Infections are serious postoperative complications, and strongly affects the mortality and prognosis of patients. Body mass index (BMI) and lipids are factors in postoperative infection, but a causal relationship has not been know. In this Mendelian randomization (MR) study, we utilized genome-wide association study (GWAS) data from the Global Lipids Genetics Consortium and FinnGen database, treating lipids and BMI as exposures. Postoperative infection GWAS data from the UK Biobank served as the outcome. We utilized linkage disequilibrium score regression (LDSC) analysis to evaluate the genetic correlations between lipids, BMI, and postoperative infections. We employed univariate and reverse MR analyses to explore the causal relationships between exposure and outcome factors. The analysis primarily utilized the inverse variance weighted method, supplemented by MR-Egger and weighted median methods. The MR-PRESSO method was used to detect horizontal pleiotropy and potential outliers. Additionally, stepwise mediation MR analysis was employed to investigate indirect factors potentially influencing the relationships between lipids, BMI, and postoperative infections. The genetic covariance analysis indicates that there is no sample overlap among all the GWAS conducted. In the LDSC analysis, genetic correlations (GC) were found between BMI(GC = 0.430, P < 0.05), HDL-C(GC = - 0.414, P < 0.05), nonHDL-C(GC = 0.137, P < 0.05), TG(GC = 0.417, P < 0.05), and postoperative infection. HDL-C showing a negative genetic association with postoperative infection, while other phenotypes showed positive associations. In MR analysis, causal relationships were identified between BMI and postoperative infection (OR = 1.36, 95% CI = 1.16-1.60, P < 0.05) and HDL-C and postoperative infection (OR = 0.87, 95% CI = 0.78-0.96, P < 0.05), with BMI showing a positive causal association and HDL-C showing a negative causal association with postoperative infection. These findings are consistent with the LDSC results. In the reverse MR analysis, there was no significant causal relationship identified between postoperative infection and both BMI and lipids. Stepwise mediation MR analysis excluded the impact of potential mediating factors between exposure and outcomes. In this study, through LDSC and MR analyses, we identified genetic correlations and causal links between BMI, HDL, and postoperative infection. It was found that BMI might increase the risk of postoperative infection, whereas HDL could potentially lower the risk of developing postoperative infection.

Air pollution is a significant public health issue and an important risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), though the underlying mechanisms of this association are unknown. Herein, we aimed to identify metabolic signatures associated with exposure to ambient air pollution and to explore their associations with the risk of MASLD.

We utilized data from the UK Biobank cohort. Annual mean concentrations of PM2.5, PM10, NO2 and NOx were assessed for each participant using bilinear interpolation. The elastic net regression model was used to identify metabolites associated with four air pollutants and to construct metabolic signatures. Associations between air pollutants, metabolic signatures and MASLD were analyzed using Cox models. Mendelian randomization (MR) analysis was used to examine potential causality. Mediation analysis was employed to examine the role of metabolic signatures in the association between air pollutants and MASLD.

A total of 244,842 participants from the UK Biobank were included in this analysis. We identified 87, 65, 76, and 71 metabolites as metabolic signatures of PM2.5, PM10, NO2, and NOx, respectively. Metabolic signatures were associated with risk of MASLD, with hazard ratios (HRs) and 95% CIs of 1.10 (1.06-1.14), 1.06 (1.02-1.10), 1.24 (1.20-1.29) and 1.14 (1.10-1.19), respectively. The four pollutants were associated with increased risk of MASLD, with HRs (95% CIs) of 1.03 (1.01-1.05), 1.02 (1.01-1.04), 1.01 (1.01-1.02) and 1.01 (1.00-1.01), respectively. MR analysis indicated an association between PM2.5, NO2 and NOx-related metabolic signatures and MASLD. Metabolic signatures mediated the association of PM2.5, PM10, NO2 and NOx with MASLD.

PM2.5, PM10, NO2 and NOx-related metabolic signatures appear to be associated with MASLD. These signatures mediated the increased risk of MASLD associated with PM2.5, PM10, NO2 and NOx.

Air pollution is a significant public health issue and an important risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), however, the mechanism by which air pollution affects MASLD remains unclear. Our study used integrated serological metabolic data of 251 metabolites from a large-scale cohort study to demonstrate that metabolic signatures play a crucial role in the elevated risk of MASLD caused by air pollution. These results are relevant to patients and policymakers because they suggest that air pollution-related metabolic signatures are not only potentially associated with MASLD but also involved in mediating the process by which PM2.5, PM10, NO2, and NOx increase the risk of MASLD. Focusing on changes in air pollution-related metabolic signatures may offer a new perspective for preventing air pollution-induced MASLD and serve as protective measures to address this emerging public health challenge.

Circulating lipids and changes in lipid profiles have long been associated with the development of stroke but causal relationships remain unclear.In this study, we aimed to assess the causal relationships between lipid species and multiple stroke phenotypes to inform stroke prevention and treatment strategies. We conducted a two-sample Mendelian randomization analysis using data from genome-wide association studies. The primary method for causal assessment was inverse variance weighting (IVW), complemented by the MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, based on MR-Egger, MR-PRESSO, and Cochran's Q statistics, were also applied to reinforce the results. In total, potential causality was observed for 133 pairs of lipids with stroke types(P < 0.05). After multiple testing correction (PFDR < 0.05), potential causal associations remained for 10 pairs of lipids, including specific sterol esters and phosphatidylcholines, with various stroke subtypes. The findings demonstrate the significant role that genetically determined lipid profiles may play in the pathogenesis of stroke. Further research is needed to establish whether these biomarkers can be used for stroke prevention or treatment.

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), decreases quality of life and causes disability. The underlying processes are not fully understood. This study uses Mendelian randomization (MR) analysis to identify cytokines that may be associated with UC and CD, aiding in early diagnosis and treatment decisions. Methods Genome-wide association study (GWAS) data for inflammatory cytokine levels were obtained from a cohort of 14,824 individuals of European descent. The outcome data were then analyzed using summary-level GWAS data for UC and CD from the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). The analysis was primarily conducted using inverse-variance weighted (IVW) methods, with MR-Egger and weighted median serving as supplementary analyses. Sensitivity analyses included Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis.The inflammatory cytokines were subjected to additional scrutiny through the application of the Steiger test and reverse Mendelian randomization analysis. Subsequently, multivariable Mendelian randomization (MVMR) was employed to examine the associations of metabolites on UC and CD, in conjunction with linkage disequilibrium score regression (LDSC) and colocalization analysis. After FDR correction, we identified significant genetic associations of two inflammatory proteins (CXCL5 and CXCL9) with UC, and CXCL5 and IL-18R1 with CD. These findings were further validated by MVMR. Colocalization analyses demonstrated substantial genetic overlap between inflammatory proteins and IBD, with CXCL5 showing strong evidence of shared genetic variants with UC, and CXCL9 exhibiting genetic colocalization with CD, suggesting common genetic determinants underlying these inflammatory protein-IBD relationships. The current work presents evidence that presents evidence of significant associations between seven inflammatory protein factors and UC, as well as three inflammatory protein factors and CD. These findings provide novel insights into the biological mechanisms of IBD, and have implications for the screening, prevention, and treatment of IBD.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by a significant health burden. There is an essential need for novel biomarkers and therapeutic targets to improve diagnosis and management. Mendelian randomization (MR) was applied to explore causal links between SLE and various biomarkers like immune cells, metabolites, and inflammatory cytokines using multiple databases. Initially, biomarkers significantly associated with SLE were identified. Bidirectional MR helped clarify these relationships, and a two-step mediation MR examined their effects on SLE risk. Intersection analysis was used to identify biomarkers with consistent effects across datasets. Four biomarkers were identified as having significant associations with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI levels [odds ratio (OR), 1.379; 95% confidence interval (CI), 1.180 to 1.613; FDR, 0.046], IL-17A levels (OR, 2.197; 95% CI, 1.412 to 3.418; FDR, 0.044), N-acetyl-aspartyl-glutamate (NAAG) levels (OR, 0.882; 95% CI, 0.831 to 0.936; FDR, 0.030), and ribitol levels (OR, 0.743; 95% CI, 0.644 to 0.857; FDR, 0.012). Bidirectional MR showed an inverse effect of NAAG on IL-17A levels (OR, 0.978; 95% CI, 0.962 to 0.994; p = 0.006). Mediation analysis indicated that NAAG influenced SLE risk both directly (beta =  - 0.108) and indirectly through IL-17A (beta =  - 0.018), highlighting the potential mediating role of IL-17A. After expanding the significance criteria to p < 0.05, intersection analysis across multiple datasets revealed 29 biomarkers with consistent beta directions, including 19 potential risk factors (beta > 0) and 10 protective factors (beta < 0) for SLE. This research has revealed significant genetic associations with SLE and demonstrated that IL-17A mediates the relationship between NAAG levels and SLE risk, highlighting potential new targets for personalized therapeutic interventions. Key Points • This study employs MR to identify significant genetic associations between various biomarkers and SLE, providing novel insights into potential biomarkers and therapeutic targets. • Four key biomarkers were identified as significantly associated with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI, IL-17A, N-acetyl-aspartyl-glutamate (NAAG), and ribitol. • The findings suggest that NAAG levels have a protective effect against SLE, partly mediated through IL-17A, indicating a complex interplay between these biomarkers in the pathogenesis of SLE. • Intersectional analysis across multiple datasets revealed 29 biomarkers with consistent effects on SLE risk, highlighting new directions for future research and potential personalized therapeutic strategies.

