Skip to main content
NCBI home page
Brain and Behavior logoLink to Brain and Behavior
. 2025 Mar 18;15(3):e70435. doi: 10.1002/brb3.70435

Two‐Sample Mendelian Randomization Analyses Identified Lipid Species Associated With Intracranial Aneurysm Formation

Junqing Yan 1,✉
PMCID: PMC11919785  PMID: 40103236

ABSTRACT

Objectives

Intracranial aneurysm (IA) poses a significant health risk, and its formation involves various factors, including lipid metabolism, while former research only focused on the standard lipid. The purpose of this study is to explore 179 lipid variants' impact on unruptured intracranial aneurysms (uIA).

Materials and Methods

Utilizing GWAS data for lipids and uIAs, MR analyses were employed with pleiotropy, heterogeneity, and sensitivity tests. Reverse MR analyses were then conducted.

Results

MR analyses revealed seven lipids associated with uIAs: TAG (51:3). SE (27:1/16:1), PC (18:2_18:2), TAG (48:1), TAG (48:2), and TAG (51:3) were identified as uIA risk factors, while SE (27:1/18:1) and SM (d34:0) exhibited protective effects. Reverse MR analysis showed no bidirectional causal relationships.

Conclusions

This study identifies specific lipid variants causally linked to uIAs, shedding light on their roles in IA formation. These findings contribute to future research on IA risk assessment and potential therapeutic interventions.

Keywords: intracranial aneurysm, lipids, Mendelian randomization, single nucleotide polymorphisms

This study investigates the causal relationship between lipid variants and unruptured intracranial aneurysm (uIA) formation using Mendelian randomization. Seven lipid species were identified as risk factors or protective agents for uIAs, offering insights into potential new biomarkers for IA risk assessment and treatment.

graphic file with name BRB3-15-e70435-g003.jpg

1. Introduction

Intracranial aneurysm (IA) is a type of intracranial vascular disorder, and different epidemiological studies show that the incidence rate of IA varies between 2% and 7% in different populations (Freneau et al. 2022; Vlak et al. 2011; Brown and Broderick 2014). Most unruptured intracranial aneurysms (uIAs) are incidentally detected through neuroimaging examinations, typically without specific symptoms, although a few may present focal neurologic dysfunction due to a positional effect (Cianfoni et al. 2013; Goldenberg‐Cohen et al. 2004). However, although the risk of rupture is low, the most dangerous and common manifestation is an aneurysmal subarachnoid hemorrhage (SAH), with a high mortality rate of up to 50% and a complication rate of 70% once it occurs (J. Jin et al. 2022). Therefore, the prevention and treatment of IA formation are crucial topics in the realm of intracranial vascular diseases.

The formation of IAs is multifactorial, involving factors such as variations in parent arterial anatomy, abnormal hemodynamic flow, oxidative stress, and inflammatory reactions (Bor et al. 2008; Etminan and Rinkel 2016; Cebral et al. 2017; Hackenberg et al. 2020). Previous studies have identified several risk factors associated with the development of cerebral aneurysms, including smoking, female gender, positive family history, alcohol consumption, hypertension, advanced age, and certain genetic conditions (Vlak et al. 2011; Lindgren et al. 2014). In addition to these factors, researchers have turned their attention to the role of lipid metabolism in IAs recently (Frosen et al. 2013; Lovik et al. 2021; Ou et al. 2020). Disruptions in lipid metabolism can impact the progression of IAs through various mechanisms, including inducing systemic inflammation and oxidative stress, altering the lipid composition and metabolism of the intracranial arterial wall, weakening the structural strength and elasticity of the intracranial arterial wall, and regulating the expression and activity of various genes, proteins, and related signaling pathways (Vanrossomme et al. 2015).

Past research has traditionally measured plasma lipids using standard lipid profiling, including high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), triglycerides (TG), and total cholesterol (TC). However, a recent genome‐wide association study (GWAS) has significantly expanded our understanding of circulating lipid variability and diversity using modern high‐throughput lipidomics technologies. This study explored 179 lipid variants across 13 major classes, including cholesterol esters (CE), ceramides (CER), cholesterol (Chol), diacylglycerols (DAG), lysophosphatidylcholines (LPC), lysophosphati‐dylethanolamine (LPE), phosphatidylcholines (PC), phosphatidylcholine ethers (PCO), phosphatidylethanolamines (PE), phosphatidylethanolamine ethers (PEO), phosphatidylinositol (PI), sphingomyelins (SM), and triacylglycerols (TAG). In comparison to standard lipids, this research has improved cardiovascular disease (CVD) risk assessment and provided new therapeutic options for CVDs (Ottensmann et al. 2023).

Given the impact of lipids on IAs, as mentioned earlier, and the similarity between many risk factors for IAs and CVDs, we hypothesize that exploring these lipid variants may also contribute to the prevention and treatment of IAs. Therefore, we conducted a Mendelian randomization (MR) study to investigate whether there is a causal relationship between these 179 lipids and the formation of IAs. Due to our study's focus on the formation of IAs, we exclusively considered data related to uIAs and did not include data on SAH.

MR analysis is a method that establishes causal inference by leveraging naturally occurring genetic variations in the population. It uses genetic variants as instrumental variables (IVs), effectively addressing issues related to confounding and reverse causation (Hong et al. 2023). This approach helps clarify relationships between variables.

2. Method

2.1. Study Design

Figure 1A demonstrates the basic principle of MR analysis. Three conditions are required for MR analysis: (1) robust connection between IVs and exposure factors (Sekula et al. 2016); (2) absence of correlation between IVs and confounders (Jia et al. 2023); (3) IVs exclusively influence outcomes through exposure factors, excluding any involvement of alternative pathways (Q. Jin et al. 2023). The analysis flow is displayed in Figure 1B.

FIGURE 1.

FIGURE 1

Open in a new tab

(A) Basic principles of Mendelian randomization. (B)Work flow of the present study.

2.2. Data Source and Preparation

We obtained GWAS data for 179 lipid species from PMC10618167, derived from 7174 Finnish individuals, categorized into 13 classes of lipid (Ottensmann et al. 2023). The GWAS data for UIAs were conducted by The International Stroke Genetics Consortium (ISGC) intracranial aneurysm working group (PMC7116530) (Bakker et al. 2020). The dataset comprises 2070 controls and 71,952 cases.

The IVs were selected by the following standard: Single nucleotide polymorphisms (SNPs) associated with each lipid species at a locus‐wide significance threshold (p < 1e−5) were chosen as potential IVs (Zeng et al. 2023). A linkage disequilibrium (LD) window analysis was conducted for all IVs then (R 2 < 0.001, clumping window size = 10,000 kb). To ensure SNPs are strongly correlated with exposure factors, only SNPs with an F value greater than 10 are retained. The formula for calculating the F value is as follows (Pierce et al. 2011; Shim et al. 2015):

F=N−2×R2/1−R2
R2=2×β2/2×β2+2×N×SE2

At last, SNP associations with any confounding factors potentially linked to the outcome were excluded using the Phenoscanner website (http://www.phenoscanner.medschl.cam.ac.uk/).

