The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies
Open accessAbstract
Active smoking is a major preventable public health problem and an established critical factor for epigenetic modification. In this systematic review, we identified 17 studies addressing the association of active smoking exposure with methylation modifications in blood DNA, including 14 recent epigenome-wide association studies (EWASs) and 3 gene-specific methylation studies (GSMSs) on the gene regions identified by EWASs. Overall, 1460 smoking-associated CpG sites were identified in the EWASs, of which 62 sites were detected in multiple (≥3) studies. The three most frequently reported CpG sites (genes) in whole blood samples were cg05575921 (AHRR), cg03636183 (F2RL3), and cg19859270 (GPR15), followed by other loci within intergenic regions 2q37.1 and 6p21.33. These significant smoking-related genes were further assessed by specific methylation assays in three GSMSs and reflected not only current but also lifetime or long-term exposure to active smoking. In conclusion, this review summarizes the evidences for the use of blood DNA methylation patterns as biomarkers of smoking exposure for research and clinical practice. In particular, it provides a reservoir for constructing a smoking exposure index score which could be used to more precisely quantify long-term smoking exposure and evaluate the risks of smoking-induced diseases.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures


![Fig. 3 Effect sizes of smoking-induced methylation for current smokers and former smokers compared with non-smokers. Chr chromosome position, used the publically available data of Zeilinger et al. (median beta values) [6]](/paper-figures/bf2e3891d5f2412e862aebc331703c5c/dna-methylation-changes-of-whole-blood-cells-in-response-to-active-smoking-figure-3.jpg)
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“While smoking status has been reported to affect global DNA methylation levels (Gao et al., 2015)”
From Epigenome-wide DNA methylation in unipolar depression: predictive biomarker of antidepressant treatment response? (2024) · cites this paper as Gao et al., 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Zhang, Q., Wang, C., Zheng, M., Li, Y., Li, J., Zhang, L., Shang, X., & Yan, C. (2014) · Cerebrovascular Diseases · doi:10.1159/000369778 · Cited by 11,609
Projections of Global Mortality and Burden of Disease from 2002 to 2030
Mathers, C. D., & Loncar, D. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030442 · Cited by 11,386
DNA methylation arrays as surrogate measures of cell mixture distribution
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., & Kelsey, K. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-86 · Cited by 3,175
Lohmueller, K. E., Pearce, C. L., Pike, M., Lander, E. S., & Hirschhorn, J. N. (2003) · Nature Genetics · doi:10.1038/ng1071 · Cited by 1,900
Panic, N., Leoncini, E., de Belvis, G., Ricciardi, W., & Boccia, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083138 · Cited by 883
Tobacco smoking leads to extensive genome-wide changes in DNA methylation
Zeilinger, S., Kühnel, B., Klopp, N., Baurecht, H., Kleinschmidt, A., Gieger, C., Weidinger, S., Lattka, E., Adamski, J., Peters, A., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063812 · Cited by 824
Tobacco-smoking-related differential DNA methylation: 27K discovery and replication
Breitling, L. P., Yang, R., Korn, B., Burwinkel, B., & Brenner, H. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.03.003 · Cited by 640
Shenker, N. S., Polidoro, S., van Veldhoven, K., Sacerdote, C., Ricceri, F., Birrell, M. A., Belvisi, M. G., Brown, R., Vineis, P., & Flanagan, J. M. (2013) · Human Molecular Genetics · doi:10.1093/hmg/dds488 · Cited by 396
Cigarette smoking and DNA methylation
Lee, K. W. K., & Pausová, Z. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00132 · Cited by 384
Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation
Guida, F., Sandanger, T. M., Castagné, R., Campanella, G., Polidoro, S., Palli, D., Krogh, V., Tumino, R., Sacerdote, C., Panico, S., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddu751 · Cited by 331
Research works printed but not linked: 1