Gastric cancer (GC) remains one of the predominant malignant tumors within the digestive tract, yet its underlying biological mechanisms remain elusive. The primary objective of this study is to delineate the causal relationship between circulating metabolites and GC.

The primary Mendelian randomization (MR) analysis was based on three large GWAS datasets. While the inverse variance weighted served as the primary analysis technique for investigating causal relationships, additional sensitivity analyses were facilitated through methods such as MR-PRESSO, the weighted median, and MR-Egger. Subsequently, replication, meta-analysis, and multivariable MR were executed using another GC GWAS.

The results of this study indicated significant associations between three metabolites 3-methyl-2-oxovalerate (OR 5.8, 95%CI: 1.53-22.05, p = 0.0099), piperine (OR 2.05, 95%CI: 1.13-3.7, p = 0.0175), Phe-Phe dipeptide (OR 0.16, 95%CI: 0.03-0.93, p = 0.0409) and GC.

The present study provides evidence supporting a causal relationship between these three circulating metabolites and GC risk. Elevated levels of 3-methyl-2-oxovalerate and piperine may increase the risk of GC, while Phe-Phe dipeptide may have a protective effect. By integrating genomics and metabolomics, we offer a novel perspective on the biological mechanisms underlying GC. Such insights have the potential to enhance strategies for the screening, prevention, and treatment of GC.

Rheumatoid arthritis (RA) is a common chronic autoimmune disorder with an incompletely elucidated pathogenesis. Emerging research indicates that dietary factors may significantly influence the onset and progression of RA. Nevertheless, the causal relationship between dietary habits and RA remains ambiguous. This investigation employed a multivariable Mendelian randomization (MVMR) methodology to rigorously assess the potential causal effects of various dietary factors on RA risk. This study utilized genome-wide association study (GWAS) summary-level data, encompassing dietary preferences (n = 113,425-159,579), and RA data (n = 302,614) from the most recent FinnGen database. The primary causal analysis was conducted using inverse-variance weighting (IVW), complemented by MR-Egger, weighted median, Bayesian weighted Mendelian randomization, and Robust Adjusted Profile Score (RAPS) methodologies. Sensitivity analyses incorporated Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. Steiger tests were employed to evaluate the identified dietary preferences. MVMR was utilized to assess the direct impact of dietary factors on RA. The study identified significant associations between RA and nine dietary preferences: F-lentils/beans liking (Fle), chips liking (Chips), coffee difference liking (CD), coffee with sugar liking (CWS), milk chocolate liking (MC), coriander liking (Coriander), pollock liking (Pollock), soft cheese liking (SC), and blue cheese liking (BC). The MVMR analysis indicated that genetically predicted coriander and MC have a direct impact on RA, independent of other dietary factors. This study presents novel causal evidence suggesting that dietary preferences may impact the risk of RA. Specifically, a reduction in the consumption of milk chocolate and coriander may contribute to the prevention and alleviation of RA. However, additional research is required to elucidate the underlying mechanisms and to validate these findings across diverse populations.

Background: Clinical studies have shown that the onset and exacerbation of chronic obstructive pulmonary disease (COPD) are related to obesity and dietary behaviours, but the genetic relationship between them is not clear.Aims: To investigate the relationship between the genetic determinants of obesity, dietary habits (alcohol consumption, intake of sweets, salt intake) and COPD.Methods: Exposure and outcome datasets were obtained from the IEU Open GWAS project. The exposure dataset includes dietary habits (Salt added to food, Sweets intake, Alcohol consumption), obesity level (represented by body mass index (BMI) and body fat percentage (BFP) data sets.). The outcome dataset includes COPD and acute COPD admissions. The collected data were imported into the RStudio software and conducted Mendelian randomisation analysis. Additionally, heterogeneity and horizontal pleiotropy tests were conducted on the data to ensure the veracity of the results.Results: The results showed that BMI was positively correlated with the risk of acute COPD admission (OR = 1.74, 95% CI 1.39-2.18) and COPD (OR = 1.81, 95%CI 1.41-2.33). In addition, BFP was also a risk factor for COPD (OR = 1.98, 95% CI 1.42-2.77) and acute exacerbation of COPD admission (OR = 1.99, 95%CI 1.43-2.77). The increase of salt, sugar and alcohol consumption will not increase the risk of COPD and the risk of hospitalisation due to COPD.Conclusion: Therefore, we should strengthen the guidance of diet and living habits of obese patients. For patients with heavier weight and higher body fat rate, they should be instructed to lose weight and fat to prevent the occurrence of COPD. For obese patients with COPD, more attention should be paid to prevent the occurrence of acute exacerbation of COPD in advance.

Cholesterol-lowering medications, blood pressure medication, insulin, and exogenous hormones (including hormone replacement therapy, oral contraceptives, and minipills) are commonly utilized in clinical practice. Recent studies indicate that the use of these medications may significantly influence the occurrence and progression of cerebral infarction. This study aims to investigate the relationship between these medications and cerebral infarction using Mendelian randomization (MR) analysis, with the goal of offering valuable insights for the clinical management of cerebral infarction.

To explore the correlation between cholesterol-lowering medication, blood pressure medication, insulin, exogenous hormones, and cerebral infarction, relevant single nucleotide polymorphisms (SNPs) were extracted from the genome-wide association study (GWAS) database. Methods for univariate MR analysis include inverse variance weighting (IVW), the weighted median method, and the MR-Egger method, with IVW being the predominant approach. Subsequently, multivariable Mendelian randomization (MVMR) was conducted on the positive results obtained from the IVW analysis to verify the independent effect of each positive exposure, with IVW still predominating. The causal relationship between this class of drugs and cerebral infarction was evaluated using odds ratio (OR) and 95% confidence interval (CI). MR-PRESSO was used to test for pleiotropy. The robustness of the findings was assessed through leave-one-out analysis, Cochran's Q test, and funnel plot.

Univariate MR results indicated that the use of blood pressure medication, insulin, and cholesterol-lowering medication was significantly associated with the occurrence of cerebral infarction (all p < 0.05). However, due to the stringent inclusion criteria for SNPs, the number of available SNPs is insufficient to elucidate the association between exogenous hormone drugs, contraceptives, and cerebral infarction. Furthermore, the MVMR analysis, which builds upon univariate MR, only identified significant causal associations between blood pressure medication, insulin, and cerebral infarction (p < 0.05). The association between cholesterol-lowering medication and cerebral infarction was confounded by other positive exposures and did not demonstrate a significant causal relationship when only its independent effects were considered. After integrating the findings from both univariate and MVMR and controlling for confounding variables to the greatest extent possible, the available evidence supports only a potential causal relationship between blood pressure medication, insulin, and cerebral infarction.

This study suggests that the misuse of blood pressure medications and insulin is a risk factor for the occurrence and progression of cerebral infarction.

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder worldwide, with an increasing incidence in recent years. While previous studies have suggested an association between the air pollutant PM2.5 and NAFLD, there is still considerable debate regarding the existence of a clear causal relationship between air pollution and NAFLD. This study aims to employ Mendelian randomization methods to evaluate the causal relationship between major air pollutants and NAFLD.

We conducted Mendelian randomization analyses on a large-scale publicly available genome-wide association study (GWAS) dataset of European populations to dissect the association between air pollutants, NAFLD, and liver function indicators. We used five different analysis methods, including Inverse-variance weighted (IVW), Weighted median, MR-Egger, Simple mode, and Weighted mode, to analyze the data. We also tested for pleiotropy, heterogeneity, and sensitivity of the results.

