Causal Relationship between Lipid Metabolome and Stroke: A Systematic Mendelian Randomization Study

doi:10.3969/j.issn.1673-5765.2025.06.003

Abstract

Abstract: Objective Abnormal lipid metabolism is closely associated with the occurrence and development of stroke. This study aims to explore the causal associations between lipid metabolome and stroke as well as its subtypes, and to analyze the impact of lipoprotein components on stroke.
Methods Based on the GWAS of 213 lipid metabolites, as well as any stroke (AS), any ischemic stroke (AIS), and its three subtypes—large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS), univariate Mendelian randomization (MR) methods were used to screen the lipid metabolites that have potential causal relationships with stroke. On this basis, multivariate MR methods were employed to integrate the genetic correlations among lipid metabolites, further identifying those with independent causal effects.
Results In the univariate MR analysis, 16, 58, and 58 lipid metabolites were identified as having potential causal associations with AS, AIS, and LAS, respectively, while only one lipid metabolite was found to be associated with each of CES and SVS. There were widespread genetic correlations among these lipid metabolites. Through further screening by multivariate MR analysis, 11, 4, and 39 lipid metabolites with high-confidence associations with AS, AIS, and LAS were identified, respectively. Among them, small-low density lipoprotein (S-LDL), medium-low density lipoprotein (M-LDL), and the ratio of phospholipids to total lipids in small-high density lipoproteins had potential protective effects on stroke and its subtypes; while lipid components such as cholesterol and cholesterol esters in S-LDL and M-LDL were significantly associated with an increased risk of LAS.
Conclusions Lipid metabolites exert the most significant effect on LAS, and the proportion of cholesterol and phospholipids in lipoprotein particles plays differentiated roles in the occurrence and development of stroke.

Key words: Lipid metabolome; Stroke; Mendelian randomization; Causal association

摘要： 目的脂质代谢异常与卒中的发生发展密切相关。本研究旨在探究脂质代谢组与卒中及其亚型的因果关联，解析脂蛋白成分对卒中的影响。
方法基于213种脂质代谢指标以及全因卒中（any stroke，AS）、全因缺血性卒中（any ischemic stroke，AIS）及其3种亚型——大动脉型卒中（large artery stroke，LAS）、心源性栓塞型卒中（cardioembolic stroke，CES）和小血管型卒中（small vessel stroke，SVS）的GWAS，通过单变量孟德尔随机化（Mendelian randomization，MR）方法初步筛选与卒中存在潜在因果关系的脂质代谢指标。在此基础上，运用多变量MR方法整合脂质代谢指标间的遗传相关性，进一步鉴定具有独立因果效应的脂质代谢指标。
结果在单变量MR中，分别识别出16种、58种和58种脂质代谢指标与AS、AIS及LAS存在潜在因果关联，仅各发现1种与CES和SVS相关的脂质代谢指标。这些脂质代谢指标间普遍存在遗传相关性。通过多变量MR的进一步筛选，分别识别出11种、4种和39种与AS、AIS及LAS相关的高置信度脂质代谢指标。其中，小颗粒低密度脂蛋白（small-low density lipoprotein，S-LDL）及中颗粒低密度脂蛋白（medium-low density lipoprotein，M-LDL）和小颗粒高密度脂蛋白中的磷脂与总脂质的比值，对卒中及其亚型具有潜在保护效应；而S-LDL和M-LDL中的胆固醇、胆固醇酯等脂质成分，则与LAS发病风险升高相关。
结论脂质代谢指标对LAS的影响最为显著，脂蛋白颗粒中的胆固醇和磷脂占比在卒中发生发展中发挥差异化作用。

关键词: 脂质代谢组; 卒中; 孟德尔随机化; 因果关联

CLC Number:

JIANG Minghui, XU Zhe, SHI Yanfeng, ZHANG Jie, LI Hao, CHENG Si. Causal Relationship between Lipid Metabolome and Stroke: A Systematic Mendelian Randomization Study[J]. Chinese Journal of Stroke, 2025, 20(6): 675-685.

姜明慧, 许喆, 石延枫, 张杰, 李昊, 程丝. 脂质代谢组与卒中的因果关系：系统孟德尔随机化研究[J]. 中国卒中杂志, 2025, 20(6): 675-685.

