Assessing the Causal Relationships Between Lipid Species and Stroke by Using Mendelian Randomization

doi:10.21203/rs.3.rs-4159975/v1

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Assessing the Causal Relationships Between Lipid Species and Stroke by Using Mendelian Randomization

https://doi.org/10.21203/rs.3.rs-4159975/v1

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

Lipids

stroke

sterol ester

phosphatidylinositol

phosphatidylcholine

Mendelian randomization

Stroke is the second most common fatal disease worldwide and carries a high risk of disability. Over the last two decades, the incidence and prevalence of stroke have increased by 70% and 85%, respectively, with an increase in the number of deaths and disability rate by 43% and 32%, respectively; these data highlight the serious threat the condition poses to people’s health and quality of life [1,2]. Stroke is usually defined as a localized interruption of blood flow to the brain due to blockage of the cerebral arteries. Approximately 12% of stroke cases are hemorrhagic (cerebrovascular rupture: 9% intracranial and 3% subarachnoid). The remaining 88% are ischemic, caused by occlusion (thrombotic or embolic) of the cerebral arteries [3], and can be further subdivided into large-artery atherosclerosis, lacunar, cardioembolic, cryptogenic, and other etiologies, according to the classification developed for the Trial of ORG 10172 in the Standards of Care for Acute Stroke [4]. High-risk factors for stroke include smoking, alcohol consumption, obesity, diabetes mellitus, hyperlipidemia, and disordered eating [5]. Of these, abnormal lipid metabolism stands out as an important vascular risk factor that is prevalent in men and children [6,7]. However, the specific mechanisms that link lipid profiles to stroke development remain uncertain.

Lipid profiles have been identified as reversible risk factors for stroke and are associated with an increased risk of the condition. Lipid measurements are recognized as biomarkers for the detection of clinically relevant diseases [8]. A recent “epidemiological reversal” phenomenon has refocused attention on lipids [9]. This phenomenon, observed in patients with ischemic stroke, suggests a paradoxical relationship between lipid profiles and functional outcomes [10]. Conventional observational studies, which are vulnerable to confounding factors and reverse causality, struggle to establish a clear causal relationship. Thus, a new approach is needed to investigate the causal links between various lipids and stroke phenotypes [11].

Mendelian Randomization (MR) serves as a supplementary alternative to randomized controlled trials. By rigorously selecting target-related instrumental variables (IVs), MR minimizes the interference of reverse causality and enables more accurate causal inference between exposures and outcomes [12,13]. This study aimed to explore the causal relationships between 179 lipid types and eight stroke phenotypes. We used MR analysis and revealed causal relationships between 10 lipid species and stroke, offering new insights into the etiology, pathogenesis, and treatment of the condition.

A schematic of the conceptual framework for this study is illustrated in Fig. 1. A detailed description of our procedures is presented in section 4.

2.1. Causal Relationships Between Lipid Species and Eight Types of Stroke

The number of eligible IVs among the 179 lipid species ranged from 4 to 43. The explained variance ranged from 0.2618–21.54%. The F-value for each single nucleotide polymorphism (SNP) was calculated to be greater than 10, as shown in Table S1 (range, 19.537–1,946.14). This finding indicates that the screened SNPs were strong IVs.

