Orthogonal partial-least squares discriminant analysis (OPLS-DA) using in-house R scripts (R foundation for statistical computing, Vienna, Austria) and the ropls package were used to interrogate the CPMG (global metabolomics) and AXINON® lipoFIT® (targeted metabolomics) spectral data to identify metabolomics differences between patient groups.24 ,25 (link) All OPLS-DA models were validated on independent test data using external 10-fold cross-validation with 100 iterations. Details of this approach have been previously published.13 (link) Briefly, this involves repeated cycles of (i) balancing class sizes; (ii) random splitting of the spectral data into a training set (90% of data) and a test set (remaining 10% of data); (iii) construction of OPLS-DA models using the training set alone; and then (iv) determining the predictive accuracy of the OPLS-DA model using the independent test set. The validity of the metabolic separation between patient groups was confirmed if the mean predictive accuracy of the ensemble of model accuracies was significantly higher than the mean predictive accuracy of a separate ensemble created by random class assignments on the same spectral data.