Table 2.

An overview of recent studies with a focus on development of DNA methylation‐based smoking scores or predictors

Purpose	Methodology	Reference
Smoking score based on 187 smoking‐associated cytosine–guanine dinucleotides (CpGs) identified in whole blood, can distinguish heavy smokers from nonsmokers (former and never)	A weighted DNA methylation score was calculated using methylation values of 187 CpGs identified by an earlier epigenome‐wide association study (EWAS) [130] as reference values.	[49]
A DNA methylation score based on two top smoking‐associated CpGs shown to be predictive of all‐cause, cardiovascular, and cancer mortality	Restricted cubic spline regression	[149]
Methylation score based on methylation values of four smoking‐associated CpGs in whole blood; can discriminate current smokers from never smokers, as well as former smokers from never smokers	EWAS followed by stepwise logistic regression with forward selection	[50]
A smoking status estimator (EpiSmokEr) that can predict the smoking status of individuals from whole‐blood methylation data	Least Absolute Shrinkage and Selection Operator (LASSO) regression	[62]
A DNA methylation smoking score that can classify newborns based on the maternal smoking exposure during pregnancy	EWAS followed by LASSO regression	[160]
A prenatal DNA methylation smoking score to predict prenatal exposure to maternal smoking	A weighted DNA methylation score calculated using the methylation values of CpGs identified by an earlier genome‐wide consortium meta‐analysis [176]	[159]
A machine‐learning based DNA methylation score that distinguishes individuals exposed to in utero smoke from individuals not exposed to in utero smoke	Elastic net regression	[161]