Abstract
Background
The relationship between ischemic stroke (IS) and lipoprotein-associated phospholipase A2 (Lp-PLA2) activity is still unclear, and there is a dearth of stratified research on the relationship between Lp-PLA2 activity and different IS subtypes. Therefore, Mendelian randomization (MR) was used in this study to examine the relationship between genetically proxied Lp-PLA2 activity and the risks of IS and its subtypes.
Methods
Based on information from a meta-analysis of genome-wide association studies, which included 13,664 European people, five single nucleotide polymorphisms related to Lp-PLA2 activity were chosen as instrumental variables. Summary statistics information about the MEGESTROKE consortium with the European group (40,585 cases and 406,111 controls) include any IS (AIS; n = 34,217), large-artery stroke (LAS; n = 4,373), cardioembolic stroke (CES; n = 7,193), and small-vessel stroke (SVS; n = 5,386). In order to determine the causal relationships between Lp-PLA2 activity and IS as well as its subtypes, the inverse-variance-weighted (IVW) approach was chosen as the primary analysis. Significant estimates were then tested by sensitivity analysis to rule out heterogeneity and pleiotropy.
Results
IVW showed that Lp-PLA2 activity was causally associated with LAS (odds ratio = 3.25, 95% confidence interval = 1.65–6.41, p = 0.0007) but not with other subtypes of stroke. Sensitivity analysis for causal estimates between Lp-PLA2 activity and LAS showed no significant heterogeneity or pleiotropy.
Conclusions
These MR analyses support a causal effect of Lp-PLA2 activity on LAS but not on AIS, CES, or SVS, which suggests that serum Lp-PLA2 activity might be a biomarker for prediction of LAS.
Keywords: Lipoprotein-associated phospholipase A2 activity, Ischemic stroke, Large-artery stroke, Mendelian randomization study, Single nucleotide polymorphisms
Introduction
The primary prevention of ischemic stroke (IS) is essential since it is the second cause of mortality and disability worldwide. Growing evidence suggests stroke is caused by multiple factors, and traditional risk factors, such as hypertension, hyperglycemia, lifestyle as well as smoking, do not sufficiently explain the entire IS risk [1]. Meanwhile, although the IS burden has been reduced in recent decades with strategies for IS prevention that aggressively control these traditional risk factors, IS remains a critical global public health issue [2]. Therefore, a better understanding of the etiology and hazard factors of IS will enable the development of novel preventive strategies. Moreover, the incidence of stroke and its outcome differ markedly among various stroke subtypes. Thus, it is necessary to better distinguish the distinct etiology and risk factors of different subtypes to refine preventive methods.
In human plasma, an enzyme called lipoprotein-associated phospholipase A2 (Lp-PLA2) produced by a variety of inflammatory cells [3, 4] is carried bound predominantly to low-density lipoproteins [5]. Oxidized phospholipids could be hydrolyzed by Lp-PLA2 to generate Lys-phosphatidylcholine, resulting in endothelial dysfunction and facilitating inflammation [6]. Thus, Lp-PLA2 as a circulating pro-inflammatory factor may be associated with cardiovascular diseases such as IS. Hitherto, numerous large-scale observational studies have concentrated on the effect of Lp-PLA2 on IS [7–11]. However, some research studies showed a substantial association [7, 9, 11], while others merely showed a weak or even no correlation [10, 12, 13]. Moreover, due to the difficulty unraveling spurious associations caused by confounding factors or reverse causality, the causality inference ability of observational studies is limited. Therefore, it is still unclear whether Lp-PLA2 is related to IS.
The limitations of observational studies could be overcome by Mendelian randomization (MR), which is based on the premise that human genetic variants are randomly distributed among the population, like the randomized grouping principle in randomized controlled trials (RCTs). Thus, MR studies could assess the role of exposures in outcomes based on genetic variants. A recent two-sample MR study by Sun et al. [14] to determine the causal relationship between Lp-PLA2 activity and coronary artery disease (CAD) as well as myocardial infarction (MI) suggests that an MR study is a potent tool for evaluating this connection. Here, we conducted MR research to evaluate the causal relationship between Lp-PLA2 activity and IS as well as its main subtypes.
