Niu Zhenhua, Wu Qingqing, Luo Yaogan, Wang Di, Zheng He, Wu Yanpu, Yang Xiaowei, Zeng Rong, Sun Liang, Lin Xu
Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd., Shanghai, 200031 China.
Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031 China.
Phenomics. 2022 Jun 14;2(5):283-294. doi: 10.1007/s43657-022-00057-y. eCollection 2022 Oct.
While disrupted lipid metabolism is a well-established risk factor for hypertension in animal models, the links between various lipidomic signatures and hypertension in human studies remain unclear. We aimed to examine associations between plasma lipidomic profiles and prevalence of hypertension among 2248 community-living Chinese aged 50-70 years. Hypertension was defined according to 2020 International Society of Hypertension global hypertension practice guidelines and 2018 Chinese guidelines. In total, 728 plasma lipidomic species were profiled using high-coverage targeted lipidomics. After multivariate adjustment, including lifestyle, body mass index, blood lipids, and sodium intake, 110 metabolites from nine lipidomic subclasses showed significant associations with hypertension, among which phosphatidylethanolamines (PEs) had the strongest association. Eleven lipidomic signals for hypertension risk were further identified from the nine subclasses, including PE(18:0/18:2) (OR per SD, 1.49; 95% confidence intervals, 1.30-1.69), phosphatidylcholine (PC) (18:0/18:2) (1.27; 1.13-1.43), phosphatidylserine (18:0/18:0) (1.24; 1.09-1.41), lysophosphatidylinositol (18:1) (1.17; 1.06-1.29), triacylglycerol (52:5) (1.38; 1.18-1.61), diacylglycerol (16:0/18:2) (1.42; 1.19-1.69), dihydroceramide (24:0) (1.25; 1.09-1.43), hydroxyl-sphingomyelins (SM[2OH])C34:1 (1.19; 1.07-1.33), lysophosphatidylcholine (20:1) (0.86; 0.78-0.95), SM(OH)C38:1 (0.87; 0.79-0.96), and PC (18:2/20:1) (0.84; 0.75-0.94). Principal component analysis also showed that a factor mainly containing specific PEs was positively associated with hypertension (1.20; 1.09-1.33). Collectively, our study revealed that disturbances in multiple circulating lipidomic subclasses and signatures, especially PEs, were significantly associated with the prevalence of hypertension in middle-aged and elderly Chinese. Future studies are warranted to confirm our findings and determine the mechanisms underlying these associations.
The online version contains supplementary material available at 10.1007/s43657-022-00057-y.
虽然脂质代谢紊乱在动物模型中是高血压的一个公认危险因素,但在人类研究中,各种脂质组学特征与高血压之间的联系仍不清楚。我们旨在研究2248名年龄在50 - 70岁的社区居住中国人群的血浆脂质组学谱与高血压患病率之间的关联。高血压根据2020年国际高血压学会全球高血压实践指南和2018年中国指南进行定义。总共使用高覆盖靶向脂质组学分析了728种血浆脂质组学物质。在进行多变量调整后,包括生活方式、体重指数、血脂和钠摄入量,来自九个脂质组学子类别的110种代谢物与高血压存在显著关联,其中磷脂酰乙醇胺(PEs)的关联最强。从这九个亚类中进一步确定了11个高血压风险的脂质组学信号,包括PE(18:0/18:2)(每标准差的比值比,1.49;95%置信区间,1.30 - 1.69)、磷脂酰胆碱(PC)(18:0/18:2)(1.27;1.13 - 1.43)、磷脂酰丝氨酸(18:0/18:0)(1.24;1.09 - 1.41)、溶血磷脂酰肌醇(18:1)(1.17;1.06 - 1.29)、三酰甘油(52:5)(1.38;1.18 - 1.61)、二酰甘油(16:0/18:2)(1.42;1.19 - 1.69)、二氢神经酰胺(24:0)(1.25;1.09 - 1.43)、羟基鞘磷脂(SM[2OH])C34:1(1.19;1.07 - 1.33)、溶血磷脂酰胆碱(20:1)(0.86;0.78 - 0.95)、SM(OH)C38:1(0.87;0.79 - 0.96)以及PC(18:2/20:1)(0.84;0.75 - 0.94)。主成分分析还表明,一个主要包含特定PEs的因子与高血压呈正相关(1.20;1.09 - 1.33)。总体而言,我们的研究表明,多种循环脂质组学子类和特征的紊乱,尤其是PEs,与中国中老年人的高血压患病率显著相关。未来的研究有必要证实我们的发现并确定这些关联背后的机制。
在线版本包含可在10.1007/s43657 - 022 - 00057 - y获取的补充材料。