The causal relationship between extensive perivascular space burden and ischemic stroke and its subtypes and transient ischemic attack: A Mendelian randomization study

The causal relationship between extensive perivascular space burden and ischemic stroke and its subtypes and transient ischemic attack: A Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The causal relationship between extensive perivascular space burden and ischemic stroke and its subtypes and transient ischemic attack: A Mendelian randomization study Xuehong Chu, Yingjie Shen, Yaolou Wang, Xiao Dong, Yuanyuan Liu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4498156/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Clinical studies suggest a strong link between extensive perivascular space (EPVS) and ischemic stroke (IS), including its subtypes, and transient ischemic attack (TIA), but it's uncertain if the relationship is genetically causal. Methods We utilize summary data from large-scale Genome-wide Association Studies (GWAS) to investigate the association between EPVS in different locations and IS, its subtypes, and TIA through Mendelian randomization (MR) analysis. Various MR methods are employed to assess the causal relationship between EPVS and IS, its subtypes, and TIA. We apply multivariable MR to mitigate potential confounding factors and conduct sensitivity analyses to enhance result robustness. Subsequently, meta-analysis is utilized to integrate causal relationships between EPVS in different locations and IS from various sources. Additionally, reverse MR is employed to observe the impact of various IS types on EPVS. Finally, linkage disequilibrium score regression is conducted to assess genetic correlations between exposures and outcomes. Results EPVS burden in both the white matter (OR, 1.12; 95% CI, 1.01–1.25; P = 0.04) and the basal ganglia (OR, 1.57; 95% CI, 1.30–1.89; P < 0.01) are significant risk factors for IS. EPVS burden in the basal ganglia is also a risk for IS (small-vessel) (OR, 4.56; 95% CI, 2.57–8.27; P = 5.95E-07). Additionally, there appears to be a potential increase in extensive basal ganglia perivascular space burden following IS and TIA. Conclusion Extensive white matter perivascular space burden and extensive basal ganglia perivascular space burden may serve as important indicators for predicting IS. extensive perivascular space burden ischemic stroke causal inference Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Stroke is a prevalent cerebrovascular disease marked by neurological deficits due to acute focal injury in the central nervous system. It is classified into hemorrhagic and ischemic types based on pathophysiology. Ischemic stroke (IS), caused by arterial blockage and decreased cerebral blood flow, is the most common form. Recently, IS incidence has been on the rise, making it a top cause of global mortality and disability, and imposing a significant socio-economic burden. 1 Perivascular spaces (PVS) are small gaps around small arteries and veins within the brain parenchyma, enclosed by the pia mater. They are thought to be essential for lymphatic drainage, waste removal, and tissue homeostasis. 2 Increasing evidence suggests that extensive perivascular space burden (EPVS) indicates impaired lymphatic drainage function, which is a characteristic of brain disorders, including small vessel disease, 3 Parkinson's disease, 4 cognitive impairment, 5 and multiple sclerosis. 6 Recent research indicates that after ischemia, cerebrospinal fluid surrounding brain tissue enters the brain via PVS within minutes, causing cerebral edema. 7 This highlights the potential impact of EPVS in IS. Approximately 98.8% of acute IS patients reportedly exhibit observable EPVS on scans within the first 7 days post-stroke. 8 A prospective study involving high-risk individuals for IS or transient ischemic attack (TIA) (n = 2002 subjects) found that a high burden of PVS in the basal ganglia is linked to recurrent stroke and IS. 9 Although traditional epidemiological studies are well-designed, prospective, and involve large population sizes, the conclusions drawn from these studies may be influenced by confounding factors or reverse causality, thus precluding the establishment of definitive causal relationships. Mendelian randomization (MR) analysis provides an alternative method for causal inference. It utilizes genetic variations as instrumental variables (IVs), which are closely linked to the exposure, unaffected by confounding factors, and not influenced by pathways other than the exposure. This effectively reduces reverse causal bias and residual confounding bias, 10 resulting in stronger evidence for causal inference. In this study, we utilize MR analysis to explore the causal relationship between EPVS in different locations and IS, its subtypes and TIA, thus offering valuable insights for clinical diagnosis and treatment. 2 Materials and Methods 2.1 Study design and ethical statement To explore the relationship between EPVS in various locations and IS, its subtypes, and TIA, we conduct MR analysis. Figure 1 outlines the study design. In the forward MR, we consider extensive white matter, hippocampal, and basal ganglia perivascular space burdens as exposures, investigating their causal links with IS, its subtypes and TIA individually. Additionally, we also utilize multivariable MR (MVMR) to adjust for confounding factors, followed by conducting a meta-analysis to evaluate the overall effect of EPVS on IS, its subtypes and TIA from different sources. The MR analysis in this study meets three core assumptions: 1) significant correlation between IVs and exposures, 11 2) no correlation between IVs and confounding factors affecting the relationship between exposures and outcomes, 11 and 3) IVs solely influencing the outcomes through exposures. 12 Ethical approval is not necessary for this study as it utilizes publicly available data that has already been approved by the relevant institutional ethics committees. Moreover, it is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization guidelines (STROBE-MR) ( Supplementary Table S1 ). 10 2.2 Data sources The summary data of EPVS in various sites are sourced from a cohort study involving 18 populations, encompassing over 8 million SNPs (minor allele frequency ≥ 1%) from more than 40,095 participants (mean age 66.3 ± 8.6 years, 51.7% female). To address variations in PVS quantification methods, image acquisition, and participant characteristics, we categorized PVS burden using thresholds closest to the upper quartile of the PVS distribution. Ultimately, 9,607 out of 39,822, 9,189 out of 40,000, and 9,339 out of 40,095 participants exhibit EPVS in white matter, hippocampus, and basal ganglia, respectively. 13 The data of IS, its subtypes and TIA are sourced from multiple publicly available Genome-wide Association Studies (GWAS) datasets. Specifically, IS data are derived from the MEGA consortium (Ncase = 34,217, sample size = 440,328), 14 a meta-analysis involving the UK Biobank (Ncase = 11,929, sample size = 484,121), 15 and the FinnGen database (Ncase = 10,551, sample size = 212,774). Based on the TOAST classification of IS, partial GWAS information for its subtypes, including large-artery atherosclerosis (Ncase = 4,373, sample size = 150,765), cardioembolism (Ncase = 7,193, sample size = 211,763), and small-vessel (Ncase = 5,386, sample size = 198,048), is obtained from the MEGA consortium. 14 Lacunar stroke (Ncase = 6,030, sample size = 225,419) is sourced from cases recruited from acute stroke hospitalization and outpatient services in Europe, the United States, South America, and Australia. This study involves a meta-analysis of MRI-diagnosed lacunar stroke patients' data and existing GWAS datasets. The patients are from hospitals in the UK, part of the UK DNA Cavernous Stroke Study and collaborators from the International Stroke Genetics Consortium. 16 GWAS data of TIA are obtained from the FinnGen database and the UK Biobank, 17 both of which conducted GWAS studies on large populations to identify risk loci for the disease. 2.3 Selection of IVs During the forward MR analysis, EPVS found in various regions are considered as the exposures, while IS, its subtypes, and TIA are examined as outcomes. To meet assumption 1, this study identifies single nucleotide polymorphisms (SNPs) across the entire genome that show significant associations with EPVS at various locations ( P < 1×10 − 5 ) and have no linkage disequilibrium (LD) (r 2 =0.01, kb = 5000), ensuring the independence of the selected IVs. To address potential confounding factors, we utilize MVMR analysis to control for common confounders of IS and TIA, including obesity, hypertension, diabetes, and alcohol, thereby satisfying assumption 2. To fulfill assumption 3, this study further excludes SNPs significantly associated with IS, its subtypes and TIA across the entire genome ( P < 1×10 − 5 ). To ensure the strength of the selected IVs, we calculate the statistical strength using the F value. Specifically, F = R²/ (1 - R²) * (N - K − 1)/K, where N represents the sample size of the exposure, K is the number of SNPs, R² is the proportion of variance explained by SNPs in the exposure dataset, and R 2 = 2× (1-MAF) (MAF) ×(β/SD) 2 , β denotes the effect size of the allele. 18 IVs with F < 10 will be excluded. Furthermore, SNPs that are inconsistent with the exposure and outcome alleles, as well as palindromic SNPs with moderate allele frequencies, are excluded. The SNPs subjected to the rigorous screening process are utilized for the final causal analysis. 2.4 MR analysis This study employs inverse variance weighted (IVW) as the primary method for MR analysis. When the selected SNPs are all effective IVs, the IVW method can provide the most accurate estimates of causal association effects. 