Methods
Study Design
In order to determine if exposure has a causal effect on the outcome in an MR investigation, genetic variants were selected as instrumental variables (IVs). Three presumptions underlie MR design (Fig. 1): (1) there is a strong association between the genetic variants and the exposure; (2) there is no association between the genetic variants and other variables; and (3) the genetic variants are only connected with the outcome through the exposure [15]. Genome-wide association studies (GWAS) that have already been published provided information on the relationships between single nucleotide polymorphisms (SNPs) and Lp-PLA2 activity and IS [16, 17].
Fig. 1.
MR study of Lp-PLA2 activity and risk of IS. The design is under the assumption that the genetic variants are associated with Lp-PLA2 activity but not with confounders, and the genetic variants influence IS and its subtypes only through Lp-PLA2 activity. SNP, single nucleotide polymorphism; Lp-PLA2, lipoprotein-associated phospholipase A2.
Exposure and Outcome Data Source
The summary statistics for Lp-PLA2 activity was generated for the exposure data by a meta-analysis of GWAS [16]. A total of 13,664 adults of European descent participated in the five independent community-based studies conducted in the USA and Europe for this meta-analysis of GWAS [18].
The MEGASTROKE GWAS meta-analysis, which published its summary genetic association data in 2018, provided the outcome data [17]. Though this was a multi-ancestry GWAS, only the European-ancestry group (40,585 cases and 406,111 controls) were selected for further analysis to prevent the bias that a multi-ancestry population causes. The cases were subtyped into any IS (AIS) regardless of the subtype (n = 34,217), large-artery stroke (LAS; n = 4,373), cardioembolic stroke (CES; n = 7,193), and small-vessel stroke (SVS; n = 5,386) according to previously published accepted judgment standards [19].
Selection of IVs
The meta-analysis of GWAS of Lp-PLA2 activity identified 59 SNPs with Lp-PLA2 activity (p < 5 × 10−8) (online suppl. Table S1; for all online suppl. material, see https://doi.org/10.1159/000535286). First, IVs with linkage disequilibrium <0.01 and distance <1 MB were chosen. Then, F-statistics was calculated to determine the strength of genetic variants, and SNPs with F-statistics less than 10 that would indicate substantial bias were removed [20]. Next, SNPs associated with IS and its subtypes were excluded. Additionally, uncertain SNPs with non-concordant alleles were rectified, as well as palindromic SNPs with ambiguous strands or SNPs with ambiguous or palindromic characteristics were eliminated from the aforementioned instrumental SNPs during harmonization. Furthermore, the association between SNPs and putative IS confounders such as blood pressure, diabetes, alcohol use, and smoking was examined using a phenome-wide association test (PhenoScanner V2) [21], and then SNPs associated with potential confounders were also excluded. Finally, the underlying outliers were eliminated before each MR analysis by the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) tests, which are best used when a horizontal pleiotropy is discovered in less than 50% of the IVs [22]. Figure 2 shows the diagram representing the study frame.
Fig. 2.
Study frame chart of the MR study revealing the causal relationship between Lp-PLA2 activity and IS and its subtypes. Lp-PLA2, lipoprotein-associated phospholipase A2; SNPs, single nucleotide polymorphisms; GWAS, genome-wide association study; MR-PRESSO, MR Pleiotropy RESidual Sum and Outlier.
Statistical Analysis
To calculate the impact of Lp-PLA2 activity on IS and its subtypes, we employed some two-sample MR methods. Because inverse-variance-weighted (IVW) offers MR evaluation by combining each Wald ratio of multiple SNPs and has the highest statistical power of all MR methods, we used it as the main analysis to estimate the causal relationship between Lp-PLA2 activity and IS as well as its subtypes [23]. In the IVW analysis, the final estimate was produced according to a weighted regression slope based on SNP outcome and SNP exposure effects, where the intercept is zero. Since they could produce more reliable estimates in a wider range of conditions despite being less efficient, the MR-Egger and weighted median were utilized to enhance the IVW estimations. The MR-Egger repression is primarily used for detecting and explaining horizontal pleiotropy [24]. MR analysis with weighted median permit to use invalid instruments, if at least 50% of all IVs, is valid [25].
