MR) methods, including Inverse Variance Weighted, MR-Egger, Weighted Median, Simple Mode, and Weighted Model, to examine the association between 1,400 metabolites and the risk of developing CTS. We obtained Single-nucleotide polymorphisms (SNPs) associated with 1,400 metabolites from the Canadian Longitudinal Study on Aging (CLSA) cohort. CTS data was derived from the FinnGen consortium, which included 11,208 cases and 1,95,047 controls of European ancestry.

Results

The results of the two-sample MR study indicated an association between 77 metabolites (metabolite ratios) and CTS. After false discovery rate (FDR) correction, a strong causal association between glucuronate levels (odd ratio (OR) [95% CI]: 0.98 [0.97-0.99], p FDR = 0.002), adenosine 5'-monophosphate (AMP) to phosphate ratio (OR [95% CI]:0.58 [0.45-0.74], p FDR = 0.009), cysteinylglycine disulfide levels (OR [95% CI]: 0.85 [0.78-0.92], p FDR = 0.047) and CTS was finally identified.

Conclusion

In summary, the results of this study suggest that the identified glucuronate, the ratio of AMP to phosphate, and cysteinylglycine disulfide levels can be considered as metabolic biomarkers for CTS screening and prevention in future clinical practice, as well as candidate molecules for future mechanism exploration and drug target selection.

Causal effects of plasma metabolites on autoimmune hepatitis (AIH): a bidirectional two-sample mendelian randomization study

Autoimmune hepatitis(AIH) is a chronic progressive inflammatory liver disease induced by loss of immune tolerance. The role of circulating metabolites in disease pathogenesis is unclear. This study aimed to investigate potential causal links between plasma metabolites and AIH risk by employing a two-sample Mendelian randomization approach. A comprehensive bidirectional two-sample Mendelian randomization analysis was conducted using genome-wide significant variant-metabolite and variant-AIH associations in European ancestry individuals. Various methods assessed causal relationships among 1400 metabolites and AIH, incorporating sensitivity analyses to evaluate pleiotropy and heterogeneity. Fifty-eight metabolites displayed possible associations, including increased AIH risk with genetically predicted higher kynurenine (p = 2.79 × 10- 5, OR: 1.64, 95% CI 1.30-2.07) and a protective effect for the dopamine sulfate ratio (p = 1.06 × 10- 5,OR: 0.62, 95% CI 0.49-0.79). Reciprocal analysis revealed a causal effect of AIH on kynurenine( p = 2.79 × 10- 5, OR: 1.64, 95% CI 1.30-2.07), but not on the dopamine sulfate ratio(p = 0.691, OR: 1.05, 95% CI 0.67-1.64). Our genetics-based approach provides evidence supporting a causal role for specific metabolite levels in AIH risk. The results deliver evidence supporting a causal effect of a specific metabolite ratio(dopamine 4-sulfate/dopamine 3-O-sulfate) on AIH risk. Experimental validation and mechanistic examinations are warranted to confirm findings.

Associations between serum metabolites and female cancers: A bidirectional two-sample mendelian randomization study

Female cancers, especially breast, ovarian, cervical, and endometrial cancers, constitute a major threat to women's health worldwide. In view of the complex genetic background of cancers cannot be fully explained with current genetic information, we used a bidirectional two-sample mendelian randomization approach to explore the causal associations between serum metabolites and four major female cancers-breast, ovarian, cervical, and endometrial cancers. We analyzed the metabolites dataset from the Canadian Longitudinal Study of Aging and cancer datasets from the 10th round of the Finngen project. Replication analyses was performed with Cancer Association Consortium and Leo's studies. Instrumental variables were analyzed using methods including the Wald ratio, inverse-variance weighted, MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane's Q, Egger's intercept, MR-PRESSO, and leave-one-out methods. After meticulous analysis, we obtained levels of 3-hydroxyoleoylcarnitine, hexadecanedioate, tetradecanedioate, and carnitine C14 with robust causal associations with breast cancer, levels of 5alpha-androstan-3alpha,17beta-diol monosulfate (1), androstenediol (3beta,17beta) monosulfate (1), androsterone sulfate, and 5alpha-androstan-3beta,17beta-diol disulfate causal associations with endometrial cancer. The reverse analysis showed that breast, ovarian, and endometrial cancer and survival of breast and ovarian cancer were found to have causal relationships with 8, 5, 2, 6, and 3 metabolites, respectively. These insights underscore the potential roles of specific metabolites in the etiology of female cancers, providing new biomarkers for early detection, risk stratification, and disease progression monitoring. Further research could elucidate how these metabolites influence specific pathways in cancer development, offering theoretical foundations for prevention and treatment strategies.

Causal effect of immune cells, metabolites, cathepsins, and vitamin therapy in diabetic retinopathy: a Mendelian randomization and cross-sectional study

Background

Diabetic retinopathy (DR) is a major microvascular complication of diabetes and a leading cause of blindness worldwide. The pathogenesis of DR involves complex interactions between metabolic disturbances, immune cells, and proteolytic enzymes such as cathepsins (CATs). Despite various studies, the precise roles of different CATs, metabolites, and vitamins in DR remain unclear.

