Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis

GC-MS urinary metabolomic analysis coupled with chemometrics is used to detect inborn errors of metabolism (IEMs), which are genetic disorders causing severe mental and physical debility and even sudden infant death. Orthogonal partial least squares discriminant analysis (OPLS-DA) is an efficient multivariate statistical method that conducts data analysis of metabolite profiling. However, performance degradation is often observed for OPLS-DA due to increasing size and complexity of metabolomic datasets. In this study, hybrid particle swarm optimization (HPSO) is employed to modify OPLS-DA by simultaneously selecting the optimal variable subset, associated weights and the appropriate number of orthogonal components, constructing a new algorithm called HPSO-OPLSDA. Investigating two IEMs, methylmalonic acidemia (MMA) and isovaleric acidemia (IVA), results suggest that HPSO-OPLSDA can significantly outperform OPLS-DA in terms of the discrimination between disease samples and healthy controls. Moreover, main discriminative metabolites are identified by HPSO-OPLSDA to aid the clinical diagnosis of IEMs, including methylmalonic-2, methylcitric-4(1) and 3-OH-propionic-2 for MMA and isovalerylglycine-1 for IVA.