Causal Relationship Between Ischemic Stroke and Vascular Dementia: A Mendelian Randomization Study

Summary

This protocol describes a reproducible two-sample Mendelian randomization workflow for evaluating the potential causal association between ischemic stroke and vascular dementia using publicly available genome-wide association study summary statistics.

Abstract

Ischemic stroke (IS) is a major cause of disability and mortality worldwide, and vascular dementia (VaD) is a common dementia subtype associated with cerebrovascular injury. Observational studies have suggested a relationship between IS and VaD, but these studies are vulnerable to confounding and reverse causality. This protocol describes a reproducible two-sample Mendelian randomization (MR) workflow for evaluating the potential causal association between IS and VaD using publicly available genome-wide association study (GWAS) summary statistics. Genetic instruments associated with IS were extracted from a public GWAS dataset, and outcome associations for VaD were obtained from a public VaD GWAS dataset. The corresponding dataset IDs are provided in the Protocol section. After outcome matching and allele harmonization, 51 single-nucleotide polymorphisms (SNPs) were retained for the final MR analysis. The workflow includes instrumental variable selection, linkage disequilibrium clumping, allele harmonization, instrument strength assessment, inverse variance weighted (IVW) analysis, weighted median analysis, MR-Egger analysis, heterogeneity testing, horizontal pleiotropy assessment, and leave-one-out sensitivity analysis. In the representative analysis, the IVW method showed a positive association between genetically predicted IS and VaD risk, and the weighted median method yielded a directionally concordant result. The MR-Egger estimate was directionally consistent but did not reach statistical significance. Therefore, these findings should be interpreted as suggestive evidence of a possible causal effect, rather than definitive proof of causality. This protocol may help researchers apply a transparent and reproducible MR workflow to investigate cerebrovascular disease-related outcomes using public GWAS data.

Introduction

Ischemic stroke (IS) is a leading cause of severe disability and death worldwide¹. According to the latest data from the Global Burden of Disease Study, the global incidence of IS has declined, but it remains a significant burden, especially in Eastern Europe, East Asia, Central Asia, and Sub-Saharan Africa^2,3. Furthermore, the incidence of IS is on the rise in countries with a low Socio-demographic Index (SDI), and it is projected to continue increasing from 2020 to 2030.

Numerous epidemiological studies have suggested a potential link between cerebrovascular diseases and vascular dementia (VaD)^4,5. Particularly in China, VaD is one of the most common types of dementia among the elderly, and the high prevalence of IS is closely associated with the onset of VaD. However, most existing studies are observational, and residual confounding, reverse causality, and differences in disease definition may limit causal interpretation. Therefore, additional analytical approaches are needed to evaluate whether IS is potentially involved in the development of VaD.

IS may influence the onset of VaD through several mechanisms. Cerebrovascular damage caused by IS can lead to insufficient cerebral perfusion and subsequent neurodegenerative changes⁶. In addition, VaD may be related to chronic cerebrovascular injury, microvascular damage, and inflammatory responses after IS^7,8. Therefore, investigating the relationship between IS and VaD is important for understanding potential disease links and guiding future prevention strategies.

Mendelian randomization (MR) is an analytical method that uses genetic variants as instrumental variables to evaluate potential causal associations between an exposure and an outcome⁹. Compared with conventional observational studies, MR can reduce confounding and reverse causality bias under valid instrumental variable assumptions. Compared with randomized controlled trials or experimental models, MR can use existing genome-wide association study (GWAS) summary statistics to evaluate clinically relevant associations when direct intervention studies are infeasible, unethical, or difficult to conduct. Recent genetic studies using Mendelian-randomization approaches have investigated the association between IS and VaD¹⁰. However, detailed protocol-level descriptions of exposure instrument selection, outcome data extraction, allele harmonization, instrument strength assessment, heterogeneity testing, pleiotropy assessment, and sensitivity analyses remain important for reproducible implementation.

This protocol is appropriate when publicly available GWAS summary statistics are available for both the exposure and outcome of interest, valid genetic instruments can be selected for the exposure, and the research question aims to evaluate a potential causal association rather than directly establish biological mechanisms. This study therefore presents a two-sample MR protocol for evaluating the potential causal association between IS and VaD using publicly available GWAS summary statistics. The aim is to provide a reproducible analytical workflow rather than to establish definitive biological mechanisms. This protocol describes genetic instrument selection, outcome matching, allele harmonization, MR estimation, and sensitivity analyses, and uses the IS-VaD association as a representative example.

