Wang Ziyu, Fei Min, Qi Yue, Yang Zhao, Li Jiangtao, Ding Shusi, Zhao Wenlang, Zhang Yunqi, Wang Na, Zhou Pan, Deng Xuan, Jia Pingping, Xue Jing, Zheng Lemin, Liu Jing
Center for Clinical and Epidemiologic Research, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, National Clinical Research Center for Cardiovascular Diseases, Ministry of Education, Beijing Municipal Key Laboratory of Clinical Epidemiology, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, China.
The Institute of Cardiovascular Sciences and Institute of Systems Biomedicine, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, 100191, Beijing, China.
Metabolomics. 2025 Sep 26;21(5):142. doi: 10.1007/s11306-025-02341-0.
Hypertension is a leading risk factor for chronic kidney disease (CKD), yet the metabolic mechanisms linking hypertension to CKD remain unclear. This study aimed to identify metabolites associated with CKD incidence and progression in hypertensive patients using untargeted metabolomics analysis.
A prospective cohort study was conducted to identify metabolites associated with the incidence and progression of CKD in hypertensive patients. Untargeted metabolomic profiling was conducted, and three statistical models-logistic regression, lasso regression, and random forest-were utilized to identify metabolites associated with CKD. Modified Poisson regression was used to assess the associations between metabolites and kidney-related outcomes.
Untargeted metabolomic profiling identified distinct metabolite patterns distinguishing hypertensive patients with CKD from those without. These metabolites were identified across the three statistical models, with 94 showing significance in at least two. Four metabolites-L-theanine, cysteine-s-sulfate, mesaconic acid, and 2-aminoadipic acid-were inversely associated with CKD incidence and progression. L-theanine and cysteine-s-sulfate were both associated with decreased estimated glomerular filtration rate (eGFR) and increased urinary albumin-to-creatinine ratio (UACR). In contrast, mesaconic acid was linked to increased UACR, and 2-aminoadipic acid was associated with decreased eGFR. Patients at higher risk of CKD progression exhibited significantly lower levels of these metabolites.
L-theanine, cysteine-s-sulfate, mesaconic acid, and 2-aminoadipic acid show an inverse association with CKD incidence and progression in hypertensive patients, suggesting their potential as biomarkers for CKD risk. Further studies are warranted to validate these findings and investigate their therapeutic implications.
高血压是慢性肾脏病（CKD）的主要危险因素，然而，将高血压与CKD联系起来的代谢机制仍不清楚。本研究旨在通过非靶向代谢组学分析确定高血压患者中与CKD发生和进展相关的代谢物。
进行了一项前瞻性队列研究，以确定高血压患者中与CKD发生和进展相关的代谢物。进行了非靶向代谢组学分析，并利用逻辑回归、套索回归和随机森林三种统计模型来确定与CKD相关的代谢物。采用修正泊松回归评估代谢物与肾脏相关结局之间的关联。
非靶向代谢组学分析确定了区分患有CKD的高血压患者和未患CKD的高血压患者的不同代谢物模式。这些代谢物在三种统计模型中均被识别出来，其中94种在至少两种模型中具有显著性。四种代谢物——L-茶氨酸、半胱氨酸-S-硫酸盐、中康酸和2-氨基己二酸——与CKD的发生和进展呈负相关。L-茶氨酸和半胱氨酸-S-硫酸盐均与估算肾小球滤过率（eGFR）降低和尿白蛋白与肌酐比值（UACR）升高有关。相比之下，中康酸与UACR升高有关，2-氨基己二酸与eGFR降低有关。CKD进展风险较高的患者这些代谢物水平显著较低。
L-茶氨酸、半胱氨酸-S-硫酸盐、中康酸和2-氨基己二酸与高血压患者CKD的发生和进展呈负相关，表明它们有可能作为CKD风险的生物标志物。有必要进一步研究以验证这些发现并探讨其治疗意义。
