GC–MS urinary metabolomics analysis of inherited metabolic diseases and stable metabolic biomarker screening by a comprehensive chemometric method

• Published in: Microchemical journal Vol. 168; p. 106350
• Main Authors: Yang, Qin, Shi, Bing-Hao, Tian, Guo-Li, Niu, Qian-Qian, Tang, Jia, Linghu, Dan-Dan, He, Hong-Qin, Wu, Ben-Qing, Yang, Jiang-Tao, Xu, Lu, Yu, Ru-Qin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: Chemometrics; GC–MS urinary metabolomics; Hybrid perturbation ensemble; Inherited metabolic diseases; Stable metabolic biomarker screening; GC–MS urinary metabolomics; Stable metabolic biomarker screening; Hybrid perturbation ensemble; Inherited metabolic diseases; Chemometrics
• ISSN: 0026-265X; 1095-9149
• DOI: 10.1016/j.microc.2021.106350

•Metabolomics coupled with chemometrics was described for stable biomarker screening.•Information-rich urinary metabolomic profiling was detected by GC–MS.•Chemometric method modeled urinary metabolomics data with desirable results.•A feasible stable biomarker screening system was developed for IMDs. Screening of stable metabolic biomarkers objectively measuring the underlying pathophysiological changes are vital to boost early diagnosis and treatment prognosis of inherited metabolic diseases (IMDs). In this study, gas chromatography-mass spectrometry (GC–MS) was invoked to perform urinary metabolomics analysis for IMDs and a comprehensive chemometric method was presented to screen stable metabolic biomarkers effectively. For this chemometric method, a novel hybrid perturbation framework based on partial least squares discriminant analysis (PLS-DA) was adopted, in which the data level utilized bootstrap (BS) method and the function level employed the strategy of multiple informative vector fusion (MIVF), forming an algorithm BS-MIVF-PLSDA. Investigated by two common IMDs (methylmalonic acidemia and propionic acidemia), GC–MS allowed the detection of a rich profile of metabolites, characterizing subtle differences between disease and healthy control groups. The proposed chemometric method BS-MIVF-PLSDA exhibited superiorities to simpler techniques in terms of identifying candidate metabolic biomarkers more biologically correlated to the metabolic mechanisms with desirable selection stability. Moreover, the chemometric method enabled good classification performance in discriminating between disease and healthy control groups using the identified top-ranked candidate metabolic biomarkers. All the results showed that GC–MS urinary metabolomics coupled with chemometrics was feasible to offer an efficient path to achieve stable metabolic biomarkers, supporting early diagnosis and guiding treatment in IMDs clinical practice.