Protocol

This study used publicly available GWAS summary statistics. The original studies had obtained institutional review board approval and informed consent from participants. No additional ethical approval was required for this secondary analysis.

1. Study design and ethics statement

1. Use a two-sample MR design to evaluate the potential causal association between IS and VaD.
2. Use only publicly available GWAS summary statistics. Because all original studies included in the GWAS datasets had obtained institutional review board approval and informed consent from participants, no additional ethical approval is required for this secondary analysis.
3. Define IS as the exposure and VaD as the outcome before data extraction.

2. Software preparation

1. Conduct all analyses using R statistical software.
2. Load the TwoSampleMR package for instrument extraction, outcome data extraction, allele harmonization, MR estimation, heterogeneity testing, and sensitivity analyses. Load the MRPRESSO package to assess global horizontal pleiotropy and potential outlier variants.
3. Use the following core functions in the workflow: extract_instruments, extract_outcome_data, harmonise_data, mr, generate_odds_ratios, mr_heterogeneity, mr_pleiotropy_test, mr_singlesnp, mr_leaveoneout, mr_scatter_plot, mr_forest_plot, mr_funnel_plot, and mr_leaveoneout_plot. Use MRPRESSO::mr_presso to perform the MR-PRESSO global test and, when applicable, outlier and distortion tests.

3. Exposure dataset selection and instrumental variable screening

1. Extract IS-associated single-nucleotide polymorphisms (SNPs) from the IEU OpenGWAS dataset ebi-a-GCST90018864. This dataset includes 11,929 IS cases and 472,192 controls, with a total sample size of 484,121 participants.
2. Select candidate SNPs associated with IS using a significance threshold of p < 5 × 10^-6.
   1. The p < 5 × 10^-6 threshold is used in this representative protocol to retain an adequate number of independent instruments for sensitivity analyses. Because this threshold is less stringent than the conventional genome-wide significance threshold, findings based on these instruments should be interpreted as exploratory and suggestive rather than definitive.
3. Perform linkage disequilibrium clumping to retain independent SNPs. Use an r² threshold of 0.001 and a clumping window of 10,000 kb.
4. Use the following R command to extract candidate instruments:
   extract_instruments(outcomes = "ebi-a-GCST90018864", p1 = 5e-06, clump = TRUE)
5. Calculate the F-statistic for each retained SNP using the formula F = (β/SE)², where β and SE are the SNP–exposure effect estimate and standard error, respectively.
6. Exclude SNPs with an F-statistic < 10 to reduce the risk of weak instrument bias. Summarize the distribution of F-statistics using the minimum, maximum, median, and interquartile range (IQR).

4. Outcome dataset extraction

1. Extract VaD outcome associations from the FinnGen GWAS dataset finn-b-F5_VASCDEM. This dataset includes 881 VaD cases and 211,508 controls.
2. Extract the outcome associations for all selected IS-associated SNPs using the extract_outcome_data function.
3. Search for proxy SNPs when target SNPs are not directly available in the outcome dataset. Retain proxy-matched variants when valid proxy information and allele correspondence are available.
4. Use the following R command to extract the outcome dataset:
   extract_outcome_data(snps = exp_dat$SNP, outcomes = "finn-b-F5_VASCDEM", access_token = NULL)

5. Allele harmonization and SNP retention

1. Harmonize the exposure and outcome datasets before MR analysis.
2. Align the effect allele and other allele so that the SNP–exposure and SNP–outcome effect estimates correspond to the same effect allele.
3. Inspect SNPs during harmonization to identify allele mismatches, unavailable outcome associations, and ambiguous allele alignment. Retain SNPs with available exposure and outcome effect estimates after harmonization.
4. Use the following R command for allele harmonization: harmonise_data(exposure_dat = exp_dat, outcome_dat = out_dat, action = 1)
5. Record the number of SNPs initially extracted from the exposure dataset, the number of SNPs with available outcome associations, the number of proxy-matched variants, and the number of SNPs retained for the final MR analysis.