Using these IVs, we identified a causal relationship between lipid types and eight stroke phenotypes (any stroke, ischemic stroke, small-vessel ischemic stroke, large-artery atheromatous ischemic stroke, cardiogenic embolic ischemic stroke, lacunar ischemic stroke, cerebral hemorrhage, and subarachnoid hemorrhage). Overall, we found potential causality (P < 0.05) between 133 lipid types and stroke phenotypes (Fig. 2). These potential causalities involved six lipid types (sterol esters, phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, sphingomyelin, and triacylglycerol), encompassing different molecular types and structures. After multiple testing corrections (P false discovery rate [FDR] < 0.05), a causal relationship was identified between 10 pairs of lipids and stroke types (Fig. 3). To remove confounding factors for these 10 pairs, we used PhenoScanner, confirming the significant causal relationships. Three lipid species were primarily identified: sterol esters, phosphatidylinositols, and phosphatidylcholines. The following relationships were observed: sterol esters (27:1/20:4) and stroke (odds ratio [OR] = 1.052, 95% confidence interval [CI]: 1.026–1.079, P = 8.05E-05, PFDR = 0.014); sterol esters (27:1/20:4) and ischemic stroke (OR = 1.056, 95% CI: 1.027–1.087, P = 0.0002, PFDR = 0.028); sterol esters (27:1/20:4) and ischemic stroke (atherosclerosis of large arteries) (OR = 1.134, 95% CI: 1.063–1.209, P = 0.0001, PFDR = 0.0144); phosphatidylinositol (18:1_18:2) and ischemic stroke (atherosclerosis of large arteries) (OR = 0.830, 95% CI: 0.754–0.914, P = 0.0002, PFDR = 0.014); phosphatidylcholine (17:0_20:4) and subarachnoid hemorrhage (OR = 0.868, 95% CI: 0.804–0.937, P = 0.0003, PFDR = 0.0479); phosphatidylcholine (18:1_18:1) and Intracerebral hemorrhage (OR = 1.485, 95% CI: 1.229–1.793, P = 4.17E-05, PFDR = 0.0037); phosphatidylcholine (18:1_18:2) and intracerebral hemorrhage (OR = 1.357, 95% CI: 1.176–1.566, P = 2.98E-05, PFDR = 0.0037); phosphatidylcholine (18:1_20:2) and intracerebral hemorrhage (OR = 1.261, 95% CI: 1.112–1.430, P = 0.0003, PFDR = 0.0138); phosphatidylcholine (O-18:1_18:2) and intracerebral hemorrhage (OR = 1.474, 95% CI: 1.196–1.816, P = 0.0003, PFDR = 0.0138); and phosphatidylcholine (16:0_18:2) and intracerebral hemorrhage (OR = 1.333, 95% CI: 1.132–1.570, P = 0.0006, PFDR = 0.0202).

2.2. Sensitivity Analyses

Sensitivity analyses were conducted using these 10 events to assess heterogeneity and horizontal pleiotropy. Cochran's Q test indicated heterogeneity for sterol ester (27:1/20:4) in its association with ischemic stroke (P = 0.041) and with ischemic stroke due to large artery atherosclerosis (P = 0.048). We found no evidence of horizontal pleiotropy, as indicated by P values greater than 0.05 in both the MR-Egger and MR-PRESSO tests. The MR results for the remaining eight pairs showed neither heterogeneity nor horizontal pleiotropy. Table 1 presents the results of the Cochran's Q, MR-Egger, and MR-PRESSO analyses for these 10 events.

Table 1

Results of the sensitivity analysis for 10 pairs of lipids–stroke types
Exposure	Outcome	Cochran's Q		MR Egger		MR PRESSO
Exposure	Outcome	Q	P	Intercept	P	Global test RSSobs	Global test P
Sterol ester (27:1/20:4)	Stroke (any type)	33.573	0.054	0.003	0.543	44.449	0.188
Sterol ester (27:1/20:4)	Ischemic stroke (any type)	33.517	0.041	0.002	0.756	38.975	0.151
Sterol ester (27:1/20:4)	Ischemic stroke (large artery atherosclerosis)	39.066	0.048	-0.003	0.770	40.923	0.121
Phosphatidylinositol (18:1_18:2)	Ischemic stroke (large artery atherosclerosis)	17.528	0.783	-0.022	0.324	19.888	0.761
Phosphatidylcholine (17:0_20:4)	Subarachnoid hemorrhage	24.836	0.687	-0.006	0.635	26.171	0.752
Phosphatidylcholine (16:0_18:2)	Intracerebral hemorrhage	29.540	0.078	-0.021	0.485	32.871	0.115
Phosphatidylcholine (18:1_18:1)	Intracerebral hemorrhage	14.238	0.286	0.006	0.867	19.264	0.308
Phosphatidylcholine (18:1_18:2)	Intracerebral hemorrhage	17.920	0.329	-0.023	0.332	19.738	0.407
Phosphatidylcholine (18:1_20:2)	Intracerebral hemorrhage	8.586	0.738	-0.023	0.348	13.641	0.601
Phosphatidylcholine (O-18:1_18:2)	Intracerebral hemorrhage	11.516	0.568	-0.045	0.204	15.664	0.496

