Response of human metabolism to ultra-low and high nicotine cigarettes based on urine metabolomics and bioinformatic analysis
Year: 2024
Type: article
Abstract: INTRODUCTION: This study aimed to evaluate the metabolomic profiles of urine samples obtained from smokers who smoked cigarettes with low and high nicotine content. METHODS: Three smokers participated in this study. They were given low-nicotine (LN) cigarettes, and urine was collected at the end of the third day for the LN group. After 1 week of not smoking, they were given high-nicotine (HN) cigarettes, and urine was collected for the HN group. Untargeted metabolomics and bioinformatic analysis methods were used for urine analysis. RESULTS: PCA showed a high degree of similarity between samples within the group and a large distance between samples between groups, indicating a significant difference between the two groups. A total of 1150 significantly differential metabolites were selected between the HN and LN groups, such as cotinine and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol-N-glucuronide. Two-way hierarchical clustering analysis also suggested noticeable differences between the two comparison groups Enrichment analysis indicates that the differential metabolites between the two groups were mainly enriched in 19 pathways, such as the protein kinase G (cGMP)-protein kinase G (PKG) signaling pathway, adenosine monophosphate (AMP)-activated protein kinase signaling pathway, mammalian target of rapamycin signaling pathway, and Parkinson's disease. CONCLUSIONS: Cigarettes with different nicotine content may alter the metabolism of smokers. A total of 1150 significantly different metabolites were identified between the HN and LN groups, which were mainly enriched in ABC transporters, protein kinase G (cGMP)-protein kinase G (PKG) signaling pathway, caffeine metabolism, and arginine biosynthesis pathways.
Source: Tobacco Induced Diseases
DOI: 10.18332/tid/196677
Authors: Mengyue Zhang, Chunting Yang, Lingling Gao, Yuanyuan Zhao, Hongzhi Shi
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,372
OpenAlex ID: W4405541172