Method

In this study, we employed Mendelian Randomization (MR) to assess causal relationships using genetic instruments selected based on genome-wide association studies (GWAS). We employed two-sample and mediation MR to explore the causal effects between nine CATs, immune cells, metabolites, vitamins, and DR. Additionally, the study also incorporated data from the NHANES survey to explore the associated relationship between vitamins and DR. We utilized cross-sectional data from the NHANES to analyze the association between vitamin intake and diabetic retinopathy (DR), adjusting for potential confounders to strengthen the validity of our findings.

Results

The MR analysis identified CAT H as a significant risk factor for both NPDR and PDR, with no evidence of reverse causality. Additionally, 62 immune cell traits were found to have causal relationships with NPDR and 49 with PDR. Enrichment analysis revealed that metabolic pathways such as sphingolipid metabolism are crucial in DR progression. Vitamins B6 and E were significantly associated with a reduced risk of PDR. Cross-sectional data indicated that vitamins B1, B2, B6, B12, and E progressively decreased with DR severity.

Conclusion

This study is the first to identify CAT H as a key risk factor for DR, while vitamins B6 and E showed significant protective effects, particularly against PDR. These findings suggest that CAT H, along with vitamins B6 and E, could serve as therapeutic targets for DR. Further validation through larger, multi-center studies is recommended to enhance the accuracy and applicability of these findings.

Relationship of metabolites and metabolic ratios with schizophrenia: a mendelian randomization study

BACKGROUND

This study aims to investigate the causal relationship of human plasma metabolites and metabolic ratios with schizophrenia (SCZ).

METHODS

We employed Mendelian Randomization (MR) approach to comprehensively analyze two large-scale metabolomics and schizophrenia Genome-Wide Association Study (GWAS) datasets, incorporating a total of 1091 metabolites and 309 metabolic ratios, with 52017 schizophrenia patients and 75889 healthy controls. The inverse variance-weighted (IVW) method was utilized to estimate the causal relationship between exposure and outcome. To provide a more comprehensive evaluation, additional Mendelian Randomization (MR) approaches were employed, including MR-Egger regression, weighted median, simple mode, and weighted mode methods. These analyses assessed the causal effects between blood metabolites, metabolic ratios, and schizophrenia. Tests for pleiotropy and heterogeneity were conducted. False Discovery Rate (FDR) correction was applied to account for multiple comparisons and heterogeneity, ensuring the robustness and reliability of our findings. Consistent with previous studies, an FDR threshold of < 0.2 was considered suggestive of a causal relationship, while an FDR of < 0.05 was considered to indicate a significant causal relationship.

RESULTS

The final results revealed that a significant causal association was found between the levels of two metabolites and schizophrenia, Alliin (OR = 0.915, 95%CI = 0.879-0.953, P = 1.93 × 10- 5, FDR = 0.013) was associated with a decreased risk of schizophrenia, N-actylcitrulline (OR = 1.058, 95%CI = 1.034-1.083, P = 1.4 × 10- 6, FDR = 0.002) was associated with increased risk of schizophrenia. When adjusting FDR to 0.2, the results showed that 4 metabolite levels and 2 metabolite ratios were suggestively causally associated with a reduced risk of schizophrenia including 2-aminooctanoate (OR = 0.904, 95%CI = 0.847-0.964, P = 0.002, FDR = 0.160), N-lactoylvaline (OR = 0.853, 95%CI = 0.775-0.938, P = 0.001,FDR = 0.122), X - 21310 (OR = 0.917, 95%CI = 0.866-0.971, P = 0.003,FDR = 0.195), X - 26111 (OR = 0.932, 95%CI = 0.890-0.976, P = 0.003,FDR = 0.189), Arachidonate (20:4n6) to oleate to vaccenate (18:1) ratio (OR = 0.945, 95%CI = 0.914-0.977, P = 8.2 × 10- 4, FDR = 0.104), and Citrulline to ornithine ratio (OR = 0.924, 95%CI = 0.881-0.969, P = 0.001, FDR = 0.122), while 4 metabolite levels and 2 metabolite ratios were suggestively causally associated with an increased risk of schizophrenia including N2, N5-diacetylornithine (OR = 1.090, 95%CI = 1.031-1.153, P = 0.003, FDR = 0.185), N - acetyl - 2-aminooctanoate (OR = 1.069, 95%CI=(1.027-1.114, P = 0.001, FDR = 0.127), N - acetyl - 2-aminoadipate (OR = 1.081, 95%CI = 1.030-1.133, P = 0.001, FDR = 0.128), X - 13844 (OR = 1.110, 95%CI = 1.036-1.190, P = 0.003, FDR = 0.196), X - 24556 (OR = 1.083, 95%CI = 1.036-1.132, P = 4.5 × 10- 4, FDR = 0.098), X - 24736 (OR = 1.065, 95%CI = 1.028-1.104, P = 5.6 × 10- 4, FDR = 0.098), N - acetylasparagine (OR = 1.048, 95%CI = 1.021-1.075, P = 4.5 × 10- 4, FDR = 0.098), N - acetylarginine (OR = 1.060, 95%CI = 1.028-1.092, P = 1.8 × 10- 4, FDR = 0.083), Cysteine to alanine ratio (OR = 1.086, 95%CI = 1.036-1.138, P = 6.5 × 10- 4, FDR = 0.101), and Benzoate to linoleoyl - arachidonoyl - glycerol (18:2 to 20:4) ratio (OR = 1.070, 95%CI = 1.025-1.117, P = 0.002, FDR = 0.158).