References

[1] GBD 2021 Stroke Risk Factor Collaborators. Global，regional，and national burden of stroke and its risk factors，1990—2021：a systematic analysis for the global burden of disease study 2021[J]. Lancet Neurol，2024，23（10）：973-1003.
[2] BUSHNELL C，KERNAN W N，SHARRIEF A Z，et al. 2024 guideline for the primary prevention of stroke：a guideline from the American Heart Association/American Stroke Association[J/OL]. Stroke，2024，55（12）：e344-e424[2025-02-16]. https://doi.org/10.1161/STR.0000000000000475.
[3] CAMPBELL B C V，DE SILVA D A，MACLEOD M R，et al. Ischaemic stroke[J/OL]. Nat Rev Dis Primers，2019，5（1）：70[2025-02-16]. https://doi.org/10.1038/s41572-019-0118-8.
[4] 唐奕欣，涂汝欣，夏健. 缺血性卒中的代谢组学应用研究及展望[J]. 中国卒中杂志，2024，19（12）：1376-1381.
TANG Y X，TU R X，XIA J. Application research and prospect of metabolomics in ischemic stroke[J]. Chin J Stroke，2024，19（12）：1376-1381.
[5] HUANG X X，LI L，JIANG R H，et al. Lipidomic analysis identifies long-chain acylcarnitine as a target for ischemic stroke[J/OL]. J Adv Res，2024，61：133-149[2025-02-16]. https://doi.org/10.1016/j.jare.2023.08.007.
[6] SLOB E A W，BURGESS S. A comparison of robust Mendelian randomization methods using summary data[J]. Genet Epidemiol，2020，44（4）：313-329.
[7] MARTÍN-CAMPOS J M，CÁRCEL-MÁRQUEZ J，LLUCIÀ-CAROL L，et al. Causal role of lipid metabolome on the risk of ischemic stroke，its etiological subtypes，and long-term outcome：a Mendelian randomization study[J/OL]. Atherosclerosis，2023，386：117382[2025-02-16]. https://doi.org/10.1016/j.atherosclerosis.2023.117382.
[8] CHEN Y H，LU T Y，PETTERSSON-KYMMER U，et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases[J]. Nat Genet，2023，55（1）：44-53.
[9] CHAN L S，MALAKHOV M M，PAN W. A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics[J]. Am J Hum Genet，2024，111（9）：1834-1847.
[10] LIN Z T，XUE H R，PAN W. Robust multivariable Mendelian randomization based on constrained maximum likelihood[J]. Am J Hum Genet，2023，110（4）：592-605.
[11] JIANG S，LI H Q，ZHANG L W Y，et al. Generic diagramming platform（GDP）：a comprehensive database of high-quality biomedical graphics[J/OL]. Nucleic Acids Res，2025，53（D1）：D1670-D1676[2025-02-16].
https://doi.org/10.1093/nar/gkae973.
[12] KARJALAINEN M K，KARTHIKEYAN S，OLIVER-WILLIAMS C，et al. Genome-wide characterization of circulating metabolic biomarkers[J]. Nature，2024，628（8006）：130-138.
[13] MACARTHUR J，BOWLER E，CEREZO M，et al. The new NHGRI-EBI catalog of published genome-wide association studies（GWAS Catalog）[J/OL]. Nucleic Acids Res，2017，45（D1）：D896-D901[2025-02-16]. https://doi.org/10.1093/nar/gkw1133.
[14] MISHRA A，MALIK R，HACHIYA T，et al. Stroke genetics informs drug discovery and risk prediction across ancestries[J]. Nature，2022，611（7934）：115-123.
[15] BULIK-SULLIVAN B，FINUCANE H K，ANTTILA V，et al. An atlas of genetic correlations across human diseases and traits[J]. Nat Genet，2015，47（11）：1236-1241.
[16] HEMANI G，TILLING K，DAVEY SMITH G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data[J/OL]. PLoS Genet，2017，13（11）：e1007081[2025-02-16]. https://doi.org/10.1371/journal.pgen.1007081.
[17] VERBANCK M，CHEN C Y，NEALE B，et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet，2018，50（5）：693-698.
