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Novel Metabolites Are Associated With Augmentation Index and Pulse Wave Velocity: Findings From the Bogalusa Heart Study
被引:21
|作者:
Li, Changwei
[1
,2
]
He, Jiang
[2
,3
]
Li, Shengxu
[2
]
Chen, Wei
[2
]
Bazzano, Lydia
[2
]
Sun, Xiao
[2
]
Shen, Luqi
[1
]
Liang, Lirong
[4
]
Shen, Ye
[1
]
Gu, Xiaoying
[5
]
Kelly, Tanika N.
[2
]
机构:
[1] Univ Georgia, Coll Publ Hlth, Dept Epidemiol & Biostat, Athens, GA 30602 USA
[2] Tulane Univ, Sch Publ Hlth & Trop Med, Dept Epidemiol, New Orleans, LA 70112 USA
[3] Tulane Univ, Dept Med, Sch Med, New Orleans, LA 70118 USA
[4] Capital Med Univ, Beijing Chaoyang Hosp, Beijing Inst Resp Med, Clin Epidemiol & Tobacco Dependence Treatment Res, Beijing, Peoples R China
[5] China Japan Friendship Hosp, Inst Clin Med Sci, Beijing, Peoples R China
关键词:
arterial stiffness;
blood pressure;
hypertension;
metabolomics;
metabolite networks;
ARTERIAL STIFFNESS;
CARDIOVASCULAR EVENTS;
BLOOD-PRESSURE;
DOPAMINE AGONISTS;
PREDICTOR;
MORTALITY;
DISEASE;
ACIDS;
RISK;
MEN;
D O I:
10.1093/ajh/hpz046
中图分类号:
R6 [外科学];
学科分类号:
1002 ;
100210 ;
摘要:
BACKGROUND Metabolomics study may help identify novel mechanisms underlying arterial stiffening. METHODS 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. RESULTS Six novel metabolites were negatively associated with AI, of which, 3-methyl-2-oxobutyrate had the lowest P value and the largest effect size (beta = -6.67, P = 5.99 x 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 (beta = 0.61, P = 6.18 x 10(-10)) was most significant, and histidine had the largest effect size (beta = -1.09, P = 2.51 x 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 x 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). CONCLUSIONS This study has identified novel and important metabolites and metabolic networks associated with arterial stiffness.
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页码:547 / 556
页数:10
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