CONCLUSION

Our study results provide valuable insights for identifying diagnostic biomarkers related to schizophrenia and offer preliminary research findings for further exploration of the mechanisms linking schizophrenia and metabolism.

Unveiling the atlas of associations between 1,400 plasma metabolites and 24 tumors: Mendelian randomization analyses

Background

Association between plasma metabolites and pan-cancer remains controversial. Herein, we performed a two-sample Mendelian randomization (MR) analysis to verify whether there is a causal relationship between the two and to point the way for cancer metabolism research.

Methods

In our research, we downloaded 1,400 plasma metabolites from a large genome-wide association study (GWAS). We also obtained GWAS summary statistics for 24 types of cancers from the publicly available GWAS database, totaling 5,003,410 European individuals. We mainly used the fixed/random-effects inverse variance-weighted (IVW) method for two-sample MR analysis.

Results

In a combined sample of 291,202 cancer cases and 4,712,208 controls, a total of 55 plasma metabolites were identified as causally associated with nine types of cancer as a result of our MR analysis [P<0.05, false discovery rate (FDR) <0.2], including methionine sulfone, gamma-glutamylcitrulline, alliin, tetradecanedioate, hexadecanedioate, glutarate, ceramide, linolenoylcarnitine, hydroxypalmitoyl sphingomyelin, 1-palmitoyl-2-linoleoyl-glycerylphosphorylcholine (1-palmitoyl-2-linoleoyl-GPC), 3-acetylphenol sulfate, retinol (vitamin a) to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) ratio, etc. Reverse MR analysis revealed a causal relationship between lung cancer and the only plasma metabolite, 1-palmitoyl-2-linoleoyl-GPC (P<0.05, FDR <0.2).

Conclusions

Our study provides a comprehensive atlas of cancer-related plasma metabolites, offering possible targets for cancer detection, as well as a reference for future research on tumorigenesis mechanisms and therapeutic targets.

Dissecting Causal Relationships Between Plasma Metabolites and Osteoporosis: A Bidirectional Mendelian Randomization Study

OBJECTIVES

To investigate the causal relationships between plasma metabolites and osteoporosis via Mendelian randomization (MR) analysis.

METHODS

Bidirectional MR was used to analyze pooled data from different genome-wide association studies (GWAS). The causal effect of plasma metabolites on osteoporosis was estimated using the inverse variance weighted method, intersections of statistically significant metabolites obtained from different sources of osteoporosis-related GWAS aggregated data was determined, and then sensitivity analysis was performed on these metabolites. Heterogeneity between single nucleotide polymorphisms was evaluated by Cochran's Q test. Horizontal pleiotropy was assessed through the application of the MR-Egger intercept method and the MR-PRESSO method. The causal effect of osteoporosis on plasma metabolites was also evaluated using the inverse variance weighted method. Additionally, pathway analysis was conducted to identify potential metabolic pathways involved in the regulation of osteoporosis.

RESULTS

Primary analysis and sensitivity analysis showed that 77 and 61 plasma metabolites had a causal relationship with osteoporosis from the GWAS data in the GCST90038656 and GCST90044600 datasets, respectively. Five common metabolites were identified via intersection. X-13684 levels and the glucose-to-maltose ratio were negatively associated with osteoporosis, whereas glycoursodeoxycholate levels and arachidoylcarnitine (C20) levels were positively associated with osteoporosis (all P < 0.05). The relationship between X-11299 levels and osteoporosis showed contradictory results (all P < 0.05). Pathway analysis indicated that glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, galactose metabolism, arginine biosynthesis, and starch and sucrose metabolism pathways were participated in the development of osteoporosis.

CONCLUSIONS

We found a causal relationship between plasma metabolites and osteoporosis. These results offer novel perspectives with important implications for targeted metabolite-focused interventions in the management of osteoporosis.

Causal relationships between immunophenotypes, plasma metabolites, and temporomandibular disorders based on Mendelian randomization

While numerous studies have underscored the implication of immune cells and metabolites in temporomandibular disorders (TMD), conclusive evidence for causality remains elusive. Consequently, our aim is to explore the causal connections between immunophenotypes and plasma metabolites in relation to TMD employing a bidirectional Mendelian randomization (MR) approach. Summary statistics data on 731 immunophenotypes (n = 3757) and 1091 plasma metabolites (n = 8299) were obtained from comprehensive genome-wide association studies (GWAS), while TMD data (5668 cases and 205,355 controls) were acquired from the FinnGen Consortium. Bidirectional MR analyses and a two-step MR approach assessed causal relationships and potential intermediaries. Various corrections and sensitivity analyses were utilized to assess the robustness of the findings. Two immunophenotypes and seven metabolites were significantly associated with TMD risk. Specifically, Alpha-hydroxyisovalerate mediated the link between CD33 on CD33dim HLA DR + CD11b + and TMD (β = 0.034, P = 5.95 × 10-5), while CD8 on NKT cells mediated the causal relationship between 5-acetylamino-6-formylamino-3-methyluracil levels and TMD (β = 0.069, P = 5.11 × 10-5). Our findings revealed the causal relationships between immunophenotypes and plasma metabolites on TMD from a genetic perspective, potentially aiding in TMD prevention.