In this MR analysis of 179 lipids with eight stroke phenotypes, we identified 133 causal relationships involving six lipid types (sterol esters, phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, sphingomyelin, and triacylglycerol) and eight stroke phenotypes. After multiple testing corrections, causal relationships remained between 10 lipids and five stroke phenotypes. This study is the first to analyze the causal relationships between different lipid molecular structures and stroke using MR. Our aim was to assess the link between specific lipid structures and stroke pathogenesis, providing insights for the study of stroke mechanisms and the development of targeted drugs.

A growing body of research indicates that lipid modulation can effectively intervene in the pathogenesis and outcome of stroke [14,15]. Lipids and lipid mediators are crucial for maintaining the normal structure and function of brain tissues, with dyslipidemia being a significant risk factor for stroke [16]. The Emerging Risk Factors Collaboration found that lipoprotein (Lpa) has a consistent and independent association with stroke [17]. Dai et al. [18] demonstrated that total cholesterol, non-high-density lipoprotein cholesterol, and triglyceride levels may predict ischemic stroke. Furthermore, Qin et al. [19] established a causal relationship between low-density lipoprotein cholesterol, apolipoprotein, and large-artery stroke through MR analysis, suggesting that elevated levels of these proteins may increase the risk of large-artery stroke. Our research primarily focused on three lipid types—sterol esters, phosphatidylinositols, and phosphatidylcholines—and examined their various molecular structures. We found that the levels of sterol ester (27:1/20:4) positively correlated with stroke (any type), ischemic stroke, and large blood vessel stroke. Conversely, phosphatidylinositol (18:1_18:2) levels negatively correlated with large blood vessel stroke. Five distinct molecular structures of phosphatidylcholine were positively associated with intracerebral hemorrhage, while phosphatidylcholine (17:0_20:4) exhibited a negative association with subarachnoid hemorrhage.

Lipids, a hydrophobic and heterogeneous group of molecules, vary in structure from simple short-chain hydrocarbons to complex molecules such as triacylglycerols, phospholipids, and sterols, along with their esters. Different lipid categories exhibit structural variation. The structural diversity of lipids and fatty acids leads to distinct metabolic pathways and functional attributes [20]. Sterol esters, which are produced through the esterification of sterols with fatty acids, play various biological roles in the regulation of cell membranes [21], energy storage [22], signal transduction [23], and hormone synthesis [24]. They serve as storage forms for sterols, with esterification leading to formation of chylomicrons, which is closely related to atherosclerosis [25,26]. Plant sterol esters can reduce cholesterol absorption in the intestine, thereby preventing hypercholesterolemia [27]. However, studies have indicated that plant sterol esters can cause endothelial damage, exacerbate cerebral ischemic diseases in experimental mice, and increase plasma sterol concentrations in humans [28]. Phosphatidylinositol, which comprises inositol kinase and phosphatase, is a vital phospholipid that can lead to the biosynthesis of seven forms and plays a crucial role in membrane dynamics as a signaling lipid. Its abnormal metabolism has been associated with various diseases [29,30]. Andres et al. demonstrated that phosphatidylinositol bisphosphate strongly inhibits proton-activated chloride channels, thereby preventing chloride entry into the cells and reducing acid-induced brain damage [31,32]. Studies on phosphatidylinositol and brain injury often focus on the neuroprotective pathway of phosphoinositide-3-kinase (PI3K)–protein kinase B (Akt) [33,34], while other reports have indicated the anti-apoptotic effects of phosphatidylinositol-4-kinase alpha (PI4KA) in the brain [35]. Clinical studies have shown that the serum level of phosphatidylinositol (3,4,5)-trisphosphate can predict acute ischemic stroke [36], and a significant increase in anti-phosphatidylinositol antibodies has been found in young patients with stroke [37]. Phosphatidylcholines are key components of cell membranes and are crucial for neural transmission as they can convert to molecules such as choline and acetylcholine. They also play roles in the blood–brain barrier and inflammatory responses [38]. Magaquian et al. found that phosphatidylcholine could restore neural stem cell plasticity under inflammatory conditions, thereby improving neuronal damage [39]. Huang et al., using liquid chromatography–mass spectrometry, identified hemolytic phosphatidylcholine as a potential biomarker for stroke recovery [40]. Lipids are important components in stroke prevention, treatment, and recovery, and understanding their roles has the potential to clarify stroke prevention and treatment strategies. Our results align with those of previous epidemiological and clinical research, suggesting that various lipids play significant roles in stroke pathophysiology, but provide stronger evidence of causality.

