Department of Chemistry, Faculty of Sciences, Universidade da Coruña, Grupo Química Analítica Aplicada (QANAP), University Institute of Research in Environmental Studies (IUMA), Centro de Investigaciones Científicas Avanzadas (CICA), Campus de A Coruña, s/n, 15071 A Coruña, Spain.
Unit of Diagnosis and Treatment of Congenital Metabolic Diseases, Department of Pediatrics, University Hospital of Santiago de Compostela, IDIS, CIBERER, A Choupana, s/n, 15706 Santiago de Compostela, Spain.
Metallomics. 2021 May 17;13(5). doi: 10.1093/mtomcs/mfab018.
Trace elements in dried blood spots (DBSs) from newborns were determined by laser ablation coupled with inductively coupled plasma mass spectrometry, and data were subjected to chemometric evaluation in an attempt to classify healthy newborns and newborns suffering from metabolic disorders. Unsupervised [principal component analysis (PCA) and cluster analysis (CA)] and supervised [linear discriminant analysis (LDA) and soft independent modeling by class analogy (SIMCA)] pattern recognition techniques were used as classification techniques. PCA and CA have shown a clear tendency to form two groups (healthy newborns and newborns suffering from metabolic disorders). LDA and SIMCA have predicted that 90.5% and 83.9% of originally grouped healthy newborn cases were correctly classified by LDA and SIMCA, respectively. In addition, these percentages were 97.6% (LDA) and 80.6% (SIMCA) for DBSs from newborns suffering from metabolic disorders. However, SIMCA has only detected one misclassified DBS from the healthy group, and the lower percentage is attributed to four DBSs from the healthy newborn group and five DBSs from newborns with disorders that were found as belonging to both categories (healthy newborns and newborns with disorders) in the training set. LDA also gave a percentage of grouped maple syrup urine disease (MSUD) cases correctly classified of 100%, although the percentage fells to 66.7% when classifying phenylketonuria (PKU) cases. Finally, essential elements such as Fe, K, Rb, and Zn were found to be matched (correlated) with the concentration of amino acids such as phenylalanine, valine, and leucine, biomarkers linked with MSUD and PKU diseases.
采用激光烧蚀与电感耦合等离子体质谱联用技术测定新生儿干血斑中的微量元素，并进行化学计量学评价，试图对健康新生儿和代谢紊乱新生儿进行分类。采用无监督[主成分分析（PCA）和聚类分析（CA）]和有监督[线性判别分析（LDA）和类间独立模式分析（SIMCA）]模式识别技术作为分类技术。PCA 和 CA 显示出明显的倾向，形成两个组（健康新生儿和代谢紊乱的新生儿）。LDA 和 SIMCA 预测，LDA 和 SIMCA 分别正确分类了 90.5%和 83.9%的原始分组健康新生儿病例。此外，对于代谢紊乱的新生儿的干血斑，这两个百分比分别为 97.6%（LDA）和 80.6%（SIMCA）。然而，SIMCA 只检测到一个来自健康组的错误分类的干血斑，较低的百分比归因于四个来自健康新生儿组的干血斑和五个来自有障碍的新生儿的干血斑，这些干血斑在训练集中被发现属于健康新生儿和有障碍的新生儿这两个类别。LDA 对分类乳清酸尿症（MSUD）病例的正确分类百分比也为 100%，尽管当分类苯丙酮尿症（PKU）病例时，百分比下降到 66.7%。最后，发现铁、钾、铷和锌等必需元素与苯丙氨酸、缬氨酸和亮氨酸等氨基酸的浓度相匹配（相关），这些氨基酸与 MSUD 和 PKU 疾病有关。