2.3. MR Analysis

We conducted MR analysis using the inverse‐variance weighting (IVW), weighted median (WM), and MR‐Egger methods. The primary results are based on the IVW method, while the WM and MR‐Egger methods serve as supplementary analyses and offer broader confidence intervals. It is important to note that the IVW method can only be applied after the impact of statistics influenced by horizontal pleiotropy is mitigated. To address this, we performed a test for horizontal pleiotropy (Burgess et al. 2016). Furthermore, we excluded results with heterogeneity among IVs during Cochran's Q test (p > 0.05) (Slob and Burgess 2020). Finally, we performed a leave‐one‐out sensitivity analysis for lipids with a p value of the IVW method < 0.05 of statistically significant causal relationships to arrive at our final results.

To bolster result credibility, we applied the same MR analysis methods described earlier to conduct a reverse MR analysis using uIAs as the exposure and lipids causally associated with uIAs as the outcomes.

All the mentioned analyses were carried out by the R package “TwoSampleMR” (Hemani et al. 2017, Hemani et al. 2018).

3. Results

Following the outlined steps, we conducted MR analysis with various lipid types as exposure factors and uIA as the outcome. Through horizontal pleiotropy tests and heterogeneity tests, we excluded certain statistical data affected by heterogeneity or horizontal pleiotropy, selecting results with p values exceeding 0.05 in both the heterogeneity Q‐test and the pleiotropy test. As shown in Figure 2, the IVW analysis revealed associations between uIAs and seven lipids (SE [27:1/16:1], SE [27:1/18:1], PC [18:2_18:2], SM [d34:0], TAG [48:1], TAG [48:2], TAG [51:3]). However, none of these lipids demonstrated a causal relationship with uIAs in MR‐Egger and WM analyses.

FIGURE 2.

FIGURE 2

Open in a new tab

Forest plot of lipids associated with uIA (p < 0.05) identified by IVW method.

Figure 2 illustrates that the causal relationships of these seven lipids, as per IVW results, persist after sensitivity analyses. Detailed information on the SNPs used in IVW and the results of the MR analysis can be found in Tables 1 and 2. IVW results demonstrated in Figure 3 indicate that SE (27:1/16:1) (IVW, p = 0.025, OR = 1.314, 95% CI = 1.035–1.668), PC (18:2_18:2) (IVW, p = 0.033, OR = 1.249, 95% CI = 1.018–1.533), TAG (48:1) (IVW, p = 0.009, OR = 1.421, 95% CI = 1.092–1.850), TAG (48:2) (IVW, p = 0.013, OR = 1.446, 95% CI = 1.080–1.938), and TAG (51:3) (IVW, p = 0.046, OR = 1.257, 95% CI = 1.004–1.575) are risk factors for uIAs. Contrarily, SE (27:1/18:1) (IVW, p = 0.039, OR = 0.791, 95% CI = 0.634–0.989) and SM (d34:0) (IVW, p = 0.034, OR = 0.712, 95% CI = 0.520–0.975) exhibit a protective effect against uIAs. The leave‐one‐out sensitivity analysis results in Figure 4 confirm the sensitivity of these results.

TABLE 1.

MR results of lipids and uIA with statistical significance by IVW methods.

Lipid Method nsnp β SE p value lo_ci up_ci OR OR_lci95 OR_uci95 p value of Cochran's Q test p value of plieotropy test
Sterol ester (27:1/16:1) Inverse variance weighted 13 0.27311567 0.12182052 0.02496452 0.03434745 0.51188388 1.31405223 1.03494414 1.66843136 0.61905496 0.762075658
MR‐Egger 13 0.35302978 0.28483886 0.24098861 −0.2052544 0.91131395 1.42337353 0.81444011 2.48758896 0.542262508
Weighted median 13 0.30191559 0.1656036 0.06828489 −0.0226675 0.62649864 1.35244706 0.97758752 1.87104788
Sterol ester (27:1/18:1) Inverse variance weighted 12 −0.2337532 0.11339833 0.03927007 −0.4560139 −0.0114924 0.79155718 0.63380504 0.98857336 0.4194889 0.146435665
MR‐Egger 12 0.07451407 0.22552471 0.74791335 −0.3675144 0.51654249 1.0773605 0.69245339 1.67622207 0.550376559
Weighted median 12 −0.2318063 0.16129725 0.15067897 −0.5479489 0.08433631 0.79309972 0.57813439 1.08799473
Phosphatidylcholine (18:2_18:2) Inverse variance weighted 14 0.22245343 0.10437085 0.03305825 0.01788656 0.42702029 1.24913764 1.01804749 1.53268376 0.47181124 0.565471847
MR‐Egger 14 0.08830073 0.25043574 0.73051238 −0.4025533 0.57915478 1.09231657 0.66861069 1.78452948 0.419259942
Weighted median 14 0.11346516 0.14452744 0.43240845 −0.1698086 0.39673893 1.12015286 0.8438263 1.48696768
Sphingomyelin (d34:0) Inverse variance weighted 11 −0.3395189 0.16014051 0.03399497 −0.6533943 −0.0256435 0.71211283 0.52027679 0.97468249 0.136447755 0.635896397
MR‐Egger 11 −0.6151872 0.58680091 0.32180243 −1.765317 0.53494259 0.5405397 0.17113253 1.70735023 0.10578214
Weighted median 11 −0.1852536 0.18719284 0.32235003 −0.5521515 0.18164442 0.83089358 0.57570982 1.19918771
Triacylglycerol (48:1) Inverse variance weighted 13 0.35168041 0.1343958 0.00887709 0.08826465 0.61509617 1.42145417 1.09227716 1.84983449 0.741592836 0.877826065
MR‐Egger 13 0.30160055 0.34549716 0.40134661 −0.3755739 0.97877498 1.35202106 0.68689497 2.66119424 0.666491067
Weighted median 13 0.28499869 0.17973416 0.11281466 −0.0672803 0.63727764 1.32976028 0.93493314 1.89132499
Triacylglycerol (48:2) Inverse variance weighted 10 0.36914213 0.14922402 0.01337052 0.07666306 0.66162121 1.44649318 1.07967822 1.93793158 0.568789918 0.651940704
MR‐Egger 10 0.62913727 0.57468717 0.30548918 −0.4972496 1.75552411 1.8759914 0.60820117 5.78647971 0.489983389
Weighted median 10 0.31876718 0.20786652 0.12514813 −0.0886512 0.72618556 1.37543106 0.91516472 2.06718042
Triacylglycerol (51:3) Inverse variance weighted 14 0.22901092 0.1149198 0.04628454 0.00376812 0.45425372 1.25735577 1.00377523 1.57499756 0.97287093 0.554358558
MR‐Egger 14 0.04803179 0.31896135 0.88280231 −0.5771325 0.67319603 1.04920401 0.5615062 1.96049312 0.966326309
Weighted median 14 0.23930311 0.14981931 0.11020371 −0.0543427 0.53294895 1.27036353 0.94710745 1.70394976
Open in a new tab

TABLE 2.

SNPs used in MR analyses.

