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. 2018 Sep 5;17(10):3492–3502. doi: 10.1021/acs.jproteome.8b00413

Table 1. Correlation (Pearson’s r) between Metabolites Detected and Quantified in 132 Urine Samples by UPLC–HRMS and DI–nESI–HRMS and Passing–Bablok regression parameters.

    Passing–Bablok regression
metabolite Pearson correlation r slope intercept
caffeic acid 0.58 31.8 0.2
carnitine 0.96 1.2 16.6
cholate 0.71 19.8 –0.4
citrate 0.9 1.04 49.6
cotinineb 0.97 1.6 0.2
creatine 0.98 1.2 73.2
creatinine 0.95 1.3 –31.2
glutamate 0.68 0.3 18.0
glycocholate 0.7 24.6 –0.8
hippurate 0.97 0.8 –23.1
homovanillatea 0.56 22.4 –17.4
3-hydroxycinnamatea 0.39 56.3 0.1
indoxyl sulfate 0.98 0.6 –0.5
isovalerylglycine 0.78 3.2 0.7
kynureninea 0.49 59.9 7.4
leucine 0.69 18.8 0.6
N-acetylneuraminate 0.88 1.2 1.1
nicotineb 0.35 1.4 1.9
phenylacetylglutamine 0.95 0.9 –12.3
phenylacetatea 0.55 21.6 –15.0
proline betaine 0.99 1.05 3.3
propionylcarnitine 0.89 1.03 0.2
saccharin 0.98 1.06 –0.01
succinate 0.6 6.03 4.5
tyraminea 0.34 42.03 5.1
vanillilmandelate 0.58 1.97 5.0
a

Metabolites showing weak or moderately weak (r < 0.6) correlation between the two MS methods.

b

Passing–Bablok regression was done between the samples from smokers only.

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