6. Primary MR analysis and sensitivity analyses

1. Perform the primary MR analysis using the inverse variance weighted (IVW) method.
2. Perform complementary MR analyses using the weighted median and MR-Egger methods to assess the consistency of the causal estimate under different assumptions.
3. Generate odds ratios (ORs) and 95% confidence intervals (CIs) from the MR estimates.
4. Use the following R commands to run the MR analysis and calculate ORs:
   res <- mr(dat)
   res <- generate_odds_ratios(res)
5. Because one prespecified primary exposure–outcome association is evaluated, define the Bonferroni-corrected threshold for the primary MR inference as 0.05/1 = 0.05. Use a two-sided p-value < 0.05 as the statistical significance threshold for the primary IVW analysis.
6. To contextualize the statistical precision of the primary analysis given the limited number of VaD cases, perform a post hoc two-sided Wald-test power calculation based on the observed IVW estimate and its standard error. Calculate the power using the non-centrality parameter βIVW/SEIVW at a two-sided α level of 0.05. Calculate the minimum detectable effect corresponding to 80% power as exp[(z0.975 + z0.80) × SE]. Because IS is a binary exposure and SNP–exposure associations are expressed on the log-odds scale, report this analysis as an effect-conditioned descriptive assessment of precision rather than as a prespecified sample-size calculation¹¹.

7. Heterogeneity, horizontal pleiotropy, and leave-one-out analyses

1. Assess between-SNP heterogeneity using Cochran’s Q statistic.
2. Use the following R command to calculate heterogeneity statistics:
   mr_heterogeneity(dat)
3. Assess directional horizontal pleiotropy using the MR-Egger intercept test.
4. Use the following R command to calculate the MR-Egger intercept:
   mr_pleiotropy_test(dat)
5. Assess global horizontal pleiotropy and potential outlier variants using MR-PRESSO with 10,000 simulations, a significance threshold of 0.05, and both outlier and distortion tests enabled¹².
6. Use the following R command for MR-PRESSO analysis:
   set.seed(20260729)
   mr_presso(
   BetaOutcome = "beta.outcome",
   BetaExposure = "beta.exposure",
   SdOutcome = "se.outcome",
   SdExposure = "se.exposure",
   OUTLIERtest = TRUE,
   DISTORTIONtest = TRUE,
   data = dat,
   NbDistribution = 10000,
   SignifThreshold = 0.05
   )
7. Perform single-SNP analysis to estimate the effect of each SNP separately.
8. Use the following R command for single-SNP analysis:
   res_single <- mr_singlesnp(dat)
9. Perform leave-one-out analysis by sequentially removing one SNP at a time and repeating the IVW analysis.
10. Use the following R command for leave-one-out analysis:
    res_loo <- mr_leaveoneout(dat)

8. Visualization and output

1. Generate a scatter plot to show the MR estimates across different methods.
2. Generate a forest plot to show the single-SNP estimates and the overall MR estimate.
3. Generate a funnel plot to visually assess the symmetry of SNP-specific estimates.
4. Generate a leave-one-out plot to assess whether the overall estimate is driven by a single SNP.
5. Use the following R commands to generate the plots:
   mr_scatter_plot(res, dat)
   mr_forest_plot(res_single)
   mr_funnel_plot(res_single)
   mr_leaveoneout_plot(res_loo)
6. Export the MR results, instrumental variable table, outcome association table, and sensitivity analysis outputs for reporting and reproducibility.

Results

Figure 1 summarizes the overall workflow of the two-sample Mendelian randomization protocol, from dataset selection and instrument screening through sensitivity analyses and result visualization. Representative outputs from each stage are presented below.

Genetic instrumental variables and harmonization quality control

A total of 52 IS-associated SNPs were initially extracted from the exposure GWAS data...

Discussion

This study presents a two-sample MR protocol for evaluating the potential causal association between IS and VaD using publicly available GWAS summary statistics. In the representative analysis, the IVW and weighted median methods supported a positive association between genetically predicted IS and VaD risk, whereas the MR-Egger estimate was directionally consistent but did not reach statistical significance. Therefore, these findings provide suggestive evidence of a possible causal effect, rather than definitive proof o...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
FinnGen GWAS dataset	FinnGen	finn-b-F5_VASCDEM	Public GWAS dataset used as the outcome source for vascular dementia
IEU OpenGWAS database	MRC Integrative Epidemiology Unit, University of Bristol	ebi-a-GCST90018864	Public GWAS dataset used as the exposure source for ischemic stroke
R software	R Foundation for Statistical Computing	Not Applicable	Statistical software used for MR analysis
RStudio	Posit Software, PBC	Not Applicable	Integrated development environment used to run R scripts
TwoSampleMR package	MRC Integrative Epidemiology Unit, University of Bristol	Not Applicable	R package used for instrument extraction, harmonization, MR analysis, and sensitivity analyses
MRPRESSO package	CRAN	Version 1.0	R package used to assess global horizontal pleiotropy and potential outlier instruments
writexl package	CRAN	Not Applicable	R package used to export result tables as xlsx files