Currently, there is no widely-accepted, non-self-report measure that simultaneously reflects smoking behaviors and is molecularly informative of general disease processes. Recently, researchers developed a smoking index (SI) using nucleated blood cells and a multi-tissue DNA methylation-based predictor of chronological age and disease (DNAm-age). To better understand the utility of this novel SI in readily accessible cell types, we used buccal cell DNA methylation to examine SI relationships with long-term tobacco smoking and moist snuff consumption.
We used a publicly available dataset comprised of buccal cell DNA methylation values from 120 middle-aged men (40 long-term smokers, 40 moist snuff consumers, and 40 non-smokers). DNAm-age (353-CpGs) and SI (66-CpGs) were calculated using CpG sites measured using the Illumina HumanMethylation450 BeadChip. We estimated associations of tobacco consumption habits with both SI and DNAm-age using linear regression models adjusted for chronological age, race, and methylation technical covariates.
In fully-adjusted models with non-smokers as the reference, smoking (β=1.08, 95%CI: 0.82, 1.33, P<0.0001) but not snuff consumption (β=0.06, 95%CI: -0.19, 0.32, P=0.63) was significantly associated with SI. SI was an excellent predictor of smoking versus non-smoking (AUC=0.92, 95%CI: 0.85, 0.98). Four DNAm-age CpGs were differentially methylated between smokers and non-smokers including cg14992253[EIF3I], which has been previously shown to be differentially methylated with exposure to long-term fine particle air pollution (PM2.5).
The 66-CpG SI appears to be a useful tool for measuring smoking-specific behaviors in buccal cells. Still, further research is needed to broadly confirm our findings and SI relationships with DNAm-age.
Our findings demonstrate that this 66-CpG blood-derived SI can reflect long-term tobacco smoking, but not long-term snuff consumption, in buccal cells. This evidence will be useful as the field works to identify an accurate non-self-report smoking biomarker that can be measured in an easily accessible tissue. Future research efforts should focus on: (1) optimizing the relationship of the SI with DNAm-age so that the metric can maximize its utility as a tool for understanding general disease processes, and (2) determining normal values for the SI CpGs so that the measure is not as study sample specific.