Lipid SNP β SE Sample size p value R 2 F
Sterol ester (27:1/16:1) rs11119973 0.110738 0.0231985 7172 1.84338e−06 0.00316706 22.7799317
rs111656006 0.185117 0.0400311 7172 3.81832e−06 0.00297279 21.3784608
rs116522970 0.55842 0.12419 7172 7.01117e−06 0.00281116 20.2128498
rs1260326 −0.0904035 0.0174668 7172 2.32931e−07 0.00372121 26.7807459
rs17603855 0.169155 0.0356528 7172 2.1289e−06 0.00312882 22.5040803
rs1800961 −0.204194 0.0372477 7172 4.34267e−08 0.00417283 30.044564
rs2464190 0.0764228 0.0167672 7172 5.24998e−06 0.00288821 20.7684304
rs4447106 0.118474 0.0242138 7172 1.01459e−06 0.00332685 23.9331067
rs58489806 −0.142087 0.0315082 7172 6.59485e−06 0.00282743 20.3301404
rs603424 −0.155845 0.0259739 7172 2.06545e−09 0.00499455 35.9907
rs7313803 0.0873092 0.0194117 7172 6.96926e−06 0.00281274 20.2242186
rs7355269 0.243229 0.0529062 7172 4.35121e−06 0.00293832 21.1298515
rs855500 0.0761958 0.0168529 7172 6.24211e−06 0.00284208 20.4358005
Sterol ester (27:1/18:1) rs10078182 0.268326 0.0586987 7174 4.92541e−06 0.00290432 20.8904337
rs10841310 −0.105591 0.019433 7174 5.69845e−08 0.00409854 29.5156949
rs10860778 −0.0815118 0.0167481 7174 1.15667e−06 0.00329092 23.6803864
rs11687710 0.08949 0.0191687 7174 3.08593e−06 0.0030289 21.7892661
rs1800961 −0.21016 0.037228 7174 1.71172e−08 0.00442257 31.8595486
rs4845593 −0.290262 0.0571029 7174 3.80059e−07 0.00358873 25.8311027
rs4906111 −0.245889 0.0540683 7174 5.50919e−06 0.00287462 20.6762455
rs701081 −0.0836375 0.0171434 7174 1.08975e−06 0.0033068 23.795075
rs73005445 −0.087242 0.0191832 7174 5.50635e−06 0.00287473 20.6770237
rs73176681 0.191002 0.0404108 7174 2.32683e−06 0.00310434 22.333657
rs7412 −0.281493 0.0371564 7174 4.00681e−14 0.00793681 57.3781906
rs7932326 0.0772058 0.0171159 7174 6.55748e−06 0.00282819 20.3413271
Phosphatidylcholine (18:2_18:2) rs1077835 0.114534 0.019222 7174 2.6626e−09 0.00492455 35.4936465
rs117017186 0.362427 0.0795703 7174 5.32894e−06 0.00288352 20.7404419
rs117675848 0.320882 0.0654187 7174 9.53745e−07 0.0033425 24.0528089
rs11869356 −0.0766319 0.0171208 7174 7.72162e−06 0.00278483 20.0285721
rs13078306 −0.0818989 0.0176422 7174 3.50558e−06 0.00299493 21.5441599
rs13150924 −0.111583 0.0250095 7174 8.25191e−06 0.00276708 19.9005444
rs173539 0.0984345 0.0184699 7174 1.01409e−07 0.00394356 28.3951717
rs174574 −0.22178 0.0168521 7174 4.16328e−39 0.02357301 173.147255
rs56158036 0.295692 0.0637559 7174 3.57995e−06 0.00298935 21.5039032
rs62238391 0.0863635 0.017876 7174 1.38372e−06 0.00324301 23.33451
rs6498540 0.0934402 0.0177453 7174 1.43628e−07 0.00385003 27.7191389
rs7717591 −0.095992 0.0178967 7174 8.4065e−08 0.00399415 28.7609398
rs7798734 −0.081769 0.0176579 7174 3.70478e−06 0.00298017 21.4376993
rs80315588 −0.0767847 0.0168318 7174 5.15184e−06 0.00289247 20.8049735
Sphingomyelin (d34:0) rs1073042 −0.0823051 0.0182851 6207 6.87365e−06 0.00325358 20.2543754
rs11631073 −0.0911807 0.0186999 6207 1.1084e−06 0.0038158 23.7677087
rs1324162 0.0899835 0.0201129 6207 7.81063e−06 0.00321437 20.0095082
rs16850360 0.227483 0.0504095 6207 6.51148e−06 0.00327016 20.3579084
rs174535 −0.0976311 0.0183889 6207 1.13756e−07 0.0045208 28.1789563
rs2808569 −0.0978734 0.0210823 6207 3.50945e−06 0.00346024 21.545343
rs3026120 −0.207247 0.0341995 6207 1.43923e−09 0.00588157 36.7110691
rs73015021 −0.15289 0.0315851 6207 1.32465e−06 0.00376076 23.4236025
rs7814780 −0.0869509 0.0192999 6207 6.74575e−06 0.00325941 20.2907494
rs8071514 0.0848261 0.0180916 6207 2.80367e−06 0.00352929 21.9768319
rs934198 0.0978059 0.0198357 6207 8.3951e−07 0.00390171 24.3049698
Triacylglycerol (48:1) rs10861498 −0.0952476 0.019381 7019 9.09524e−07 0.00342917 24.1452637
rs12565526 0.0795461 0.0175846 7019 6.17408e−06 0.00290692 20.4573337
rs1260326 −0.118511 0.0176221 7019 1.88871e−11 0.00640232 45.2145525
rs147463852 −0.440217 0.096887 7019 5.61791e−06 0.00293259 20.6385336
rs16970164 −0.202438 0.0425919 7019 2.04282e−06 0.00320818 22.5842817
rs17192812 0.165787 0.0366097 7019 6.03367e−06 0.00291317 20.5014623
rs3127050 0.0882816 0.018625 7019 2.17713e−06 0.00319069 22.4607401
rs6079205 −0.0765162 0.0172598 7019 9.41841e−06 0.00279219 19.6476903
rs6664147 −0.0967534 0.0216447 7019 7.93696e−06 0.0028387 19.9758636
rs6804331 −0.0803188 0.0180013 7019 8.24665e−06 0.00282827 19.9022839
rs7031238 0.0750969 0.0168493 7019 8.43547e−06 0.00282213 19.8589612
rs7721676 −0.0907963 0.0204262 7019 8.91782e−06 0.00280715 19.7531962
rs79152531 −0.188193 0.0360273 7019 1.80354e−07 0.00387243 27.2784527
Triacylglycerol (48:2) rs10084264 0.172089 0.0363614 7071 2.25621e−06 0.0031577 22.3924789
rs10861498 −0.0879739 0.0192951 7071 5.21205e−06 0.00293128 20.782164
rs1260326 −0.137885 0.0175462 7071 4.45849e−15 0.00865786 61.7369316
rs1832326 0.0832489 0.0187957 7071 9.59737e−06 0.00276666 19.6117859
rs2546043 −0.0802241 0.0167299 7071 1.65631e−06 0.0032414 22.9879456
rs3127050 0.0885169 0.0185552 7071 1.87213e−06 0.00320808 22.750883
rs6664147 −0.0959268 0.0215573 7071 8.71861e−06 0.00279252 19.7955844
rs6804331 −0.081343 0.0179395 7071 5.86932e−06 0.0028992 20.5540234
rs72738698 0.101596 0.0220537 7071 4.15883e−06 0.00299232 21.2161921
rs79152531 −0.194281 0.0359655 7071 6.80166e−08 0.00410978 29.1719594
Triacylglycerol (51:3) rs10147474 −0.205161 0.0449078 7119 4.9914e−06 0.00292318 20.8652709
rs1042034 0.107098 0.0186743 7119 1.01247e−08 0.00459889 32.8815311
rs1047974 0.143421 0.0316816 7119 6.0771e−06 0.00287041 20.4875135
rs111568723 0.242108 0.0546607 7119 9.58185e−06 0.00274823 19.6130834
rs1260326 −0.13791 0.0174639 7119 3.27832e−15 0.00868365 62.3429025
rs12635725 0.0753128 0.0167629 7119 7.13444e−06 0.00282742 20.1798184
rs138427786 0.301941 0.0665321 7119 5.76122e−06 0.00288475 20.5901584
rs139278484 0.252416 0.0563323 7119 7.53858e−06 0.0028124 20.0722828
rs139500046 −0.37314 0.0798691 7119 3.03631e−06 0.00305659 21.8204652
rs15285 −0.105751 0.0189806 7119 2.61547e−08 0.00434151 31.033236
rs16996148 −0.148112 0.0334981 7119 9.93836e−06 0.00273861 19.5442098
rs35332062 −0.150726 0.0254703 7119 3.41151e−09 0.00489506 35.0095235
rs390082 0.150707 0.0313089 7119 1.51269e−06 0.00324415 23.1637678
rs7846649 0.0948767 0.0212506 7119 8.1378e−06 0.00279218 19.9275873
Open in a new tab