- Zhang Y, Schottker B, Ordonez-Mena J, Holleczek B, Yang R, Burwinkel B et al. F2RL3 methylation, lung cancer incidence and mortality. Int J Cancer. 2015. doi: 10.1002/ijc.29537 . · DOI
Cited by
Sorted by citations
Items 401–417 of 417
Additional file 4 of Refining epigenetic prediction of chronological and biological age
Bernabeu, E., McCartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., Wrobel, N., Campbell, A., Harris, S. E., Liewald, D. C. M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571949
Föhr, T., Waller, K. L., Anne, V., Sanchez, R., Ollikainen, M., Rantanen, T., Jaakko, K., & Sillanpää, E. (2021) · Figshare · doi:10.6084/m9.figshare.14776663
Tan, Q., Møller, A. M. J., Qiu, C., Madsen, J. S., Shen, H., Bechmann, T., Delaisse, J.-M., Kristensen, B. W., HongWen, D., Karasik, D., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22619372
Langdon, R., Beynon, R. A., Ingarfield, K., Marioni, R. E., McCartney, D. L., Martin, R. M., Ness, A., Pawlita, M., Waterboer, T., Relton, C. L., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12177894
Grieshober, L., Graw, S., Barnett, M. J., Thornquist, M. D., Goodman, G. E., Chen, C., Koestler, D. C., Marsit, C. J., & Doherty, J. A. (2020) · Figshare · doi:10.6084/m9.figshare.12991724
Fitzgerald, S. J., Bhat, B., Print, C., & Jones, G. T. (2024) · Figshare · doi:10.6084/m9.figshare.25478836
Zhu, L., Zhu, C., Wang, J., Yang, R., & Zhao, X. (2022) · Figshare · doi:10.6084/m9.figshare.20486687
Additional file 1 of DNA methylation biomarkers of myocardial infarction and cardiovascular disease
Fernández‐Sanlés, A., Sayols-Baixeras, S., Subirana, I., Sentı́, M., Pérez-Fernández, S., Moura, M. D. C., Esteller, M., Marrugat, J., & Elosúa, R. (2021) · Figshare · doi:10.6084/m9.figshare.14462547
Epigenetic Signatures in an Italian Cohort of Parkinson’s Disease Patients from Sicily
Salluzzo, M. G., Ferraresi, F., Marcolungo, L., Pirazzini, C., Kwiatkowska, K. M., Dall’Olio, D., Castellani, G., Sala, C., Zago, E. D., Gentilini, D., et al. (2025) · Brain Sciences · doi:10.3390/brainsci16010031
General Introduction to Obesity Genetics and Genomics
Jansen, P. R., Uhm, J., & van Haelst, M. M. (2025) · Springer eBooks · doi:10.1007/978-3-032-04095-4_1
Stevenson‐Hoare, J. O., Stoffel, M., Schöttker, B., Holleczek, B., Zaugg, J. B., Ditzen, B., & Brenner, H. (2026) · The Journals of Gerontology Series A · doi:10.1093/gerona/glag036
Multi-omic molecular signatures from peripheral blood samples of patients with mood disorders
Mokhtari, A. (2022) · theses.fr (ABES) · doi:10.70675/6e6b049ez0ae2z40f2za015z47c5c58102d3
Shang, K.-L., Luo, C., Tan, X., Xu, M.-L., Li, B., Wang, H.-H., & Cheng, F.-H. (2026) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-026-02237-7
Tsutsumi, T., Ogata, A., Nishizawa, E., Furukawa, A., Yonehara, Y., Aboshi, H., & Kondo, M. (2026) · Journal of Hard Tissue Biology · doi:10.2485/jhtb.35.81
Zhong, X., Wang, Z., Huang, S., Tang, B., Wu, H., You, Q., Liu, Y., Xu, W., Li, Q., Zhang, C. J., et al. (2026) · Current Atherosclerosis Reports · doi:10.1007/s11883-026-01420-4 · Cited by 1
Li, W., Li, Y., Dong, P., Tang, J.-X., Liu, S., Tu, L., & Xu, X.-Z. (2026) · Tobacco Induced Diseases · doi:10.18332/tid/216384
Bello‐Chavolla, O. Y., & Fermín‐Martínez, C. A. (2026) · Advances in biogerontology · doi:10.1016/bs.abger.2026.08.002
Metadata
- Authors
- Gao, X., Jia, M., Zhang, Y., Breitling, L. P., & Brenner, H.
- Affiliations
- German Cancer Research Center (DE)
- Corresponding author
- Brenner, Hermann
- Venue
- Clinical Epigenetics
- Published
- 2015-10-16
- Volume
- 7
- Issue
- 1
- Pages
- 113-113
- ISSN
- 1868-7075 (ISSN-L)
- 1868-7083
- PMC
- PMC4609112
- OpenAlex
- W1918724965
- Cited by
- 386
- References
- 41
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 31 · 2023: 21 · 2022: 36 · 2021: 32 · 2020: 53 · 2019: 51 · 2018: 50 · 2017: 55 · 2016: 29 · 2015: 1
- FWCI
- 4.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Clinical Epigenetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- China Scholarship Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Smoking Behavior and Cessation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- active smoking, DNA methylation, whole blood, epigenome-wide association study, gene-specific methylation, AHRR, F2RL3, GPR15, CpG sites
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review