Metabolic alterations caused by smoking: the use of 1H-NMR in blood plasma analysis to unravel underlying mechanisms of lung cancers leading risk factor

TL;DR

This study demonstrates that 1H-NMR metabolomics can effectively distinguish smokers from non-smokers with high accuracy, revealing metabolic alterations caused by smoking, which may aid in lung cancer detection.

Contribution

The paper introduces a pathway-specific variable reduction method that maintains classification accuracy while significantly reducing data complexity in blood plasma metabolomics.

Findings

Classification model achieved 96% sensitivity and 94% specificity.

Metabolic profile of smokers differs significantly from non-smokers.

Variable reduction preserves model performance, enabling efficient biomarker discovery.

Abstract

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.