We are the first to assess the causal relationship between 179 lipid species and multiple stroke phenotypes, covering a broader range of lipids and a larger sample size for each phenotype than previous studies [41]. Although MR mitigates common confounders and reverse causality issues in traditional observational studies by using genetic variation as IVs, some limitations of our study should be acknowledged. First, the effectiveness of the MR analysis depends on the quality of the IVs used, and a P value of 5×10 − 5 was chosen to ensure a sufficient number of SNPs. Second, despite using genetic variants highly correlated with lipids, we cannot completely rule out confounding effects on other biological pathways related to stroke phenotypes. Lastly, the SNP source in this study is mainly of European origin, with some coverage of Asian origin. Considering that the population structure is relatively simple, the applicability of the results to various races is limited to a certain extent. Future studies should include more diverse populations for validation.

In summary, our study extensively analyzed the causal relationships between 179 lipids and eight stroke phenotypes. The findings highlight a strong association between lipid profiles and stroke incidence, underscoring the significance of lipids as potential targets for stroke prevention and treatment. Future research, especially focusing on the biological functions of different structural lipids, is expected to offer new insights into stroke prevention and therapeutic strategies.

4.1. Study Design

We performed a univariate two-sample MR analysis to assess the causal relationship between various lipid species and stroke phenotypes, including any stroke and ischemic stroke and its subtypes (small-blood vessel, large artery atherosclerosis, cardioembolism, and lacunar), as well as cerebral hemorrhage and subarachnoid hemorrhage. MR analysis is predicated on three key assumptions [42]: (1) genetic variants are strongly associated with the exposure of interest; (2) these variants are not associated with confounding factors; and (3) the impact of these variants on the outcome is mediated exclusively through the exposure. As this study used publicly available GWAS summary data, no separate ethical approval was required.

4.2. Data Sources for 179 Lipid Species

The data regarding lipids were derived from the GWAS results of the GeneRISK cohort [43]. That study used birdshot lipidomics to examine lipid species in 7,174 Finnish individuals and detected 179 lipids in 13 lipid classes.

4.3. Data Sources for Stroke

The stroke-related GWAS data were obtained from the study by Malik et al. [44], which comprised 446,696 stroke samples (40,585 cases and 406,111 controls) and 440,328 samples of ischemic stroke (34,217 cases and 406,111 controls). Specific ischemic stroke subtypes included 1,980,048 small-blood-vessel (5,386 cases and 192,662 controls), 150,765 large-artery atherosclerosis (4,373 cases and 406,111 controls), and 211,763 cardioembolism (7,193 cases and 406,111 controls). The GWAS data for lacunar stroke were obtained from the study by Traylor et al. [45] in an Asian mixed population and included 232,596 participants (7,338 cases and 225,258 controls). GWAS data for cerebral hemorrhage from 153,478 individuals (1,456 cases and 152,022 controls) and subarachnoid hemorrhages from 473,255 individuals (1,693 cases and 471,562 controls) were identified from the study by Sakaue et al. [46]. The data sources used for this study are listed in Table 2.

