Data_Sheet_1_Targeted Metabolomic Analysis of Serum Fatty Acids for the Prediction of Autoimmune Diseases.docx

Autoimmune diseases (ADs) are rapidly increasing worldwide and accumulating data support a key role of disrupted metabolism in ADs. This study aimed to identify an improved combination of Total Fatty Acids (TFAs) biomarkers as a predictive factor for the presence of autoimmune diseases. A retrospective nested case-control study was conducted in 403 individuals. In the case group, 240 patients diagnosed with rheumatoid arthritis, thyroid disease, multiple sclerosis, vitiligo, psoriasis, inflammatory bowel disease, and other AD were included and compared to 163 healthy individuals. Targeted metabolomic analysis of serum TFAs was performed using GC-MS, and 28 variables were used as input for the predictive models. The primary analysis identified 12 variables that were statistically significantly different between the two groups, and metabolite-metabolite correlation analysis revealed 653 significant correlation coefficients with 90% level of significance (p < 0.05). Three predictive models were developed, namely (a) a logistic regression based on Principal Component Analysis (PCA), (b) a straightforward logistic regression model and (c) an Artificial Neural Network (ANN) model. PCA and straightforward logistic regression analysis, indicated reasonably well adequacy (74.7 and 78.9%, respectively). For the ANN, a model using two hidden layers and 11 variables was developed, resulting in 76.2% total predictive accuracy. The models identified important biomarkers: lauric acid (C12:0), myristic acid (C14:0), stearic acid (C18:0), lignoceric acid (C24:0), palmitic acid (C16:0) and heptadecanoic acid (C17:0) among saturated fatty acids, Cis-10-pentadecanoic acid (C15:1), Cis-11-eicosenoic acid (C20:1n9), and erucic acid (C22:1n9) among monounsaturated fatty acids and the Gamma-linolenic acid (C18:3n6) polyunsaturated fatty acid. The metabolic pathways of the candidate biomarkers are discussed in relation to ADs. The findings indicate that the metabolic profile of serum TFAs is associated with the presence of ADs and can be an adjunct tool for the early diagnosis of ADs.

自身免疫性疾病（Autoimmune Diseases, ADs）在全球范围内呈快速增长趋势，且越来越多的研究数据证实代谢紊乱在自身免疫性疾病中发挥关键作用。本研究旨在筛选优化的总脂肪酸（Total Fatty Acids, TFAs）生物标志物组合，将其作为自身免疫性疾病患病风险的预测因子。本研究纳入403名受试者，开展了一项回顾性巢式病例对照研究：病例组纳入240名确诊为类风湿关节炎、甲状腺疾病、多发性硬化、白癜风、银屑病、炎症性肠病及其他自身免疫性疾病的患者，并与163名健康个体进行对照。采用气相色谱-质谱联用（Gas Chromatography-Mass Spectrometry, GC-MS）技术对血清总脂肪酸进行靶向代谢组学分析，选取28个变量作为预测模型的输入特征。初步分析显示，两组间存在12个具有统计学显著性差异的变量；代谢物-代谢物相关性分析共得到653个显著相关系数，显著性水平为90%（p < 0.05）。本研究构建了三种预测模型：(a) 基于主成分分析（Principal Component Analysis, PCA）的逻辑回归模型，(b) 标准逻辑回归模型，以及(c) 人工神经网络（Artificial Neural Network, ANN）模型。主成分分析与标准逻辑回归模型均表现出较好的拟合效果，准确率分别为74.7%与78.9%。针对人工神经网络模型，本研究构建了含2个隐藏层、输入11个变量的模型，其整体预测准确率达76.2%。模型筛选得到的关键生物标志物包括：饱和脂肪酸类中的月桂酸（C12:0）、肉豆蔻酸（C14:0）、硬脂酸（C18:0）、木蜡酸（C24:0）、棕榈酸（C16:0）与十七烷酸（C17:0）；单不饱和脂肪酸类中的顺-10-十五碳烯酸（C15:1）、顺-11-二十碳烯酸（C20:1n9）与芥酸（C22:1n9）；以及多不饱和脂肪酸类中的γ-亚麻酸（C18:3n6）。本研究探讨了候选生物标志物相关的代谢通路与自身免疫性疾病的关联。研究结果表明，血清总脂肪酸的代谢特征与自身免疫性疾病的患病风险显著相关，可作为辅助工具用于自身免疫性疾病的早期诊断。