Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China.
Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China.
Metabolomics. 2024 Jan 5;20(1):13. doi: 10.1007/s11306-023-02081-z.
The burden of stroke in patients with hypertension is very high, and its prediction is critical.
We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.
This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.
Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).
We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.
高血压患者中风负担非常高，预测其发病至关重要。
本研究旨在利用血浆脂质组学分析鉴定出预测高血压患者中风的脂质生物标志物。
这是一项巢式病例对照研究。从 30 例新发中风的高血压患者、30 例匹配的高血压患者、30 例中风高危患者和 30 例匹配的健康对照者中采集基线血浆样本。采用超高效液相色谱-串联质谱法进行脂质组学分析，使用多变量和单变量统计方法筛选差异脂质代谢物。采用机器学习方法（最小绝对收缩和选择算子、随机森林）鉴定预测高血压患者中风的候选生物标志物。
共表达网络分析显示，中风脂质网络的关键分子改变涉及甘油磷脂代谢和胆碱代谢。多变量统计和机器学习方法鉴定出 6 种脂质代谢物作为候选生物标志物，分别为磷脂酰胆碱（40:3p）（rep）、胆固醇酯（20:5）、单甘油酯（29:5）、甘油三酯（18:0p/18:1/18:1）、甘油三酯（18:1/18:2/21:0）和辅酶（q9）。这 6 种脂质生物标志物的组合具有良好的诊断和预测能力，能够早期提示高血压患者发生中风的风险（曲线下面积=0.870；95%置信区间：0.783-0.957）。
本研究确定了与未来中风发展相关的脂质组学特征，并鉴定出预测高血压患者中风的新脂质生物标志物。这些生物标志物具有转化潜力，因此可能成为预测高血压性中风的基于血液的生物标志物。