This study utilized four common exposures related to air pollution and four outcomes related to NAFLD. The results regarding the association between air pollutants and NAFLD (PM2.5: P=0.808, 95% CI=0.37-3.56; PM10: P=0.238, 95% CI=0.33-1.31; nitrogen dioxide: P=0.629, 95% CI=0.40-4.61; nitrogen oxides: P=0.123, 95% CI=0.13-1.28) indicated no statistically significant correlation between them. However, notably, there was a causal relationship between PM10 and serum albumin (ALB) levels (P=0.019, 95% CI=1.02-1.27).

This MR study found no evidence of a causal relationship between air pollution and NAFLD in European populations. However, a statistically significant association was observed between PM10 and ALB levels, suggesting that the air pollutant PM10 may impact the liver's ability to synthesize proteins.

Common side effect of Herpes Zoster, postherpetic neuralgia (PHN), causes persistent pain that seriously affects quality of life. Lack of dependable biomarkers makes the clinical diagnosis and treatment of PHN difficult, so complicating the assessment of therapeutic efficacy. Blood metabolites are becoming more and more well known as significant disease markers. With an aim to find possible biomarkers for diagnosis and treatment, this work investigates the causal link between blood metabolites and PHN using Mendelian randomization.

This work evaluated causal relationships between PHN and 1,091 plasma metabolites using Mendelian randomization (MR). Complementing MR-Egger and weighted median approaches, the main causality analysis was done using inverse variance weighted (IVW) and Wald ratio (WR) approaches. Robustness was checked using sensitivity analyses including CAUSE, Cochran's Q tests, leave-one-out analysis, MR-PRESSO, and MR-Egger intercept analysis. Reverse MR analysis and linkage disequilibrium score regression (LDSC) was used to assess significant correlations as well. Two-step MR analysis was also used to look at the mediating function of positively correlated metabolites in the causal pathway.

The results of this study indicated a significant association between N-acetyl-aspartyl-glutamate (NAAG) and PHN, with an odds ratio (OR) of 0.83 (95% CI: 0.76-0.91, p = 2.68E-05). Moreover, five potential associated metabolites were identified: Gamma-glutamylthreonine (OR = 1.60, 95% CI: 1.16-2.20, p = 0.004), 3-hydroxyphenylacetoylglutamine (OR = 1.43, 95% CI: 1.00-2.05, p = 0.048), Caprate (10:0) (OR = 1.86, 95% CI: 1.11-3.12, p = 0.018), X-12013 (OR = 1.64, 95% CI: 1.03-2.60, p = 0.035), and X-17328 (OR = 1.50, 95% CI: 1.04-2.18, p = 0.032). Additionally, NAAG likely acts as a complete mediator between FOLH1(CGPII) and postherpetic neuralgia in the causal pathway.

The results of this study indicated a significant association between N-acetyl-aspartyl-glutamate (NAAG) and PHN, with an odds ratio (OR) of 0.83 (95% CI: 0.76-0.91, p = 2.68E-05). Furthermore five possible related metabolites were found: Glutamylthreonine gamma-wise (OR = 1.60, 95% CI: 1.16-2.20, p = 0.004), 3-hydroxyphenylacetoylglutamine (OR = 1.43, 95% CI: 1.00-2.05, p = 0.048), Caprate (10:0) (OR = 1.86, 95% CI: 1.11-3.12, p = 0.018), X-12013 (OR = 1.64, 95% CI: 1.03-2.60, p = 0.035), and X-17328 (OR = 1.50, 95% CI: 1.04-2.18, p = 0.032). Furthermore, in the causal pathway NAAG most certainly serves as a complete mediator between FOLH1(CGPII) and postherpetic neuralgia.

Prior investigations have suggested a significant association between coagulation factors and ovarian cancer; however, the precise nature of the causal relationship remains elusive. Our objective is to thoroughly investigate this causal link and delineate the influence of coagulation factors on the risk of ovarian cancer through a rigorous two-sample Mendelian randomization (MR) analysis.

Genetic instrumental variables representing coagulation factors were sourced from four distinct data repositories. Summary statistics pertaining to ovarian cancer were obtained from two extensive Genome-Wide Association Studies (GWAS) for primary and replication analyses, respectively. The primary Mendelian randomization (MR) analysis utilized the inverse-variance weighted (IVW) method. To fortify the reliability of our findings, additional analyses were conducted, including the weighted-median method, MR-Egger regression, MR pleiotropy residual sum and outlier test, Cochran's Q statistic test, MR-Egger intercept analysis, and leave-one-out method, among others.

We identified four coagulation factors that were associated with the risk of ovarian cancer in the primary analysis, [odds ratio (OR): 1.365, 95% confidence interval (CI): 1.209-1.542, P <0.001 for von Willebrand factor measurement(vWF); OR: 1.060, 95% CI: 1.018-1.104, P = 0.005 for A disintegrin and metalloproteinase with thrombospondin motifs 13 (ADATMS13); OR: 1.317, 95% CI: 1.002-1.730, P = 0.048 for activated partial thromboplastin time (aPTT); OR: 1.139, 95% CI: 1.063-1.221, P <0.001 for coagulation Factor VIII (FVIII)]. In the meta-analysis, we found that higher levels of coagulation factor VII measurement(FVII) (OR=1.0007, 95% CI: 1.0001-1.0013, P=1.0007) was associated with increased ovarian cancer risk. The results of sensitivity analyses for these coagulation factors were consistent (P<0.05).

Our systematic analyses have furnished evidence suggesting a plausible causal association between FVII and the susceptibility to ovarian cancer. Further investigations are warranted to delineate the mechanistic pathways through which coagulation factors influence the progression of ovarian cancer.

Enhancing therapy choices for varying stages of esophageal cancer and improving patient survival depend on timely and precise diagnosis. Blood metabolites may play a role in either causing or preventing esophageal cancer, but further research is needed to determine whether blood metabolites constitute a genetic risk factor for the disease. In order to tackle these problems, we evaluated the causal association between esophageal cancer and 486 blood metabolites that functioned as genetic proxies using a two-sample Mendelian randomization (MR) study.

We utilized two-sample MR analyses to evaluate the causal links between blood metabolites and esophageal cancer. For the exposure, we used a genome-wide association study (GWAS) of 486 metabolites, and a GWAS study on esophageal cancer from Sakaue et al. was used for preliminary analyses. Causal analyses employed randomized inverse variance weighted (IVW) as the main method, supplemented by MR-Egger and weighted median (WM) analyses. Sensitivity analyses included the MR-Egger intercept test, Cochran Q test, MR-PRESSO, and leave-one-out analysis. Additionally, independent esophageal cancer GWAS data were utilized for replication and meta-analysis. FDR correction was applied to discern features with causal relationships. For conclusive metabolite identification, we conducted Steiger tests, linkage disequilibrium score regression, and colocalization analyses. Moreover, we utilized the program MetaboAnalyst 5.0 to analyze metabolic pathways.

This study found an important association between esophageal cancer and three metabolites: 1-linoleoylglycerophosphoethanolamine* [odds ratio (OR) = 3.21, 95% confidence interval (CI): 1.42-7.26, p < 0.01], pyroglutamine* (OR = 1.92, 95% CI: 1.17-3.17, p < 0.01), and laurate (12:0) (OR = 3.06, 95% CI: 1.38-6.78, p < 0.01).

This study establishes a causal link between three defined blood metabolites and esophageal cancer, offering fresh insights into its pathogenesis.

Peripheral Artery Disease (PAD) is a common vascular disorder typically caused by atherosclerosis, leading to impaired blood supply to the lower extremities, resulting in pain, necrosis, and even amputation. Despite extensive research into the pathogenesis of PAD, many mysteries remain, particularly regarding its association with human blood metabolites.

To explore the causal relationship between 1,400 serum metabolites and PAD, a two-sample Mendelian randomization (MR) analysis was conducted. The Inverse Variance-Weighted (IVW) method was the primary technique used to estimate the causal impact of the metabolites on PAD. To enhance the analysis, several additional methods were employed: MR-Egger regression, weighted median, simple mode, and weighted mode. These methods provided a comprehensive evaluation beyond the primary IVW estimation. To ensure the validity of the MR findings, sensitivity analysis was performed. Furthermore, a bidirectional MR approach was applied to explore the possibility of a reverse causal effect between PAD and potential candidate metabolites.

