Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population

Table 1 Statistics of multivariate analysis models demonstrating an association between ¹H NMR spectroscopic data and several biological and lifestyle factors.

Model Description	Y variable	Number of LVs	R²X	Q²Y	Significance (Pvalue)
A. PLS	ln(U-Cd)	3	0.251	0.237	< 0.01
B. PLS (current smokers excluded)	ln(U-Cd)	5	0.308	0.330	< 0.001
C. PLS (past and current smokers excluded)	ln(U-Cd)	1	0.0729	0.142	< 0.001
D. PLS	sex	3	0.241	0.104	> 0.05
E. PLS	age	2	0.216	0.224	< 0.001
F. PLS	ln(U-NAG)	1	0.054	0.162	< 0.001
G. PLS-DA	Smoking history^a	2	0.194	0.185	< 0.01

^aSmoking history was defined as either 1 = never smoked and past smoker (n = 106) or 2 = current smoker (n = 20), one individual did not complete the lifestyle questionnaire. Spectra that exhibited signs of bacterial contamination, analgesics or ethanol were excluded from these analyses. All variables were mean-centred and scaled to unit variance. NMR data were reduced to 1,127 data points of δ 0.01 resolution. Sample numbers for PLS models: A, D, E and F: n = 127. B: n = 106. C: n = 79. PLS-DA (model G) n = 126. Number of latent variables in a model were auto-fitted in SIMCA-P+. All models were assessed for validity by Y variable permutation analysis (1,000 permutations, see additional file 1 Figure S4). Scores scatter plots for each multivariate model can also be found in additional file 1 (Figure S5). ln(U-Cd), natural logarithm of urinary cadmium; ln(U-NAG), natural logarithm of urinary-N-acetyl-β-D-glucosaminidase; LV, latent variable; n, sample number; PLS, partial least squares; PLS-DA, partial least squares - discriminant analysis. R²X is the proportion of variance in the X matrix (i.e. spectral NMR data) described by the PLS model. Q²Y is the ability of the PLS model to predict the Y-score (ln(U-Cd), sex, age, ln(U-NAG) or smoking status) of a novel sample or the "cross-validated goodness-of-fit".

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