Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

Panagiotis Georgiadis, Dennie G Hebels, Ioannis Valavanis, Irene Liampa, Ingvar A Bergdahl, Anders Johansson, Domenico Palli, Marc Chadeau-Hyam, Aristotelis Chatziioannou, Danyel G J Jennen, Julian Krauskopf, Marlon J Jetten, Jos C S Kleinjans, Paolo Vineis, Soterios A Kyrtopoulos, EnviroGenomarkers consortium

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    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.

    Original languageEnglish
    Article number20544
    Pages (from-to)1-15
    Number of pages15
    JournalScientific Reports
    Volume6
    DOIs
    Publication statusPublished - 3 Feb 2016

    Keywords

    • Molecular biology
    • Predictive markers
    • Risk factors

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