19 Additionally, Bayesian weighted, weighted median (WM), weighted mode, and simple mode are used as supplementary analyses. Bayesian weighted Mendelian randomization explicitly accounts for uncertainty related to weak effects from polygenic traits and can identify outliers, addressing instrumental variable assumption violations due to pleiotropy. 20 The WM method provides effective causal estimates when over half of the SNPs are valid IVs. 21 Weighted mode is reliable when most individual instruments' causal effect estimates come from valid instruments, even if some IVs are considered invalid. 22 Furthermore, the simple mode can serve as an unweighted empirical density function for estimating causality. 23 To enhance the robustness of results, we require consistent directions of β values across all methods while ensuring significance in IVW and Bayesian weighted results. Moreover, we use false discovery rate (FDR) correction for P -values. Significant causal relationships between EPVS and outcomes are indicated when P < 0.05 and P FDR < 0.05, and potential causal relationships are suggested when P 0.05. 2.5 Sensitivity analysis We use IVW and MR Egger regression to detect heterogeneity and calculate Cochran’s Q statistic to quantify its magnitude. P < 0.05 indicates significant heterogeneity, warranting the use of a random-effects model for causal inference. 24 MR-Egger intercept test is utilized to analyze horizontal pleiotropy, estimating directional inference by calculating the intercept and resulting in a directional P -value. P > 0.05 suggests the absence of horizontal pleiotropy, demonstrating the robustness of the MR analysis results. 25 The MR-PRESSO Global test identifies outliers, whose presence is confirmed by P < 0.05, requiring their exclusion for subsequent analysis. 26 Besides, leave-one-out analysis assesses individual SNPs' influence on the MR results. After removing outlier SNPs, P < 0.05 in the MR Egger regression renders the MR results unreliable. In forward MR analysis, we utilize MR Steiger to ensure directional accuracy. This method assumes that the genetic variants should explain more variance during exposure than outcome, meeting the legitimate requirements of MR investigation and aiding in identifying potential bidirectional effects. 27 Finally, reverse MR analysis is used to observe bidirectional effects between EPVS and IS, its subtypes, and TIA, with SNP selection criteria consistent with forward MR. 2.6 MVMR analysis MVMR analysis can evaluate direct causal effects between exposure and outcome. 28 Thus, to adjust for potential confounders (Obesity, hypertension, type 2 diabetes, and ongoing alcohol addiction), we perform MVMR analysis following univariable MR (UVMR) analysis to examine the independent impact of EPVS in different locations on IS, its subtypes, and TIA. We utilize Multivariable IVW, Multivariable Egger, and Multivariable Median methods, with Multivariable IVW serving as the primary method. Also, to assess result stability, Cochran’s Q statistic and I 2 detect result heterogeneity, while the Egger-intercept test identifies horizontal pleiotropy. 2.7 Meta- analysis To mitigate biases stemming from various sources of GWAS data on IS, following MR analysis, we conduct meta-analysis to examine the overall impact of EPVS in different locations on IS and its subtypes, as well as TIA. Additionally, we employ I2 to assess the heterogeneity of the findings. 2.8 Linkage disequilibrium score regression and directionality tests We employ linkage disequilibrium score regression (LDSC) analysis to summarize GWAS data and estimate heritability and genetic correlations based on single-nucleotide variants. The LD reference panel from the 1000 Genomes Project is used to compute LD scores. Finally, we utilize the LDSC tool to further evaluate the genetic associations between EPVS at different locations and IS and its subtypes. 29 All MR-related analyses are conducted in R (version 4.3.0) using the "TwoSampleMR", 23 "MR-PRESSO", 26 and "Mendelian Randomization" R packages. 3 Results 3.1 Results of causality between EPVS and outcomes by UVMR After rigorous IV selection, we establish a variable number of IVs for EPVS in different locations based on various outcomes. Detailed information on all IVs can be found in Supplementary Table S2 . The F-values for all SNPs were greater than 10, indicating that the current results are not biased by weak IVs. Genetically predicted extensive white matter perivascular space burden is significantly associated with a higher risk of IS (IEU database) (OR, 1.24; 95%CI, 1.09–1.42; P = 1.09E-03; P FDR = 9.82E-03). Considering extensive hippocampal perivascular space burden as the exposure reveals a potential causal link with IS (large-artery atherosclerosis) (OR, 1.95; 95%CI, 1.02–3.74; P = 4.50E-02; P FDR =3.77E-01). Moreover, extensive basal ganglia perivascular space burden exhibits significant causal associations with IS and its subtypes. Specifically, it notably increases the risk of IS (IEU database) (OR, 1.53; 95%CI, 1.20–1.94; P = 5.37E-04; P FDR = 1.91E-02), IS (FinnGen) (OR, 1.63; 95%CI, 1.17–2.29; P = 4.23E-03; P FDR = 3.80E-02), and IS (small vessel) (OR, 2.66; 95%CI, 1.49–4.75; P = 9.46E-04; P FDR = 1.91E-02) ( Supplementary Table S3 , Fig. 2 and Fig. 3 ). 3.2 Results of sensitivity analysis for UVMR To ensure the reliability of our findings, we conduct several sensitivity analyses. When extensive hippocampal perivascular space burden is considered as the exposure and IS (small-vessel) as the outcome, Cochran’s Q test reveals heterogeneity, prompting the use of the random-effects model of IVW despite the absence of outliers. For IS (MEGA STROKE) as the outcome, MR-PRESSO identifies three outliers. After their removal, no heterogeneity or horizontal pleiotropy is detected. Similarly, when extensive white matter perivascular space burden is the exposure and IS (MEGA STROKE) or IS (IEU database) is the outcome, we find two outliers in each case. After excluding them, the results remain robust. Additionally, with extensive basal ganglia perivascular space burden as the exposure and IS (MEGA STROKE) or Lacunar stroke as the outcome, two outliers are detected in each analysis. Removing these outliers enhances the robustness of all sensitivity analyses ( Supplementary Tables S4 and S5 ). Furthermore, leave-one-out analysis confirms that individual SNPs do not drive the current results (Fig. 4 ). 3.3 Results of Meta-analysis after UVMR To mitigate biases arising from varied GWAS data sources on IS, we perform meta-analyses after UVMR to synthesize the findings regarding EPVS in different locations on IS from diverse sources (Fig. 5 ). The results indicate that when extensive white matter perivascular space burden is considered as the exposure and IS from various sources as the outcomes, no discernible causal relationship is observed, albeit with moderate heterogeneity ( P = 0.07, I 2 = 63%). Conversely, when extensive basal ganglia perivascular space burden is considered as the exposure and IS from different sources as the outcome, a significant causal relationship is detected, with no observed heterogeneity in the current causal association ( P = 0.43, I 2 = 0%). 3.4 Results of MVMR analysis between EPVS and outcomes To assess the direct causal link between EPVS in different locations and IS and its subtypes, we perform MVMR analyses. In these analyses, we adjust for confounders such as obesity, hypertension, type 2 diabetes, and ongoing alcohol addiction. The results are shown in Supplementary Table S6 and Fig. 6 . After accounting for confounding factors, the outcomes of UVMR change. Notably, when extensive white matter perivascular space burden is considered as the exposure and IS (IEU database) as the outcome, the causal relationship remains significant (OR, 1.23; 95%CI, 1.05–1.43; P = 9.65E-03). However, when extensive hippocampal perivascular space burden is the exposure, the causal link with IS (large artery atherosclerosis) becomes non-significant (OR, 1.55; 95%CI, 0.75–3.18; P = 2.36E-01). Conversely, when extensive basal ganglia perivascular space burden is the exposure, a causal relationship is established with IS (MEGA STROKE) (OR, 1.32; 95%CI, 1.01–1.71; P = 4.11E-02). Moreover, the causal associations between extensive basal ganglia perivascular space burden and IS (IEU database) (OR, 1.76; 95%CI, 1.37–2.25; P = 8.51E-06), IS (FinnGen) (OR, 1.68; 95%CI, 1.21–2.34; P = 1.97E-03), and IS (small-vessel) (OR, 4.56; 95%CI, 2.51–8.27; P = 5.95E-07) persist (Table 1 ). Table 1 Multivariable mendelian randomization analysis results between perivascular space burden and ischemic stroke by adjusting for all confounders Type of Perivascular space measurement / Exposure Data source Type of ischemic stroke nSNP Methods of multivariable MR Beta SE P-value OR (95%CI) Extensive white matter perivascular space burden MEGA STROKE Ischemic stroke 193 Multivariable IVW 0.045 0.086 6.06E-01 1.05 (0.88–1.24) Multivariable Median -0.049 0.127 6.96E-01 0.95 (0.74–1.22) Multivariable Egger -0.016 0.119 8.92E-01 0.98 (0.78–1.24) IEU database Ischemic stroke 193 Multivariable IVW 0.204 0.079 9.65E-03 1.23 (1.05–1.43) Multivariable Median 0.146 0.109 1.79E-01 1.16 (0.94–1.43) Multivariable Egger 0.295 0.105 5.16E-03 1.34 (1.09–1.65) FinnGen Ischemic stroke 195 Multivariable IVW 0.068 0.109 5.29E-01 1.07 (0.87–1.32) Multivariable Median 0.241 0.157 1.25E-01 1.27 (0.94–1.73) Multivariable Egger 0.244 0.144 9.15E-02 1.28 (0.96–1.69) Extensive hippocampal perivascular space burden MEGA STROKE Ischemic stroke (large artery atherosclerosis) 163 Multivariable IVW 0.436 0.368 2.36E-01 1.55 (0.75–3.18) Multivariable Median 0.953 0.476 4.53E-02 2.59 (1.02–6.60) Multivariable Egger 1.001 0.472 3.42E-02 2.72 (1.08–6.87) Extensive basal ganglia perivascular space burden MEGA STROKE Ischemic stroke 165 Multivariable IVW 0.275 0.134 4.11E-02 1.32 (1.01–1.71) Multivariable Median 0.534 0.183 3.55E-03 1.71 (1.19–2.44) Multivariable Egger 0.156 0.178 3.79E-01 1.17 (0.83–1.66) IEU database Ischemic stroke 168 Multivariable IVW 0.563 0.126 8.51E-06 1.76 (1.37–2.25) Multivariable Median 0.455 0.170 7.29E-03 1.58 (1.13–2.20) Multivariable Egger 0.650 0.173 1.76E-04 1.92 (1.36–2.69) FinnGen Ischemic stroke 168 Multivariable IVW 0.520 0.168 1.97E-03 1.68 (1.21–2.34) Multivariable Median 0.422 0.253 9.49E-02 1.52 (0.93–2.50) Multivariable Egger 0.515 0.221 1.96E-02 1.67 (1.09–2.58) MEGA STROKE Ischemic stroke (small-vessel) 167 Multivariable IVW 1.517 0.304 5.95E-07 4.56 (2.51–8.27) Multivariable Median 1.547 0.440 4.38E-04 4.70 (1.98–1.13) Multivariable Egger 1.202 0.404 2.94E-03 3.33 (1.51–7.35) MR, mendelian randomization; IVW, inverse variance weighted. Meanwhile, we conduct sensitivity analyses to affirm the stability of the results, employing Cochran's Q test and I 2 to detect heterogeneity. We find no significant heterogeneity in any of the results. Additionally, no instances of horizontal pleiotropy are identified ( Supplementary Table S7 ). 