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2000
Volume 26, Issue 1
  • ISSN: 1871-5303
  • E-ISSN: 2212-3873
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Abstract

Background

Atrial Fibrillation (AF) is the most prevalent form of cardiac arrhythmia, with a complex etiology that implicates lipid metabolism. This study employs Mendelian Randomization (MR) to dissect the causal relationships between lipidomic profiles and AF, utilizing comprehensive genetic data to clarify these associations.

Methods

Summary statistics for 179 lipid species across 13 classes were retrieved from the GWAS Catalog, encompassing 7,174 Finnish individuals from the GeneRISK study. For AF, data were synthesized from six major studies comprising over one million subjects. Our Two-Sample MR (TSMR) approach was implemented using Inverse Variance Weighting (IVW), MR-Egger, and MR-PRESSO for sensitivity analysis. Additionally, we uniquely integrated the Mendelian Randomization-Bayesian Model Averaging (MR_BMA) method to robustly prioritize the most likely causal lipid determinants of AF, and performed bidirectional MR analysis to assess potential reverse causality.

Results

The TSMR analysis, reinforced by MR_BMA, revealed significant causal associations between specific lipid species and AF risk. In particular, Phosphatidylcholine (17:0_18:2) was associated with a decreased risk of AF (OR = 0.96, 95% CI 0.93–0.99, <0.05), whereas Phosphatidylcholine (16:0_20:5) and Phosphatidylcholine (17:0_20:4) were linked to increased risks (OR = 1.04, 95% CI 1.01–1.07, <0.01; and OR = 1.02, 95% CI 1.00–1.05, <0.05, respectively). Furthermore, elevated levels of Phosphatidylethanolamine (18:0_20:4) (OR = 1.03, 95% CI 1.01–1.06, <0.01) and Triacylglycerol (50:4) (OR = 1.04, 95% CI 1.00–1.07, <0.05) were also associated with increased AF risk. In addition, Sphingomyelin (d34:2), Sterol ester (27:1/18:0), and Sterol ester (27:1/18:3) emerged as further risk factors, thereby expanding the spectrum of lipidomic determinants implicated in AF. The bidirectional MR analysis provided no evidence of reverse causation, reinforcing the directionality of the lipid-driven association. Sensitivity analyses demonstrated robust findings with no indication of pleiotropy or heterogeneity.

Conclusion

This study provides strong evidence for thecausal role of specific lipid species in the development of AF. Our comprehensive MR analysis not only deepens our understanding of AF pathophysiology but also highlights the therapeutic potential of targeting these lipid alterations. Notably, the absence of reverse causation supports a unidirectional relationship wherein altered lipid species drive AF risk.