For significant estimates, several sensitivity tests were conducted to determine whether the results were robust and whether the conclusions were reliable. Cochran’s Q test was used to test heterogeneity [26]. Horizontal pleiotropy was further assessed using the MR-Egger intercept test and leave-one-out analyses [24]. An online technique that was previously disclosed was used to calculate statistical power (https://shiny.cnsgenomics.com/mRnd/) [27].
The R software (version 4.2.1) with TwoSampleMR (version 0.5.6) packages were used for all MR analyses. Given the 4 MR estimations, statistical significance was defined as a p value <0.0125 (0.05/4) after the Bonferroni correction; a p value between 0.05 and 0.0125 was regarded as nominally significant.
Results
After 52 SNPs were excluded because of high linkage disequilibrium, the remaining 7 SNPs were selected as genetic IVs of Lp-PLA2 activity for the MR studies and these SNPs had strong strength (F-statistics, 33.65–116.66, online suppl. Table S2) and were not related to potential confounders. The associations between each SNP and IS as well as its subtypes are presented in Table 1. Before the primary MR analysis of the causal effect from Lp-PLA2 activity to LAS, the MR-PRESSO test excluded one outlier (rs7756935); however, no outlier was detected for the other 3 MR analyses.
Table 1.
Characteristics of the 7 SNPs associated with Lp-PLA2 activity and IS
| SNP | Parameter | Lp-PLA2 activity | IS | |||
|---|---|---|---|---|---|---|
| AIS | LAS | CES | SVS | |||
| rs10846744 | p value | 6.07E-09 | 0.3603 | 0.1660 | 0.3213 | 0.7998 |
| Chr: 12 | Beta | 0.0290 | 0.0132 | 0.0486 | 0.0274 | 0.0084 |
| Location: 123878378 | Standard error | 0.0050 | 0.0144 | 0.0351 | 0.0276 | 0.0331 |
| Effect allele/other allele: C/G | Effect allele frequency | 0.1604 | 0.1621 | 0.1593 | 0.1597 | |
| rs4420638 | p value | 4.91E-30 | 0.9771 | 0.4186 | 0.1889 | 0.3163 |
| Chr: 19 | Beta | −0.054 | 4.00E-04 | −0.0307 | 0.0294 | −0.0342 |
| Location: 50114786 | Standard error | 0.0050 | 0.0146 | 0.0380 | 0.0386 | 0.0341 |
| Effect allele/other allele: A/G | Effect allele frequency | 0.8199 | 0.8242 | 0.8231 | 0.8201 | |
| rs445925 | p value | 3.28E-10 | 0.1062 | 0.1169 | 0.3008 | 0.3778 |
| Chr: 19 | Beta | −0.0710 | −0.0298 | −0.0723 | −0.0362 | −0.0365 |
| Location: 50107480 | Standard error | 0.0110 | 0.0184 | 0.0461 | 0.0350 | 0.0413 |
| Effect allele/other allele: A/G | Effect allele frequency | 0.1029 | 0.1014 | 0.1000 | 0.1030 | |
| rs6511720 | p value | 2.61E-11 | 4.20E-05 | 0.0424 | 0.0430 | 0.6972 |
| Chr: 19 | Beta | −0.0450 | 0.0010 | −0.0837 | −0.0660 | 0.0144 |
| Location: 11063306 | Standard error | 0.0070 | 0.01650 | 0.04130 | 0.0326 | 0.0370 |
| Effect allele/other allele: T/G | Effect allele frequency | 0.1131 | 0.1148 | 0.1140 | 0.1156 | |
| rs7528419 | p value | 1.30E-17 | 0.2909 | 0.0084 | 0.5770 | 0.7469 |
| Chr: 1 | Beta | 0.0350 | 0.0128 | 0.0799 | 0.0130 | 0.0091 |
| Location: 109618715 | Standard error | 0.0040 | 0.0121 | 0.0303 | 0.0234 | 0.0283 |
| Effect allele/other allele: A/G | Effect allele frequency | 0.7806 | 0.7808 | 0.7809 | 0.7810 | |