Metabolite, immunocyte phenotype, and lymphoma: a Mendelian randomization study

Background

Recent studies have confirmed that metabolites and immunocyte phenotype may be associated with the risk of lymphoma. However, the bidirectional causality between metabolites, immunocyte phenotype, disease risk, and whether immunity is an intermediate mediator between metabolism and lymphoma causality is still unclear.

Objective

To elucidate the causal relationship between metabolites, immune cell phenotypes, and lymphomas, we used two-sample Mendelian randomization (MR) and two-step MR analysis.

Methods

Applying large-scale genome-wide association studies (GWAS) pooled data, we selected 1400 metabolites and 731 immunocyte phenotypes with eight lymphoma subtypes for two-sample bi-directional MR analysis. In addition, we used two-step MR to quantify the proportion of metabolite effects on lymphomas mediated by immunocyte phenotype.

Results

This study yielded a bidirectional causal relationship between 17 metabolites and lymphoma and a bidirectional causal relationship between 12 immunocyte phenotypes and lymphoma. In addition, we found causal associations between metabolites and lymphomas, three groups of which were mediated by immunocyte phenotypes. Among them, CD27 on plasmablast/plasma cell (PB/PC) was a mediator of the positive association of arginine to glutamate ratio with chronic lymphocytic leukemia, with a mediator ratio of 14.60% (95% CI=1.29-28.00%, P=3.17 × 10-2). Natural killer (NK) cells as a percentage of all lymphocytes(NK %lymphocyte) was a mediator of the negative association of X-18922(unknown metabolite) levels with diffuse large B-cell lymphoma, with a mediation proportion of -8.940% (95% CI=-0.063-(-17.800) %, P=4.84 × 10-2). CD25 on IgD- CD24- B cell was the mediator of the positive association between X-24531(unknown metabolite) levels and diffuse large B-cell lymphoma, with a mediation proportion of 13.200% (95% CI=-0.156-26.200%, P=4.73 × 10-2).

Conclusion

In the present study, we identified a causal relationship between metabolites and lymphoma, in which immunocyte phenotypes as mediators are involved in only a minor part. The mediators by which most metabolites affect the risk of lymphoma development remain unclear and require further exploration in the future.

Microbiome-metabolome analysis insight into the effects of high-salt diet on hemorheological functions in SD rats

The present study examined the effect of two dietary regimens with elevated salt concentrations (4% and 8% salt) on hemorheological functions of SD rats, and explored the underlying mechanisms mainly through microbiome-metabolome analysis. An 8% HSD substantially altered the hemorheological parameters, and compromised intestinal barrier integrity and reduced the short-chain fatty acid levels. The microbiome-metabolome analysis revealed that 49 genus-specific microorganisms and 156 metabolites showed a consistent trend after exposure to both 4% and 8% HSDs. Pathway analysis identified significant alterations in key metabolites within bile acid and arachidonic acid metabolism pathways. A two-sample Mendelian randomization (MR) analysis verified the link between high dietary salt intake and hemorheology. It also suggested that some key microbes and metabolites (such as Ruminococcaceae_UCG-005, Lachnospiraceae_NK4A136, Ruminiclostridium_6, and Ruminococcaceae_UCG-010, TXB-2, 11,12-diHETrE, glycochenodeoxycholate) may involve in abnormalities in blood rheology caused by high salt intake. Collectively, our findings underscored the adverse effects of high dietary salt on hemorheological functions and provide new insight into the underlying mechanism based on microbiome-metabolome analysis.

Causal relationship between fertility nutrients supplementation and PCOS risk: a Mendelian randomization study

Background

Polycystic ovary syndrome (PCOS), a prevalent endocrine disorder in women of reproductive age, is mainly ameliorated through drugs or lifestyle changes, with limited treatment options. To date, numerous researchers have found that fertility nutrient supplements may benefit female reproductive health, but their direct impact on polycystic ovary syndrome risk remains unclear.

Methods

Our research employs Mendelian Randomization to assess how fertility nutrients affect PCOS risk. Initially, we reviewed 49 nutrients and focused on 10: omega-3 fatty acids, calcium, dehydroepiandrosterone, vitamin D, betaine, D-Inositol, berberine, curcumin, epigallocatechin gallate, and metformin. Using methodologies of Inverse Variance Weighting and Mendelian Randomization-Egger regression, we examined their potential causal relationships with PCOS risk.

