Statistical analysis and visualization
of metabolite concentrations, glycoproteins, lipoproteins, NFL, YKL-40,
metal elements, and demographic data were performed using R, SPSS
v.20, and Metaboanalyst v.5.0. The gender distribution was analyzed
with Pearson’s Chi-Square, while the age at inclusion was analyzed
with one-way analysis of variance. EDSS was analyzed with an independent
sample t-test with the assumption of equal variance
based on Levene’s test for equality of variance, while equal
variance was not assumed for the disease duration for the same test.
All concentrations were Pareto scaled prior to multivariate analysis.
Due to the relatively high number of variables relative to the number
of observations, sparse partial least squares discriminant analysis
(sPLSDA) with leave-one-out cross-validation was used to identify
the metabolites with the greatest contribution to group separation.
Since some of the concentration data were not normally distributed
based on Kolmogorov–Smirnov and Shapiro–Wilk tests,
nonparametric tests, such as the Kruskal–Wallis test for multiple
comparisons and the Mann–Whitney U test for
pairwise comparisons, were performed. Descriptive statistics for the
biomarkers are reported as the median and interquartile range (IQR),
while those for the demographic data are reported as the mean and
standard deviation (SD). Due to multiple comparisons and unless otherwise
stated, p-values lower than 0.01 were considered
to be statistically significant, while p-values higher
than 0.01 but less than 0.05 were considered as trends. Spearman’s
correlation coefficients were also calculated for relevant metabolites.
Receiver operator characteristic (ROC) curves were also constructed
for significant metabolites using SPSS v.20. The multidimensional
scaling method ALSCAL was used to project the significant features
in two dimensions with the dissimilarity matrix based on the Euclidean
distance.
of metabolite concentrations, glycoproteins, lipoproteins, NFL, YKL-40,
metal elements, and demographic data were performed using R, SPSS
v.20, and Metaboanalyst v.5.0. The gender distribution was analyzed
with Pearson’s Chi-Square, while the age at inclusion was analyzed
with one-way analysis of variance. EDSS was analyzed with an independent
sample t-test with the assumption of equal variance
based on Levene’s test for equality of variance, while equal
variance was not assumed for the disease duration for the same test.
All concentrations were Pareto scaled prior to multivariate analysis.
Due to the relatively high number of variables relative to the number
of observations, sparse partial least squares discriminant analysis
(sPLSDA) with leave-one-out cross-validation was used to identify
the metabolites with the greatest contribution to group separation.
Since some of the concentration data were not normally distributed
based on Kolmogorov–Smirnov and Shapiro–Wilk tests,
nonparametric tests, such as the Kruskal–Wallis test for multiple
comparisons and the Mann–Whitney U test for
pairwise comparisons, were performed. Descriptive statistics for the
biomarkers are reported as the median and interquartile range (IQR),
while those for the demographic data are reported as the mean and
standard deviation (SD). Due to multiple comparisons and unless otherwise
stated, p-values lower than 0.01 were considered
to be statistically significant, while p-values higher
than 0.01 but less than 0.05 were considered as trends. Spearman’s
correlation coefficients were also calculated for relevant metabolites.
Receiver operator characteristic (ROC) curves were also constructed
for significant metabolites using SPSS v.20. The multidimensional
scaling method ALSCAL was used to project the significant features
in two dimensions with the dissimilarity matrix based on the Euclidean
distance.
