Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term −80 °C Storage: A TOFI_Asia Sub-Study

Abstract

Biological samples of lipids and metabolites degrade after extensive years in −80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of −80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS–DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of −80 °C storage. With receiver operator characteristic curves, sparse PLS–DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at −80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Keywords

• frozen
• −80 °C storage
• lipidomics
• metabolomics
• multivariate modelling
• predictions