  Nowadays, the screening methods for the early detection of lung cancer
struggle with several limitations such as many false positive results and low
sensitivity. The detection of specific biomarkers is of high interest to
complement these conventional screening methods. The objective of this study is
to prove the power of 1H-NMR in metabolomics for the detection of smoking
behavior, which is the leading risk factor for lung cancer, and as such gain
more insights in the metabolic alterations that are caused by smoking. In this
research, 1H-NMR spectra of human blood plasma samples were divided in 110
integration regions, from which the integration values were used to train an
OPLS-DA classification model that underwent further data reduction. Results
show that a classification model could discriminate between individuals based
on their smoking status with a sensitivity of 96 percent and a specificity of
94 percent. This study also demonstrates that by performing a pathway-specific
variable reduction of almost 50 percent, the sensitivity and specificity of the
model almost remains the same. To conclude, 1H-NMR analyses show that the
bloods metabolic profile of a smoker is altered compared to that of a
non-smoker. Also, pathway-specific variable reduction shows great potential to
perform overall data reduction. This workflow could be interesting to apply in
the identification of lung cancer, to potentially detect specific biomarkers.