Results

Our findings indicate omega-3 fatty acids reduced PCOS risk (OR=0.73, 95% CI: 0.57-0.94, P=0.016), whereas betaine increased it (OR=2.60, 95% CI: 1.09-6.17, P=0.031). No definitive causal relations were observed for calcium, dehydroepiandrosterone, vitamin D, D-Inositol, and metformin (P>0.05). Drug target Mendelian Randomization analysis suggested that increased expression of the berberine target gene BIRC5 in various tissues may raise PCOS risk (OR: 3.00-4.88; P: 0.014-0.018), while elevated expressions of curcumin target gene CBR1 in Stomach and epigallocatechin gallate target gene AHR in Adrenal Gland were associated with reduced PCOS risk (OR=0.48, P=0.048; OR=0.02, P=0.018, respectively).

Conclusions

Our research reveals that specific fertility nutrients supplementation, such as omega-3 fatty acids, berberine, and curcumin, may reduce the risk of PCOS by improving metabolic and reproductive abnormalities associated with it.

Genetically predicted N-Acetyl-L-Alanine mediates the association between CD3 on activated and secreting Tregs and Guillain-Barre syndrome

Objective

This study sought to explore the potential causal relationships among immune cell traits, Guillain-Barre syndrome (GBS) and metabolites.

Methods

Employing a two-sample Mendelian randomization (MR) approach, the study investigated the causal associations between 731 immune cell traits, 1400 metabolite levels and GBS leveraging summary-level data from a genome-wide association study (GWAS). To ensure the reliability of our findings, we further assessed horizontal pleiotropy and heterogeneity and evaluated the stability of MR results using the Leave-one-out method.

Results

This study revealed a causal relationship between CD3 on activated & secreting Tregs and GBS. Higher CD3 on activated and secreting Regulatory Tregs increased the risk of GBS (primary MR analysis odds ratio (OR) 1.31/SD increase, 95% confidence interval (CI) 1.08-1.58, p = 0.005). There was no reverse causality for GBS on CD3 on activated & secreting Tregs (p = 0.36). Plasma metabolite N-Acetyl-L-Alanine (ALA) was significantly positively correlated with GBS by using the IVW method (OR = 2.04, 95% CI, 1.26-3.30; p = 0.00038). CD3 on activated & secreting Tregs was found to be positively associated with ALA risk (IVW method, OR, 1.04; [95% CI, 1.01-1.07], p = 0.0078). Mediation MR analysis indicated the mediated proportion of CD3 on activated & secreting Tregs mediated by ALA was 10% (95%CI 2.63%, 17.4%).

Conclusion

In conclusion, our study identified a causal relationship between the level of CD3 on activated & secreting Tregs and GBS by genetic means, with a considerable proportion of the effect mediated by ALA. In clinical practice, thus providing guidance for future clinical research.

Causality of multiple serum metabolites on emotional lability: A two-sample Mendelian randomization study

BACKGROUND

Emotional lability (EL)-a transdiagnostic feature characterized by rapid emotional shifts-contributes significantly to functional impairment across psychiatric disorders, such as depression, bipolar disorder, and schizophrenia. Despite its clinical significance, its etiology remains poorly understood, hindering effective screening and interventions. Growing evidence suggests that metabolic alterations may play a crucial role in the pathophysiology of psychiatric disorders.

METHODS

A comprehensive Mendelian randomization (MR) design incorporated summary-level data from extensive genome-wide association studies (GWAS) on serum metabolites (8299 European participants) and EL (3268 European samples) to investigate causal associations between genetically determined metabolite levels and EL. Assumptions of instrumental variables, heterogeneity, horizontal pleiotropy, and directionality were assessed alongside sensitivity analyses.

RESULTS

Out of 1400 metabolites and ratios analyzed, 30 metabolites demonstrated causal associations with an increased risk of EL based on the inverse-variance weighted method. Sensitivity analyses identified three potential causal metabolites: hydrocinnamate (OR: 1.277, CI: 1.071-1.522, P = 0.0063), which is associated with an increased risk, while glycolithocholate (OR: 0.779, CI: 0.667-0.911, P = 0.0017) and 3β-hydroxy-5-cholenoic acid (OR: 0.857, CI: 0.756-0.971, P = 0.015) are associated with a decreased risk.

CONCLUSION

This MR study supports a causal link between hydrocinnamate, glycolithocholate, and 3β-hydroxy-5-cholenoic acid levels and the incidence of EL, offering potential metabolic biomarkers and therapeutic targets for EL in psychiatric disorders.

Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis

Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches.

Related Factors with Vascular Dementia: A Two-Sample Mendelian Randomization Study

Pathogenesis of vascular dementia (VD) is still unclear, there are currently no effective prevention and treatment methods. We applied Mendelian randomization (MR) using summary statistics from large-scale GWAS of metabolites and VD to reveal the causal effect of metabolites on the VD. One set of genetics instrument was used for analysis, derived from publicly available genetic summary data. Which was 32 single-nucleotide polymorphisms robustly associated with metabolites. Inverse-variance weighted, weighted median method, MR-Egger regression, and MR Pleiotropy RESidual Sum and Outlier test were used for MR analyses. Strong evidence for a positive effect of metabolites, which means N6-threonylcarbamoyladenosine (t6A) on VD was found in inverse-variance weighted (odds ratios [OR]: 0.667, 95% confidence interval [CI]: 0.548-0.812, p < 0.001), MR-Egger (OR: 0.647, 95% CI: 0.458-0.913, p = 0.019), and weighted median (OR: 0.650, 95% CI: 0.466-0.908, p = 0.012). The MR analysis indicated that metabolites (t6A) may be causally associated with a positive effect on VD.

