Department of Neurology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Department of Neurology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China; Department of Pediatrics, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Clin Chim Acta. 2024 Jan 1;552:117652. doi: 10.1016/j.cca.2023.117652. Epub 2023 Nov 17.
Stroke is a prominent contributor to global mortality and morbidity, thus necessitating the establishment of dependable diagnostic indicators. The objective of this study was to ascertain metabolites linked to sphingolipid metabolism and assess their viability as diagnostic markers for stroke.
Two cohorts, consisting of 56 S patients and 56 healthy volunteers, were incorporated into this investigation. Metabolite data was obtained through the utilization of Ultra Performance Liquid Chromatography and Tandem Mass Spectrometry (UPLC-MS/MS). The mass spectrometry data underwent targeted analysis and quantitative evaluation utilizing the multiple reaction monitoring mode of triple quadrupole mass spectrometry. Various data analysis techniques, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM), logistic regression, and Receiver Operating Characteristic (ROC) curves were employed.
A comprehensive analysis detected a total of 129 metabolites related to sphingolipid metabolism, encompassing ceramides, 1-phosphoceramides, phytoceramides, glycosphingolipids, sphingomyelins, and sphingomyelins. The implementation of OPLS-DA analysis revealed significant disparities between individuals with stroke and controls, as it successfully identified 31 metabolites that exhibited significant differential expression between the two groups. Furthermore, functional enrichment analysis indicated the participation of these metabolites in diverse biological processes. Six metabolic markers, namely CerP(d18:1/20:3), CerP(d18:1/18:1), CerP(d18:1/18:0), CerP(d18:1/16:0), SM(d18:1/26:1), and Cer(d18:0/20:0), were successfully validated as potential diagnostic markers for stroke. The utilization of ROC analysis further confirmed their diagnostic potential, while a logistic regression model incorporating these markers demonstrated robust efficacy in distinguishing stroke patients from healthy controls.
these identified metabolic markers exhibit clinical significance and hold promise as valuable tools for the diagnosis of stroke.
中风是全球死亡率和发病率的主要原因，因此需要建立可靠的诊断指标。本研究的目的是确定与鞘脂代谢有关的代谢物，并评估其作为中风诊断标志物的可行性。
本研究纳入了 56 例 S 患者和 56 例健康志愿者的两个队列。通过使用超高效液相色谱和串联质谱（UPLC-MS/MS）获得代谢物数据。采用三重四极杆质谱的多反应监测模式对质谱数据进行靶向分析和定量评估。使用正交偏最小二乘判别分析（OPLS-DA）、最小绝对收缩和选择算子（LASSO）回归、支持向量机（SVM）、逻辑回归和受试者工作特征（ROC）曲线等多种数据分析技术。
全面分析共检测到与鞘脂代谢相关的 129 种代谢物，包括神经酰胺、1-磷酸神经酰胺、植物神经酰胺、糖鞘脂、神经鞘磷脂和神经鞘磷脂。OPLS-DA 分析结果显示，中风患者和对照组之间存在显著差异，成功鉴定出两组间差异表达的 31 种代谢物。此外，功能富集分析表明这些代谢物参与了多种生物学过程。CerP(d18:1/20:3)、CerP(d18:1/18:1)、CerP(d18:1/18:0)、CerP(d18:1/16:0)、SM(d18:1/26:1)和 Cer(d18:0/20:0)等 6 种代谢标志物被成功验证为中风的潜在诊断标志物。ROC 分析进一步证实了它们的诊断潜力，而包含这些标志物的逻辑回归模型在区分中风患者和健康对照组方面表现出较强的疗效。
这些鉴定出的代谢标志物具有临床意义，有望成为中风诊断的有用工具。