3.5 Results of Meta-analysis after MVMR Following adjustment for relevant confounding factors in the MVMR analysis, we conducted a meta-analysis to synthesize the results of EPVS in different locations on IS from various sources (Fig. 7 ). The findings reveal a significant causal relationship between extensive white matter perivascular space burden as the exposure and IS from different sources as the outcome, without any observed heterogeneity ( P = 0.35, I 2 = 6%). Additionally, when considering extensive basal ganglia perivascular space burden as the exposure and IS from different sources as the outcome, the causal relationship remains significant, with no observed heterogeneity in the current causal association ( P = 0.26, I 2 = 25%). 3.6 Reverse causal relationship between IS, TIA and EPVS In the reverse MR analysis, IS and TIA are treated as exposures, while EPVS in different locations are considered as outcomes. Through comprehensive sensitivity analyses, outliers are removed to ensure result stability, and causal associations affected by horizontal pleiotropy are eliminated. Ultimately, we find potential causal relationships between extensive basal ganglia perivascular space burden and IS (FinnGen) (β, 0.021; 95%CI, 0.002–0.041; P = 3.46E-02, P FDR = 1.01E-01) and TIA (FinnGen) (β, 0.016; 95%CI, 0.000-0.032; P = 4.50E-02, P FDR =1.01E-01) ( Supplementary Tables S8 and S9 ). Furthermore, there is no evidence of heterogeneity or horizontal pleiotropy in these results. Leave-one-out analysis confirms that the observed causal effects are not driven by individual SNPs ( Supplementary Tables S10 , S11 and Fig. 8 ). 3.7 Genetic correlations between EPVS and IS and its subtypes As indicated in Supplementary Table S12, in addition to a comprehensive MR analysis, we also utilize LDSC regression to evaluate the genetic correlations between EPVS in different locations and IS and its subtypes. We observe genetic correlations between extensive basal ganglia perivascular space burden and IS from various sources. Specifically, extensive basal ganglia perivascular space burden shows positive genetic correlations with IS (MEGA STROKE) (Rg = 0.504, P = 1.43E-05), IS (IEU database) (Rg = 0.390, P = 1.97E-03), IS (FinnGen) (Rg = 0.616, P = 8.05E-03), and IS (small-vessel) (Rg = 0.877, P = 4.96E-03). 4 Discussion This study thoroughly investigates the relationship between EPVS in various locations and IS, its subtypes, and TIA using MR analysis. After adjusting for confounders and integrating data from multiple sources, we confirm that EPVS in the white matter and basal ganglia region increases the risk of IS. Moreover, EPVS in the basal ganglia region is associated with the occurrence of IS (small-vessel subtype). Reverse MR results suggest a potential increase in extensive basal ganglia perivascular space burden following the occurrence of IS and TIA. Therefore, monitoring EPVS in these regions could be valuable for preventing, monitoring, and treating IS in clinical practice. PVSs, also known as Virchow-Robin spaces, are potential gaps between the brain's vascular system and the meningeal layers, initially described by pathologists Rudolf Virchow and Charles Robin in the 19th century. 30 These spaces create pathways along small arteries and veins in the subarachnoid space, facilitating the transport of cerebrospinal fluid and the exchange of substances within cells to effectively clear metabolic byproducts from the brain. 31 IS is the most common type of stroke, characterized by high incidence, mortality, and disability rates. 32 Clinical risk factors for IS are categorized into controllable and uncontrollable types. Uncontrollable risk factors include age, gender, and race, while controllable factors encompass hypertension, diabetes, hyperlipidemia, smoking, alcohol abuse, carotid atherosclerosis, heart disease, and blood disorders. However, even with effective management of known risk factors, many patients still experience worsening or recurrence of ischemia. Therefore, there remains a need to identify and control additional unknown risk factors. Recent research on IS and EPVS has sparked debate. Some studies suggest that EPVS serves as a risk factor for IS, with varying relationships observed between EPVS in different regions and IS. 9 Conversely, other research poses that EPVS is merely an MRI manifestation of cerebral small vessel disease and lacks significant association with IS onset. 33 This ongoing debate highlights the controversial nature of the EPVS-IS relationship. Moreover, several studies indicate that risk factors for cerebrovascular disease, such as hypertension, 34 diabetes, 35 age, 36 and gender, 37 may also influence EPVS. Consequently, EPVS might be linked to the occurrence of IS. Recent research suggests that atherosclerosis in the carotid artery system may disrupt pulsatile blood flow dynamics and impede interstitial fluid drainage in the brain, leading to EPVS. 38 In IS patients, over 50% of intracranial atherosclerotic stenosis is independently associated with more than 20 centrum semiovale EPVS, but not with basal ganglia EPVS. 9 Another study indicates that atherosclerosis in the conduit arteries between the heart and the brain may correlate with a higher burden of EPVS, suggesting that EPVS could serve as a marker of systemic arterial aging and its impact on pulsatile hemodynamics. 39 Given that atherosclerosis is a key pathogenic mechanism of IS, 40 we speculate whether EPVS may contribute to IS by facilitating the occurrence and progression of atherosclerosis. Further longitudinal studies are essential to elucidate the relationship between EPVS burden and luminal narrowing progression. In our study, we investigated the genetic association between EPVS burden in various locations and the onset of IS and its subtypes, revealing a clear causal link between EPVS burden in the white matter and basal ganglia regions and IS occurrence. Moreover, our findings suggest a potential increase in EPVS burden in the basal ganglia region following IS and TIA, underscoring the need for further exploration of underlying mechanisms. It is essential to recognize several limitations in our study. Firstly, setting the genome-wide significance threshold at 1×10 − 5 may introduce bias. Secondly, our study samples were exclusively from European ancestry populations, limiting the generalizability of our findings to other ethnic groups. Lastly, due to dataset constraints, we couldn't assess the individual-level impact of EPVS burden on IS. Therefore, prospective cohort studies are still necessary. Nevertheless, we rigorously evaluated the causal relationship between EPVS burden in various locations and IS, its subtypes, and TIA through comprehensive MR analysis, providing a foundation for further clinical diagnosis and treatment. 5 Conclusion This MR analysis confirms that an increased EPVS burden in the white matter and basal ganglia regions significantly raises the risk of IS. Moreover, elevated EPVS burden in the basal ganglia region also contributes to the occurrence of IS (small-vessel). Therefore, clinical monitoring of EPVS burden is essential. However, future research should delve deeper into the associations and potential mechanisms involved. Declarations Declaration of competing interest The authors declare that they have no any competing interests. Acknowledgment We would like to thank the Duperron MG et.al, MEGA STRIOKE consortium, FinnGen consortium, the IEU open GWAS project, and the UK biobank for providing complete GWAS data. Data availability statement The original GWAS data for perivascular space burden can be found at the GWAS CatLog (https://www.ebi.ac.uk/gwas/studies/GCST90134433). The GWAS data for ischemic stroke and its subtype can be found at https://www.finngen.fi/en, https://gwas.mrcieu.ac.uk/, and http://www.megastroke.org/index.html. Funding This work was supported by the National Natural Science Foundation of China (82071468 and 82271507). CRediT author contributions Xuehong Chu : Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft. Yingjie Shen : Conceptualization, Data Curation, Software and Writing - Review & Editing. Yaolou Wang : Investigation, Data Curation, and Visualization. Xiao Dong : Software, Resources, and Visualization. Yuanyuan Liu : Conceptualization, Validation, and Resources. Chuanhui Li : Software and Writing - Review & Editing. Wenbo Zhao : Validation, and Formal analysis. Xunming Ji : Supervision, Project administration, and Funding acquisition. Miaowen Jiang : Writing - Review & Editing, Investigation, Resources. Software, and Visualization. Ming Li : Writing – review & editing, Software and Visualization, Funding acquisition, Resources, Supervision. Chuanjie Wu : Conceptualization, Writing - Review & Editing, Supervision, Project administration, and Funding acquisition. All participating authors give their consent for this work to be published. Ethics approval No additional ethical approvals were required for this study, as we used publicly available data that had been approved by the relevant ethical and institutional review boards. Supporting information Additional information can be found in the Supplemental Tables (1-12). References Ding Y, DeGracia D, Geng X, et al. Perspectives on effect of spleen in ischemic stroke. Brain Circ. 2022;8:117–20. 