FIGURE 3.

FIGURE 3

Open in a new tab

Scatter plots for the causal association between lipids and uIA identified by IVW method.

FIGURE 4.

FIGURE 4

Open in a new tab

Leave‐one‐out plots for the causal association between lipids and uIA identified by IVW method.

In the reverse MR analysis, we used uIA GWAS data as exposure and the aforementioned seven lipids as outcomes, and detailed reverse MR results indicate no bidirectional causal relationship between these lipids and uIAs (Table 3). SNPs used as IVs are provided in Table 4. The sensitivity test results of this step are shown in Figure 5.

TABLE 3.

Results of the reverse MR analyses.

Lipid Method nsnp β SE p value lo_ci up_ci OR OR_lci95 OR_uci95 p value of Cochran's Q test p value of plieotropy test
Sterol ester (27:1/16:1) Inverse variance weighted 15 −0.0111488 0.02320979 0.63097773 −0.05664 0.03434233 0.98891307 0.94493416 1.03493884 0.476912826 0.467332845
MR‐Egger 15 −0.0621994 0.07204397 0.40359054 −0.2034056 0.07900674 0.93969545 0.8159472 1.08221162 0.442002712
Weighted median 15 −0.0130895 0.03310102 0.69251794 −0.0779675 0.05178852 0.98699582 0.92499451 1.053153
Sterol ester (27:1/18:1) Inverse variance weighted 15 0.01605737 0.02979205 0.58989945 −0.0423351 0.0744498 1.01618699 0.95854856 1.07729126 0.058509544 0.937617013
MR‐Egger 15 0.02328463 0.09570366 0.81157052 −0.1642945 0.2108638 1.02355784 0.84849208 1.23474417 0.040511803
Weighted median 15 −0.0073018 0.03345764 0.82724124 −0.0728788 0.05827513 0.99272476 0.92971351 1.0600066
Phosphatidylcholine (18:2_18:2) Inverse variance weighted 15 0.01513174 0.02977265 0.611283 −0.0432227 0.07348613 1.0152468 0.95769813 1.07625361 0.059329482 0.832808868
MR‐Egger 15 0.03458989 0.09546036 0.72291435 −0.1525124 0.2216922 1.03519508 0.85854823 1.24818712 0.041962848
Weighted median 15 −0.0392082 0.03436573 0.25390795 −0.106565 0.02814868 0.96155053 0.89891662 1.02854859
Sphingomyelin (d34:0) Inverse variance weighted 15 0.02823919 0.02494714 0.25765083 −0.0206572 0.07713559 1.0286417 0.97955469 1.08018853 0.548985476 0.856623974
MR‐Egger 15 0.04169092 0.07713488 0.59799539 −0.1094934 0.19287528 1.04257219 0.89628805 1.21273153 0.472648918
Weighted median 15 0.03776757 0.03525381 0.28403244 −0.0313299 0.10686504 1.03848983 0.9691558 1.11278406
Triacylglycerol (48:1) Inverse variance weighted 15 −0.0334031 0.02346 0.15449563 −0.0793847 0.01257854 0.96714866 0.92368455 1.01265799 0.462430251 0.353077668
MR‐Egger 15 0.0327216 0.0725565 0.65943385 −0.1094891 0.17493233 1.03326283 0.8962919 1.1911656 0.455351514
Weighted median 15 −0.0477989 0.03367784 0.15581202 −0.1138075 0.01820966 0.95332548 0.89242976 1.01837647
Triacylglycerol (48:2) Inverse variance weighted 15 −0.0246066 0.02338199 0.29262771 −0.0704353 0.02122208 0.97569367 0.93198804 1.02144887 0.45914209 0.172845623
MR‐Egger 15 0.07416298 0.0723576 0.32408123 −0.0676579 0.21598387 1.07698232 0.93458013 1.24108236 0.544650721
Weighted median 15 −0.0366348 0.03404725 0.28192728 −0.1033674 0.03009777 0.9640281 0.90179555 1.03055529
Triacylglycerol (51:3) Inverse variance weighted 15 −0.0225039 0.02910945 0.43947558 −0.0795584 0.03455061 0.97774742 0.92352407 1.03515442 0.080285299 0.085553138
MR‐Egger 15 0.12382717 0.08309871 0.16005089 −0.0390463 0.28670065 1.13182024 0.96170617 1.33202541 0.185538013
Weighted median 15 −0.0105491 0.03405797 0.75675899 −0.0773027 0.05620452 0.98950634 0.9256096 1.05781401
Open in a new tab

TABLE 4.

SNPs used in the reverse MR analyses.

