Kasakin Marat F, Rogachev Artem D, Predtechenskaya Elena V, Zaigraev Vladimir J, Koval Vladimir V, Pokrovsky Andrey G
Institute of Chemical Biology and Fundamental Medicine, The Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Novosibirsk Institute of Organic Chemistry, The Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Mult Scler Int. 2020 Jul 15;2020:9010937. doi: 10.1155/2020/9010937. eCollection 2020.
McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS ( = 16) and control ( = 12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.
麦克唐纳标准和磁共振成像（MRI）用于多发性硬化症（MS）的诊断；然而，做出临床诊断需要相当长的时间。MS患者的氨基酸和脂肪酸代谢途径会受到干扰，这一信息可能有助于诊断。我们研究的目的是寻找氨基酸和酰基肉碱血浆谱的变化，以区分多发性硬化症患者和健康对照者。我们采用了一种靶向代谢组学方法，基于对干血斑中氨基酸和酰基肉碱的串联质谱分析，然后进行多变量统计分析，以发现MS组（n = 16）和对照组（n = 12）之间的差异。结果发现，与主成分线性判别分析和随机森林算法相比，偏最小二乘判别分析产生了更好的组分类。所有这三种模型都检测到MS组相对于对照组在氨基酸和酰基肉碱谱上有明显变化。我们的结果为临床决策支持系统的进一步发展带来了希望。