10.4103/bc.bc_53_22 . Rasmussen MK, Mestre H, Nedergaard M. Fluid transport in the brain. 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Med Res Rev 2022; 42: 259–305. 20210506. 10.1002/med.21817 . Doubal FN, MacLullich AM, Ferguson KJ, et al. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010;41:450–45420100107. 10.1161/strokeaha.109.564914 . Zhu YC, Tzourio C, Soumaré A, et al. Severity of dilated Virchow-Robin spaces is associated with age, blood pressure, and MRI markers of small vessel disease: a population-based study. Stroke. 2010;41:2483–90. 10.1161/strokeaha.110.591586 . Ferguson SC, Blane A, Perros P, et al. Cognitive ability and brain structure in type 1 diabetes: relation to microangiopathy and preceding severe hypoglycemia. Diabetes. 2003;52:149–56. 10.2337/diabetes.52.1.149 . Zhu YC, Dufouil C, Mazoyer B, et al. Frequency and location of dilated Virchow-Robin spaces in elderly people: a population-based 3D MR imaging study. AJNR Am J Neuroradiol. 2011;32:709–13. 10.3174/ajnr.A2366 . Patankar TF, Mitra D, Varma A, et al. Dilatation of the Virchow-Robin space is a sensitive indicator of cerebral microvascular disease: study in elderly patients with dementia. AJNR Am J Neuroradiol. 2005;26:1512–20. Mikami T, Tamada T, Suzuki H, et al. Influence of hemodynamics on enlarged perivascular spaces in atherosclerotic large vessel disease. Neurol Res. 2018;40:1021–102720180829. 10.1080/01616412.2018.1509827 . Gutierrez J, DiTullio M, YK KC, et al. Brain arterial dilatation modifies the association between extracranial pulsatile hemodynamics and brain perivascular spaces: the Northern Manhattan Study. Hypertens Res. 2019;42:1019–102820190401. 10.1038/s41440-019-0255-1 . Bir SC, Kelley RE. Carotid atherosclerotic disease: A systematic review of pathogenesis and management. Brain Circ 2022; 8: 127–136. 20220921. 10.4103/bc.bc_36_22 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Supplemental Tables (1-12). 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University","correspondingAuthor":false,"prefix":"","firstName":"Yingjie","middleName":"","lastName":"Shen","suffix":""},{"id":311640405,"identity":"5222e70e-f270-474b-94b0-5d4566fad941","order_by":2,"name":"Yaolou Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaolou","middleName":"","lastName":"Wang","suffix":""},{"id":311640406,"identity":"dcbd8ef9-16c7-4f87-9469-47d38bea0081","order_by":3,"name":"Xiao Dong","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Dong","suffix":""},{"id":311640407,"identity":"d62969b2-943e-4d0a-b17e-b05748373554","order_by":4,"name":"Yuanyuan Liu","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Liu","suffix":""},{"id":311640408,"identity":"10bb1b88-569b-41a7-b08d-8b7f9160b8e8","order_by":5,"name":"Yan Feng","email":"","orcid":"","institution":"Suzhou Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Feng","suffix":""},{"id":311640409,"identity":"bda470b3-4557-42f0-88ba-2f6266057e94","order_by":6,"name":"Chuanhui Li","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chuanhui","middleName":"","lastName":"Li","suffix":""},{"id":311640410,"identity":"0754b8c3-e9af-4a52-9111-1cd783d6b154","order_by":7,"name":"Wenbo Zhao","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Li","suffix":""},{"id":311640415,"identity":"5e9232e6-1dcf-4808-880d-645da0151ebd","order_by":11,"name":"Chuanjie Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHACNhDBzMbAfAAqkEC0FjaYUiK1AAGPAXFaDG4kP3vMU3OHnY+95+OHnzmHGfjZcwwYfu7ApyXN3Jjn2DNmNp6zmyV7tx1mkOx5Y8DYewa3FrMbOWzSPGyHmdkkcrcxMwK1GNzIMWBmbCOk5R9Qi/ybZ2At9kRp4W0D2cLDBrFFgoAW+zPPzCTn9gG18KQZA/2SziNx5lnBwV48WiTbk59JvPl2OFm+/fDDDz+3WcvxtydvfPATjxYYSIYxeEDEAcIaGBjsiFE0CkbBKBgFIxQAAG2nSbk1GAz6AAAAAElFTkSuQmCC","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chuanjie","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-05-29 15:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4498156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4498156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58208266,"identity":"ac8283ae-2f00-4287-83c9-1a9cfb83f158","added_by":"auto","created_at":"2024-06-12 12:50:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52889,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart based on the three core assumptions of mendelian randomization. 1) Instrumental variables (IVs) show a significant association with exposures. 2) IVs are unrelated to any confounders of the exposure-outcomes association. 3) IVs exclusively influence the outcome through the exposure. SNP, single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/9b21b0a00104e5341bddab14.png"},{"id":58208269,"identity":"c71caf0f-8bc2-4246-9792-2073d4a3dd3f","added_by":"auto","created_at":"2024-06-12 12:50:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43072,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of univariate MR of different types of perivascular space burden with multiple types of ischemic stroke and TIA. The depth of the purple color represents the value of the OR. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/bb3bc844152de673107f9f4e.png"},{"id":58208273,"identity":"0d3eee4f-f529-4f77-adac-c98236cbe45a","added_by":"auto","created_at":"2024-06-12 12:50:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67130,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of univariate MR of the perivascular space with different types and sources of ischemic stroke. The different colors represent differences in exposure and different sources of outcome. MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/b7bac96fd5d2aafb243dcf8b.png"},{"id":58208268,"identity":"c5732a46-922d-4b5f-a8e9-f3dd05aa7a83","added_by":"auto","created_at":"2024-06-12 12:50:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36844,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plots for MR analysis of different types of perivascular spaces with different types and sources of ischemic stroke. MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/8acb13c8fe721ac1c838b68a.png"},{"id":58208264,"identity":"8dbecb2c-88ab-4099-96c1-8f79a860803c","added_by":"auto","created_at":"2024-06-12 12:50:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44142,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of meta-analysis of different types of perivascular spaces with different sources of ischemic stroke based on univariable MR results. MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/548d942bd7fa17b247dc01e0.png"},{"id":58208707,"identity":"d9e52af9-3df8-4477-9ebb-bda813536346","added_by":"auto","created_at":"2024-06-12 12:58:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70696,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of different types of perivascular spaces with different types and sources of ischemic stroke based on multivariable MR results. MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/c27fd2b1ad75f588b13898a4.png"},{"id":58208272,"identity":"13e54897-40ce-49dc-bd6c-f059a7d696ed","added_by":"auto","created_at":"2024-06-12 12:50:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43369,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of meta-analysis of different types of perivascular spaces with different sources of ischemic stroke based on multivariable MR results. MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/960d2400a8987ccd08f963dc.png"},{"id":58208271,"identity":"8f04b7a9-5f99-4bf6-a2d0-212cf75119a1","added_by":"auto","created_at":"2024-06-12 12:50:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":20131,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plots for MR analysis of TIA and ischemic stroke with extensive basal ganglia perivascular space burden. MR, Mendelian randomization. TIA, Transient ischemic attack.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/4cf3dd572f93caeaa37bb4e9.png"},{"id":58821626,"identity":"f345196c-ab41-4a59-b1a5-5e462f4b25ba","added_by":"auto","created_at":"2024-06-21 15:12:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1395934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/eb535271-ddd9-49f5-aa63-b0886545ce2f.pdf"},{"id":58208270,"identity":"20b388e2-9460-411a-b87b-77929ed2f820","added_by":"auto","created_at":"2024-06-12 12:50:11","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":308342,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Tables (1-12).\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4498156/v1/c0c7c81568e6a881c0c1aa24.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The causal relationship between extensive perivascular space burden and ischemic stroke and its subtypes and transient ischemic attack: A Mendelian randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eStroke is a prevalent cerebrovascular disease marked by neurological deficits due to acute focal injury in the central nervous system. It is classified into hemorrhagic and ischemic types based on pathophysiology. Ischemic stroke (IS), caused by arterial blockage and decreased cerebral blood flow, is the most common form. Recently, IS incidence has been on the rise, making it a top cause of global mortality and disability, and imposing a significant socio-economic burden.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePerivascular spaces (PVS) are small gaps around small arteries and veins within the brain parenchyma, enclosed by the pia mater. They are thought to be essential for lymphatic drainage, waste removal, and tissue homeostasis.