TY - JOUR
T1 - Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking
AU - Georgiadis, Panagiotis
AU - Hebels, Dennie G.
AU - Valavanis, Ioannis
AU - Liampa, Irene
AU - Bergdahl, Ingvar A.
AU - Johansson, Anders
AU - Palli, Domenico
AU - Chadeau-Hyam, Marc
AU - Chatziioannou, Aristotelis
AU - Jennen, Danyel G. J.
AU - Krauskopf, Julian
AU - Jetten, Marlon J.
AU - Kleinjans, Jos C. S.
AU - Vineis, Paolo
AU - Kyrtopoulos, Soterios A.
PY - 2016/2/3
Y1 - 2016/2/3
N2 - The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.
AB - The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.
U2 - 10.1038/srep20544
DO - 10.1038/srep20544
M3 - Article
C2 - 26837704
SN - 2045-2322
VL - 6
JO - Scientific Reports
JF - Scientific Reports
M1 - 20544
ER -