Causal pathways in lymphoid leukemia: the gut microbiota, immune cells, and serum metabolites

Background

We employed Mendelian randomization (MR) to investigate the causal relationship between the gut microbiota and lymphoid leukemia, further exploring the causal relationships among immune cells, lymphoid leukemia, and potential metabolic mediators.

Methods

We utilized data from the largest genome-wide association studies to date, encompassing 418 species of gut microbiota, 713 types of immune cells, and 1,400 serum metabolites as exposures. Summary statistics for lymphoid leukemia, acute lymphocytic leukemia (ALL), and chronic lymphocytic leukemia (CLL) were obtained from the FinnGen database. We performed bidirectional Mendelian analyses to explore the causal relationships among the gut microbiota, immune cells, serum metabolites, and lymphoid leukemia. Additionally, we conducted a two-step mediation analysis to identify potential intermediary metabolites between immune cells and lymphoid leukemia.

Results

Several gut microbiota were found to have causal relationships with lymphoid leukemia, ALL, and CLL, particularly within the Firmicutes and Bacteroidetes phyla. In the two-step MR analysis, various steroid hormone metabolites (such as DHEAS, pregnenolone sulfateprogestogen derivatives, and androstenediol-related compounds) were identified as potential intermediary metabolites between lymphoid leukemia and immune cells. In ALL, the causal relationship between 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) and ALL was mediated by CD62L-plasmacytoid DC%DC (mediated proportion=-2.84%, P=0.020). In CLL, the causal relationship between N6,n6,n6-trimethyllysine and CLL was mediated by HLA DR+ CD8br AC (mediated proportion=4.07%, P=0.021).

Conclusion

This MR study provides evidence supporting specific causal relationships between the gut microbiota and lymphoid leukemia, as well as between certain immune cells and lymphoid leukemia with potential intermediary metabolites.

Effect of inflammatory cytokines and plasma metabolome on OSA: a bidirectional two- sample Mendelian randomization study and mediation analysis

Background

Obstructive sleep apnea (OSA) is a common sleep disorder. Inflammatory factors and plasma metabolites are important in assessing its progression. However, the causal relationship between them and OSA remains unclear, hampering early clinical diagnosis and treatment decisions.

Methods

We conducted a large-scale study using data from the FinnGen database, with 43,901 cases and 366,484 controls for our discovery MR analysis. We employed 91 plasma proteins from 11 cohorts (totaling 14,824 participants of European descent) as instrumental variables (IVs). Additionally, we conducted a GWAS involving 13,818 cases and 463,035 controls to replicate the MR analysis. We primarily used the IVW method, supplemented by MR Egger, weighted median, simple mode, and weighted mode methods. Meta-analysis was used to synthesize MR findings, followed by tests for heterogeneity, pleiotropy, and sensitivity analysis (LOO). Reverse MR analysis was also performed to explore causal relationships.

Results

The meta-analysis showed a correlation between elevated Eotaxin levels and an increased risk of OSA (OR=1.050, 95% CI: 1.008-1.096; p < 0.05). Furthermore, we found that the increased risk of OSA could be attributed to reduced levels of X-11849 and X-24978 (decreases of 7.1% and 8.4%, respectively). Sensitivity analysis results supported the reliability of these findings.

Conclusions

In this study, we uncovered a novel biomarker and identified two previously unknown metabolites strongly linked to OSA. These findings underscore the potential significance of inflammatory factors and metabolites in the genetic underpinnings of OSA development and prognosis.

A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank

The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate "biomarker - disease" causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.

Causal effects of genetically determined metabolites and metabolite ratios on esophageal diseases: a two-sample Mendelian randomization study

BACKGROUND

Esophageal diseases (ED) are a kind of common diseases of upper digestive tract. Previous studies have proved that metabolic disorders are closely related to the occurrence and development of ED. However, there is a lack of evidence for causal relationships between metabolites and ED, as well as between metabolite ratios representing enzyme activities and ED. Herein, we explored the causality of genetically determined metabolites (GDMs) on ED through Mendelian Randomization (MR) study.

METHODS

Two-sample Mendelian randomization analysis was used to assess the causal effects of genetically determined metabolites and metabolite ratios on ED. A genome-wide association analysis (GWAS) encompassing 850 individual metabolites along with 309 metabolite ratios served as the exposures. Meanwhile, the outcomes were defined by 10 types of ED phenotypes, including Congenital Malformations of Esophagus (CME), Esophageal Varices (EV), Esophageal Obstructions (EO), Esophageal Ulcers (EU), Esophageal Perforations (EP), Gastroesophageal Reflux Disease (GERD), Esophagitis, Barrett's Esophagus (BE), Benign Esophageal Tumors (BETs), and Malignant Esophageal Neoplasms (MENs). The standard inverse variance weighted (IVW) method was applied to estimate the causal relationship between exposure and outcome. Sensitivity analyses were carried out using multiple methods, including MR-Egger, Weighted Median, MR-PRESSO, Cochran's Q test, and leave-one-out analysis. P < 0.05 was conventionally considered statistically significant. After applying the Bonferroni correction for multiple testing, a threshold of P < 4.3E-05 (0.05/1159) was regarded as indicative of a statistically significant causal relationship. Furthermore, metabolic pathway analysis was performed using the web-based MetaboAnalyst 6.0 software.