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Increasing evidence suggests that extensive perivascular space burden (EPVS) indicates impaired lymphatic drainage function, which is a characteristic of brain disorders, including small vessel disease,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Parkinson's disease,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e cognitive impairment,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and multiple sclerosis.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Recent research indicates that after ischemia, cerebrospinal fluid surrounding brain tissue enters the brain via PVS within minutes, causing cerebral edema.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e This highlights the potential impact of EPVS in IS. Approximately 98.8% of acute IS patients reportedly exhibit observable EPVS on scans within the first 7 days post-stroke.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e A prospective study involving high-risk individuals for IS or transient ischemic attack (TIA) (n\u0026thinsp;=\u0026thinsp;2002 subjects) found that a high burden of PVS in the basal ganglia is linked to recurrent stroke and IS.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Although traditional epidemiological studies are well-designed, prospective, and involve large population sizes, the conclusions drawn from these studies may be influenced by confounding factors or reverse causality, thus precluding the establishment of definitive causal relationships.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis provides an alternative method for causal inference. It utilizes genetic variations as instrumental variables (IVs), which are closely linked to the exposure, unaffected by confounding factors, and not influenced by pathways other than the exposure. This effectively reduces reverse causal bias and residual confounding bias,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e resulting in stronger evidence for causal inference. In this study, we utilize MR analysis to explore the causal relationship between EPVS in different locations and IS, its subtypes and TIA, thus offering valuable insights for clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and ethical statement\u003c/h2\u003e \u003cp\u003eTo explore the relationship between EPVS in various locations and IS, its subtypes, and TIA, we conduct MR analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the study design. In the forward MR, we consider extensive white matter, hippocampal, and basal ganglia perivascular space burdens as exposures, investigating their causal links with IS, its subtypes and TIA individually. Additionally, we also utilize multivariable MR (MVMR) to adjust for confounding factors, followed by conducting a meta-analysis to evaluate the overall effect of EPVS on IS, its subtypes and TIA from different sources. The MR analysis in this study meets three core assumptions: 1) significant correlation between IVs and exposures,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e 2) no correlation between IVs and confounding factors affecting the relationship between exposures and outcomes,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and 3) IVs solely influencing the outcomes through exposures.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Ethical approval\u003c/strong\u003e is not necessary for this study as it utilizes publicly available data that has already been approved by the relevant institutional ethics committees. Moreover, it is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization guidelines (STROBE-MR) (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources\u003c/h2\u003e \u003cp\u003eThe summary data of EPVS in various sites are sourced from a cohort study involving 18 populations, encompassing over 8\u0026nbsp;million SNPs (minor allele frequency\u0026thinsp;\u0026ge;\u0026thinsp;1%) from more than 40,095 participants (mean age 66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6 years, 51.7% female). To address variations in PVS quantification methods, image acquisition, and participant characteristics, we categorized PVS burden using thresholds closest to the upper quartile of the PVS distribution. Ultimately, 9,607 out of 39,822, 9,189 out of 40,000, and 9,339 out of 40,095 participants exhibit EPVS in white matter, hippocampus, and basal ganglia, respectively.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe data of IS, its subtypes and TIA are sourced from multiple publicly available Genome-wide Association Studies (GWAS) datasets. Specifically, IS data are derived from the MEGA consortium (Ncase\u0026thinsp;=\u0026thinsp;34,217, sample size\u0026thinsp;=\u0026thinsp;440,328),\u003csup\u003e14\u003c/sup\u003e a meta-analysis involving the UK Biobank (Ncase\u0026thinsp;=\u0026thinsp;11,929, sample size\u0026thinsp;=\u0026thinsp;484,121),\u003csup\u003e15\u003c/sup\u003e and the FinnGen database (Ncase\u0026thinsp;=\u0026thinsp;10,551, sample size\u0026thinsp;=\u0026thinsp;212,774). Based on the TOAST classification of IS, partial GWAS information for its subtypes, including large-artery atherosclerosis (Ncase\u0026thinsp;=\u0026thinsp;4,373, sample size\u0026thinsp;=\u0026thinsp;150,765), cardioembolism (Ncase\u0026thinsp;=\u0026thinsp;7,193, sample size\u0026thinsp;=\u0026thinsp;211,763), and small-vessel (Ncase\u0026thinsp;=\u0026thinsp;5,386, sample size\u0026thinsp;=\u0026thinsp;198,048), is obtained from the MEGA consortium.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Lacunar stroke (Ncase\u0026thinsp;=\u0026thinsp;6,030, sample size\u0026thinsp;=\u0026thinsp;225,419) is sourced from cases recruited from acute stroke hospitalization and outpatient services in Europe, the United States, South America, and Australia. This study involves a meta-analysis of MRI-diagnosed lacunar stroke patients' data and existing GWAS datasets. The patients are from hospitals in the UK, part of the UK DNA Cavernous Stroke Study and collaborators from the International Stroke Genetics Consortium.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e GWAS data of TIA are obtained from the FinnGen database and the UK Biobank,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e both of which conducted GWAS studies on large populations to identify risk loci for the disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of IVs\u003c/h2\u003e \u003cp\u003eDuring the forward MR analysis, EPVS found in various regions are considered as the exposures, while IS, its subtypes, and TIA are examined as outcomes. To meet assumption 1, this study identifies single nucleotide polymorphisms (SNPs) across the entire genome that show significant associations with EPVS at various locations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) and have no linkage disequilibrium (LD) (r\u003csup\u003e2\u003c/sup\u003e=0.01, kb\u0026thinsp;=\u0026thinsp;5000), ensuring the independence of the selected IVs. To address potential confounding factors, we utilize MVMR analysis to control for common confounders of IS and TIA, including obesity, hypertension, diabetes, and alcohol, thereby satisfying assumption 2. To fulfill assumption 3, this study further excludes SNPs significantly associated with IS, its subtypes and TIA across the entire genome (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). To ensure the strength of the selected IVs, we calculate the statistical strength using the F value. Specifically, F\u0026thinsp;=\u0026thinsp;R\u0026sup2;/ (1 - R\u0026sup2;) * (N - K \u0026minus;\u0026thinsp;1)/K, where N represents the sample size of the exposure, K is the number of SNPs, R\u0026sup2; is the proportion of variance explained by SNPs in the exposure dataset, and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2\u0026times; (1-MAF) (MAF) \u0026times;(β/SD)\u003csup\u003e2\u003c/sup\u003e, β denotes the effect size of the allele.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e IVs with F\u0026thinsp;\u0026lt;\u0026thinsp;10 will be excluded. Furthermore, SNPs that are inconsistent with the exposure and outcome alleles, as well as palindromic SNPs with moderate allele frequencies, are excluded. The SNPs subjected to the rigorous screening process are utilized for the final causal analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 MR analysis\u003c/h2\u003e \u003cp\u003eThis study employs inverse variance weighted (IVW) as the primary method for MR analysis. When the selected SNPs are all effective IVs, the IVW method can provide the most accurate estimates of causal association effects.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Additionally, Bayesian weighted, weighted median (WM), weighted mode, and simple mode are used as supplementary analyses. Bayesian weighted Mendelian randomization explicitly accounts for uncertainty related to weak effects from polygenic traits and can identify outliers, addressing instrumental variable assumption violations due to pleiotropy.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The WM method provides effective causal estimates when over half of the SNPs are valid IVs.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Weighted mode is reliable when most individual instruments' causal effect estimates come from valid instruments, even if some IVs are considered invalid.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Furthermore, the simple mode can serve as an unweighted empirical density function for estimating causality.