| rs7756935 | p value | 1.26E-10 | 0.0945 | 0.0046 | 0.1977 | 0.6159 |
| Chr: 6 | Beta | −0.0270 | 0.0209 | 0.0895 | 0.0314 | 0.0145 |
| Location: 46782984 | Standard error | 0.0040 | 0.0125 | 0.0315 | 0.0244 | 0.0289 |
| Effect allele/other allele: A/C | Effect allele frequency | 0.7984 | 0.7988 | 0.7992 | 0.8002 | |
| rs964184 | p value | 8.37E-11 | 0.2325 | 0.8720 | 0.6806 | 0.8379 |
| Chr: 11 | Beta | −0.0320 | 0.0181 | 0.0060 | 0.0122 | −0.0071 |
| Location: 116154127 | Standard error | 0.0050 | 0.0152 | 0.0373 | 0.0297 | 0.0349 |
| Effect allele/other allele: C/G | Effect allele frequency | 0.8578 | 0.8437 | 0.8643 | 0.8646 | |
SNPs, single nucleotide polymorphisms; Lp-PLA2, lipoprotein-associated phospholipase A2; AIS, any ischemic stroke; LAS, large-artery stroke; CES, cardioembolic stroke; SVS, small-vessel stroke; Chr, chromosome.
Main Findings
Figure 3 shows MR results for the role of Lp-PLA2 activity in IS and its subtypes. Lp-PLA2 activity had a demonstrable causal relationship with the incidence of LAS (odds ratio [OR] = 3.25, 95% confidence interval [CI] = 1.65–6.41, p = 0.0007 in IVW). The results from the weighted median showed good consistency with IVW (p < 0.05). Although there was no potent connection between Lp-PLA2 activity and LAS in MR-Egger analysis (OR = 1.68, 95% CI = 0.17–16.6, p = 0.68), the direction of this estimate was similar to other MR methods. There was no significant causal role of Lp-PLA2 activity in AIS (OR = 1.32, 95% CI = 0.81–2.15, p = 0.25), CES (OR = 1.18, 95% CI = 0.62–2.23, p = 0.61), or SVS (OR = 1.31, 95% CI = 0.73–2.37, p = 0.37) in IVW analysis. Figure 4 displays the correlations between each SNP and Lp-PLA2 activity as well as LAS risk.
Fig. 3.
OR plot for Lp-PLA2 activity and IS and its subtypes. AIS, any ischemic stroke; LAS, large-artery stroke; CES, cardioembolic stroke; SVS, small-vessel stroke; MR, Mendelian randomization; IVW, inverse-variance weighted; OR, odds ratio; CI, confidence interval.
Fig. 4.
Scatter plots of MR estimates from genetically predicted Lp-PLA2 activity effect on LAS. The lines indicate the estimate of effect using IVW, weighted median, and MR-Egger. Circles indicate marginal genetic associations with Lp-PLA2 activity and risk of LAS for each variant. Error bars indicate 95% CIs. MR, Mendelian randomization; LAS, large-artery stroke; Lp-PLA2 lipoprotein-associated phospholipase A2; SNP, single nucleotide polymorphism; IVW, inverse-variance-weighted.
Sensitivity Analysis
Further sensitivity analysis was carried out for the genetic impact of Lp-PLA2 activity on LAS. Cochran Q test results for the IVW study showed no significant heterogeneity (Q = 4.53, p = 0.48). In addition, no significant intercept was observed (intercept = 0.03; SE = 0.05. p = 0.59), demonstrating the absence of significant directional pleiotropy. No single SNP significantly changed the causal relationship between Lp-PLA2 activity and LAS, according to leave-one-out sensitivity analysis (Fig. 5). Additionally, well-designed GWAS with a large sample size were used to obtain the summary statistics data for Lp-PLA2 activity, IS, and its subtypes, enabling causal inferences with 100% statistical power.
Fig. 5.
