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首页> 外文期刊>Metabolomics >Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool
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Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool

机译：使用UPLC-MS作为代谢谱分析工具来改善PLSDA模型结果以预测人类非酒精性脂肪肝疾病的化学计量学方法

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An MS-based metabolomics strategy including variable selection and PLSDA analysis has been assessed as a tool to discriminate between non-steatotic and steatotic human liver profiles. Different chemometric approaches for uninformative variable elimination were performed by using two of the most common software packages employed in the field of metabolomics (i.e., MATLAB and SIMCA-P). The first considered approach was performed with MATLAB where the PLS regression vector coefficient values were used to classify variables as informative or not. The second approach was run under SIMCA-P, where variable selection was performed according to both the PLS regression vector coefficients and VIP scores. PLSDA models performance features, such as model validation, variable selection criteria, and potential biomarker output, were assessed for comparison purposes. One interesting finding is that variable selection improved the classification predictiveness of all the models by facilitating metabolite identification and providing enhanced insight into the metabolic information acquired by the UPLC-MS method. The results prove that the proposed strategy is a potentially straightforward approach to improve model performance. Among others, GSH, lysophospholipids and bile acids were found to be the most important altered metabolites in the metabolomic profiles studied. However, further research and more in-depth biochemical interpretations are needed to unambiguously propose them as disease biomarkers.
机译：包括变量选择和PLSDA分析在内的基于MS的代谢组学策略已被评估为区分非脂肪变性和脂肪变性人类肝脏特征的工具。通过使用代谢组学领域中使用的两种最常用的软件包（即MATLAB和SIMCA-P），执行了用于消除无信息变量的不同化学计量学方法。第一种考虑的方法是使用MATLAB执行的，其中使用PLS回归向量系数值将变量分类为信息量还是非信息量。第二种方法是在SIMCA-P下运行的，其中根据PLS回归向量系数和VIP分数进行变量选择。为了比较，评估了PLSDA模型的性能特征，例如模型验证，变量选择标准和潜在的生物标志物输出。一个有趣的发现是，变量选择通过促进代谢物鉴定和增强对通过UPLC-MS方法获取的代谢信息的洞察力，改善了所有模型的分类预测性。结果证明，所提出的策略是提高模型性能的潜在直接方法。在研究的代谢组学研究中，发现GSH，溶血磷脂和胆汁酸是最重要的代谢物改变。然而，需要进一步的研究和更深入的生化解释，以明确地将其作为疾病生物标志物。

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