Lipid SNP β SE Sample size p value R 2 F
Sterol ester (27:1/16:1) rs10087339 0.1966 0.0434 74022 6.005e−06 0.00027714 20.519928
rs10893077 −0.2538 0.054 74022 2.634e−06 0.00029834 22.0894032
rs11646044 −0.2051 0.0398 74022 2.534e−07 0.00035863 26.5554364
rs11662668 −0.1761 0.0391 74022 6.554e−06 0.00027396 20.2839935
rs1537373 −0.1954 0.0342 74022 1.075e−08 0.0004408 32.6426322
rs1998891 −0.151 0.0339 74022 8.38e−06 0.00026796 19.8400501
rs2417658 −0.2178 0.046 74022 2.23e−06 0.00030277 22.4175606
rs4705938 0.1482 0.0335 74022 9.802e−06 0.00026432 19.5701908
rs571138 −0.2042 0.0397 74022 2.75e−07 0.00035728 26.4556677
rs62349022 −0.2468 0.0517 74022 1.839e−06 0.00030776 22.7875424
rs6798962 −0.1876 0.0409 74022 4.612e−06 0.00028414 21.0381389
rs72705377 −0.5121 0.1094 74022 2.857e−06 0.00029593 21.9110492
rs73349742 0.8042 0.1775 74022 5.915e−06 0.00027724 20.5267262
rs77028772 −0.2715 0.0567 74022 1.696e−06 0.00030966 22.92777
rs893176 0.3235 0.0716 74022 6.158e−06 0.0002757 20.4131859
Sterol ester (27:1/18:1) rs10087339 0.1966 0.0434 74022 6.005e−06 0.00027714 20.519928
rs10893077 −0.2538 0.054 74022 2.634e−06 0.00029834 22.0894032
rs11646044 −0.2051 0.0398 74022 2.534e−07 0.00035863 26.5554364
rs11662668 −0.1761 0.0391 74022 6.554e−06 0.00027396 20.2839935
rs1537373 −0.1954 0.0342 74022 1.075e−08 0.0004408 32.6426322
rs1998891 −0.151 0.0339 74022 8.38e−06 0.00026796 19.8400501
rs2417658 −0.2178 0.046 74022 2.23e−06 0.00030277 22.4175606
rs4705938 0.1482 0.0335 74022 9.802e−06 0.00026432 19.5701908
rs571138 −0.2042 0.0397 74022 2.75e−07 0.00035728 26.4556677
rs62349022 −0.2468 0.0517 74022 1.839e−06 0.00030776 22.7875424
rs6798962 −0.1876 0.0409 74022 4.612e−06 0.00028414 21.0381389
rs72705377 −0.5121 0.1094 74022 2.857e−06 0.00029593 21.9110492
rs73349742 0.8042 0.1775 74022 5.915e−06 0.00027724 20.5267262
rs77028772 −0.2715 0.0567 74022 1.696e−06 0.00030966 22.92777
rs893176 0.3235 0.0716 74022 6.158e−06 0.0002757 20.4131859
Phosphatidylcholine (18:2_18:2) rs10087339 0.1966 0.0434 74022 6.005e−06 0.00027714 20.519928
rs10893077 −0.2538 0.054 74022 2.634e−06 0.00029834 22.0894032
rs11646044 −0.2051 0.0398 74022 2.534e−07 0.00035863 26.5554364
rs11662668 −0.1761 0.0391 74022 6.554e−06 0.00027396 20.2839935
rs1537373 −0.1954 0.0342 74022 1.075e−08 0.0004408 32.6426322
rs1998891 −0.151 0.0339 74022 8.38e−06 0.00026796 19.8400501
rs2417658 −0.2178 0.046 74022 2.23e−06 0.00030277 22.4175606
rs4705938 0.1482 0.0335 74022 9.802e−06 0.00026432 19.5701908
rs571138 −0.2042 0.0397 74022 2.75e−07 0.00035728 26.4556677
rs62349022 −0.2468 0.0517 74022 1.839e−06 0.00030776 22.7875424
rs6798962 −0.1876 0.0409 74022 4.612e−06 0.00028414 21.0381389
rs72705377 −0.5121 0.1094 74022 2.857e−06 0.00029593 21.9110492
rs73349742 0.8042 0.1775 74022 5.915e−06 0.00027724 20.5267262
rs77028772 −0.2715 0.0567 74022 1.696e−06 0.00030966 22.92777
rs893176 0.3235 0.0716 74022 6.158e−06 0.0002757 20.4131859
Sphingomyelin (d34:0) rs10087339 0.1966 0.0434 74022 6.005e−06 0.00027714 20.519928
rs10893077 −0.2538 0.054 74022 2.634e−06 0.00029834 22.0894032
rs11646044 −0.2051 0.0398 74022 2.534e−07 0.00035863 26.5554364
rs11662668 −0.1761 0.0391 74022 6.554e−06 0.00027396 20.2839935
rs1537373 −0.1954 0.0342 74022 1.075e−08 0.0004408 32.6426322
rs1998891 −0.151 0.0339 74022 8.38e−06 0.00026796 19.8400501
rs2417658 −0.2178 0.046 74022 2.23e−06 0.00030277 22.4175606
rs4705938 0.1482 0.0335 74022 9.802e−06 0.00026432 19.5701908
rs571138 −0.2042 0.0397 74022 2.75e−07 0.00035728 26.4556677
rs62349022 −0.2468 0.0517 74022 1.839e−06 0.00030776 22.7875424
rs6798962 −0.1876 0.0409 74022 4.612e−06 0.00028414 21.0381389
rs72705377 −0.5121 0.1094 74022 2.857e−06 0.00029593 21.9110492
rs73349742 0.8042 0.1775 74022 5.915e−06 0.00027724 20.5267262
rs77028772 −0.2715 0.0567 74022 1.696e−06 0.00030966 22.92777
rs893176 0.3235 0.0716 74022 6.158e−06 0.0002757 20.4131859
Triacylglycerol (48:1) rs10087339 0.1966 0.0434 74022 6.005e−06 0.00027714 20.519928
rs10893077 −0.2538 0.054 74022 2.634e−06 0.00029834 22.0894032
rs11646044 −0.2051 0.0398 74022 2.534e−07 0.00035863 26.5554364
rs11662668 −0.1761 0.0391 74022 6.554e−06 0.00027396 20.2839935
rs1537373 −0.1954 0.0342 74022 1.075e−08 0.0004408 32.6426322
rs1998891 −0.151 0.0339 74022 8.38e−06 0.00026796 19.8400501
rs2417658 −0.2178 0.046 74022 2.23e−06 0.00030277 22.4175606
rs4705938 0.1482 0.0335 74022 9.802e−06 0.00026432 19.5701908
rs571138 −0.2042 0.0397 74022 2.75e−07 0.00035728 26.4556677
rs62349022 −0.2468 0.0517 74022 1.839e−06 0.00030776 22.7875424
rs6798962 −0.1876 0.0409 74022 4.612e−06 0.00028414 21.0381389
rs72705377 −0.5121 0.1094 74022 2.857e−06 0.00029593 21.9110492
rs73349742 0.8042 0.1775 74022 5.915e−06 0.00027724 20.5267262
rs77028772 −0.2715 0.0567 74022 1.696e−06 0.00030966 22.92777
rs893176 0.3235 0.0716 74022 6.158e−06 0.0002757 20.4131859
Triacylglycerol (48:2) rs10087339 0.1966 0.0434 74022 6.005e−06 0.00027714 20.519928
rs10893077 −0.2538 0.054 74022 2.634e−06 0.00029834 22.0894032
rs11646044 −0.2051 0.0398 74022 2.534e−07 0.00035863 26.5554364
rs11662668 −0.1761 0.0391 74022 6.554e−06 0.00027396 20.2839935
rs1537373 −0.1954 0.0342 74022 1.075e−08 0.0004408 32.6426322
rs1998891 −0.151 0.0339 74022 8.38e−06 0.00026796 19.8400501
rs2417658 −0.2178 0.046 74022 2.23e−06 0.00030277 22.4175606
rs4705938 0.1482 0.0335 74022 9.802e−06 0.00026432 19.5701908
rs571138 −0.2042 0.0397 74022 2.75e−07 0.00035728 26.4556677
rs62349022 −0.2468 0.0517 74022 1.839e−06 0.00030776 22.7875424
rs6798962 −0.1876 0.0409 74022 4.612e−06 0.00028414 21.0381389
rs72705377 −0.5121 0.1094 74022 2.857e−06 0.00029593 21.9110492
rs73349742 0.8042 0.1775 74022 5.915e−06 0.00027724 20.5267262
rs77028772 −0.2715 0.0567 74022 1.696e−06 0.00030966 22.92777
rs893176 0.3235 0.0716 74022 6.158e−06 0.0002757 20.4131859
Triacylglycerol (51:3) rs10087339 0.1966 0.0434 74022 6.005e−06 0.00027714 20.519928
rs10893077 −0.2538 0.054 74022 2.634e−06 0.00029834 22.0894032
rs11646044 −0.2051 0.0398 74022 2.534e−07 0.00035863 26.5554364
rs11662668 −0.1761 0.0391 74022 6.554e−06 0.00027396 20.2839935
rs1537373 −0.1954 0.0342 74022 1.075e−08 0.0004408 32.6426322
rs1998891 −0.151 0.0339 74022 8.38e−06 0.00026796 19.8400501
rs2417658 −0.2178 0.046 74022 2.23e−06 0.00030277 22.4175606
rs4705938 0.1482 0.0335 74022 9.802e−06 0.00026432 19.5701908
rs571138 −0.2042 0.0397 74022 2.75e−07 0.00035728 26.4556677
rs62349022 −0.2468 0.0517 74022 1.839e−06 0.00030776 22.7875424
rs6798962 −0.1876 0.0409 74022 4.612e−06 0.00028414 21.0381389
rs72705377 −0.5121 0.1094 74022 2.857e−06 0.00029593 21.9110492
rs73349742 0.8042 0.1775 74022 5.915e−06 0.00027724 20.5267262
rs77028772 −0.2715 0.0567 74022 1.696e−06 0.00030966 22.92777
rs893176 0.3235 0.0716 74022 6.158e−06 0.0002757 20.4131859
Open in a new tab

