Lattau Seyed Siyawasch Justus, Borsch Lisa-Marie, Auf dem Brinke Kristina, Klose Christian, Vinhoven Liza, Nietert Manuel, Fitzner Dirk
Department of Neurology, University Medical Center Göttingen, 37075 Göttingen, Germany.
Lipotype GmbH, 01307 Dresden, Germany.
Int J Mol Sci. 2024 Feb 20;25(5):2483. doi: 10.3390/ijms25052483.
This investigation explores the potential of plasma lipidomic signatures for aiding in the diagnosis of Multiple Sclerosis (MS) and evaluating the clinical course and disease activity of diseased patients. Plasma samples from 60 patients with MS (PwMS) were clinically stratified to either a relapsing-remitting (RRMS) or a chronic progressive MS course and 60 age-matched controls were analyzed using state-of-the-art direct infusion quantitative shotgun lipidomics. To account for potential confounders, data were filtered for age and BMI correlations. The statistical analysis employed supervised and unsupervised multivariate data analysis techniques, including a principal component analysis (PCA), a partial least squares discriminant analysis (oPLS-DA) and a random forest (RF). To determine whether the significant absolute differences in the lipid subspecies have a relevant effect on the overall composition of the respective lipid classes, we introduce a class composition visualization (CCV). We identified 670 lipids across 16 classes. PwMS showed a significant increase in diacylglycerols (DAG), with DAG 16:0;0_18:1;0 being proven to be the lipid with the highest predictive ability for MS as determined by RF. The alterations in the phosphatidylethanolamines (PE) were mainly linked to RRMS while the alterations in the ether-bound PEs (PE O-) were found in chronic progressive MS. The amount of CE species was reduced in the CPMS cohort whereas TAG species were reduced in the RRMS patients, both lipid classes being relevant in lipid storage. Combining the above mentioned data analyses, distinct lipidomic signatures were isolated and shown to be correlated with clinical phenotypes. Our study suggests that specific plasma lipid profiles are not merely associated with the diagnosis of MS but instead point toward distinct clinical features in the individual patient paving the way for personalized therapy and an enhanced understanding of MS pathology.
本研究探讨了血浆脂质组学特征在辅助多发性硬化症（MS）诊断以及评估患病患者临床病程和疾病活动度方面的潜力。对60例MS患者（PwMS）的血浆样本进行临床分层，分为复发缓解型（RRMS）或慢性进展型MS病程，并使用最先进的直接进样定量鸟枪法脂质组学对60名年龄匹配的对照进行分析。为了考虑潜在的混杂因素，对数据进行了年龄和BMI相关性筛选。统计分析采用了监督和非监督多变量数据分析技术，包括主成分分析（PCA）、偏最小二乘判别分析（oPLS-DA）和随机森林（RF）。为了确定脂质亚类的显著绝对差异是否对各自脂质类别的整体组成有相关影响，我们引入了类组成可视化（CCV）。我们在16个类别中鉴定出670种脂质。PwMS患者的二酰基甘油（DAG）显著增加，其中DAG 16:0;0_18:1;0被证明是RF确定的对MS预测能力最高的脂质。磷脂酰乙醇胺（PE）的改变主要与RRMS相关，而醚键连接的PE（PE O-）的改变则在慢性进展型MS中发现。CPMS队列中CE物种的数量减少，而RRMS患者中TAG物种减少，这两种脂质类别在脂质储存中都很重要。综合上述数据分析，分离出了不同的脂质组学特征，并显示与临床表型相关。我们的研究表明，特定的血浆脂质谱不仅与MS的诊断相关，还指向个体患者的不同临床特征，为个性化治疗和加深对MS病理学的理解铺平了道路。
