Assessing the causal relationship between metabolic biomarkers and coronary artery disease by Mendelian randomization studies.

The development of coronary artery disease (CAD) is significantly affected by impaired endocrine and metabolic status. Under this circumstance, improved prevention and treatment of CAD may result from knowing the connection between metabolites and CAD. This study aims to delve into the causal relationship between human metabolic biomarkers and CAD by using two-sample Mendelian randomization (MR). Utilizing two-sample bidirectional MR analysis, we assessed the correlation between 1400 blood metabolites and CAD, and the metabolites data from the CLSA, encompassing 8299 participants. Metabolite analysis identified 1091 plasma metabolites and 309 ratios as instrumental variables. To evaluate the causal link between metabolites and CAD, we analyzed three datasets: ebi-a-GCST005195 (547,261 European & East Asian samples), bbj-a-159 (29,319 East Asian CAD cases & 183,134 East Asian controls), and ebi-a-GCST005194 (296,525 European & East Asian samples). To estimate causal links, we utilized the IVW method. To conduct sensitivity analysis, we used MR-Egger, Weighted Median, and MR-PRESSO. Additionally, we employed MR-Egger interception and Cochran's Q statistic to assess potential heterogeneity and pleiotropy. What's more, replication and reverse analyses were performed to verify the reliability of the results and the causal order between metabolites and disease. Furthermore, we conducted a pathway analysis to identify potential metabolic pathways. 59 blood metabolites and 27 metabolite ratios nominally associated with CAD (P < 0.05) were identified by IVW analysis method. A total of four known blood metabolites, namely beta-hydroxyisovaleroylcarnitine (OR 1.06, 95% CI 1.027-1.094, FDR 0.07), 1-palmitoyl-2-arachidonoyl (OR 1.07, 95% CI 1.029-1.110, FDR 0.09), 1-stearoyl-2- docosahexaenoyl (OR 1.07, 95% CI 1.034-1.113, FDR 0.07) and Linoleoyl-arachidonoyl-glycerol, (OR 1.07, 95% CI 1.036-1.105, FDR 0.05), and two metabolite ratios, namely spermidine to N-acetylputrescine ratio (OR 0.94, 95% CI 0.903-0.972, FDR 0.09) and benzoate to linoleoyl-arachidonoyl-glycerol ratio (OR 0.87, 95% CI 0.879-0.962, FDR 0.07), were confirmed as having a significant causal relationship with CAD, after correcting for the FDR method (p < 0. 1). A causal relationship was found to be established between beta -hydroxyisovalerylcarnitine and CAD with the validation in other two datasets. Moreover, multiple metabolic pathways were discovered to be associated with CAD. Our study supports the hypothesis that metabolites have an impact on CAD by demonstrating a causal relationship between human metabolites and CAD. This study is important for new strategies for the prevention and treatment of CAD.

Yang K ，Li J ，Hui X ，Wang W ，Liu Y ... -  《Scientific Reports》

Causal effects of genetically determined blood metabolites on asthma: a bidirectional Mendelian randomization study.

Lin L ，Wu Z ，Zhong A ，Luo H ，Xu W ，Luo W ... -  《-》

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

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

Huang Y ，Wang H ，Zheng J ，Zhou N ... -  《Annals of General Psychiatry》

Non-targeted metabolomics revealed novel links between serum metabolites and primary ovarian insufficiency: a Mendelian randomization study.

Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms. Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis. Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weighted-median =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants. By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention.

Chen S ，Zhou Z ，Zhou Z ，Liu Y ，Sun S ，Huang K ，Yang Q ，Guo Y ... -  《Frontiers in Endocrinology》

Genetically predicted 1091 blood metabolites and 309 metabolite ratios in relation to risk of type 2 diabetes: a Mendelian randomization study.

Metabolic dysregulation represents a defining characteristic of Type 2 diabetes (T2DM). Nevertheless, there remains an absence of substantial evidence establishing a direct causal link between circulating blood metabolites and the promotion or prevention of T2DM. In addressing this gap, we employed Mendelian randomization (MR) analysis to investigate the potential causal association between 1,091 blood metabolites, 309 metabolite ratios, and the occurrence of T2DM. Data encompassing single-nucleotide polymorphisms (SNPs) for 1,091 blood metabolites and 309 metabolite ratios were extracted from a Canadian Genome-wide association study (GWAS) involving 8,299 participants. To evaluate the causal link between these metabolites and Type 2 diabetes (T2DM), multiple methods including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode were employed. p-values underwent correction utilizing False Discovery Rates (FDR). Sensitivity analyses incorporated Cochran's Q test, MR-Egger intercept test, MR-PRESSO, Steiger test, leave-one-out analysis, and single SNP analysis. The causal effects were visualized via Circos plot, forest plot, and scatter plot. Furthermore, for noteworthy, an independent T2DM GWAS dataset (GCST006867) was utilized for replication analysis. Metabolic pathway analysis of closely correlated metabolites was conducted using MetaboAnalyst 5.0. The IVW analysis method utilized in this study revealed 88 blood metabolites and 37 metabolite ratios demonstrating a significant causal relationship with T2DM (p < 0.05). Notably, strong causal associations with T2DM were observed for specific metabolites: 1-linoleoyl-GPE (18:2) (IVW: OR:0.930, 95% CI: 0.899-0.962, p = 2.16 × 10-5), 1,2-dilinoleoyl-GPE (18:2/18:2) (IVW: OR:0.942, 95% CI: 0.917-0.968, p = 1.64 × 10-5), Mannose (IVW: OR:1.133, 95% CI: 1.072-1.197, p = 1.02 × 10-5), X-21829 (IVW: OR:1.036, 95% CI: 1.036-1.122, p = 9.44 × 10-5), and Phosphate to mannose ratio (IVW: OR:0.870, 95% CI: 0.818-0.926, p = 1.29 × 10-5, FDR = 0.008). Additionally, metabolic pathway analysis highlighted six significant pathways associated with T2DM development: Valine, leucine and isoleucine biosynthesis, Phenylalanine metabolism, Glycerophospholipid metabolism, Alpha-Linolenic acid metabolism, Sphingolipid metabolism, and Alanine, aspartate, and glutamate metabolism. This study identifies both protective and risk-associated metabolites that play a causal role in the development of T2DM. By integrating genomics and metabolomics, it presents novel insights into the pathogenesis of T2DM. These findings hold potential implications for early screening, preventive measures, and treatment strategies for T2DM.

Li J ，Wang W ，Liu F ，Qiu L ，Ren Y ，Li M ，Li W ，Gao F ，Zhang J ... -  《Frontiers in Genetics》