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e To enhance the robustness of results, we require consistent directions of β values across all methods while ensuring significance in IVW and Bayesian weighted results. Moreover, we use false discovery rate (FDR) correction for \u003cem\u003eP\u003c/em\u003e-values. Significant causal relationships between EPVS and outcomes are indicated when \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt; 0.05, and potential causal relationships are suggested when \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026gt; 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eWe use IVW and MR Egger regression to detect heterogeneity and calculate Cochran\u0026rsquo;s Q statistic to quantify its magnitude. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates significant heterogeneity, warranting the use of a random-effects model for causal inference.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e MR-Egger intercept test is utilized to analyze horizontal pleiotropy, estimating directional inference by calculating the intercept and resulting in a directional \u003cem\u003eP\u003c/em\u003e-value. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 suggests the absence of horizontal pleiotropy, demonstrating the robustness of the MR analysis results.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The MR-PRESSO Global test identifies outliers, whose presence is confirmed by \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, requiring their exclusion for subsequent analysis.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Besides, leave-one-out analysis assesses individual SNPs' influence on the MR results. After removing outlier SNPs, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the MR Egger regression renders the MR results unreliable. In forward MR analysis, we utilize MR Steiger to ensure directional accuracy. This method assumes that the genetic variants should explain more variance during exposure than outcome, meeting the legitimate requirements of MR investigation and aiding in identifying potential bidirectional effects.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Finally, reverse MR analysis is used to observe bidirectional effects between EPVS and IS, its subtypes, and TIA, with SNP selection criteria consistent with forward MR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 MVMR analysis\u003c/h2\u003e \u003cp\u003eMVMR analysis can evaluate direct causal effects between exposure and outcome.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Thus, to adjust for potential confounders (Obesity, hypertension, type 2 diabetes, and ongoing alcohol addiction), we perform MVMR analysis following univariable MR (UVMR) analysis to examine the independent impact of EPVS in different locations on IS, its subtypes, and TIA. We utilize Multivariable IVW, Multivariable Egger, and Multivariable Median methods, with Multivariable IVW serving as the primary method. Also, to assess result stability, Cochran\u0026rsquo;s Q statistic and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e detect result heterogeneity, while the Egger-intercept test identifies horizontal pleiotropy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Meta- analysis\u003c/h2\u003e \u003cp\u003eTo mitigate biases stemming from various sources of GWAS data on IS, following MR analysis, we conduct meta-analysis to examine the overall impact of EPVS in different locations on IS and its subtypes, as well as TIA. Additionally, we employ I2 to assess the heterogeneity of the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Linkage disequilibrium score regression and directionality tests\u003c/h2\u003e \u003cp\u003eWe employ linkage disequilibrium score regression (LDSC) analysis to summarize GWAS data and estimate heritability and genetic correlations based on single-nucleotide variants. The LD reference panel from the 1000 Genomes Project is used to compute LD scores. Finally, we utilize the LDSC tool to further evaluate the genetic associations between EPVS at different locations and IS and its subtypes.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAll MR-related analyses are conducted in R (version 4.3.0) using the \"TwoSampleMR\",\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e \"MR-PRESSO\",\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and \"Mendelian Randomization\" R packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Results of causality between EPVS and outcomes by UVMR\u003c/h2\u003e \u003cp\u003eAfter rigorous IV selection, we establish a variable number of IVs for EPVS in different locations based on various outcomes. Detailed information on all IVs can be found in \u003cb\u003eSupplementary Table S2\u003c/b\u003e. The F-values for all SNPs were greater than 10, indicating that the current results are not biased by weak IVs. Genetically predicted extensive white matter perivascular space burden is significantly associated with a higher risk of IS (IEU database) (OR, 1.24; 95%CI, 1.09\u0026ndash;1.42; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09E-03; \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 9.82E-03). Considering extensive hippocampal perivascular space burden as the exposure reveals a potential causal link with IS (large-artery atherosclerosis) (OR, 1.95; 95%CI, 1.02\u0026ndash;3.74; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.50E-02; \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=3.77E-01). Moreover, extensive basal ganglia perivascular space burden exhibits significant causal associations with IS and its subtypes. Specifically, it notably increases the risk of IS (IEU database) (OR, 1.53; 95%CI, 1.20\u0026ndash;1.94; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.37E-04; \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 1.91E-02), IS (FinnGen) (OR, 1.63; 95%CI, 1.17\u0026ndash;2.29; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.23E-03; \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 3.80E-02), and IS (small vessel) (OR, 2.66; 95%CI, 1.49\u0026ndash;4.75; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.46E-04; \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 1.91E-02) (\u003cb\u003eSupplementary Table S3\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Results of sensitivity analysis for UVMR\u003c/h2\u003e \u003cp\u003eTo ensure the reliability of our findings, we conduct several sensitivity analyses. When extensive hippocampal perivascular space burden is considered as the exposure and IS (small-vessel) as the outcome, Cochran\u0026rsquo;s Q test reveals heterogeneity, prompting the use of the random-effects model of IVW despite the absence of outliers. For IS (MEGA STROKE) as the outcome, MR-PRESSO identifies three outliers. After their removal, no heterogeneity or horizontal pleiotropy is detected. Similarly, when extensive white matter perivascular space burden is the exposure and IS (MEGA STROKE) or IS (IEU database) is the outcome, we find two outliers in each case. After excluding them, the results remain robust. Additionally, with extensive basal ganglia perivascular space burden as the exposure and IS (MEGA STROKE) or Lacunar stroke as the outcome, two outliers are detected in each analysis. Removing these outliers enhances the robustness of all sensitivity analyses (\u003cb\u003eSupplementary Tables S4\u003c/b\u003e and \u003cb\u003eS5\u003c/b\u003e). Furthermore, leave-one-out analysis confirms that individual SNPs do not drive the current results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Results of Meta-analysis after UVMR\u003c/h2\u003e \u003cp\u003eTo mitigate biases arising from varied GWAS data sources on IS, we perform meta-analyses after UVMR to synthesize the findings regarding EPVS in different locations on IS from diverse sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results indicate that when extensive white matter perivascular space burden is considered as the exposure and IS from various sources as the outcomes, no discernible causal relationship is observed, albeit with moderate heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;63%). Conversely, when extensive basal ganglia perivascular space burden is considered as the exposure and IS from different sources as the outcome, a significant causal relationship is detected, with no observed heterogeneity in the current causal association (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Results of MVMR analysis between EPVS and outcomes\u003c/h2\u003e \u003cp\u003eTo assess the direct causal link between EPVS in different locations and IS and its subtypes, we perform MVMR analyses. In these analyses, we adjust for confounders such as obesity, hypertension, type 2 diabetes, and ongoing alcohol addiction. The results are shown in \u003cb\u003eSupplementary Table S6\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. After accounting for confounding factors, the outcomes of UVMR change. Notably, when extensive white matter perivascular space burden is considered as the exposure and IS (IEU database) as the outcome, the causal relationship remains significant (OR, 1.23; 95%CI, 1.05\u0026ndash;1.43; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.65E-03). However, when extensive hippocampal perivascular space burden is the exposure, the causal link with IS (large artery atherosclerosis) becomes non-significant (OR, 1.55; 95%CI, 0.75\u0026ndash;3.18; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.36E-01). Conversely, when extensive basal ganglia perivascular space burden is the exposure, a causal relationship is established with IS (MEGA STROKE) (OR, 1.32; 95%CI, 1.01\u0026ndash;1.71; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.11E-02). Moreover, the causal associations between extensive basal ganglia perivascular space burden and IS (IEU database) (OR, 1.76; 95%CI, 1.37\u0026ndash;2.25; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.51E-06), IS (FinnGen) (OR, 1.68; 95%CI, 1.21\u0026ndash;2.34; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.97E-03), and IS (small-vessel) (OR, 4.56; 95%CI, 