© 2026 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-04-29
2026-01-03

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References

  1. LiH. SongX. LiangY. BaiX. Liu-HuoW.S. TangC. ChenW. ZhaoL. Global, regional, and national burden of disease study of atrial fibrillation/flutter, 1990–2019: Results from a global burden of disease study, 2019.BMC Public Health2022221201510.1186/s12889‑022‑14403‑236329400
     [Google Scholar]
  2. DaiH. ZhangQ. MuchA.A. MaorE. SegevA. BeinartR. AdawiS. LuY. BragazziN.L. WuJ. Global, regional, and national prevalence, incidence, mortality, and risk factors for atrial fibrillation, 1990–2017: Results from the Global Burden of Disease Study 2017.Eur. Heart J. Qual. Care Clin. Outcomes20217657458210.1093/ehjqcco/qcaa06132735316
     [Google Scholar]
  3. KornejJ. BenjaminE.J. MagnaniJ.W. Atrial fibrillation: Global burdens and global opportunities.Heart2021heartjnl31848010.1136/heartjnl‑2020‑318480
     [Google Scholar]
  4. AmadiP.U. GuH.M. YinK. JiangX.C. ZhangD. Editorial: Lipid metabolism and human diseases.Front. Physiol.202213107290310.3389/fphys.2022.107290336406978
     [Google Scholar]
  5. SegattoM. CutoneA. PallottiniV. Fat checking: Emerging role of lipids in metabolism and disease.Int. J. Mol. Sci.202123221384210.3390/ijms232213842
     [Google Scholar]
  6. SagrisD. HarrisonS.L. LipG.Y.H. Lipids and atrial fibrillation: New insights into a paradox.PLoS Med.2022198100406710.1371/journal.pmed.100406735951513
     [Google Scholar]
  7. JiangQ. YangL. ChenM.L. HuaF. LiJ.J. Lipid profile and atrial fibrillation: Is there any link?Rev. Cardiovasc. Med.202223827210.31083/j.rcm230827239076640
     [Google Scholar]
  8. SmithD.G. HemaniG. Mendelian randomization: Genetic anchors for causal inference in epidemiological studies.Hum. Mol. Genet.201423R1R89R9810.1093/hmg/ddu32825064373
     [Google Scholar]
  9. LarssonS.C. ButterworthA.S. BurgessS. Mendelian randomization for cardiovascular diseases: Principles and applications.Eur. Heart J.202344474913492410.1093/eurheartj/ehad73637935836
     [Google Scholar]
  10. LenskiM. SchleiderG. AdrianL. KohlhaasM. MaackC. BoehmM. LaufsU. Cardiac metabolism during atrial fibrillation is characterized by increased lipid accumulation and glycogen synthesis.Eur. Heart J.201334Suppl. 13259
     [Google Scholar]
  11. ToledoE. WittenbecherC. RazquinC. Ruiz-CanelaM. ClishC.B. LiangL. AlonsoA. Hernández-AlonsoP. Becerra-TomásN. Arós-BorauF. CorellaD. RosE. EstruchR. García-RodríguezA. FitóM. LapetraJ. FiolM. Alonso-GomezÁ.M. Serra-MajemL. DeikA. Salas-SalvadóJ. HuF.B. Martínez-GonzálezM.A. Plasma lipidome and risk of atrial fibrillation: Results from the PREDIMED trial.J. Physiol. Biochem.202379235536437004634
     [Google Scholar]
  12. CaoY. AiM. LiuC. The impact of lipidome on breast cancer: A Mendelian randomization study.Lipids Health Dis.202423110938622701
     [Google Scholar]
  13. ZuberV. GillD. Ala-KorpelaM. LangenbergC. ButterworthA. BottoloL. BurgessS. High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease.Int. J. Epidemiol.202150389390133130851
     [Google Scholar]
  14. ZuberV. ColijnJ.M. KlaverC. BurgessS. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization.Nat. Commun.20201112931911605
     [Google Scholar]
  15. OttensmannL. TabassumR. RuotsalainenS.E. GerlM.J. KloseC. WidénE. SimonsK. RipattiS. PirinenM. Genome-wide association analysis of plasma lipidome identifies 495 genetic associations.Nat. Commun.2023141693410.1038/s41467‑023‑42532‑837907536
     [Google Scholar]
