## 2 Materials and Methods
### NMR Data Preprocessing and Multivariate Statistical Analysis  
The phase and baseline of NMR spectra were corrected using ACDlab Processor Academic Edition (Advanced Chemistry Development, 12.01, 2010, Toronto, ON, Canada). The spectral region comprising the signal of residual water and urea (4.5-6.0 ppm) was removed. The final spectral regions considered were between 0.5 and 4.5 ppm as well as 6.0 and 9.5 ppm. The ACD Labs intelligent bucketing method was used for spectral integration [14]. A 0.01 ppm bucket width was defined with an allowed 50% looseness. The intelligent bucket method finds local minima in spectra and adjusts the buckets accordingly. In this way, a peak is integrated into one bucket. The area of bucketed regions was normalized using Median Fold Change Normalization [15], largely preferred to total sum normalization when studying urine samples, and a matrix was generated. The resultant data sets were then imported into SIMCA software (Version 15.0, Sartorius Stedim Biotech, Umea, Sweden) for multivariate statistical analysis. The data sets were then Pareto scaled. Pareto scaling, where each variable is divided by the square root of the standard deviation, gives greater weight to NMR variables with low intensity, but it is not as extreme as the UV (Unit Variance) scaling method. Principal component analysis (PCA) and Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) were used for multivariate statistical analyses of NMR data. PCA was performed to identify any possible relation (trends, outliers) between the samples. As far as the outliers are concerned, [PERSON]'s T2 and DModX tests were applied. OPLS-DA analysis was used to reduce model complexity and to better highlight sample discrimination. The goodness of the model was evaluated using a 7-fold cross-validation and a \"permutation test\" (400 permutations). The permutation test was calculated by randomizing the Y-matrix (classification components) while the X-matrix (peak intensity in NMR spectra) was kept constant. The permutation plot shows the correlation coefficient between the original and the permuted y-variables on the \(x\)-axis, versus the cumulative R2 and Q2 on the \(y\)-axis, and draws the regression line. Q2Y intercept values are a measure of the overfit, and values \(<\)0.05 are indicative of a valid model. To highlight potential metabolites that mainly contributed to group separation, an S-plot for the OPLS-DA model was created. The S-plot reveals the contribution of each variable to the predictive component, matching the covariance p and the correlation \(p\)(corr) obtained from the OPLS-DA model. The axes plotted in the S-plot from the predictive component are p1 versus \(p\)(corr)1, representing the magnitude (modelled covariance) and reliability (modelled correlation) respectively. The variables characterized by high magnitude and reliability values have an important role in the separation of different groups of samples. In the S-plot both magnitude (intensity) and reliability are plotted. The statistical significance of the difference in metabolite concentrations, quantified using Chenomx NMR suite 7.1 (Chenomx Inc., Edmonton, AB, Canada), was calculated using an unpaired Welch \(t\)-test with a 95% confidence interval. Chenomx NMR Suite software is useful for identifying and quantifying the metabolites in NMR spectra [16]. It is equipped with reference libraries containing numerous compound models that are identical to the spectra of pure compounds obtained under similar experimental conditions. Basically,a Lorentzian peak shape model of each reference compound is created from database information and is overlapped with the actual spectrum. The linear combination of modeled metabolites gives rise to the total spectral fit, which can be assessed with a summation line [16]. The metabolites with both VIP \(>\) 1 (Variable Importance for the Projection) and a \(p\)-value \(\leq\) 0.05 were considered statistically significant.