Vojinovic Dina, Kalaoja Marita, Trompet Stella, Fischer Krista, Shipley Martin J, Li Shuo, Havulinna Aki S, Perola Markus, Salomaa Veikko, Yang Qiong, Sattar Naveed, Jousilahti Pekka, Amin Najaf, Satizabal Claudia L, Taba Nele, Sabayan Behnam, Vasan Ramachandran S, Ikram M Arfan, Stott David J, Ala-Korpela Mika, Jukema J Wouter, Seshadri Sudha, Kettunen Johannes, Kivimaki Mika, Esko Tonu, van Duijn Cornelia M
From the Department of Epidemiology (D.V., N.A., M.A.I., C.M.v.D.), Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Computational Medicine (M. Kalaoja, M.A.-K., J.K.), Faculty of Medicine, University of Oulu and Biocenter Oulu, Finland; Departments of Gerontology and Geriatrics (S.T.), and Cardiology (S.T., J.W.J.), Leiden University Medical Center, the Netherlands; Estonian Genome Centre (K.F., N.T., T.E.), Institute of Genomics, and Institute of Molecular and Cell Biology (N.T.), University of Tartu, Estonia; Department of Epidemiology and Public Health (M.J.S., M. Kivimaki), UCL, London, UK; Department of Biostatistics (S.L., O.Y.), School of Public Health, Boston University, MA; Department of Public Health Solutions (A.S.H., M.P., V.S., P.J., J.K.), Finnish Institute for Health and Welfare; Institute for Molecular Medicine Finland (A.S.H., M.P.), University of Helsinki; BHF Glasgow Cardiovascular Research Centre (N.S.), Faculty of Medicine, UK; Department of Neurology (B.S.), Feinberg School of Medicine, Northwestern University, Chicago, IL; Framingham Heart Study (C.L.S., R.S.V., S.S.), MA; Department of Radiology and Nuclear Medicine (M.A.I.), Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Institute of Cardiovascular and Medical Sciences (D.J.S.), College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Systems Epidemiology (M.A.-K.), Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; NMR Metabolomics Laboratory (M.A.-K.), School of Pharmacy, University of Eastern Finland, Kuopio; Population Health Science (M.A.-K.), Bristol Medical School, and Medical Research Council Integrative Epidemiology Unit (M.A.-K.), University of Bristol, UK; Department of Epidemiology and Preventive Medicine (M.A.-K.), School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia; Netherlands Heart Institute (J.W.J.), Utrecht, the Netherlands; Department of Neurology (C.L.S., S.S.), Boston University School of Medicine; Broad Institute of MIT and Harvard (T.E.), Boston, MA; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (C.L.S., S.S.), UT Health San Antonio, TX; Nuffield Department of Population Health (C.M.v.D.), University of Oxford, UK. D.V. is currently at the Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, the Netherlands, and K.F. is currently at the Institute of Mathematics and Statistics, University of Tartu, Estonia.
Neurology. 2021 Feb 22;96(8):e1110-e1123. doi: 10.1212/WNL.0000000000011236.
To conduct a comprehensive analysis of circulating metabolites and incident stroke in large prospective population-based settings.
We investigated the association of metabolites with risk of stroke in 7 prospective cohort studies including 1,791 incident stroke events among 38,797 participants in whom circulating metabolites were measured by nuclear magnetic resonance technology. The relationship between metabolites and stroke was assessed with Cox proportional hazards regression models. The analyses were performed considering all incident stroke events and ischemic and hemorrhagic events separately.
The analyses revealed 10 significant metabolite associations. Amino acid histidine (hazard ratio [HR] per SD 0.90, 95% confidence interval [CI] 0.85, 0.94; = 4.45 × 10), glycolysis-related metabolite pyruvate (HR per SD 1.09, 95% CI 1.04, 1.14; = 7.45 × 10), acute-phase reaction marker glycoprotein acetyls (HR per SD 1.09, 95% CI 1.03, 1.15; = 1.27 × 10), cholesterol in high-density lipoprotein (HDL) 2, and several other lipoprotein particles were associated with risk of stroke. When focused on incident ischemic stroke, a significant association was observed with phenylalanine (HR per SD 1.12, 95% CI 1.05, 1.19; = 4.13 × 10) and total and free cholesterol in large HDL particles.
We found association of amino acids, glycolysis-related metabolites, acute-phase reaction markers, and several lipoprotein subfractions with the risk of stroke. These findings support the potential of metabolomics to provide new insights into the metabolic changes preceding stroke.
在基于人群的大型前瞻性研究中，对循环代谢物与新发中风进行全面分析。
我们在7项前瞻性队列研究中调查了代谢物与中风风险的关联，这些研究包括38797名参与者中的1791例新发中风事件，通过核磁共振技术测量了他们的循环代谢物。使用Cox比例风险回归模型评估代谢物与中风之间的关系。分析分别考虑了所有新发中风事件以及缺血性和出血性事件。
分析揭示了10种显著的代谢物关联。氨基酸组氨酸（每标准差风险比[HR]为0.90，95%置信区间[CI]为0.85至0.94；=4.45×10）、糖酵解相关代谢物丙酮酸（每标准差HR为1.09，95%CI为1.04至1.14；=7.45×10）、急性期反应标志物糖蛋白乙酰化产物（每标准差HR为1.09，95%CI为1.03至1.15；=1.27×10）、高密度脂蛋白（HDL）2中的胆固醇以及其他几种脂蛋白颗粒与中风风险相关。当聚焦于新发缺血性中风时，观察到与苯丙氨酸（每标准差HR为1.12，95%CI为1.05至1.19；=4.13×10）以及大HDL颗粒中的总胆固醇和游离胆固醇存在显著关联。
我们发现氨基酸、糖酵解相关代谢物、急性期反应标志物以及几种脂蛋白亚组分与中风风险存在关联。这些发现支持了代谢组学在为中风前代谢变化提供新见解方面的潜力。