  16. TabassumR. RipattiS. Integrating lipidomics and genomics: Emerging tools to understand cardiovascular diseases.Cell. Mol. Life Sci.20217862565258410.1007/s00018‑020‑03715‑433449144
     [Google Scholar]
  17. AlshehryZ.H. MundraP.A. BarlowC.K. MellettN.A. WongG. McConvilleM.J. SimesJ. TonkinA.M. SullivanD.R. BarnesE.H. NestelP.J. KingwellB.A. MarreM. NealB. PoulterN.R. RodgersA. WilliamsB. ZoungasS. HillisG.S. ChalmersJ. WoodwardM. MeikleP.J. Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus.Circulation2016134211637165027756783
     [Google Scholar]
  18. MundraP.A. BarlowC.K. NestelP.J. BarnesE.H. KirbyA. ThompsonP. SullivanD.R. AlshehryZ.H. MellettN.A. HuynhK. JayawardanaK.S. GilesC. McConvilleM.J. ZoungasS. HillisG.S. ChalmersJ. WoodwardM. WongG. KingwellB.A. SimesJ. TonkinA.M. MeikleP.J. Large-scale plasma lipidomic profiling identifies lipids that predict cardiovascular events in secondary prevention.JCI Insight201831712132610.1172/jci.insight.12132630185661
     [Google Scholar]
  19. NielsenJ.B. ThorolfsdottirR.B. FritscheL.G. ZhouW. SkovM.W. GrahamS.E. HerronT.J. McCarthyS. SchmidtE.M. SveinbjornssonG. SurakkaI. MathisM.R. YamazakiM. CrawfordR.D. GabrielsenM.E. SkogholtA.H. HolmenO.L. LinM. WolfordB.N. DeyR. DalenH. SulemP. ChungJ.H. BackmanJ.D. ArnarD.O. ThorsteinsdottirU. BarasA. O’DushlaineC. HolstA.G. WenX. HornsbyW. DeweyF.E. BoehnkeM. KheterpalS. MukherjeeB. LeeS. KangH.M. HolmH. KitzmanJ. ShavitJ.A. JalifeJ. BrummettC.M. TeslovichT.M. CareyD.J. GudbjartssonD.F. StefanssonK. AbecasisG.R. HveemK. WillerC.J. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology.Nat. Genet.20185091234123930061737
     [Google Scholar]
  20. VerbanckM. ChenC.Y. NealeB. DoR. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.Nat. Genet.201850569369810.1038/s41588‑018‑0099‑729686387
     [Google Scholar]
  21. LawlorD.A. HarbordR.M. SterneJ.A.C. TimpsonN. SmithD.G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology.Stat. Med.20082781133116310.1002/sim.303417886233
     [Google Scholar]
  22. ReesJ.M.B. WoodA.M. DudbridgeF. BurgessS. Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates.PLoS One2019149022236210.1371/journal.pone.022236231545794
     [Google Scholar]
  23. BurgessS. ThompsonS.G. Interpreting findings from Mendelian randomization using the MR-Egger method.Eur. J. Epidemiol.201732537738910.1007/s10654‑017‑0255‑x28527048
     [Google Scholar]
  24. LinZ. DengY. PanW. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model.PLoS Genet.20211711100992210.1371/journal.pgen.100992234793444
     [Google Scholar]
  25. GaoC. Investigating the association between blood metabolites and telomere length: A mendelian randomization study.PLoS One2024193029817238457472
     [Google Scholar]
  26. BowdenJ. SmithD.G. BurgessS. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression.Int. J. Epidemiol.201544251252510.1093/ije/dyv08026050253
     [Google Scholar]
  27. BowdenJ. HemaniG. SmithD.G. Invited commentary: Detecting individual and global horizontal pleiotropy in Mendelian randomization—a job for the humble heterogeneity statistic?Am. J. Epidemiol.2018187122681268510.1093/aje/kwy18530188969
     [Google Scholar]
  28. HemaniG. ZhengJ. ElsworthB. WadeK.H. HaberlandV. BairdD. LaurinC. BurgessS. BowdenJ. LangdonR. The MR-Base platform supports systematic causal inference across the human phenome.Elife20187e3440810.7554/eLife.34408
     [Google Scholar]
  29. HarrisonS. LipG. LaneD. MastejM. KasperczykS. BanachM. JozwiakJ. The cholesterol paradox in atrial fibrillation: Results from the LIPIDOGRAM 2015 study.Europ. Heart J.202041Supplement_2ehaa946.045110.1093/ehjci/ehaa946.0451
     [Google Scholar]