RESULTS

The findings revealed that initially, a total of 869 candidate causal association pairs ( P ivw < 0.05) were identified, involving 442 metabolites, 145 metabolite ratios and 10 types of ED. However, upon applying the Bonferroni correction for multiple testing, only 36 pairs remained significant, involving 28 metabolites (predominantly lipids and amino acids), 5 metabolite ratios and 6 types of ED. Sensitivity analyses and reverse MR were performed for these 36 causal association pairs, where the results showed that the pair of EV and 1-(1-enyl-palmitoyl)-2-linoleoyl-GPE (p-16:0/18:2) did not withstand the sensitivity tests, and Hexadecenedioate (C16:1-DC) was found to have a reverse causality with GERD. The final 34 robust causal pairs included 26 metabolites, 5 metabolite ratios and 5 types of ED. The involved 26 metabolites predominantly consisted of methylated nucleotides, glycine derivatives, sex hormones, phospholipids, bile acids, fatty acid dicarboxylic acid derivatives, and N-acetylated amino acids. Furthermore, through metabolic pathway analysis, we uncovered 8 significant pathways that played pivotal roles in five types of ED conditions.

CONCLUSIONS

This study integrated genomics with metabolomics to assess causal relationships between ED and both metabolites and metabolite ratios, uncovering several key metabolic features in ED pathogenesis. These findings have potential as novel biomarkers for ED and provide insights into the disease's etiology and progression. However, further clinical and experimental validations are necessary.

Causal influence of plasma metabolites on age-related macular degeneration: A Mendelian randomization study

Using genome-wide association study data from European populations, this research clarifies the causal relationship between plasma metabolites and age-related macular degeneration (AMD) and employs Metabo Analyst 5.0 for enrichment analysis to investigate their metabolic pathways. Employing Mendelian randomization analysis, this study leveraged single nucleotide polymorphisms significantly associated with plasma metabolites as instrumental variables. This approach established a causal link between metabolites and AMD. Analytical methods such as inverse-variance weighted, Mendelian randomization-Egger, and weighted median were applied to validate causality. Mendelian Randomization Pleiotropy Residual Sum and Outlier was utilized for outlier detection and correction, and Cochran's Q test was conducted to assess heterogeneity. To delve deeper into the metabolic characteristics of AMD, metabolic enrichment analysis was performed using Metabo Analyst 5.0. These combined methods provided a robust framework for elucidating the metabolic underpinnings of AMD. The 2-sample MR analysis, after meticulous screening, identified causal relationships between 88 metabolites and AMD. Of these, 16 metabolites showed a significant causal association. Following false discovery rate correction, 3 metabolites remained significantly associated, with androstenediol (3 beta, 17 beta) disulfate (2) exhibiting the most potent protective effect against AMD. Further exploration using Metabo Analyst 5.0 highlighted 4 metabolic pathways potentially implicated in AMD pathogenesis. This pioneering MR study has unraveled the causal connections between plasma metabolites and AMD. It identified several metabolites with a causal impact on AMD, with 3 maintaining significance after FDR correction. These insights offer robust causal evidence for future clinical applications and underscore the potential of these metabolites as clinical biomarkers in AMD screening, treatment, and prevention strategies.

Proteome-wide Mendelian randomization identified potential drug targets for migraine

BACKGROUND

Migraine is a highly prevalent and complex neurovascular disease. However, the currently available therapeutic drugs often fall to adequately meet clinical needs due to limited effectiveness and numerous undesirable side effects. This study aims to identify putative novel targets for migraine treatment through proteome-wide Mendelian randomization (MR).

METHODS

We utilized MR to estimate the causal effects of plasma proteins on migraine and its two subtypes, migraine with aura (MA) and without aura (MO). This analysis integrated plasma protein quantitative trait loci (pQTL) data with genome-wide association studies (GWAS) findings for these migraine phenotypes. Moreover, we conducted a phenome-wide MR assessment, enrichment analysis, protein-protein interaction networks construction, and mediation MR analysis to further validate the pharmaceutical potential of the identified protein targets.

RESULTS

We identified 35 protein targets for migraine and its subtypes (p < 8.04 × 10-6), with prioritized targets showing minimal side effects. Phenome-wide MR identified novel protein targets-FCAR, UBE2L6, LATS1, PDCD1LG2, and MMP3-that have no major disease side effects and interacted with current acute migraine medication targets. Additionally, MMP3, PDCD1LG2, and HBQ1 interacted with current preventive migraine medication targets. The causal effects of plasma protein on migraine were partly mediated by plasma metabolites (proportion of mediation from 3.8% to 21.0%).

CONCLUSIONS

A set of potential protein targets for migraine and its subtypes were identified. These proteins showed rare side effects and were responsible for biological mechanisms involved in migraine pathogenesis, indicating priority for the development of migraine treatments.