After rigorous selection, significant associations were found between 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) and X-17653 levels with PAD. 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) was positively associated with increased PAD risk (IVW OR = 1.13, 95% CI, 1.06-1.21; P < 0.001). X-17653 levels were associated with decreased PAD risk (IVW OR = 0.88, 95% CI, 0.83-0.94; P < 0.001). In the reverse direction, PAD was positively associated with increased 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) levels (IVW OR = 1.16, 95% CI, 1.01-1.34; P = 0.036). PAD was not associated with X-17653.

Among 1,400 blood metabolites, 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) and X-17653 are significantly associated with PAD risk. Importantly, in the reverse direction, PAD was found to be positively associated with increased levels of 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4). This highlights the bidirectional nature of the association and suggests a potential feedback mechanism between PAD and this specific lipid species. 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) may serve as potential biomarkers for PAD, aiding early diagnosis and providing novel avenues for personalized treatment and management. However, further validation and research are warranted despite the promising results.

Limited and inconclusive data from observational studies and randomized controlled trials exist on the levels of circulating micronutrients in the blood and their association with respiratory infections.

A Mendelian randomization (MR) analysis was conducted to assess the impact of 12 micronutrients on the risk of three types of infections [upper respiratory tract infections (URTI), lower respiratory tract infections (LRTI), and pneumonia] and their 14 subtypes. This study utilized a bidirectional MR approach to evaluate causal relationships and included a range of sensitivity analyses and multivariable MR to address potential heterogeneity and pleiotropy. The threshold for statistical significance was set at p < 1.39 × 10-3.

Meta-analysis revealed that higher levels of circulating copper were significantly associated with a reduced risk of URTI (odds ratio (OR) = 0.926, 95% CI: 0.890 to 0.964, p = 0.000195). Additionally, copper demonstrated a suggestive association with a reduced risk of LRTI (p = 0.0196), and Vitamin B6 was nominally associated with a reduced risk of pneumonia (p = 0.048). Subtype analyses further indicated several suggestive associations: copper reduces the risk of acute pharyngitis (p = 0.029), vitamin C increases the risk of critical care admissions for pneumonia (p = 0.032) and LRTI (p = 0.021), and folate reduces the risk of viral pneumonia (p = 0.042). No significant connections were observed for other micronutrients.

We observed a genetically predicted potential protective effect of copper in susceptibility to upper respiratory infections. This provides new insights for further research into the role of micronutrients in the prevention and treatment of infection.

Hypercholesterolemia, a critical contributor to cardiovascular disease, is not fully understood in terms of its relationship with serum metabolites and their role in disease pathogenesis.

This study leveraged GWAS data to explore the relationship between serum metabolites and hypercholesterolemia, pinpointing significant metabolites via Mendelian Randomization (MR) and KEGG pathway enrichment analysis. Data on metabolites were sourced from a European population, with analysis focusing on individuals diagnosed with hypercholesterolemia.

Out of 486 metabolites analyzed, ten showed significant associations with hypercholesterolemia, categorized into those enhancing risk and those with protective effects. Specifically, 2-methoxyacetaminophen sulfate and 1-oleoylglycerol (1-monoolein) were identified as risk-enhancing, with odds ratios (OR) of 1.545 (95% CI: 1.230-1.939; P_FDR = 3E-04) and 1.462 (95% CI: 1.036-2.063; P_FDR = 0.037), respectively. On the protective side, 3-(cystein-S-yl)acetaminophen, hydroquinone sulfate, and 2-hydroxyacetaminophen sulfate demonstrated ORs of 0.793 (95% CI: 0.735-0.856; P_FDR = 6.18E-09), 0.641 (95% CI: 0.423-0.971; P_FDR = 0.042), and 0.607 (95% CI: 0.541-0.681; P_FDR = 5.39E-17), respectively. In addition, KEGG pathway enrichment analysis further revealed eight critical pathways, comprising "biosynthesis of valine, leucine, and isoleucine", "phenylalanine metabolism", and "pyruvate metabolism", emphasizing their significant role in the pathogenesis of hypercholesterolemia.

This study underscores the potential causal links between particular serum metabolites and hypercholesterolemia, offering innovative viewpoints on the metabolic basis of the disease. The identified metabolites and pathways offer promising targets for therapeutic intervention and warrant further investigation.

Circulating metabolites, which play a crucial role in our health, have been reported to be disordered in basal cell carcinoma (BCC). Despite these findings, evidence is still lacking to determine whether these metabolites directly promote or prevent BCC's progression. Therefore, our study aims to examine the potential effects of circulating metabolites on BCC progression.

We conducted a two-sample Mendelian randomization (MR) analysis using data from two separate genome-wide association studies (GWAS). The primary study included data for 123 blood metabolites from a GWAS with 25,000 Finnish individuals, while the secondary study had data for 249 blood metabolites from a GWAS with 114,000 UK Biobank participants.GWAS data for BCC were obtained from the UK Biobank for the primary analysis and the FinnGen consortium for the secondary analysis. Sensitivity analyses were performed to assess heterogeneity and pleiotropy.

In the primary analysis, significant causal relationships were found between six metabolic traits and BCC with the inverse variance weighted (IVW) method after multiple testing [P < 4 × 10-4 (0.05/123)]. Four metabolic traits were discovered to be significantly linked with BCC in the secondary analysis, with a significance level of P < 2 × 10-4 (0.05/249). We found that all the significant traits are linked to Polyunsaturated Fatty Acids (PUFAs) and their degree of unsaturation.

Our research has revealed a direct link between the susceptibility of BCC and Polyunsaturated Fatty Acids and their degree of unsaturation. This discovery implies screening and prevention of BCC.

Blood metabolite abnormalities have revealed an association with cholestatic liver diseases (CLDs), while the underlying metabolic mechanisms have remained sluggish yet. Accordingly, the present evaluation aims to investigate the causal relationship between blood metabolites and the risk of two major CLDs, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC).

Univariable and multivariable Mendelian randomization (MR) approaches were employed to uncover potential causal associations between blood metabolites and 2 CLDs, including PBS and PSC, through extracting instrumental variables (IVs) for metabolites from genome-wide association studies (GWAS) conducted on European individuals. The GWAS summary data of PBC or PSC were sourced from two distinct datasets. The initial analysis employed inverse variance weighted (IVW) and an array of sensitivity analyses, followed by replication and meta-analysis utilizing FinnGen consortium data. Finally, a multivariable MR analysis was carried out to ascertain the independent effects of each metabolite. Furthermore, the web-based tool MetaboAnalyst 5.0 was used to perform metabolic pathway examination.

A genetic causality between 15 metabolites and CLDs was recognized after preliminary analysis and false discovery rate (FDR) correction. Subsequently, 9 metabolites consistently represented an association through replication and meta-analysis. Additionally, the independent causal effects of 7 metabolites were corroborated by multivariable MR analysis. Specifically, the metabolites isovalerylcarnitine (odds ratio [OR] = 3.146, 95% confidence intervals [CI]: 1.471-6.726, p = 0.003), valine (OR = 192.44, 95%CI: 4.949-7483.27, p = 0.005), and mannose (OR = 0.184, 95%CI: 0.068-0.499, p < 0.001) were found to have a causal relationship with the occurrence of PBC. Furthermore, erythrose (OR = 5.504, 95%CI: 1.801-16.821, p = 0.003), 1-stearoylglycerophosphocholine (OR = 6.753, 95%CI: 2.621-17.399, p = 7.64 × 10-5), X-11847 (OR = 0.478, 95%CI: 0.352-0.650, p = 2.28 × 10-6), and X-12405 (OR = 3.765, 95%CI: 1.771-8.005, p = 5.71 × 10-4) were independently associated with the occurrence of PSC. Furthermore, the analysis of metabolic pathways identified seven significant pathways in two CLDs.

The findings of the present study have unveiled robust causal relationships between 7 metabolites and 2 CLDs, thereby providing novel insights into the metabolic mechanisms and therapeutic strategies for these disorders.

We aimed to elucidate the causal relationship between plasma metabolites and the vulnerability to Osteoarthritis (OA), encompassing both hip OA and knee OA.

We conducted a two-way two-sample Mendelian randomization (MR) analysis to investigate the association of 1,400 plasma metabolites with OA. The Inverse Variance Weighted (IVW) model served as the primary two-sample MR Analysis method, with supplementary analysis using the Weighted Median (WM) and MR Egger methods. To ensure the robustness of our findings, sensitivity analyses were performed, incorporating Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and Leave-One-Out analyses. To validate the identified metabolites, we utilized the Steiger test and linkage disequilibrium score regression.