2.51\u0026ndash;8.27; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.95E-07) persist (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable mendelian randomization analysis results between perivascular space burden and ischemic stroke by adjusting for all confounders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Perivascular space measurement / Exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType of ischemic stroke\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethods of multivariable MR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eExtensive white matter perivascular space burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMEGA STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.06E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.05 (0.88\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.96E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95 (0.74\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.92E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.98 (0.78\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIEU database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.65E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.23 (1.05\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.79E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.16 (0.94\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.16E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.34 (1.09\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.29E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.07 (0.87\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.27 (0.94\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.15E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.28 (0.96\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExtensive hippocampal perivascular space burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMEGA STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke (large artery atherosclerosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.36E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.55 (0.75\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.53E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.59 (1.02\u0026ndash;6.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.42E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.72 (1.08\u0026ndash;6.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eExtensive basal ganglia perivascular space burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMEGA STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.11E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.32 (1.01\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.55E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.71 (1.19\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.79E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.17 (0.83\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIEU database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.51E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.76 (1.37\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.29E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.58 (1.13\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.76E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.92 (1.36\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.97E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.68 (1.21\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.49E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.52 (0.93\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.96E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.67 (1.09\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMEGA STROKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIschemic stroke (small-vessel)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable IVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.95E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.56 (2.51\u0026ndash;8.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.38E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.70 (1.98\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.94E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.33 (1.51\u0026ndash;7.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eMR, mendelian randomization; IVW, inverse variance weighted.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, we conduct sensitivity analyses to affirm the stability of the results, employing Cochran's Q test and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e to detect heterogeneity. We find no significant heterogeneity in any of the results. Additionally, no instances of horizontal pleiotropy are identified (\u003cb\u003eSupplementary Table S7\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Results of Meta-analysis after MVMR\u003c/h2\u003e \u003cp\u003eFollowing adjustment for relevant confounding factors in the MVMR analysis, we conducted a meta-analysis to synthesize the results of EPVS in different locations on IS from various sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The findings reveal a significant causal relationship between extensive white matter perivascular space burden as the exposure and IS from different sources as the outcome, without any observed heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.35, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6%). Additionally, when considering extensive basal ganglia perivascular space burden as the exposure and IS from different sources as the outcome, the causal relationship remains significant, with no observed heterogeneity in the current causal association (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;25%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Reverse causal relationship between IS, TIA and EPVS\u003c/h2\u003e \u003cp\u003eIn the reverse MR analysis, IS and TIA are treated as exposures, while EPVS in different locations are considered as outcomes. Through comprehensive sensitivity analyses, outliers are removed to ensure result stability, and causal associations affected by horizontal pleiotropy are eliminated. Ultimately, we find potential causal relationships between extensive basal ganglia perivascular space burden and IS (FinnGen) (β, 0.021; 95%CI, 0.002\u0026ndash;0.041; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.46E-02, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 1.01E-01) and TIA (FinnGen) (β, 0.016; 95%CI, 0.000-0.032; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.50E-02, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e =1.01E-01) (\u003cb\u003eSupplementary Tables S8\u003c/b\u003e and \u003cb\u003eS9\u003c/b\u003e). Furthermore, there is no evidence of heterogeneity or horizontal pleiotropy in these results. Leave-one-out analysis confirms that the observed causal effects are not driven by individual SNPs (\u003cb\u003eSupplementary Tables S10\u003c/b\u003e, \u003cb\u003eS11\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Genetic correlations between EPVS and IS and its subtypes\u003c/h2\u003e \u003cp\u003eAs indicated in Supplementary Table S12, in addition to a comprehensive MR analysis, we also utilize LDSC regression to evaluate the genetic correlations between EPVS in different locations and IS and its subtypes. We observe genetic correlations between extensive basal ganglia perivascular space burden and IS from various sources. Specifically, extensive basal ganglia perivascular space burden shows positive genetic correlations with IS (MEGA STROKE) (Rg\u0026thinsp;=\u0026thinsp;0.504, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.43E-05), IS (IEU database) (Rg\u0026thinsp;=\u0026thinsp;0.390, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.97E-03), IS (FinnGen) (Rg\u0026thinsp;=\u0026thinsp;0.616, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.05E-03), and IS (small-vessel) (Rg\u0026thinsp;=\u0026thinsp;0.877, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.96E-03).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study thoroughly investigates the relationship between EPVS in various locations and IS, its subtypes, and TIA using MR analysis. After adjusting for confounders and integrating data from multiple sources, we confirm that EPVS in the white matter and basal ganglia region increases the risk of IS. Moreover, EPVS in the basal ganglia region is associated with the occurrence of IS (small-vessel subtype). Reverse MR results suggest a potential increase in extensive basal ganglia perivascular space burden following the occurrence of IS and TIA. Therefore, monitoring EPVS in these regions could be valuable for preventing, monitoring, and treating IS in clinical practice.\u003c/p\u003e \u003cp\u003ePVSs, also known as Virchow-Robin spaces, are potential gaps between the brain's vascular system and the meningeal layers, initially described by pathologists Rudolf Virchow and Charles Robin in the 19th century.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e These spaces create pathways along small arteries and veins in the subarachnoid space, facilitating the transport of cerebrospinal fluid and the exchange of substances within cells to effectively clear metabolic byproducts from the brain.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e IS is the most common type of stroke, characterized by high incidence, mortality, and disability rates.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Clinical risk factors for IS are categorized into controllable and uncontrollable types. Uncontrollable risk factors include age, gender, and race, while controllable factors encompass hypertension, diabetes, hyperlipidemia, smoking, alcohol abuse, carotid atherosclerosis, heart disease, and blood disorders. However, even with effective management of known risk factors, many patients still experience worsening or recurrence of ischemia. Therefore, there remains a need to identify and control additional unknown risk factors.