  30. DuanJ. MoffatM. Protective effects of phosphatidylcholine against mechanisms of ischemia and reperfusion-induced arrhythmias in isolated guinea pig ventricular tissues.Naunyn Schmiedebergs Arch. Pharmacol.1990342334234810.1007/BF001694472280801
     [Google Scholar]
  31. LiY. GrayA. XueL. FarbM.G. AyalonN. AnderssonC. KoD. BenjaminE.J. LevyD. VasanR.S. LarsonM.G. RongJ. XanthakisV. LiuC. FettermanJ.L. GopalD.M. Metabolomic profiles, ideal cardiovascular health, and risk of heart failure and atrial fibrillation: Insights from the Framingham heart study.J. Am. Heart Assoc.2023121202802210.1161/JAHA.122.02802237301766
     [Google Scholar]
  32. VugtV.M. FinanC. ChopadeS. ProvidenciaR. BezzinaC.R. AsselbergsF.W. SettenV.J. SchmidtA.F. Integrating metabolomics and proteomics to identify novel drug targets for heart failure and atrial fibrillation.Genome Med.202416112010.1186/s13073‑024‑01395‑439434187
     [Google Scholar]
  33. Del Greco MF. FocoL. TeumerA. VerweijN. PagliaG. MeravigliaV. MelottiR. VukovicV. RauheW. JoshiP.K. DemirkanA. FelixS.B. PietznerM. SaidM.A. van de VegteY.J. van der HarstP. WrightA.F. HicksA.A. CampbellH. DörrM. SniederH. WilsonJ.F. PramstallerP.P. RossiniA. PattaroC. Lipidomics, atrial conduction, and body mass index: Evidence from association, mediation, and Mendelian randomization models.Circ. Genom. Precis. Med.201912700238410.1161/CIRCGEN.118.00238431306056
     [Google Scholar]
  34. HuangF. LiuX. LiuJ. XieY. ZhaoL. LiuD. ZengZ. LiuX. ZhengS. XiaoZ. Phosphatidylethanolamine aggravates Angiotensin II-induced atrial fibrosis by triggering ferroptosis in mice.Front. Pharmacol.202314114841010.3389/fphar.2023.114841037288112
     [Google Scholar]
  35. ZhaoM. LiuX. BuX. LiY. WangM. ZhangB. SunW. LiC. Application of plasma metabolome for monitoring the effect of rivaroxaban in patients with nonvalvular atrial fibrillation.PeerJ2022101385310.7717/peerj.1385335966924
     [Google Scholar]
  36. LiuJ. LiuX. LuoY. HuangF. XieY. ZhengS. JiaB. XiaoZ. Sphingolipids: Drivers of cardiac fibrosis and atrial fibrillation.J. Mol. Med.2024102214916510.1007/s00109‑023‑02391‑838015241
     [Google Scholar]
  37. Quiroz-AcostaT. BermeoK. ArenasI. GarciaD.E. Inactivation of potassium channels by ceramide in rat pancreatic β-cells.Arch. Biochem. Biophys.202373510952010.1016/j.abb.2023.10952036646267
     [Google Scholar]
  38. GruenbergJ. An MBoC Favorite: Functional interactions between sphingolipids and sterols in biological membranes regulating cell physiology.Mol. Biol. Cell201223132402240210.1091/mbc.e12‑03‑019022745339
     [Google Scholar]
  39. DingW.Y. ProttyM.B. DaviesI.G. LipG.Y.H. Relationship between lipoproteins, thrombosis, and atrial fibrillation.Cardiovasc. Res.2022118371673110.1093/cvr/cvab01733483737
     [Google Scholar]
  40. RafaqatS. SharifS. MajeedM. NazS. ManzoorF. RafaqatS. Biomarkers of metabolic syndrome: Role in pathogenesis and pathophysiology of atrial fibrillation.J. Atr. Fibrillation20211422020049510.4022/jafib.2020049534950373
     [Google Scholar]
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Unraveling the Lipidomic Determinants of Atrial Fibrillation: An Extensive Mendelian Randomization Study
Endocr Metab Immune Disord Drug Targets 26, E18715303378914 (2026); https://doi.org/10.2174/0118715303378914250418095928
/content/journals/emiddt/10.2174/0118715303378914250418095928
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  • Article Type:     Research Article
Keyword(s): atrial fibrillation; cardiovascular genetics; causal inference; genetic epidemiology; lipidome; Mendelian randomization

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