A total of 94 plasma metabolites were associated with osteoarthritis, with 60 associated with hip OA and 106 associated with knee OA. IVW analysis revealed that tryptophan levels showed the strongest positive association with hip OA (OR [95% CI]: 1.119 [1.024, 1.223]), while X-24757 levels exhibited the highest positive association with knee osteoarthritis (OR [95% CI]: 1.095 [1.032, 1.162]). Ethylparaben sulfate levels were found to have the greatest positive association with hip OA (OR [95% CI]: 1.118 [1.015, 1.231]). Notably, the plasma metabolite X-2475 showed a strong robust random effect across all three types of osteoarthritis. Metabolic pathway analysis revealed that the pathogenesis of osteoarthritis in the hip was mediated by acetylarginine, specifically in four important metabolic pathways: ethanol degradation (p = 0.044), amino sugar metabolism (p = 0.090), fatty acid biosynthesis (p = 0.095), and aspartate metabolism (p = 0.097816).

There is a significant association between tryptophan levels and the risk of hip OA, as well as X-24757 levels and the risk of knee osteoarthritis. Additionally, X-24757 levels are also linked to the risk of hip OA. Moreover, this study has identified four crucial metabolic pathways in hip osteoarthritis, which are all regulated by acetylarginine. These findings provide valuable insights into potential biomarkers for OA and highlight potential pathways for its prevention and clinical intervention.

The relationship between dietary factors and hernias is currently unclear.

The UK Biobank was used to extract dietary factors that were used as exposures, including intake of alcohol, non-oily fish, beef, fresh fruit, oily fish, salad/raw vegetables, dried fruit, coffee, cereal, salt, tea, water, cooked vegetables, cheese, Lamb/mutton, pork, poultry, processed meat, and bread. The FinnGen biobank was used to obtain GWAS data on hernias as outcomes. The main analysis of this study was performed using the weighted median, MR-Egger, and IVW methods. Cochran's Q test was utilized to assess heterogeneity. To find potential outliers, the MR-PRESSO method was used. Leave-one-out analysis was employed to assess the IVW method's robustness.

Alcoholic consumption per week (OR: 0.614; p = 0.00614) reduced the risk of inguinal hernia. Alcohol intake frequency (OR: 1.309; p = 0.0477) increased the risk of ventral hernia (mainly including incisional hernia and parastomal hernia). The intake of non-oily fish (OR: 2.945; p = 0.0214) increased the risk of inguinal hernia. Salt added to food (OR: 1.841; p = 0.00267) increased the risk of umbilical hernia. Cheese intake (OR: 0.434; p = 0.000536) and dried fruit intake (OR: 0.322; p = 0.00716) decreased the risk of ventral hernia, while cooked vegetable intake (OR: 4.475; p = 0.0380) increased the risk of ventral hernia. No causal relationships were found with hernias from other dietary factors.

Inguinal, umbilical, and ventral hernias are all related to dietary factors.

Previous research has hinted at a crucial link between gut microbiota and arterial embolism and thrombosis, yet the causal relationship remains enigmatic. To gain a deeper understanding, we aimed to comprehensively explore the causal relationship and elucidate the impact of the gut microbiota on the risk through a two-sample Mendelian randomization (MR) study.

Genetic instrumental variables for gut microbiota were identified from a genome-wide association study (GWAS) of 18,340 participants. Summary statistics for IBS were drawn from a GWAS including 1,076 cases and 381,997 controls. We used the inverse-variance weighted (IVW) method as the primary analysis. To test the robustness of our results, we further performed the weighted median method, MR-Egger regression, and MR pleiotropy residual sum and outlier test.

We identified three bacterial traits that were associated with the risk of arterial embolism and thrombosis: odds ratio (OR): 1.58, 95% confidence interval (CI): 1.08-2.31, p = 0.017 for genus Catenibacterium; OR: 0.64, 95% CI: 0.42-0.96, p = 0.031 for genus Dialister; and OR: 2.08, 95% CI: 1.25-3.47, p = 0.005 for genus Odoribacter. The results of sensitivity analyses for these bacterial traits were consistent (P<0.05).

Our systematic analyses provided evidence to support a potential causal relationship between several gut microbiota taxa and the risk of arterial embolism and thrombosis. More studies are required to show how the gut microbiota affects the development of arterial embolism and thrombosis.

Epidemiological studies show dietary habits can have an impact on the risk of cholelithiasis, but the relationship is still unclear. We used a comprehensive Mendelian randomization (MR) study to explore the relationship between dietary habits and cholelithiasis.

The 18 dietary habits were divided into six categories: meat foods, cereals, vegetables, fruits, dairy products, beverages, and condiments. Cholelithiasis data came from a GWAS meta-analysis and the FinnGen consortium. The inverse variance weighted (IVW), the weighted median (WM), and MR-Egger approaches were used as the main MR analysis methods. In addition, multiple sensitivity analysis and meta-analysis were performed to verify the robustness of the results.

Dried fruit intake [odds ratio (OR) = 0.568; 95% confidence interval (CI), 0.405-0.797; p = 0.001] was discovered to reduce the risk of cholelithiasis. The sensitivity analysis and meta-analysis showed reliable results for the relationship between dried fruit intake and cholelithiasis.

Our study found that dried fruit intake is a protective factor in the development of cholelithiasis. However, the mechanisms of action need to be further explored.

Substantial focus has been placed on atrial fibrillation (AF) treatment and associated stroke prevention rather than preventing AF itself. We employed Mendelian randomization (MR) approach to examine the causal relationships between 50 modifiable risk factors (RFs) and AF.

Instrumental variables for genetically predicted exposures were derived from corresponding genome-wide association studies (GWASs). Summary-level statistical data for AF were obtained from a GWAS meta-analysis (discovery dataset, N = 1,030,836) and FinnGen (validation dataset, N = 208,594). Univariable and multivariable MR analyses were performed, primarily using inverse variance weighted method with a series of robust sensitivity analyses.

Genetic predisposition to insomnia, daytime naps, apnea, smoking initiation, moderate to vigorous physical activity and obesity traits, including body mass index, waist-hip ratio, central and peripheral fat/fat-free mass, exhibited significant associations with an increased risk of AF. Coffee consumption and ApoB had suggestive increased risks. Hypertension (odds ratio (OR) 95% confidence interval (CI): 5.26 (4.42, 6.24)), heart failure (HF) (OR 95% CI, 4.77 (2.43, 9.37)) and coronary artery disease (CAD) (OR 95% CI: 1.20 (1.16, 1.24)) were strongly associated with AF, while college degree, higher education attachment and HDL levels were associated with a decreased AF risk. Reverse MR found a bidirectional relationship between genetically predicted AF and CAD, HF and ischemic stroke. Multivariable analysis further indicated that obesity-related traits, systolic blood pressure and lower HDL levels independently contributed to the development of AF.

This study identified several lifestyles and cardiometabolic factors that might be causally related to AF, underscoring the importance of a holistic approach to AF management and prevention.

This study employs a two-sample bidirectional Mendelian randomization (MR) approach to systematically evaluate the causal relationship between gut microbiota and oral cavity cancer (OCC).

To address the challenge in establishing the causal relationship between gut microbiota and OCC, we applied a systematic MR analysis.

Utilizing GWAS data from the MiBioGen consortium (18,340 individuals) and UK Biobank (n = 264,137), we selected instrumental variables and employed MR-Egger, weighted median, IVW, and weighted mode analyses. Heterogeneity and pleiotropy were assessed using Cochran's Q test and MR-Egger intercept test.

Our findings indicate, at the order level, Bacteroidales (OR = 0.9990, 95% CI = 0.9980-1.0000, P = 0.046), Burkholderiales (OR = 1.0009, 95% CI = 1.0001-1.0018, P = 0.033), and Victivallales (OR = 0.9979, 95% CI = 0.9962-0.9995, P = 0.037) exhibit causality on OCC in the Weighted median, IVW, and MR-Egger analyses, respectively. At the family level, Alcaligenaceae (OR = 1.0012, 95% CI = 1.0004-1.0019, P = 0.002) and Clostridiaceae1 (OR = 0.9970, 95% CI = 0.9948-0.9992, P = 0.027) show causality on OCC in IVW and MR-Egger analyses. At the genus level, Clostridiumsensustricto1 (IVW, OR = 0.9987, 95% CI = 0.9980-0.9995, P = 0.001; MR-Egger, OR = 0.9978, 95% CI = 0.9962-0.9995, P = 0.035), Desulfovibrio (IVW, OR = 1.0008, 95% CI = 1.0001-1.0015, P = 0.016), Eggerthella (IVW, OR = 0.9995, 95% CI = 0.9990-1.0000, P = 0.048), Eubacterium fissicatena group (IVW, OR = 1.0005, 95% CI = 1.0000-1.0009, P = 0.032), and Holdemanella (IVW, OR = 0.9994, 95% CI = 0.9989-0.9999, P = 0.018) are implicated in causing OCC in related analyses.