FIGURE 5.

FIGURE 5

Open in a new tab

Leave‐one‐out plots for the reverse MR analyses.

4. Discussion

IA is a common cerebrovascular disorder with diverse clinical manifestations, ranging from asymptomatic cases to those causing neurological dysfunction or compression of adjacent structures (W. Li et al. 2021). However, the most severe consequence is the rupture of IAs, leading to SAH, which poses a life‐threatening condition. Therefore, investigating the risk factors for the formation of IAs is of paramount importance.

Recent research has revealed the critical role of lipid metabolism in the onset of IAs. For instance, an MR study found that genetically determined levels of HDL‐C and LDL‐C were associated with a reduced risk of IAs and IA rupture, shedding light on the impact of lipid‐modifying drugs on IAs (Karhunen et al. 2021). However, these findings were not widely accepted, for another study did not identify a correlation between IA and TG or LDL‐C (Zhang et al. 2022).

Furthermore, certain proteins and genetic variations related to lipid metabolism have been confirmed to be associated with IAs. For instance, apolipoprotein E (APOE), a key regulatory factor in lipid metabolism, has been linked to genetic susceptibility to arterial aneurysms (Liu et al. 2016). Differences in gene expression related to lipid metabolism in IA patients also underscore the potential role of lipid metabolism in the pathogenesis of IAs, such as increased expression of the LDLR gene in IAs and specific genotypes (A/G) and alleles (A) of the APOA1 gene contributing to an increased risk of IAs (Synowiec et al. 2016).

In terms of mechanisms, pathological changes in IAs involve lipid deposition and alterations in the vascular wall structure and inflammatory responses within the damaged endothelial layer, induced by the interplay of lipid metabolism and blood flow. This ultimately leads to apoptosis of endothelial cells and smooth muscle cells, weakening the mechanical strength of the vascular wall, causing local outward bulging, and resulting in the formation of an arterial aneurysm (J. Jin et al. 2022).

However, former studies have focused solely on standard lipids such as HDL‐C, LDL‐C, TG, and TC. This makes our research strengths stand out. Firstly, we utilized data from a GWAS research that encompasses 179 lipid variants, enabling us to explore the impact of these diverse lipid isomers on the formation of uIAs and more accurately predict the risk of uIAs. (Ottensmann et al. 2023)

The obtained results highlight the significant impact of these lipid isomers on IAs, which has been scarcely explored in previous research, particularly in relation to vascular‐related disorders. The novelty of this study is underscored by the limited recognition of these isomers in prior investigations. Although the direct traces of these isomers were absent in previous studies, we have nonetheless summarized the roles of the major lipid classes they belong to.

For SEs, studies have indicated that their elevation may contribute to the process of “lipid raft aging,” leading to increased viscosity and reduced fluidity of lipid rafts (Diaz et al. 2023), and may be advantageous for the pro‐inflammatory state in cerebral tissue (Stables and Gilroy 2011). As IA is a cerebrovascular disease influenced by hemodynamics, we sought relevant research on the impact of SEs on hemodynamics and found only a mild effect (Hallikainen et al. 2006). Interestingly, our research identified two SE isomers with opposing effects on IAs, suggesting a potential mutual offsetting of the isomers leading to an overall diminished effect of SEs.

PCs are major components of mitochondrial membranes and are crucial for the synthesis of mature phospholipids in the heart, playing a vital role in maintaining mitochondrial function (X. Li et al. 2015). Limited information is available on the specific impact of PC (18:2_18:2) isomer on cerebrovascular diseases, as only one previous study associated this subtype with the severity of bronchiolitis (Kyo et al. 2023). On the contrary, other PC subtypes such as PC (22:6/18:2), PC (22:6/18:1), PC (20:4/16:1), and PC (16:1/18:3) have shown potential implications in diseases like myocardial infarction, and PC (16:0/16:0) has been associated with hypertension (Dong et al. 2017, Shoghli et al. 2023). Despite this, it is noteworthy that the PC (18:2_18:2) constitutes a significant proportion (34%) of soybean PC, suggesting a potential association between soybean intake and IA risk, although further research is needed (Le Grandois et al. 2009).

TAG, also known as TG, is acknowledged as a crucial risk factor in the formation of IAs, playing a key role in atherosclerosis‐related CVDs. Intracranial atherosclerosis leads to the deposition of lipid plaques, endothelial cell damage, and rupture of elastic fibers, resulting in weakened walls of intracranial arteries. Elevated intracranial pressure can cause localized dilation of these weakened arteries, ultimately leading to the formation of intracranial (Gutierrez et al. 2022; Holmstedt et al. 2013). However, recent MR studies investigating the relationship between lipids and IAs have not found any association between TGs and IAs (Karhunen et al. 2021; Zhang et al. 2022). Our study identified a specific association with only the following TAG subspecies: TAG (48:1), TAG (48:2), and TAG (51:3). This finding may offer new directions for understanding the role of TAGs in the context of IAs.

SM belongs to the class of sphingolipids and serves as a component of cell membranes, functioning as a bioactive signaling molecule (Ruangsiriluk et al. 2012). Clinically, plasma levels of SM have been associated with the progression of CADs and are considered an independent risk factor for CADs (Jiang et al. 2000). Unfortunately, there is a lack of research specifically addressing this particular isomer in the context of IAs.

In this study, we employed MR methodology, enhancing our ability to formulate causal hypotheses and increasing confidence in our findings. This approach mitigates various issues in observational studies, including confounding, selection bias, and memory bias, thereby aiding in clarifying relationships between variables and providing more reliable causal inferences (Smith and Ebrahim 2003). Additionally, the use of a two‐sample MR design allows for the combination of nonoverlapping exposure and outcome data to reduce bias.

