Lin Feng, Song Yige, Cao Hongli, Liao Fengye, Deng Yanping, Wei Qinyu, Hong Weimin, Yao Guifeng, Ding Chunguang, Chen Xianyang
Department of Neurology, Sanming First Hospital Affiliated to Fujian Medical University, Sanming, Fujian, China.
Bao Feng Key Laboratory of Genetics and Metabolism, Beijing, China.
Sci Rep. 2024 Dec 28;14(1):31337. doi: 10.1038/s41598-024-82808-7.
Many lipid biomarkers of stroke have been identified, but the lipid metabolism in elderly patients with leukoaraiosis remains poorly understood. This study aims to explore lipid metabolic processes in stroke among leukoaraiosis patients, which could provide valuable insights for guiding future antithrombotic therapy. In a cohort of 215 individuals undergoing MRI, 13 stroke patients were matched with controls, and 48 stroke patients with leukoaraiosis were matched with 40 leukoaraiosis patients. Serum lipidomics was profiled using UPLC-TOF, and OPLS-DA was applied for metabolite identification. Partial Least Squares Path Model (PLS-PM) assessed pathway weights of novel metabolites in stroke risk, while linear regression explored correlations with clinical outcomes. Lipid profiling identified 168 distinct compounds. From these, 25 lipid molecules were associated with glycerolipid, glycerophospholipid, and sphingolipid metabolism. PLS-PM identified 12 key metabolites, including DG 36:4 (OR = 6.40) as a significant risk factor. Metabolites such as PE 38:5 and FA 16:1;O showed significant correlations with stroke in leukoaraiosis, particularly when the Fazekas score was ≥ 4. Twelve metabolites were identified as key factors in stroke incidence among leukoaraiosis patients. Lipid disturbances in glycerolipids and glycerophospholipids provide valuable insights for further studies on the progression from leukoaraiosis to stroke.
许多中风的脂质生物标志物已被确定,但白质疏松症老年患者的脂质代谢仍知之甚少。本研究旨在探索白质疏松症患者中风中的脂质代谢过程,这可为指导未来的抗血栓治疗提供有价值的见解。在一组215名接受磁共振成像(MRI)的个体中,13名中风患者与对照组匹配,48名患有白质疏松症的中风患者与40名白质疏松症患者匹配。使用超高效液相色谱-飞行时间质谱(UPLC-TOF)对血清脂质组进行分析,并应用正交偏最小二乘法判别分析(OPLS-DA)进行代谢物鉴定。偏最小二乘路径模型(PLS-PM)评估了新代谢物在中风风险中的通路权重,而线性回归则探索了与临床结果的相关性。脂质分析鉴定出168种不同的化合物。其中,25种脂质分子与甘油酯、甘油磷脂和鞘脂代谢有关。PLS-PM确定了12种关键代谢物,包括二酰甘油36:4(优势比=6.40)作为一个显著的危险因素。代谢物如磷脂酰乙醇胺38:5和脂肪酸16:1;O与白质疏松症患者的中风有显著相关性,特别是当 Fazekas 评分≥4时。12种代谢物被确定为白质疏松症患者中风发病率的关键因素。甘油酯和甘油磷脂中的脂质紊乱为进一步研究白质疏松症向中风的进展提供了有价值的见解。