Our study identifies Burkholderiales order, Alcaligenaceae family, Desulfovibrio genus, and Eubacterium fissicatena group as causally increasing OCC risk. In contrast, Bacteroidales order, Victivallales order, Clostridiaceae1 family, Clostridiumsensustricto1 genus, Eggerthella genus, and Holdemanella genus are causally associated with a decreased OCC risk. However, further investigations are essential to delineate an optimal gut microbiota composition and unravel the underlying mechanisms of specific bacterial taxa in OCC pathophysiology.

Numerous studies have established a link between coronary heart disease and metabolic disorders. Yet, causal evidence connecting metabolites and Coronary Heart Disease (CHD) remains scarce. To address this, we performed a bidirectional Mendelian Randomization (MR) analysis investigating the causal relationship between blood metabolites and CHD.

Data were extracted from published genome-wide association studies (GWASs) on metabolite levels, focusing on 1,400 metabolite summary data as exposure measures. Primary analyses utilized the GWAS catalog database GCST90199698 (60,801 cases and 123,504 controls) and the FinnGen cohort (43,518 cases and 333,759 controls). The primary method used for causality analysis was random inverse variance weighting (IVW). Supplementary analyses included MR-Egger, weighted mode, and weighted median methods. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Reverse MR analysis was employed to evaluate the direct impact of metabolites on coronary heart disease. Additionally, replication and meta-analysis were performed. We further conducted the Steiger test and colocalization analysis to reflect the causality deeply.

This study identified eight metabolites associated with lipids, amino acids and metabolite ratios that may influence CHD risk. Findings include: 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels: OR = 1.08; 95% CI 1.04-1.12; P = 8.21E-06; 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels: OR = 1.07; 95% CI 1.04-1.11; P = 9.01E-05; Linoleoyl-arachidonoyl-glycerol (18:2/20:4): OR = 1.08; 95% CI 1.04-1.22; P = 0.0001; Glycocholenate sulfate: OR = 0.93; 95% CI 0.90-0.97; P = 0.0002; 1-stearoyl-2-arachidonoyl-GPE (OR = 1.07; 95% CI 1.03-1.11; P = 0.0002); N-acetylasparagine (OR = 1.04; 95% CI 1.02-1.07; P = 0.0030); Octadecenedioate (C18:1-DC) (OR = 0.93; 95% CI 0.90-0.97; P = 0.0004); Phosphate to linoleoyl-arachidonoyl-glycerol (18:2-20:4) (1) ratio (OR = 0.92; 95% CI 0.88-0.97; P = 0.0005).

The integration of genomics and metabolomics offers novel insights into the pathogenesis of CHD and holds significant importance for the screening and prevention of CHD.

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.

Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention.

We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary two-sample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings.

After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data.

By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD.

Allergic conjunctivitis (AC) afflicts a significant portion of the global populace. Yet, its metabolic foundations remain largely unexplored.

We applied Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC) to scrutinize a cohort comprising 20 958 AC cases and 356 319 controls. Data were amalgamated from the metabolomics GWAS server and the FinnGen project, under strict quality control protocols.

Using two-sample MR analysis, 486 blood metabolites were investigated in relation to AC. The IVW approach highlighted 18 metabolites as closely tied to AC risk; of these, 16 retained significance post sensitivity assessments for heterogeneity and horizontal pleiotropy. LDSC analysis, adopted to bolster our findings and negate confounders from shared genetic markers, revealed 8 metabolites with marked heritability, including: palmitate (OR = 0.614), 3-methoxytyrosine (OR = 0.657), carnitine (OR = 1.368), threonate (OR = 0.828), N-[3-(2-Oxopyrrolidin-1-yl)propyl]acetamide (OR = 1.257), metoprolol acid metabolite (OR = 0.982), oleoylcarnitine (OR = 0.635), and 2-palmitoylglycerophosphocholine (OR = 1.351).

AC is precipitated by ocular responses to environmental allergens. Our study unveils a causal link between 8 blood metabolites and AC. This insight accentuates the role of metabolites in AC onset, suggesting novel avenues for its early prediction, targeted prevention, and tailored therapeutic interventions.

Background: Sepsis, a global health challenge, necessitates a nuanced understanding of modifiable factors for effective prevention and intervention. The role of trace micronutrients in sepsis pathogenesis remains unclear, and their potential connection, especially with genetic influences, warrants exploration. Methods: We employed Mendelian randomization (MR) analyses to assess the causal relationship between genetically predicted blood levels of nine micronutrients (calcium, β-carotene, iron, magnesium, phosphorus, vitamin C, vitamin B6, vitamin D, and zinc) and sepsis susceptibility, severity, and subtypes. The instrumental variables for circulating micronutrients were derived from nine published genome-wide association studies (GWAS). In the primary MR analysis, we utilized summary statistics for sepsis from two independent databases (UK Biobank and FinnGen consortium), for initial and replication analyses. Subsequently, a meta-analysis was conducted to merge the results. In secondary MR analyses, we assessed the causal effects of micronutrients on five sepsis-related outcomes (severe sepsis, sepsis-related death within 28 days, severe sepsis-related death within 28 days, streptococcal septicaemia, and puerperal sepsis), incorporating multiple sensitivity analyses and multivariable MR to address potential heterogeneity and pleiotropy. Results: The study revealed a significant causal link between genetically forecasted zinc levels and reduced risk of severe sepsis-related death within 28 days (odds ratio [OR] = 0.450; 95% confidence interval [CI]: 0.263, 0.770; p = 3.58 × 10-3). Additionally, suggestive associations were found for iron (increased risk of sepsis), β-carotene (reduced risk of sepsis death) and vitamin C (decreased risk of puerperal sepsis). No significant connections were observed for other micronutrients. Conclusion: Our study highlighted that zinc may emerges as a potential protective factor against severe sepsis-related death within 28 days, providing theoretical support for supplementing zinc in high-risk critically ill sepsis patients. In the future, larger-scale data are needed to validate our findings.

Background: Ischemic stroke (IS) is a major cause of death and disability worldwide. Previous studies have reported associations between metabolic disorders and IS. However, evidence regarding the causal relationship between blood metabolites and IS lacking. Methods: A two-sample Mendelian randomization analysis (MR) was used to assess the causal relationship between 1,400 serum metabolites and IS. The inverse variance-weighted (IVW) method was employed to estimate the causal effect between exposure and outcome. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode approaches were employed as supplementary comprehensive evaluations of the causal effects between blood metabolites and IS. Tests for pleiotropy and heterogeneity were conducted. Results: After rigorous selection, 23 known and 5 unknown metabolites were identified to be associated with IS. Among the 23 known metabolites, 13 showed significant causal effects with IS based on 2 MR methods, including 5-acetylamino-6-formylamino-3-methyluracil, 1-ribosyl-imidazoleacetate, Behenoylcarnitine (C22), N-acetyltyrosine, and N-acetylputrescine to (N (1) + N (8))-acetate,these five metabolites were positively associated with increased IS risk. Xanthurenate, Glycosyl-N-tricosanoyl-sphingadienine, Orotate, Bilirubin (E,E), Bilirubin degradation product, C17H18N2O, Bilirubin (Z,Z) to androsterone glucuronide, Bilirubin (Z,Z) to etiocholanolone glucuronide, Biliverdin, and Uridine to pseudouridine ratio were associated with decreased IS risk. Conclusion: Among 1,400 blood metabolites, this study identified 23 known metabolites that are significantly associated with IS risk, with 13 being more prominent. The integration of genomics and metabolomics provides important insights for the screening and prevention of IS.

Chronic kidney disease (CKD) is often accompanied by alterations in the metabolic profile of the body, yet the causative role of these metabolic changes in the onset of CKD remains a subject of ongoing debate. This study investigates the causative links between metabolites and CKD by leveraging the results of genomewide association study (GWAS) from 486 blood metabolites, employing bulk two-sample Mendelian randomization (MR) analyses. Building on the metabolites that exhibit a causal relationship with CKD, we delve deeper using enrichment analysis to identify the metabolic pathways that may contribute to the development and progression of CKD.