Gut microbiota influence on lung cancer risk through blood metabolite mediation: from a comprehensive Mendelian randomization analysis and genetic analysis

Background

Gut microbiota (GM) and metabolic alterations play pivotal roles in lung cancer (LC) development and host genetic variations are known to contribute to LC susceptibility by modulating the GM. However, the causal links among GM, metabolite, host genes, and LC remain to be fully delineated.

Method

Through bidirectional MR analyses, we examined the causal links between GM and LC, and utilized two-step mediation analysis to identify potential mediating blood metabolite. We employed diverse MR methods, including inverse-variance-weighted (IVW), weighted median, MR-Egger, weighted mode, and simple mode, to ensure a robust examination of the data. MR-Egger intercept test, Radial MR, MR-PRESSO, Cochran Q test and Leave-one-out (LOO) analysis were used for sensitivity analyses. Analyses were adjusted for smoking, alcohol intake frequency and air pollution. Linkage disequilibrium score regression and Steiger test were used to probe genetic causality. The study also explored the association between specific host genes and the abundance of gut microbes in LC patients.

Results

The presence of Bacteroides clarus was associated with an increased risk of LC (odds ratio [OR] = 1.07, 95% confidence interval [CI]: 1.03-1.11, p = 0.012), whereas the Eubacteriaceae showed a protective effect (OR = 0.82, 95% CI: 0.75-0.89, p = 0.001). These findings remained robust after False Discovery Rate (FDR) correction. Our mediator screening identified 13 blood metabolites that significantly influence LC risk after FDR correction, underscoring cystine and propionylcarnitine in reducing LC risk, while linking specific lipids and hydroxy acids to an increased risk. Our two-step mediation analysis demonstrated that the association between the bacterial pathway of synthesis of guanosine ribonucleotides and LC was mediated by Fructosyllysine, with mediated proportions of 11.38% (p = 0.037). LDSC analysis confirmed the robustness of these associations. Our study unveiled significant host genes ROBO2 may influence the abundance of pathogenic gut microbes in LC patients. Metabolic pathway analysis revealed glutathione metabolism and glutamate metabolism are the pathways most enriched with significant metabolites related to LC.

Conclusion

These findings underscore the importance of GM in the development of LC, with metabolites partly mediating this effect, and provide dietary and lifestyle recommendations for high-risk lung cancer populations.

Exploring genetic associations between metabolites and atopic dermatitis: insights from bidirectional Mendelian randomization analysis in European population

Introduction

There is growing evidence indicating a complex interaction between blood metabolites and atopic dermatitis (AD). The objective of this study was to investigate and quantify the potential influence of plasma metabolites on AD through Mendelian randomization (MR) analysis.

Methods

Our procedures followed these steps: instrument variable selection, primary analysis, replication analysis, Meta-analysis of results, reverse MR analysis, and multivariate MR (MVMR) analysis. In our study, the exposure factors were derived from the Canadian Longitudinal Study on Aging (CLSA), encompassing 8,299 individuals of European descent and identifying 1,091 plasma metabolites and 309 metabolite ratios. In primary analysis, AD data, was sourced from the GWAS catalog (Accession ID: GCST90244787), comprising 60,653 cases and 804,329 controls. For replication, AD data from the Finnish R10 database included 15,208 cases and 367,046 controls. We primarily utilized the inverse variance weighting method to assess the causal relationship between blood metabolites and AD.

Results

Our study identified significant causal relationships between nine genetically predicted blood metabolites and AD. Specifically, 1-palmitoyl-2-stearoyl-GPC (16:0/18:0) (OR = 0.92, 95% CI 0.89-0.94), 1-methylnicotinamide (OR = 0.93, 95% CI 0.89-0.98), linoleoyl-arachidonoyl-glycerol (18:2/20:4) [1] (OR = 0.94, 95% CI 0.92-0.96), and 1-arachidonoyl-GPC (20:4n6) (OR = 0.94, 95% CI 0.92-0.96) were associated with a reduced risk of AD. Conversely, phosphate / linoleoyl-arachidonoyl-glycerol (18:2/20:4) [2] (OR = 1.07, 95% CI 1.04-1.10), docosatrienoate (22:3n3) (OR = 1.07, 95% CI 1.04-1.10), retinol (Vitamin A) / linoleoyl-arachidonoyl-glycerol (18:2/20:4) [2] (OR = 1.08, 95% CI 1.05-1.11), retinol (Vitamin A) / linoleoyl-arachidonoylglycerol (18:2/20:4) [1] (OR = 1.08, 95% CI 1.05-1.12), and phosphate / linoleoyl-arachidonoyl-glycerol (18:2/20:4) [1] (OR = 1.09, 95% CI 1.07-1.12 were associated with an increased risk of AD. No evidence of reverse causality was found in the previously significant results. MVMR analysis further confirmed that 1-palmitoyl-2-stearoyl-GPC (16:0/18:0) and 1-methylnicotinamide are independent and dominant contributors to the development of AD.

Conclusion

Our study revealed a causal relationship between genetically predicted blood metabolites and AD. This discovery offers specific targets for drug development in the treatment of AD patients and provides valuable insights for investigating the underlying mechanisms of AD in future research.