MR leave-one-out sensitivity analysis for Lp-PLA2 activity on LAS. Circles indicate MR estimates for Lp-PLA2 on LAS using the IVW method if each SNP was omitted in turn. The bars indicate the CI. MR, Mendelian randomization; Lp-PLA2 lipoprotein-associated phospholipase A2; LAS, large-artery stroke.
Discussion
The findings of the current study demonstrated that Lp-PLA2 activity had a causal relationship with the elevated risk of LAS but not with the risks of AIS, CES, or SVS. Moreover, sensitivity analyses excluded heterogeneity and pleiotropy on the significant estimate of the causal role of Lp-PLA2 activity.
Increasing observational data over the past few decades have shown that Lp-PLA2 activity was connected to the emergence of IS. The Lp-PLA2 activity was first found to be associated with IS in the Rotterdam study (case-cohort study), which demonstrated that it was a standalone predictor of IS among the general population (n = 7,983) [9]. Meanwhile, Persson et al. conducted a prospective analysis of 5,393 participants and discovered that higher Lp-PLA2 activity was independent of conventional risk factors of IS and related to the occurrence of IS [7]. Additionally, Lp-PLA2 activity was strongly correlated with the risk of cardiovascular disease in a multiethnic cohort comprising 5,456 people [11]. Some research studies however could not find a connection between Lp-PLA2 activity and the risks of IS. The Northern Manhattan Study, a case-control study with 1,946 participants, found no correlation between Lp-PLA2 activity levels and total IS risk [12]. Another case-cohort study comprising 1821 CVD cases and a reference subcohort of 1992 women found that increased Lp-PLA2 activity was not an independent hazard factor for cardiovascular disease [13]. The reason for this difference may be that observational studies are affected by potential confounding factors and reserve causality. Moreover, these studies did not stratify the subtypes of IS.
MR analysis of the causal influence of Lp-PLA2 activity on IS and expanded to its subtypes was conducted to address the drawback of observational research. To the best of our knowledge, this was the first study to explore this causal association. Though our results supported no evidence about the causal role of Lp-PLA2 activity in overall IS, CES, and SVS, LAS was found to be positively correlated with genetically represented Lp-PLA2 activity. Our finding indicated that one potential reason for the difference in the outcome between various observational studies about IS was that subtype proportions in each study were dramatically different. Lp-PLA2 activity and IS may be significantly correlated in studies that include a considerable number of LAS patients. According to the results of the current study, patients who have a high level of Lp-PLA2 activity should focus more on LAS prevention and targeted diagnosis.
Recently, another MR analysis about the causal role of Lp-PLA2 activity in CAD and MI produced a similar positive causal conclusion [14]. As atherosclerosis formation and plaque rupture are the common causes of CAD, MI, and LAS [28, 29], the potential role of Lp-PLA2 includes advancing atherothrombosis or atherosclerotic plaque instability. In some studies, Lp-PLA2 has been shown to hydrolyze oxidized phospholipids, releasing Lys-phosphatidylcholine [30], which has pro-inflammatory properties resulting in endothelial dysfunction and macrophage adhesion and migration to trigger atherosclerotic plaque formation [6], expanding the necrotic core in plaques [30] and inducing adhesion molecules and cytokines to be released from activated plaques [31].
The strength of the present study included the following: (1) observational studies could not draw conclusions about causal association due to reverse causation and confounding factors; however, the MR method uses genetic variables from large sample size GWAS to make reasonable inferences, thereby avoiding the biases previously described; (2) the current study can imitate RCTs utilizing the MR design; however, RCTs as widely accepted for studying causality cause huge economic burden and are frequently difficult to implement; (3) we performed MR analysis between Lp-PLA2 activity and overall IS as well as its subtypes, which makes it possible to identify the different effects of Lp-PLA2 activity on each subtype. However, several limitations were also present. First, the use of genetic variants could affect the validity of the MR study because the potential for typical instrumental variable assumptions to be broken could skew MR analysis. Yet, the sensitivity analysis revealed no indication of violations. Second, though IVW and weighted median analysis yielded positive results about the causal effect between Lp-PLA2 activity on LAS, MR-Egger did not, which might result from that MR-Egger assumes all SNPs are invalid IVs; thus, its statistical power is low [24]. Nevertheless, the direction of MR-Egger estimate is similar to other MR methods. To sum up, we still robustly believe in the positive result. Third, all GWAS data came from the European population; it was unknown whether the results would be consistent in other ethnic groups.
