Department of Health Management, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Human Disease Genes Key Laboratory of Sichuan Province and Institute of Laboratory Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Clin Exp Med. 2023 Sep;23(5):1729-1739. doi: 10.1007/s10238-022-00958-2. Epub 2022 Dec 2.
Ankylosing spondylitis (AS) is an autoimmune rheumatic disease that mostly affects the axial skeleton. This study aimed to investigate reliable diagnostic serum biomarkers for AS. Serum samples were collected from 20 AS patients and 20 healthy controls (HCs) and analyzed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differential metabolites between the AS patients and HCs were profiled using univariate and multivariate statistical analyses. Pathway analysis and a heat map were also conducted. Random forest (RF) analysis and the least absolute shrinkage and selection operator (LASSO) were used to establish predictive and diagnostic models. After controlling the variable importance in the projection (VIP) value > 1 and false discovery rate (FDR) < 0.05, a total of 61 differential metabolites were identified from 995 metabolites, which exhibited significant differences in the pathway analysis and heat map between the AS patients and HCs. RF as a predictive model also identified differential metabolites with 95% predictive accuracy and a high area under the curve (AUC) of 1. A diagnostic model comprising nine metabolites (cysteinylglycine disulfide, choline, N6, N6, N6-trimethyllysine, histidine, sphingosine, fibrinopeptide A, glycerol 3-phosphate, 1-linoleoyl-GPA (18:2), and fibrinopeptide A (3-16)) was generated using LASSO regression, capable of distinguishing HCs from AS with a high AUC of 1. Our results indicated that the UPLC-MS/MS analysis method is a powerful tool for identifying AS metabolite profiles. We developed a nine-metabolites-based model serving as a diagnostic tool to separate AS patients from HCs, and the identified diagnostic biomarkers appeared to have a diagnostic value for AS.
强直性脊柱炎（AS）是一种主要影响中轴骨骼的自身免疫性风湿病。本研究旨在探索用于 AS 的可靠诊断血清生物标志物。收集了 20 例 AS 患者和 20 例健康对照者（HCs）的血清样本，并使用超高效液相色谱-串联质谱法（UPLC-MS/MS）进行分析。使用单变量和多变量统计分析来分析 AS 患者和 HCs 之间的差异代谢物。还进行了途径分析和热图。随机森林（RF）分析和最小绝对收缩和选择算子（LASSO）用于建立预测和诊断模型。在控制变量重要性投影（VIP）值>1 和错误发现率（FDR）<0.05 后，从 995 种代谢物中总共鉴定出 61 种差异代谢物，它们在 AS 患者和 HCs 之间的途径分析和热图中表现出显著差异。RF 作为预测模型也确定了具有 95%预测准确性和高曲线下面积（AUC）的 1.0 的差异代谢物。使用 LASSO 回归生成了包含九个代谢物（二硫胱氨酸、胆碱、N6、N6、N6-三甲基赖氨酸、组氨酸、神经鞘氨醇、纤维蛋白肽 A、甘油 3-磷酸、1-亚油酸-GPA（18:2）和纤维蛋白肽 A（3-16）的诊断模型，能够以高 AUC 1.0 区分 HCs 和 AS。我们的结果表明，UPLC-MS/MS 分析方法是一种识别 AS 代谢物谱的有力工具。我们开发了一种基于九个代谢物的模型作为一种诊断工具，将 AS 患者与 HCs 分开，并且鉴定出的诊断生物标志物似乎对 AS 具有诊断价值。