In conducting the Mendelian randomization analysis, we treated the GWAS data for 486 metabolic traits as exposure variables while using GWAS data for estimated glomerular filtration rate based on serum creatinine (eGFRcrea), microalbuminuria, and the urinary albumin-to-creatinine ratio (UACR) sourced from the CKDGen consortium as the outcome variables. Inverse-variance weighting (IVW) analysis was used to identify metabolites with a causal relationship to outcome. Using Bonferroni correction, metabolites with more robust causal relationships are screened. Additionally, the IVW-positive results were supplemented with the weighted median, MR-Egger, weighted mode, and simple mode. Furthermore, we performed sensitivity analyses using the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out (LOO) test. Pathway enrichment analysis was conducted using two databases, KEGG and SMPDB, for eligible metabolites.

During the batch Mendelian randomization (MR) analyses, upon completion of the inverse-variance weighted (IVW) approach, sensitivity analysis, and directional consistency checks, 78 metabolites were found to meet the criteria. The following four metabolites satisfy Bonferroni correction: mannose, N-acetylornithine, glycine, and bilirubin (Z, Z), and mannose is causally related to all outcomes of CKD. By pathway enrichment analysis, we identified eight metabolic pathways that contribute to CKD occurrence and progression.

Based on the present analysis, mannose met Bonferroni correction and had causal associations with CKD, eGFRcrea, microalbuminuria, and UACR. As a potential target for CKD diagnosis and treatment, mannose is believed to play an important role in the occurrence and development of CKD.

"Sick quitting," a phenomenon describing reductions in alcohol consumption following poor health, may explain observations that alcohol appears protective for frailty risk. We examined associations between frailty and reductions in drinking frequency among people with HIV (PWH). At six Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) sites between January 2012 and August 2021, we assessed whether frailty, measured through validated modified frailty phenotype, precedes reductions in drinking frequency. We associated time-updated frailty with quitting and reducing frequency of any drinking and heavy episodic drinking (HED), adjusted for demographic and clinical characteristics in Cox models. Among 5,654 PWH reporting drinking, 60% reported >monthly drinking and 18% reported ≥monthly HED. Over an average of 5.4 years, frail PWH had greater probabilities of quitting (HR: 1.56, 95% confidence interval [95% CI] [1.13-2.15]) and reducing (HR: 1.35, 95% CI [1.13-1.62]) drinking frequency, as well as reducing HED frequency (HR: 1.58, 95% CI [1.20-2.09]) versus robust PWH. Sick quitting likely confounds the association between alcohol use and frailty risk, requiring investigation for control.

Metabolic abnormalities constitute a significant characteristic of systemic lupus erythematosus (SLE). We utilised a two-sample Mendelian randomisation (MR) study to evaluate the potential causal association between 486 blood metabolites and SLE. Exposure data at the metabolite level were extracted from 7824 European Genome-wide association studies (GWAS). Preliminary analysis utilised SLE GWAS data from FinnGen. The primary method for causal analysis relied on random inverse variance weighting (IVW). To ensure robustness, sensitivity analyses included the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. Steiger testing and linkage disequilibrium score regression were employed to validate the identified metabolites. This study identified 12 metabolites, comprising six known chemical structures: 1,5-anhydroglucitol(1,5-AG) [odds ratio (OR) = 0.100, 95% confidence interval (CI): 0.015-0.773, P = 0.027), gamma-glutamylthreonine (OR = 0.077, 95% CI: 0.010-0.574, P = 0.012), 5-dodecenoate(12:1n7) (OR = 0.205, 95% CI: 0.061-0.685, P = 0.010), linoleoylglycerophosphoethanolamine * (OR = 0.159, 95% CI: 0.027-0.933, P = 0.044), erythrose (OR = 88.331,95% CI:1.098-63.214, P = 0.040) and 1-, adrenate (22:4n6) (OR = 9.876, 95% CI: 1.753-55.639, P = 0.001)]. Additionally, we found associations between SLE and six unknown chemical structures: X-06351 (OR = 0.071, 95% CI: 0.006-0.817, P = 0.034), X-10810 (OR = 4.268 95% CI: 1.260-14.459, P = 0.020), X-11412 (OR = 5.418 95% CI: 1.068-27.487, P = 0.041), X-11905 (OR = 0.551, 95%CI: 0.304-0.997, P = 0.049), X-12038 (OR = 0.178 95%CI: 0.032-0.988, P = 0.045), X-12217 (OR = 0.174 95%CI: 0.044-0.680, P = 0.014). This study offers evidence supporting a causal relationship between SLE and 12 circulating metabolites, six of which have known chemical structures and six that remain unidentified. These findings introduce a new perspective for further exploration of SLE mechanisms.

Gastroesophageal reflux disease (GERD) affects approximately 13% of the global population. However, the pathogenesis of GERD has not been fully elucidated. The development of metabolomics as a branch of systems biology in recent years has opened up new avenues for the investigation of disease processes. As a powerful statistical tool, Mendelian randomization (MR) is widely used to explore the causal relationship between exposure and outcome.

To analyze of the relationship between 486 blood metabolites and GERD.

Two-sample MR analysis was used to assess the causal relationship between blood metabolites and GERD. A genome-wide association study (GWAS) of 486 metabolites was the exposure, and two different GWAS datasets of GERD were used as endpoints for the base analysis and replication and meta-analysis. Bonferroni correction is used to determine causal correlation features (P < 1.03 × 10-4). The results were subjected to sensitivity analysis to assess heterogeneity and pleiotropy. Using the MR Steiger filtration method to detect whether there is a reverse causal relationship between metabolites and GERD. In addition, metabolic pathway analysis was conducted using the online database based MetaboAnalyst 5.0 software.

In MR analysis, four blood metabolites are negatively correlated with GERD: Levulinate (4-oxovalerate), stearate (18:0), adrenate (22:4n6) and p-acetamidophenylglucuronide. However, we also found a positive correlation between four blood metabolites and GERD: Kynurenine, 1-linoleoylglycerophosphoethanolamine, butyrylcarnitine and guanosine. And bonferroni correction showed that butyrylcarnitine (odd ratio 1.10, 95% confidence interval: 1.05-1.16, P = 7.71 × 10-5) was the most reliable causal metabolite. In addition, one significant pathways, the "glycerophospholipid metabolism" pathway, can be involved in the pathogenesis of GERD.

Our study found through the integration of genomics and metabolomics that butyrylcarnitine may be a potential biomarker for GERD, which will help further elucidate the pathogenesis of GERD and better guide its treatment. At the same time, this also contributes to early screening and prevention of GERD. However, the results of this study require further confirmation from both basic and clinical real-world studies.

Recent cross-sectional research has demonstrated a substantial link between tuberculosis (TB) and gut microbiota. Nevertheless, the causal impact of the gut microbiota on TB susceptibility in humans remains unknown.

The Mendelian randomization (MR) method was utilized for investigating the causality between them. The main method used for MR analysis was the inverse variance weighted (IVW) test, with the MR-Egger, weighted median, weighted mode, and simple median methods serving as supplements. And several sensitivity tests were carried out to validate the MR findings.

The IVW outcomes suggested that three bacterial traits exhibited associations with susceptibility to respiratory TB after Bonferroni correction, namely Lachnospiraceae UCG010 (odds ratio [OR] 1.73, 95% confidence interval [CI] 1.17-2.55, P = 0.005), Eubacterium (brachy group) (OR 1.33, 95% CI 1.07-1.65, P = 0.009), and Ruminococcaceae UCG005 (OR 0.71, 95% CI 0.52-0.98, P = 0.034). Sensitivity analyses demonstrated that horizontal pleiotropy and heterogeneity were absent, thereby guaranteeing the reliability of the results.

This research sheds light on the causal impact of gut microbiota on respiratory tuberculosis susceptibility, improving our knowledge of therapeutic strategies for managing TB.

Although it is known that variation in the aldehyde dehydrogenase 2 (ALDH2) gene family influences the East Asian alcohol flushing response, knowledge about other genetic variants that affect flushing symptoms is limited.

We performed a genome-wide association study meta-analysis and heritability analysis of alcohol flushing in 15,105 males of East Asian ancestry (Koreans and Chinese) to identify genetic associations with alcohol flushing. We also evaluated whether self-reported flushing can be used as an instrumental variable for alcohol intake.

