3.2 Metabolomic profiling of no AKI vs. AKI patients
Following XCMS processing of chromatographic data collected from
untargeted metabolomics, exclusion of features with high variability in
pooled quality control samples, and removal of potential
adducts/isotopes, 758 serum features and 484 urine features were
included for subsequent multivariate analysis. The derivatized
metabolomics dataset was similarly processed, without the removal of
potential adducts or isotopes. Additionally, any duplicate features
detected in both the derivatized and untargeted datasets were removed.
Ultimately, a total of 975 serum features and 2355 urine features
remained after processing of derivatized data. After cross referencing
the derivatized features with a library of 263 derivatized small
molecule standards, 35 serum and 117 urine features were matched bym/z and retention time with the library of standards.
To select the features most important in discriminating between no AKI
and AKI patients at each timepoint, OPLS-DA models were sequentially
generated, each time excluding features with VIP values < 1.
This sequential exclusion of features was repeated until model
statistics of OPLS-DA models were maximized. With the remaining
features, PCA score plots were generated to visualize the metabolic
differences between no AKI and AKI patients at each timepoint. Score
plots of urine samples showed moderate separation at the pre
(Figure 1A ) and 24-48h timepoints (Figure 1B ), and
clear separation at the post timepoint (Figure 1C ). In serum,
strong separation was observed in both the pre (Figure 2A ) and
post (Figure 2C ) timepoints, with moderate separation observed
at the 24-48h timepoint (Figure 2B ). Corresponding OPLS-DA
models comparing no AKI and AKI patients at each timepoint mirrored the
visual separation observed in the PCA score plots, with a high degree of
fit (R2Y) and predictive ability (Q2Y) at the post timepoint for both
urine and serum (Figure 1F, 2F ), as well as the pre timepoint
for serum (Figure 2D ). Moderate model statistics were observed
for the 24-48h timepoint for both urine and serum (Figure 1E,
2E ), as well as the pre timepoint for urine (Figure 1D ).
Features with 0.4 < p(corr) < -0.4 and VIP
> 1 in the OPLS-DA models were considered as important
discriminators of AKI and were thus followed up for identification.
Identified metabolites that were significantly different between no AKI
and AKI groups by two-way ANOVA are summarized in Table 2 .