[18] HEMANI G，ZHENG J，ELSWORTH B，et al. The MR-base platform supports systematic causal inference across the human phenome[J/OL]. eLife，2018，7：e34408[2025-02-16]. https://doi.org/10.7554/eLife.34408.
[19] XUE H R，SHEN X T，PAN W. Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects[J]. Am J Hum Genet，2021，108（7）：1251-1269.
[20] ZHAO Q Y，WANG J S，HEMANI G，et al. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score[J]. Ann Statist，2020，48（3）：1742-1769.
[21] BURGESS S，THOMPSON S G. Multivariable Mendelian randomization：the use of pleiotropic genetic variants to estimate causal effects[J]. Am J Epidemiol，2015，181（4）：251-260.
[22] BURGESS S，FREITAG D F，KHAN H，et al. Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions[J/OL]. PLoS One，2014，9（10）：e108891[2025-02-16]. https://doi.org/10.1371/journal.pone.0108891.
[23] HACKAM D G，HEGELE R A. Cholesterol lowering and prevention of stroke[J]. Stroke，2019，50（2）：537-541.
[24] MA Q F，LI R，WANG L J，et al. Temporal trend and attributable risk factors of stroke burden in China，1990—2019：an analysis for the global burden of disease study 2019[J/OL]. Lancet Public Health，2021，6（12）：e897-e906[2025-02-16]. https://doi.org/10.1016/S2468-2667 (21) 00228-0.
[25] YAGHI S，ELKIND M S. Lipids and cerebrovascular disease：research and practice[J]. Stroke，2015，46（11）：3322-3328.
[26] BJÖRKEGREN J L M，LUSIS A J. Atherosclerosis：recent developments[J]. Cell，2022，185（10）：1630-1645.
[27] STÜRZEBECHER P E，KATZMANN J L，LAUFS U. What is‘remnant cholesterol’？[J]. Eur Heart J，2023，44（16）：1446-1448.
[28] SNIDERMAN A D，THANASSOULIS G，GLAVINOVIC T，et al. Apolipoprotein B particles and cardiovascular disease：a narrative review[J]. JAMA Cardiol，2019，4（12）：1287-1295.
[29] POWNALL H J，ROSALES C，GILLARD B K，et al. High-density lipoproteins，reverse cholesterol transport and atherogenesis[J]. Nat Rev Cardiol，2021，18（10）：712-723.
[30] WALLDIUS G，JUNGNER I. The ApoB/ApoA-I ratio：a strong，new risk factor for cardiovascular disease and a target for lipid-lowering therapy—a review of the evidence[J]. J Intern Med，2006，259（5）：493-519.
[31] VAN TUYEN N，HOANG NGOC N，QUOC HOAN P，et al. Differential distribution of plasma ApoA-I and ApoB levels and clinical significance of ApoB/ApoA-I ratio in ischemic stroke subtypes[J/OL]. Front Neurol，2024，15：1398830[2025-02-16]. https://doi.org/10.3389/fneur.2024.1398830.
[32] LIU D，ZHANG Y，WANG C C，et al. Association of the ApoB/ApoA-I ratio with stroke risk：findings from the China health and nutrition survey（CHNS）[J]. Nutr Metab Cardiovasc Dis，2022，32（1）：203-209.
[33] SUN L L，CLARKE R，BENNETT D，et al. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults[J]. Nat Med，2019，25（4）：569-574.
[34] ZHANG Y R，TUOMILEHTO J，JOUSILAHTI P，et al. Total and high-density lipoprotein cholesterol and stroke risk[J]. Stroke，2012，43（7）：1768-1774.
[35] GRUNDY S M，STONE N J，BAILEY A L，et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol：executive summary：a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines[J]. J Am Coll Cardiol，2019，73（24）：3168-3209.
[36] CHENG S，XU Z，BIAN S Z，et al. The STROMICS genome study：deep whole-genome sequencing and analysis of 10 k Chinese patients with ischemic stroke reveal complex genetic and phenotypic interplay[J/OL]. Cell Discov，2023，9（1）：75[2025-02-16]. https://doi.org/10.1038/s41421-023-00582-8.