We identified variants in the region of ALDH2 strongly associated with alcohol flushing, replicating previous studies conducted in East Asian populations. Additionally, we identified variants in the alcohol dehydrogenase 1B (ADH1B) gene region associated with alcohol flushing. Several novel variants were identified after adjustment for the lead variants (ALDH2-rs671 and ADH1B-rs1229984), which need to be confirmed in larger studies. The estimated SNP-heritability on the liability scale was 13% (S.E. = 4%) for flushing, but the heritability estimate decreased to 6% (S.E. = 4%) when the effects of the lead variants were controlled for. Genetic instrumentation of higher alcohol intake using these variants recapitulated known associations of alcohol intake with hypertension. Using self-reported alcohol flushing as an instrument gave a similar association pattern of higher alcohol intake and cardiovascular disease-related traits (e.g. stroke).

This study confirms that ALDH2-rs671 and ADH1B-rs1229984 are associated with alcohol flushing in East Asian populations. Our findings also suggest that self-reported alcohol flushing can be used as an instrumental variable in future studies of alcohol consumption.

Previous observational studies have showed that certain psychiatric disorders may be linked to breast cancer risk, there is, however, little understanding of relationships between mental disorders and a variety of breast diseases. This study aims to investigate if mental disorders influence the risks of overall breast cancer, the two subtypes of breast cancer (ER+ and ER-), breast benign tumors and breast inflammatory diseases.

During our research, genome-wide association study (GWAS) data for seven psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, post-traumatic stress disorder, panic disorder, obsessive-compulsive disorder and anorexia nervosa) from the Psychiatric Genomics Consortium (PGC) and the UK Biobank were selected, and single-nucleotide polymorphisms (SNPs) significantly linked to these mental disorders were identified as instrumental variables. GWAS data for breast diseases came from the Breast Cancer Association Consortium (BCAC) as well as the FinnGen consortium. We performed two-sample Mendelian randomization (MR) analyses and multivariable MR analyses to assess these SNPs' effects on various breast diseases. Both heterogeneity and pleiotropy were evaluated by sensitivity analyses.

When the GWAS data of psychiatric disorders were derived from the PGC, our research found that schizophrenia significantly increased the risks of overall breast cancer (two-sample MR: OR 1.05, 95%CI [1.03-1.07], p = 3.84 × 10^-6; multivariable MR: OR 1.06, 95%CI [1.04-1.09], p = 2.34 × 10^-6), ER+ (OR 1.05, 95%CI [1.02-1.07], p = 5.94 × 10^-5) and ER- (two-sample MR: OR 1.04, 95%CI [1.01-1.07], p = 0.006; multivariable MR: OR 1.06, 95%CI [1.02-1.10], p = 0.001) breast cancer. Nevertheless, major depressive disorder only showed significant positive association with overall breast cancer (OR 1.12, 95%CI [1.04-1.20], p = 0.003) according to the two-sample MR analysis, but not in the multivariable MR analysis. In regards to the remainder of the mental illnesses and breast diseases, there were no significant correlations. While as for the data from the UK Biobank, schizophrenia did not significantly increase the risk of breast cancer.

The correlation between schizophrenia and breast cancer found in this study may be false positive results caused by underlying horizontal pleiotropy, rather than a true cause-and-effect relationship. More prospective studies are still needed to be carried out to determine the definitive links between mental illnesses and breast diseases.

Background: Observational studies have shown that anti-tumor necrosis factor (TNF) therapy may be beneficial for patients with coronavirus disease 2019 (COVID-19). Nevertheless, because of the methodological restrictions of traditional observational studies, it is a challenge to make causal inferences. This study involved a two-sample Mendelian randomization analysis to investigate the causal link between nine TNFs and COVID-19 severity using publicly released genome-wide association study summary statistics. Methods: Summary statistics for nine TNFs (21,758 cases) were obtained from a large-scale genome-wide association study. Correlation data between single-nucleotide polymorphisms and severe COVID-19 (18,152 cases vs. 1,145,546 controls) were collected from the COVID-19 host genetics initiative. The causal estimate was calculated by inverse variance-weighted (IVW), MR-Egger, and weighted median methods. Sensitivity tests were conducted to assess the validity of the causal relationship. Results: Genetically predicted TNF receptor superfamily member 6 (FAS) positively correlated with the severity of COVID-19 (IVW, odds ratio = 1.10, 95% confidence interval = 1.01-1.19, p = 0.026), whereas TNF receptor superfamily member 5 (CD40) was protective against severe COVID-19 (IVW, odds ratio = 0.92, 95% confidence interval = 0.87-0.97, p = 0.002). Conclusion: Genetic evidence from this study supports that the increased expression of FAS is associated with the risk of severe COVID-19 and that CD40 may have a potential protective effect against COVID-19.

Non-alcoholic fatty liver disease(NAFLD) is common worldwide and has previously been reported to be associated with sleep traits. However, it is not clear whether NAFLD changes sleep traits or whether the changes in sleep traits lead to the onset of NAFLD. The purpose of this study was to investigate the causal relationship between NAFLD and changes in sleep traits using Mendelian randomization.

We proposed a bidirectional Mendelian randomization (MR) analysis and performed validation analyses to dissect the association between NAFLD and sleep traits. Genetic instruments were used as proxies for NAFLD and sleep. Data of genome-wide association study(GWAS) were obtained from the center for neurogenomics and cognitive research database, Open GWAS database and GWAS catalog. Three MR methods were performed, including inverse variance weighted method(IVW), MR-Egger, weighted median.

In total,7 traits associated with sleep and 4 traits associated with NAFLD are used in this study. A total of six results showed significant differences. Insomnia was associated with NAFLD (OR(95% CI)= 2.25(1.18,4.27), P = 0.01), Alanine transaminase levels (OR(95% CI)= 2.79(1.70, 4.56), P =4.71×10-5) and percent liver fat(OR(95% CI)= 1.31(1.03,1.69), P = 0.03). Snoring was associated with percent liver fat (1.15(1.05,1.26), P =2×10-3), alanine transaminase levels (OR(95% CI)= 1.27(1.08,1.50), P =0.04).And dozing was associated with percent liver fat(1.14(1.02,1.26), P =0.02).For the remaining 50 outcomes, no significant or definitive association was yielded in MR analysis.

Genetic evidence suggests putative causal relationships between NAFLD and a set of sleep traits, indicating that sleep traits deserves high priority in clinical practice. Not only the confirmed sleep apnea syndrome, but also the sleep duration and sleep state (such as insomnia) deserve clinical attention. Our study proves that the causal relationship between sleep characteristics and NAFLD is the cause of the change of sleep characteristics, while the onset of non-NAFLD is the cause of the change of sleep characteristics, and the causal relationship is one-way.

The real causal relationship between human gut microbiota and T1D remains unclear and difficult to establish. Herein, we adopted a two-sample bidirectional mendelian randomization (MR) study to evaluate the causality between gut microbiota and T1D.

We leveraged publicly available genome-wide association study (GWAS) summary data to perform MR analysis. The gut microbiota-related GWAS data from 18,340 individuals from the international consortium MiBioGen were used. The summary statistic data for T1D (n = 264,137) were obtained from the latest release from the FinnGen consortium as the outcome of interest. The selection of instrumental variables conformed strictly to a series of preset inclusion and exclusion criteria. MR-Egger, weighted median, inverse variance weighted (IVW), and weighted mode methods were used to assess the causal association. The Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis were conducted to identify heterogeneity and pleiotropy.

At the phylum level, only Bacteroidetes was indicated to have causality on T1D (OR = 1.24, 95% CI = 1.01-1.53, P = 0.044) in the IVW analysis. When it comes to their subcategories, Bacteroidia class (OR = 1.28, 95% CI = 1.06-1.53, P = 0.009, P _FDR = 0.085), Bacteroidales order (OR = 1.28, 95% CI = 1.06-1.53, P = 0.009, P _FDR = 0.085), and Eubacterium eligens group genus (OR = 0.64, 95% CI = 0.50-0.81, P = 2.84×10^-4, P _FDR = 0.031) were observed to have a causal relationship with T1D in the IVW analysis. No heterogeneity and pleiotropy were detected.

The present study reports that Bacteroidetes phylum, Bacteroidia class, and Bacteroidales order causally increase T1D risk, whereas Eubacterium eligens group genus, which belongs to the Firmicutes phylum, causally decreases T1D risk. Nevertheless, future studies are warranted to dissect the underlying mechanisms of specific bacterial taxa's role in the pathophysiology of T1D.