Conclusion
These MR analyses support a causal effect of Lp-PLA2 activity on LAS but not on AIS, CES, or SVS, which indicated that serum Lp-PLA2 activity might be a biomarker for prediction of LAS.
Acknowledgments
We thank the investigators and participants of the original GWAS. We are grateful for all GWAS sharing summary data used in this study.
Statement of Ethics
Ethical approval and consent are not required for this study in accordance with local or national guidelines.
Conflict of Interest Statement
The authors declare that they have no competing interests.
Funding Sources
This project was supported by grants from the National Natural Science Foundation of China (82027802, 82071466).
Author Contributions
Yang Zhang: conceptualization, formal analysis, investigation, methodology, software, and writing – original draft; Miaowen Jiang, Yuan Gao, and Yi Xu: software; Yifan Zhou: investigation; Di Wu, Chen Zhou, and Guiyou Liu: writing – review and editing; Li Ming: methodology and writing – review and editing; Xunming Ji: conceptualization, project administration, and writing – review and editing.
Funding Statement
This project was supported by grants from the National Natural Science Foundation of China (82027802, 82071466).
Data Availability Statement
All data used in our study are publicly available. For Lp-PLA2 activity in the present study, corresponding summary statistics data were available from the original meta-analysis of GWAS research studies: https://doi.org/10.1093/eurheartj/ehr372. Summary statistics data for IS and its subtypes were from the MEGASTROKE Consortium website: http://www.megastroke.org/index.html. Further inquiries can be directed to the corresponding author.
Supplementary Material.
Supplementary Material.
References
- 1. Boehme AK, Esenwa C, Elkind MSV. Stroke risk factors, genetics, and prevention. Circ Res. 2017;120(3):472–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2020 update: a report from the American heart association. Circulation. 2020;141(9):e139–596. [DOI] [PubMed] [Google Scholar]
- 3. Zalewski A, Macphee C. Role of lipoprotein-associated phospholipase A2 in atherosclerosis: biology, epidemiology, and possible therapeutic target. Arterioscler Thromb Vasc Biol. 2005;25(5):923–31. [DOI] [PubMed] [Google Scholar]
- 4. Mouchlis VD, Hayashi D, Vasquez AM, Cao J, McCammon JA, Dennis EA. Lipoprotein-associated phospholipase A2: a paradigm for allosteric regulation by membranes. Proc Natl Acad Sci U S A. 2022;119(2):e2102953118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Stafforini DM, Tjoelker LW, McCormick SP, Vaitkus D, McIntyre TM, Gray PW, et al. Molecular basis of the interaction between plasma platelet-activating factor acetylhydrolase and low density lipoprotein. J Biol Chem. 1999;274(11):7018–24. [DOI] [PubMed] [Google Scholar]
- 6. Kume N, Cybulsky MI, Gimbrone MA. Lysophosphatidylcholine, a component of atherogenic lipoproteins, induces mononuclear leukocyte adhesion molecules in cultured human and rabbit arterial endothelial cells. J Clin Invest. 1992;90(3):1138–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Persson M, Berglund G, Nelson JJ, Hedblad B. Lp-PLA2 activity and mass are associated with increased incidence of ischemic stroke: a population-based cohort study from Malmö, Sweden. Atherosclerosis. 2008;200(1):191–8. [DOI] [PubMed] [Google Scholar]
- 8. Li J, Lin J, Pan Y, Wang M, Meng X, Li H, et al. Interleukin-6 and YKL-40 predicted recurrent stroke after ischemic stroke or TIA: analysis of 6 inflammation biomarkers in a prospective cohort study. J Neuroinflammation. 2022;19(1):131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Oei H-HS, van der Meer IM, Hofman A, Koudstaal PJ, Stijnen T, Breteler MMB, et al. Lipoprotein-associated phospholipase A2 activity is associated with risk of coronary heart disease and ischemic stroke: the Rotterdam Study. Circulation. 2005;111(5):570–5. [DOI] [PubMed] [Google Scholar]