\u003c/p\u003e \u003cp\u003eRecent research on IS and EPVS has sparked debate. Some studies suggest that EPVS serves as a risk factor for IS, with varying relationships observed between EPVS in different regions and IS.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Conversely, other research poses that EPVS is merely an MRI manifestation of cerebral small vessel disease and lacks significant association with IS onset.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e This ongoing debate highlights the controversial nature of the EPVS-IS relationship. Moreover, several studies indicate that risk factors for cerebrovascular disease, such as hypertension,\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e diabetes,\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e age,\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and gender,\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e may also influence EPVS. Consequently, EPVS might be linked to the occurrence of IS.\u003c/p\u003e \u003cp\u003eRecent research suggests that atherosclerosis in the carotid artery system may disrupt pulsatile blood flow dynamics and impede interstitial fluid drainage in the brain, leading to EPVS.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e In IS patients, over 50% of intracranial atherosclerotic stenosis is independently associated with more than 20 centrum semiovale EPVS, but not with basal ganglia EPVS.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Another study indicates that atherosclerosis in the conduit arteries between the heart and the brain may correlate with a higher burden of EPVS, suggesting that EPVS could serve as a marker of systemic arterial aging and its impact on pulsatile hemodynamics.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven that atherosclerosis is a key pathogenic mechanism of IS,\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e we speculate whether EPVS may contribute to IS by facilitating the occurrence and progression of atherosclerosis. Further longitudinal studies are essential to elucidate the relationship between EPVS burden and luminal narrowing progression. In our study, we investigated the genetic association between EPVS burden in various locations and the onset of IS and its subtypes, revealing a clear causal link between EPVS burden in the white matter and basal ganglia regions and IS occurrence. Moreover, our findings suggest a potential increase in EPVS burden in the basal ganglia region following IS and TIA, underscoring the need for further exploration of underlying mechanisms.\u003c/p\u003e \u003cp\u003eIt is essential to recognize several limitations in our study. Firstly, setting the genome-wide significance threshold at 1\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;5 may introduce bias. Secondly, our study samples were exclusively from European ancestry populations, limiting the generalizability of our findings to other ethnic groups. Lastly, due to dataset constraints, we couldn't assess the individual-level impact of EPVS burden on IS. Therefore, prospective cohort studies are still necessary. Nevertheless, we rigorously evaluated the causal relationship between EPVS burden in various locations and IS, its subtypes, and TIA through comprehensive MR analysis, providing a foundation for further clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis MR analysis confirms that an increased EPVS burden in the white matter and basal ganglia regions significantly raises the risk of IS. Moreover, elevated EPVS burden in the basal ganglia region also contributes to the occurrence of IS (small-vessel). Therefore, clinical monitoring of EPVS burden is essential. However, future research should delve deeper into the associations and potential mechanisms involved.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no any competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Duperron MG et.al, MEGA STRIOKE consortium, FinnGen consortium, the IEU open GWAS project, and the UK biobank for providing complete GWAS data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original GWAS data for perivascular space burden can be found at the GWAS CatLog (https://www.ebi.ac.uk/gwas/studies/GCST90134433). The GWAS data for ischemic stroke and its subtype can be found at https://www.finngen.fi/en, \u0026nbsp; \u0026nbsp;https://gwas.mrcieu.ac.uk/, and http://www.megastroke.org/index.html.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82071468 and 82271507).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT author contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXuehong Chu\u003c/strong\u003e: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft.\u0026nbsp;\u003cstrong\u003eYingjie Shen\u003c/strong\u003e: Conceptualization, Data Curation, Software and Writing - Review \u0026amp; Editing.\u0026nbsp;\u003cstrong\u003eYaolou Wang\u003c/strong\u003e: Investigation, Data Curation, and Visualization.\u0026nbsp;\u003cstrong\u003eXiao Dong\u003c/strong\u003e: Software, Resources, and Visualization.\u0026nbsp;\u003cstrong\u003eYuanyuan Liu\u003c/strong\u003e: Conceptualization, Validation, and Resources.\u0026nbsp;\u003cstrong\u003eChuanhui Li\u003c/strong\u003e: Software and Writing - Review \u0026amp; Editing.\u0026nbsp;\u003cstrong\u003eWenbo Zhao\u003c/strong\u003e: Validation, and Formal analysis.\u0026nbsp;\u003cstrong\u003eXunming Ji\u003c/strong\u003e: Supervision, Project administration, and Funding acquisition.\u0026nbsp;\u003cstrong\u003eMiaowen Jiang\u003c/strong\u003e: Writing - Review \u0026amp; Editing, Investigation, Resources. Software, and Visualization.\u0026nbsp;\u003cstrong\u003eMing Li\u003c/strong\u003e: Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing, Software and Visualization, Funding acquisition, Resources, Supervision.\u0026nbsp;\u003cstrong\u003eChuanjie Wu\u003c/strong\u003e: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Project administration, and Funding acquisition. All participating authors give their consent for this work to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo additional ethical approvals were required for this study, as we used publicly available data that had been approved by the relevant ethical and institutional review boards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional information can be found in the Supplemental Tables (1-12).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDing Y, DeGracia D, Geng X, et al. Perspectives on effect of spleen in ischemic stroke. 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Carotid atherosclerotic disease: A systematic review of pathogenesis and management. \u003cem\u003eBrain Circ\u003c/em\u003e 2022; 8: 127\u0026ndash;136. 20220921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/bc.bc_36_22\u003c/span\u003e\u003cspan address=\"10.4103/bc.bc_36_22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"extensive perivascular space burden, ischemic stroke, causal inference, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4498156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4498156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClinical studies suggest a strong link between extensive perivascular space (EPVS) and ischemic stroke (IS), including its subtypes, and transient ischemic attack (TIA), but it's uncertain if the relationship is genetically causal.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilize summary data from large-scale Genome-wide Association Studies (GWAS) to investigate the association between EPVS in different locations and IS, its subtypes, and TIA through Mendelian randomization (MR) analysis. Various MR methods are employed to assess the causal relationship between EPVS and IS, its subtypes, and TIA. We apply multivariable MR to mitigate potential confounding factors and conduct sensitivity analyses to enhance result robustness. Subsequently, meta-analysis is utilized to integrate causal relationships between EPVS in different locations and IS from various sources. Additionally, reverse MR is employed to observe the impact of various IS types on EPVS. Finally, linkage disequilibrium score regression is conducted to assess genetic correlations between exposures and outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEPVS burden in both the white matter (OR, 1.12; 95% CI, 1.01\u0026ndash;1.25; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and the basal ganglia (OR, 1.57; 95% CI, 1.30\u0026ndash;1.89; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) are significant risk factors for IS. EPVS burden in the basal ganglia is also a risk for IS (small-vessel) (OR, 4.56; 95% CI, 2.57\u0026ndash;8.27; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.95E-07). Additionally, there appears to be a potential increase in extensive basal ganglia perivascular space burden following IS and TIA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eExtensive white matter perivascular space burden and extensive basal ganglia perivascular space burden may serve as important indicators for predicting IS.\u003c/p\u003e","manuscriptTitle":"The causal relationship between extensive perivascular space burden and ischemic stroke and its subtypes and transient ischemic attack: A Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 12:50:06","doi":"10.21203/rs.3.rs-4498156/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06db12f9-f2d4-4fc8-a95b-6c38a98e29b9","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-19T05:46:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 12:50:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4498156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4498156","identity":"rs-4498156","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}