Download Supplemental Methods Statistical analysis. All NMR spectra were

Supplemental Methods
Statistical analysis. All NMR spectra were converted into 22 K data points over the range of
δH 0.2-10.0 ppm and imported into the MATLAB software (version 7.0; The Math-Works
Inc., Natick, MA) excluding the water residue (water δH=4.50-5.19 ppm). The spectra were
normalized to a constant total sum of all intensities within the specified range and auto scaled.
Chemometric analysis was performed using the software package SIMCA-P+ (version 11.5,
Umetrics AB, Umeå, Sweden) and in-house developed MATLAB routines in order to detect
the presence of inherent similarities between metabolic profiles. In order to detect the
presence of similarities between metabolic profiles, Principal Component Analysis (PCA) (1),
Projection to Latent Structure (PLS) (2) and the Orthogonal Projection to Latent Structures
(O-PLS) (3) were used. Seven-fold cross validation was used to assess the validity of the
model (4). The classification accuracy of the O-PLS-DA model was established from the
predicted samples in the 7-fold cross-validation cycle.
Intermediates metabolites from β-oxidation, amino acid, NAD, SCFA, TCA and Urea
metabolic pathways assigned in urine and stool 1H NMR spectra were relatively quantified by
peak integration. The urinary and fecal excretions of these metabolites were further analysed
using Wilcoxon-Mann-Whitney tests over time and between groups. Metabolic pathway
analysis was conducted using metabolite set enrichment analysis using the web-based
inference of biological patterns, functions and pathways from metabolomic data using
MetaboAnalyst 2.0 (5,6).
Z-scores for weight, at 3 to 12 months were calculated based on the WHO 2006 Child Growth
Standards (7). Z-scores were compared by visit (cross-sectionally). Comparison between
groups was performed using ANCOVA correcting for the predicting covariates maternal BMI
(kg/m2), gender (m/f), and the respective anthropometric parameter at 3 months. Other
covariates, such as ethnicity, presence of complementary foods and antibiotic use that were
found to be non-predicting were not used in ANCOVA.
Supplemental Figure Captions
Supplemental Figure S1: Longitudinal analysis of body weight for age z-score.
Supplemental Figure S2: O-PLS-DA scores plot describing the distribution of the urine
metabolic profiles according to the age of the infants in breast-fed (left panel) and
infants fed with a high protein formula (right panel). Horizontal axis corresponds to the
first latent component and the vertical axis corresponds to the first orthogonal component.
Supplemental Figure S3: O-PLS-DA scores plot describing the distribution of the stool
metabolic profiles according to the age (3M, 3 months; 6M, six months; 12M, twelve
months) of the infants in breast-fed (left panel) and infants fed with a high protein
formula (right panel). Horizontal axis corresponds to the first latent component and the
vertical axis corresponds to the second latent component.
References
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