- 10. Lp-PLA2 Studies Collaboration; Thompson A, Gao P, Orfei L, Watson S, Di Angelantonio E, et al. Lipoprotein-associated phospholipase A(2) and risk of coronary disease, stroke, and mortality: collaborative analysis of 32 prospective studies. Lancet. 2010;375(9725):1536–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Garg PK, McClelland RL, Jenny NS, Criqui MH, Greenland P, Rosenson RS, et al. Lipoprotein-associated phospholipase A2 and risk of incident cardiovascular disease in a multi-ethnic cohort: the multi ethnic study of atherosclerosis. Atherosclerosis. 2015;241(1):176–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Katan M, Moon YP, Paik MC, Wolfert RL, Sacco RL, Elkind MSV. Lipoprotein-associated phospholipase A2 is associated with atherosclerotic stroke risk: the Northern Manhattan Study. PLoS One. 2014;9(1):e83393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Cook NR, Paynter NP, Manson JE, Martin LW, Robinson JG, Wassertheil-Smoller S, et al. Clinical utility of lipoprotein-associated phospholipase A for cardiovascular disease prediction in a multiethnic cohort of women. Clin Chem. 2012;58(9):1352–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Sun L, Zhu Z, Shi M, Jia Y, Yang P, Wang Y, et al. Causal effect of Lipoprotein-associated phospholipase A2 activity on coronary artery disease and myocardial Infarction: a Two-Sample Mendelian Randomization study. Clin Chim Acta. 2021;523:491–6. [DOI] [PubMed] [Google Scholar]
- 15. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318(19):1925–6. [DOI] [PubMed] [Google Scholar]
- 16. Grallert H, Dupuis J, Bis JC, Dehghan A, Barbalic M, Baumert J, et al. Eight genetic loci associated with variation in lipoprotein-associated phospholipase A2 mass and activity and coronary heart disease: meta-analysis of genome-wide association studies from five community-based studies. Eur Heart J. 2012;33(2):238–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50(4):524–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Psaty BM, O’Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, et al. Cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2(1):73–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Adams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24(1):35–41. [DOI] [PubMed] [Google Scholar]
- 20. Burgess S, Thompson SG; CRP C, HD Genetics Collaboration . Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64. [DOI] [PubMed] [Google Scholar]
- 21. Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019;35(22):4851–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology. 2017;28(1):30–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Greco M FD, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34(21):2926–40. [DOI] [PubMed] [Google Scholar]
- 27. Brion M-JA, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42(5):1497–501. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Turan TN, Zaidat OO, Gronseth GS, Chimowitz MI, Culebras A, Furlan AJ, et al. Stroke prevention in symptomatic large artery intracranial atherosclerosis practice advisory: report of the AAN guideline subcommittee. Neurology. 2022;98(12):486–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Gresham GA, Howard AN. Atherosclerosis and coronary heart-disease. Lancet. 1960;2(7149):527. [DOI] [PubMed] [Google Scholar]
- 30. Wilensky RL, Macphee CH. Lipoprotein-associated phospholipase A(2) and atherosclerosis. Curr Opin Lipidol. 2009;20(5):415–20. [DOI] [PubMed] [Google Scholar]
- 31. Stewart RAH, White HD. The role of lipoprotein-associated phospholipase a₂ as a marker and potential therapeutic target in atherosclerosis. Curr Atheroscler Rep. 2011;13(2):132–7. [DOI] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
All data used in our study are publicly available. For Lp-PLA2 activity in the present study, corresponding summary statistics data were available from the original meta-analysis of GWAS research studies: https://doi.org/10.1093/eurheartj/ehr372. Summary statistics data for IS and its subtypes were from the MEGASTROKE Consortium website: http://www.megastroke.org/index.html. Further inquiries can be directed to the corresponding author.