Table 2

Data sources used for this study
Phenotype	Sample size (cases/controls)	Population	Journal	Reference
179 lipids	7,174	European	Nat Commun	[43]
Cerebral hemorrhage	(446,696) 40,585/406,111	European	Nat Genet	[44]
Ischemic stroke (any type)	(440,328) 34,217/406,111	European	Nat Genet	[44]
Ischemic stroke (small blood vessel)	(198,048) 5,386/192,662	European	Nat Genet	[44]
Ischemic stroke (large artery atherosclerosis)	(150,765) 4,373/406,111	European	Nat Genet	[44]
Ischemic stroke (cardioembolism)	(211,763) 7,193/406,111	European	Nat Genet	[44]
Lacunar stroke	(232,596) 7,338/225,258	Mixed	Lancet Neurol	[45]
Subarachnoid hemorrhage	(473,255) 1,693/47,1562	European	Nat Genet	[46]
Cerebral hemorrhage	(153,478) 1,456/152,022	European	Nat Genet	[46]

4.4. Selection of Instrumental Variables

In this study, few SNPs met the criteria for IV screening at a significance threshold of P < 5E-8. Therefore, the threshold was adjusted to P < 1E-5 to obtain a sufficient number of SNPs for analysis [47]. The linkage disequilibrium removal threshold was set to r2 < 0.001 and a distance of 10,000 kb [48]. To comply with the first basic assumption of MR, we used the formula F = beta2 / se2 to calculate the statistics of IVs, considering an F-statistics value > 10 indicative of a strong IV [49]. Additionally, the Steiger test was applied to strengthen the exclusion assumption in the MR analysis, filtering out SNPs significantly associated with the outcome and enhancing the robustness of IVs [50]. SNPs associated with common confounders (smoking, alcohol consumption, or diabetes) were excluded using the PhenoScanner database (http://www.phenoscanner.medschl.cam.ac.uk/) to minimize analysis bias [51].

4.5. Data Analysis

After eligible SNPs were extracted, we performed a univariate two-sample MR analysis, using inverse variance weighting (IVW) as the primary method, to assess the causal relationship between lipid species and stroke. IVW is an effective MR method that summarizes the overall estimates using the ratios of IVs [52]. We supplemented IVW with the MR-Egger, weighted median, and weighted mode methods [53]. Heterogeneity was examined using Cochran's Q statistic and the leave-one-out method to enhance the stability of the causal effects by eliminating potential outliers [54]. Horizontal pleiotropy was assessed and corrected using the MR-Egger intercept and MR-PRESSO global tests to confirm the independent association of IVs with lipids and stroke [55]. A P value of less than 0.05 in IVW indicated potential causality. Owing to multiple hypothesis testing, positive results can be subject to random errors. Therefore, we employed the Benjamini–Hochberg method to control for FDR. A causal relationship was considered for PFDR values less than 0.05 [56]. The results are presented as ORs, 95% CI, and PFDR values. The statistical analysis was primarily conducted using the TwoSampleMR 0.4.25 and MR-PRESSO 1.0 packages in R software (version 4.3.1).

Author Contributions: Conceptualization: S.Q.W. and H.Z.; methodology: X.S.H.; formal analysis: H.Z.; investigation: Q.Z.; data curation: R.B.C.; writing-original draft: S.Q.W. and X.S.H.; writing-review and editing: F.X.; supervision: F.X.; project administration: Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by the Health Commission of Henan Province (Outstanding Young Talent Program 2022JDZX125) and the Health Commission of Henan Province (2021JDZX2007).

Data Availability: Consent was obtained from all participants in the original GWAS studies.

Ethics Approval: Ethical review and approval were waived for this study because ethical approval was obtained in the original studies. Patients or the public were not involved in the design, conduct, reporting, or dissemination of our research.

Consent to Participate: Not applicable.

Consent for Publication: All authors read and approved the final manuscript.

Competing Interests: The authors declare no competing interests.

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You are reading this latest preprint version

Assessing the Causal Relationships Between Lipid Species and Stroke by Using Mendelian Randomization

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Abstract

Figures

1. Introduction

2. Results

2.1. Causal Relationships Between Lipid Species and Eight Types of Stroke

2.2. Sensitivity Analyses

3. Discussion

4. Materials and Methods

4.1. Study Design

4.2. Data Sources for 179 Lipid Species

4.3. Data Sources for Stroke

4.4. Selection of Instrumental Variables

4.5. Data Analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1