Study Design and data sources
This study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) (additional checklist) [9,10]. Figure 1 illustrates the overall study design. We conducted two-sample MR analyses using summary-level GWAS data. All data in this study was available publicly thus ethical approval was not required. Three core assumptions for instrumental variables were made. SNPs must be associated with the exposure, are are independent of the confounding factors and only affect the outcome through their effects on the exposure. Data from 23 GWASs for exposures and one GWAS for the outcome were included in the study.
We performed systematic search and retrieved data on GWASs of participants of only European ancestry (until March,2024) in the GWAS Catalog, UK Biobank and FinnGen. The exposures are genetically-predicted hemostatic factors in plasma and the outcome is acute mesenteric ischemia. The largest GWAS summary statistics were included. After identification and evaluation, a total of 23 hemostatic parameters were included as instrumental variants were retrieved from GWAS catalog: vWF level (n=5,356) [11], TM (thrombomodulin) level (n=21,758) [12], platelet count (n=408,112) [13], plateletcrit (n=408,112) [13], mean platelet volume (n=408,112) [13], platelet glycoprotein (n=3,301) [14], prothrombin (n=10,708) [15], PT/INR(n=34,919) [16], fibrinogen level (n=10,708) [15], coagulation factors (V,VII,VIII,IX,Xa) (n=10,708) [15], thrombin level (n=10,708) [15], tissue factor level (n=21,758) [12], antithrombin level(n=10,708) [15], activated protein C level (n=3,301) [14], D-dimer level (n=10,708) [15], plasminogen level (n=10,708) [15], tissue-type plasminogen activator level (n=21,758) [12], plasminogen activator inhibitor level (n=341,448) [17], alpha-2-antiplasmin level (n=3,301) [14], plasmin level (n=3,301) [14], tissue factor pathway inhibitor level (n=3,301) [14], protein C level (n=3,301) [14] and protein S level (n=21,758) [12]. Summary-level data for AMI was obtained from GWAS catalog(143 cases, 456,205 controls) [18]. Diagnosis of AMI was made based on International Classification of Diseases codes (ICD-9,Acute vascular insufficiency of intestine). Full summary statistics for the exposures and outcome are available for download from the GWAS catalog website (https://www.ebi.ac.uk/gwas/downloads/summary-statistics). Overview of GWAS used was shown in Supplement table 1.
Mendelian Randomization
Independent genome-wide significant SNPs of each hemostatic factor were used as genetic instruments. Included instrumental variables should be strongly associated with the exposures (p ≤5×10−8) and SNPs with an F statistic <10 were excluded [19]. Linkage disequilibrium (LD) clumping was performed with r2 < 0.001 and window size of 10,000 kb [20]. The strength of each genetic instrument was assessed through the F statistic, calculated as F = R2 (N − 2)/(1 − R2), where R2 denotes the proportion of variance explained by the genetic instrument and N represents the effective sample size of the GWAS for the SNP-micronutrient association [19]. The R2 value was determined using the formula 2 × MAF (1 − MAF) beta2, where beta signifies the effect estimate of the genetic variant on the exposure, measured in standard deviation (SD) units, and MAF indicates the minor allele frequency [19-20]. Then, we removed SNPs associated with diseases or risk factors potentially associated with AMI (http://www.phenoscanner.medschl.cam.ac.uk/). The remaining SNPs were used in the MR analysis. The inverse variance-weighted (IVW) method was the main method for MR analysis and and the weighted median and MR-Egger methods were conducted to improve the IVW model-based estimation [21]. P < 0.05 was considered nominally significant. Wald ratios for all SNPs were calculated. MR analyses estimated odds ratio (OR) of per SD change of the genetically predicted circulating levels of hemostatic factors. All MR analyses were performed using the TwoSampleMR package (version ) in R (version ) [22].
Pleiotropy and and Sensitivity Analysis
We performed MR-Egger regression to examine the potential presence of pleiotropic effects of the SNPs. The intercept term in MR-Egger regression serves as a valuable indicator to determine if directional horizontal pleiotropy is influencing the results of the MR analysis. We employed both the IVW method and MR-Egger regression to identify heterogeneity. The degree of heterogeneity was measured using the Cochran Q statistic, and a P value of < 0.05 was considered significant heterogeneity. Moreover, to identify any potentially influential SNPs, we conducted leave-one-out test, whereby the MR was re-run while excluding each SNP in turn.