BACKGROUND: Most biomarkers of exposure tend to have short half-lives. This includes cotinine, a metabolite of nicotine widely used to assess smoke exposure. Cotinine is thus unsuitable as a determinant of past exposure to cigarette smoke. METHODS: We used bisulphite pyrosequencing of a set of four genomic loci (AHRR, 6p21, and two at 2q37) that had differential DNA methylation levels in peripheral blood DNA dependent on tobacco exposure to create a predictive model of smoking status. RESULTS: Combining four gene loci into a single methylation index provided high positive predictive and sensitivity values for predicting former smoking status in both test (n = 81) and validation (n = 180) sample sets. CONCLUSIONS: This study provides a direct molecular measure of prior exposure to tobacco that can be performed using the quantitative approach of bisulphite pyrosequencing. Epigenetic changes that are detectable in blood may more generally act as molecular biomarkers for other exposures that are also difficult to quantify in epidemiological studies.
BACKGROUND: Most biomarkers of exposure tend to have short half-lives. This includes cotinine, a metabolite of nicotine widely used to assess smoke exposure. Cotinine is thus unsuitable as a determinant of past exposure to cigarette smoke. METHODS: We used bisulphite pyrosequencing of a set of four genomic loci (AHRR, 6p21, and two at 2q37) that had differential DNA methylation levels in peripheral blood DNA dependent on tobacco exposure to create a predictive model of smoking status. RESULTS: Combining four gene loci into a single methylation index provided high positive predictive and sensitivity values for predicting former smoking status in both test (n = 81) and validation (n = 180) sample sets. CONCLUSIONS: This study provides a direct molecular measure of prior exposure to tobacco that can be performed using the quantitative approach of bisulphite pyrosequencing. Epigenetic changes that are detectable in blood may more generally act as molecular biomarkers for other exposures that are also difficult to quantify in epidemiological studies.
Authors: Alexandra J White; Jia Chen; Lauren E McCullough; Xinran Xu; Yoon Hee Cho; Susan L Teitelbaum; Alfred I Neugut; Mary Beth Terry; Hanina Hibshoosh; Regina M Santella; Marilie D Gammon Journal: Cancer Causes Control Date: 2015-09-25 Impact factor: 2.506
Authors: Kristina M Jordahl; Amanda I Phipps; Timothy W Randolph; Hilary A Tindle; Simin Liu; Lesley F Tinker; Karl T Kelsey; Emily White; Parveen Bhatti Journal: Epigenetics Date: 2019-06-23 Impact factor: 4.528
Authors: Josep C Jiménez-Chillarón; Mark J Nijland; António A Ascensão; Vilma A Sardão; José Magalhães; Michael J Hitchler; Frederick E Domann; Paulo J Oliveira Journal: Epigenetics Date: 2015-03-16 Impact factor: 4.528
Authors: Jiaxuan Liu; Wei Zhao; Farah Ammous; Stephen T Turner; Thomas H Mosley; Xiang Zhou; Jennifer A Smith Journal: Epigenetics Date: 2019-03-14 Impact factor: 4.528
Authors: Linda Valeri; Sarah L Reese; Shanshan Zhao; Christian M Page; Wenche Nystad; Brent A Coull; Stephanie J London Journal: Epigenomics Date: 2017-02-21 Impact factor: 4.778
Authors: Kathleen A McGinnis; Amy C Justice; Janet P Tate; Henry R Kranzler; Hilary A Tindle; William C Becker; John Concato; Joel Gelernter; Boyang Li; Xinyu Zhang; Hongyu Zhao; Kristina Crothers; Ke Xu Journal: Addict Biol Date: 2018-10-04 Impact factor: 4.280
Authors: Christine Ladd-Acosta; Chang Shu; Brian K Lee; Nicole Gidaya; Alison Singer; Laura A Schieve; Diana E Schendel; Nicole Jones; Julie L Daniels; Gayle C Windham; Craig J Newschaffer; Lisa A Croen; Andrew P Feinberg; M Daniele Fallin Journal: Environ Res Date: 2015-11-21 Impact factor: 6.498