There are still some limitations to our study: Firstly, due to the limited number of SNPs filtered by the conventional GWAs significance threshold (p < 5e−8), we lowered the standard to p < 1e−5, but rigorous pleiotropy and sensitivity tests helped remedy this deficiency. Secondly, MR analysis is influenced by demographic factors and genetic sequencing errors, and the current study population consists of individuals of European descent, limiting its generalizability. Furthermore, while MR analysis can provide evidence of causality, the interpretation of biological mechanisms may still be complex and require further experimental research. At last, we only focused on the formation of IAs and lacked a study on SAH. Further studies are needed to investigate whether these lipids have an effect on the rupture of IA.

5. Conclusion

Through MR analysis, we identified seven lipids that have a causal relationship with IAs, potentially offering new perspectives and directions for the risk assessment of IAs.

Author Contributions

Junqing Yan: conceptualization, methodology, software, data curation, supervision, formal analysis, validation, visualization, writing–review and editing, writing – original draft, investigation, resources, project administration.

Conflicts of Interest

The author declares no conflicts of interest.

Peer Review

The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.70435

Declaration

The work has not been fully or partially published previously and is not under consideration for publication elsewhere.

Its publication is approved by all authors, and if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically, without the written consent of the copyright holder.

Acknowledgments

There was no funding source for this study. I would like to thank all the institutions and individuals who contributed data to the public database.

Funding: The author received no specific funding for this work.

