Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, Georgia, USA.
Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.
Am J Hypertens. 2019 May 9;32(6):547-556. doi: 10.1093/ajh/hpz046.
Metabolomics study may help identify novel mechanisms underlying arterial stiffening.
We performed untargeted metabolomics profiling among 1,239 participants of the Bogalusa Heart Study. After quality control, 1,202 metabolites were evaluated for associations with augmentation index (AI) and pulse wave velocity (PWV), using multivariate linear regression adjusting for age, sex, race, education, smoking, drinking, body weight, body height, physical activity, and estimated glomerular filtration rate. Heart rate, blood pressure and antihypertensive medication usage, lipids, and fasting glucose were sequentially adjusted in the sensitivity analyses for significant metabolites. Weighted correlation network analysis was applied to build metabolite networks.
Six novel metabolites were negatively associated with AI, of which, 3-methyl-2-oxobutyrate had the lowest P value and the largest effect size (β = -6.67, P = 5.99 × 10-6). Heart rate contributed to a large proportion (25%-58%) of the association for each metabolite. Twenty-one novel metabolites were identified for PWV, of which, fructose (β = 0.61, P = 6.18 × 10-10) was most significant, and histidine had the largest effect size (β = -1.09, P = 2.51 × 10-7). Blood pressure played a major contribution (9%-54%) to the association for each metabolite. Furthermore, 16 metabolites were associated with arterial stiffness independent of traditional risk factors. Network analysis identified 2 modules associated with both AI and PWV (P < 8.00 × 10-4). One was composed of metabolites from the glycerolipids synthesis and recycling pathway, and the other was involved in valine, leucine, and isoleucine metabolism. One module related to sphingomyelin metabolism was associated with PWV only (P = 0.002).
This study has identified novel and important metabolites and metabolic networks associated with arterial stiffness.
代谢组学研究可能有助于确定动脉僵硬的新机制。
我们在博加拉卢萨心脏研究的 1239 名参与者中进行了非靶向代谢组学分析。经过质量控制,使用多元线性回归方法,对 1202 种代谢物与增强指数(AI)和脉搏波速度(PWV)之间的关系进行了评估,调整因素包括年龄、性别、种族、教育程度、吸烟、饮酒、体重、身高、体力活动和估计肾小球滤过率。在对显著代谢物的敏感性分析中,依次调整心率、血压和降压药物使用、血脂和空腹血糖。应用加权相关网络分析构建代谢物网络。
有 6 种新型代谢物与 AI 呈负相关,其中 3-甲基-2-氧代丁酸的 P 值最低,效应量最大(β=-6.67,P=5.99×10-6)。心率对每种代谢物的相关性贡献较大(25%-58%)。发现 21 种新型代谢物与 PWV 相关,其中果糖(β=0.61,P=6.18×10-10)最显著,组氨酸的效应量最大(β=-1.09,P=2.51×10-7)。血压对每种代谢物的相关性贡献较大(9%-54%)。此外,有 16 种代谢物与传统危险因素无关,与动脉僵硬相关。网络分析确定了与 AI 和 PWV 都相关的 2 个模块(P<8.00×10-4)。一个模块由甘油磷脂合成和再循环途径中的代谢物组成,另一个模块涉及缬氨酸、亮氨酸和异亮氨酸代谢。一个与鞘磷脂代谢相关的模块仅与 PWV 相关(P=0.002)。
本研究确定了与动脉僵硬相关的新型重要代谢物和代谢网络。