Data Availability Statement

The data that supports the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Bakker, M. K. , van der Spek R. A. A., van Rheenen W., et al. 2020. “Genome‐Wide Association Study of Intracranial Aneurysms Identifies 17 Risk Loci and Genetic Overlap With Clinical Risk Factors.” Nature Genetics 52, no. 12: 1303–1313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bor, A. S. , Velthuis B. K., Majoie C. B., and Rinkel G. J.. 2008. “Configuration of Intracranial Arteries and Development of Aneurysms: A Follow‐Up Study.” Neurology 70, no. 9: 700–705. [DOI] [PubMed] [Google Scholar]
  3. Brown, R. D. Jr. , and Broderick J. P.. 2014. “Unruptured Intracranial Aneurysms: Epidemiology, Natural History, Management Options, and Familial Screening.” Lancet Neurology 13, no. 4: 393–404. [DOI] [PubMed] [Google Scholar]
  4. Burgess, S. , Dudbridge F., and Thompson S. G.. 2016. “Combining Information on Multiple Instrumental Variables in Mendelian Randomization: Comparison of Allele Score and Summarized Data Methods.” Statistics in Medicine 35, no. 11: 1880–1906. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cebral, J. , Ollikainen E., Chung B. J., et al. 2017. “Flow Conditions in the Intracranial Aneurysm Lumen Are Associated With Inflammation and Degenerative Changes of the Aneurysm Wall.” AJNR American Journal of Neuroradiology 38, no. 1: 119–126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cianfoni, A. , Pravatà E., de Blasi R., Tschuor C. S., and Bonaldi G.. 2013. “Clinical Presentation of Cerebral Aneurysms.” European Journal of Radiology 82, no. 10: 1618–1622. [DOI] [PubMed] [Google Scholar]
  7. Diaz, M. , Pereda de Pablo D., Valdés‐Baizabal C., Santos G., and Marin R.. 2023. “Molecular and Biophysical Features of Hippocampal ‘Lipid Rafts Aging’ Are Modified by Dietary n‐3 Long‐Chain Polyunsaturated Fatty Acids.” Aging Cell 22, no. 8: e13867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dong, S. , Zhang R., Liang Y., et al. 2017. “Changes of Myocardial Lipidomics Profiling in a Rat Model of Diabetic Cardiomyopathy Using UPLC/Q‐TOF/MS Analysis.” Diabetology & Metabolic Syndrome 9: 56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Etminan, N. , and Rinkel G. J.. 2016. “Unruptured Intracranial Aneurysms: Development, Rupture and Preventive Management.” Nature Reviews Neurology 12, no. 12: 699–713. [DOI] [PubMed] [Google Scholar]
  10. Freneau, M. , Baron‐Menguy C., Vion A. C., and Loirand G.. 2022. “Why Are Women Predisposed to Intracranial Aneurysm?” Frontiers in Cardiovascular Medicine 9: 815668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Frosen, J. , Tulamo R., Heikura T., et al. 2013. “Lipid Accumulation, Lipid Oxidation, and Low Plasma Levels of Acquired Antibodies Against Oxidized Lipids Associate With Degeneration and Rupture of the Intracranial Aneurysm Wall.” Acta Neuropathologica Communications 1: 71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Goldenberg‐Cohen, N. , Curry C., Miller N. R., Tamargo R. J., and Murphy K. P.. 2004. “Long Term Visual and Neurological Prognosis in Patients With Treated and Untreated Cavernous Sinus Aneurysms.” Journal of Neurology, Neurosurgery, and Psychiatry 75, no. 6: 863–867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gutierrez, J. , Turan T. N., Hoh B. L., and Chimowitz M. I.. 2022. “Intracranial Atherosclerotic Stenosis: Risk Factors, Diagnosis, and Treatment.” Lancet Neurology 21, no. 4: 355–368. [DOI] [PubMed] [Google Scholar]
  14. Hackenberg, K. A. M. , Rajabzadeh‐Oghaz H., and Dreier R.. 2020. “Collagen Turnover in Relation to Risk Factors and Hemodynamics in Human Intracranial Aneurysms.” Stroke 51, no. 5: 1624–1628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hallikainen, M. , Lyyra‐Laitinen T., Laitinen T., et al. 2006. “Endothelial Function in Hypercholesterolemic Subjects: Effects of Plant Stanol and Sterol Esters.” Atherosclerosis 188, no. 2: 425–432. [DOI] [PubMed] [Google Scholar]
  16. Hemani, G. , Tilling K., and Smith G.. 2017. “Davey, Orienting the Causal Relationship Between Imprecisely Measured Traits Using GWAS Summary Data.” PLoS Genet 13, no. 11: e1007081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hemani, G. , Zheng J., Elsworth B., et al. 2018. “The MR‐Base Platform Supports Systematic Causal Inference Across the Human Phenome.” eLife 7: e34408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Holmstedt, C. A. , Turan T. N., and Chimowitz M. I.. 2013. “Atherosclerotic Intracranial Arterial Stenosis: Risk Factors, Diagnosis, and Treatment.” Lancet Neurology 12, no. 11: 1106–1114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hong, W. , Huang G., Wang D., et al. 2023. “Gut Microbiome Causal Impacts on the Prognosis of Breast Cancer: A Mendelian Randomization Study.” BMC Genomics [Electronic Resource] 24, no. 1: 497. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Jia, Y. , Yao P., Li J., et al. 2023. “Causal Associations of Sjogren's Syndrome With Cancers: a Two‐sample Mendelian Randomization Study.” Arthritis Research & Therapy 25, no. 1: 171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jiang, X. C. , Paultre F., Pearson T. A., et al. 2000. “Plasma Sphingomyelin Level as a Risk Factor for Coronary Artery Disease.” Arteriosclerosis, Thrombosis, and Vascular Biology 20, no. 12: 2614–2618. [DOI] [PubMed] [Google Scholar]
  22. Jin, J. , Duan J., Du L., Xing W., Peng X., and Zhao Q.. 2022. “Inflammation and Immune Cell Abnormalities in Intracranial Aneurysm Subarachnoid Hemorrhage (SAH): Relevant Signaling Pathways and Therapeutic Strategies.” Frontiers in Immunology 13: 1027756. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Jin, Q. , Ren F., Dai D., Sun N., Qian Y., and Song P.. 2023. “The Causality Between Intestinal Flora and Allergic Diseases: Insights From a Bi‐Directional Two‐Sample Mendelian Randomization Analysis.” Frontiers in immunology 14: 1121273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Karhunen, V. , Bakker M. K., Ruigrok Y. M., Gill D., and Larsson S. C.. 2021. “Modifiable Risk Factors for Intracranial Aneurysm and Aneurysmal Subarachnoid Hemorrhage: A Mendelian Randomization Study.” Journal of the American Heart Association 10, no. 22: e022277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kyo, M. , Zhu Z., Shibata R., et al. 2023. “Respiratory Virus‐Specific Nasopharyngeal Lipidome Signatures and Severity in Infants With Bronchiolitis: A Prospective Multicenter Study.” Journal of Infectious Diseases 228, no. 10: 1410–1420. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Le Grandois, J. , Marchioni E., Zhao M., Giuffrida F., Ennahar S., and Bindler F.. 2009. “Investigation of Natural Phosphatidylcholine Sources: Separation and Identification by Liquid Chromatography‐electrospray Ionization‐tandem Mass Spectrometry (LC‐ESI‐MS2) of Molecular Species.” Journal of Agricultural and Food Chemistry 57, no. 14: 6014–6020. [DOI] [PubMed] [Google Scholar]
  27. Li, W. , Ye M., Cimpoca A., et al. 2021. “Avenir(R) vs. Axium(TM) Coils for the Treatment of Intracranial Aneurysms: Results of a Multicenter Randomized Controlled Trial With Short‐Term Follow‐Up.” Frontiers in Neurology 12: 817989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Li, X. X. , Tsoi B., Li Y. F., Kurihara H., and He R. R.. 2015. “Cardiolipin and Its Different Properties in Mitophagy and Apoptosis.” Journal of Histochemistry and Cytochemistry 63, no. 5: 301–311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lindgren, A. E. , Kurki M. I., Riihinen A., et al. 2014. “Hypertension Predisposes to the Formation of Saccular Intracranial Aneurysms in 467 Unruptured and 1053 Ruptured Patients in Eastern Finland.” Annals of Medicine 46, no. 3: 169–176. [DOI] [PubMed] [Google Scholar]
  30. Liu, H. , Mao P., Xie C., Xie W., Wang M., and Jiang H.. 2016. “Apolipoprotein E Polymorphism and the Risk of Intracranial Aneurysms in a Chinese Population.” BMC Neurology [Electronic Resource] 16: 14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lovik, K. , Laupsa‐Borge J., Logallo N., and Helland C. A.. 2021. “Dyslipidemia and Rupture Risk of Intracranial Aneurysms—A Systematic Review.” Neurosurgical Review 44, no. 6: 3143–3150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ottensmann, L. , Tabassum R., Ruotsalainen S. E., et al. 2023. “Genome‐Wide Association Analysis of Plasma Lipidome Identifies 495 Genetic Associations.” Nature Communications 14, no. 1: 6934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ou, C. , Qian Y., Zhang X., et al. 2020. “Elevated Lipid Infiltration Is Associated with Cerebral Aneurysm Rupture.” Frontiers in Neurology 11: 154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pierce, B. L. , Ahsan H., and Vanderweele T. J.. 2011. “Power and Instrument Strength Requirements for Mendelian Randomization Studies Using Multiple Genetic Variants.” International Journal of Epidemiology 40, no. 3: 740–752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Ruangsiriluk, W. , Grosskurth S. E., Ziemek D., Kuhn M., des Etages S. G., and Francone O. L.. 2012. “Silencing of Enzymes Involved in Ceramide Biosynthesis Causes Distinct Global Alterations of Lipid Homeostasis and Gene Expression.” Journal of Lipid Research 53, no. 8: 1459–1471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Sekula, P. , del Greco M. F., Pattaro C., and Köttgen A.. 2016. “Mendelian Randomization as an Approach to Assess Causality Using Observational Data.” Journal of the American Society of Nephrology 27, no. 11: 3253–3265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Shim, H. , Chasman D. I., Smith J. D., et al. 2015. “A Multivariate Genome‐Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians.” PLoS One 10, no. 4: e0120758. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Shoghli, M. , Lokki A. I., Lääperi M., et al. 2023. “The Novel Ceramide‐ and Phosphatidylcholine‐Based Risk Score for the Prediction of New‐Onset of Hypertension.” Journal of Clinical Medicine 12, no. 24: 7524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Slob, E. A. W. , and Burgess S.. 2020. “A Comparison of Robust Mendelian Randomization Methods Using Summary Data.” Genetic Epidemiology 44, no. 4: 313–329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Smith, G. D. , and Ebrahim S.. 2003. “‘Mendelian Randomization’: Can Genetic Epidemiology Contribute to Understanding Environmental Determinants of Disease?” International Journal of Epidemiology 32, no. 1: 1–22. [DOI] [PubMed] [Google Scholar]
  41. Stables, M. J. , and Gilroy D. W.. 2011. “Old and New Generation Lipid Mediators in Acute Inflammation and Resolution.” Progress in Lipid Research 50, no. 1: 35–51. [DOI] [PubMed] [Google Scholar]
  42. Synowiec, E. , Wojcik K. A., Wójcik R., et al. 2016. “Expression and Variability of Lipid Metabolism Genes in Intracranial Aneurysm.” Cellular and Molecular Biology 62, no. 4: 73–82. [PubMed] [Google Scholar]
  43. Vanrossomme, A. E. , Eker O. F., Thiran J. P., Courbebaisse G. P., and Zouaoui Boudjeltia K.. 2015. “Intracranial Aneurysms: Wall Motion Analysis for Prediction of Rupture.” AJNR American Journal of Neuroradiology 36, no. 10: 1796–1802. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Vlak, M. H. , Algra A., Brandenburg R., and Rinkel G. J.. 2011. “Prevalence of Unruptured Intracranial Aneurysms, With Emphasis on Sex, Age, Comorbidity, Country, and Time Period: A Systematic Review and Meta‐Analysis.” Lancet Neurology 10, no. 7: 626–636. [DOI] [PubMed] [Google Scholar]
  45. Zeng, Y. , Cao S., and Yang H.. 2023. “Roles of Gut Microbiome in Epilepsy Risk: A Mendelian Randomization Study.” Frontiers in Microbiology 14: 1115014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhang, B. , Dong S., Miao Y., et al. 2022. “Effects of Blood Lipids and Lipid‐Modifying Drugs on Intracranial Aneurysms.” European Journal of Neurology 29, no. 10: 2967–2975. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that supports the findings of this study are available from the corresponding author upon reasonable request.

Articles from Brain and Behavior are provided here courtesy of Wiley

RESOURCES