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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.
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![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)
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“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
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Chen, Z., Wanqing, W., Cai, Q., Long, J., Wang, Y., Lin, W., Shu, X.-O., Zheng, W., & Guo, X. (2020) · Figshare · doi:10.6084/m9.figshare.12955303
Chen, Z., Wanqing, W., Cai, Q., Long, J., Wang, Y., Lin, W., Shu, X.-O., Zheng, W., & Guo, X. (2020) · Figshare · doi:10.6084/m9.figshare.12955306
Chen, Z., Wanqing, W., Cai, Q., Long, J., Wang, Y., Lin, W., Shu, X.-O., Zheng, W., & Guo, X. (2020) · Figshare · doi:10.6084/m9.figshare.12955309
Song, M.-A., Mori, K. M., McElroy, J. P., Freudenheim, J. L., Weng, D. Y., Reisinger, S. A., Brasky, T. M., Wewers, M. D., & Shields, P. G. (2024) · Figshare · doi:10.6084/m9.figshare.24295767
Zhu, L., Zhu, C., Wang, J., Yang, R., & Zhao, X. (2022) · Figshare · doi:10.6084/m9.figshare.20486684
Bruzzone, S. E. P., Ozenne, B., Fisher, P. M., Ortega, G., Jensen, P. S., Dam, V. H., Svarer, C., Knudsen, G. M., Lesch, K., & Frokjaer, V. G. (2024) · Figshare · doi:10.6084/m9.figshare.26726348
Bruzzone, S. E. P., Ozenne, B., Fisher, P. M., Ortega, G., Jensen, P. S., Dam, V. H., Svarer, C., Knudsen, G. M., Lesch, K., & Frokjaer, V. G. (2024) · Figshare · doi:10.6084/m9.figshare.26726345
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.22619369
Bruzzone, S. E. P., Ozenne, B., Fisher, P. M., Ortega, G., Jensen, P. S., Dam, V. H., Svarer, C., Knudsen, G. M., Lesch, K., & Frokjaer, V. G. (2024) · Figshare · doi:10.6084/m9.figshare.26726339
Bruzzone, S. E. P., Ozenne, B., Fisher, P. M., Ortega, G., Jensen, P. S., Dam, V. H., Svarer, C., Knudsen, G. M., Lesch, K., & Frokjaer, V. G. (2024) · Figshare · doi:10.6084/m9.figshare.26726336
Chen, Z., Wanqing, W., Cai, Q., Long, J., Wang, Y., Lin, W., Shu, X.-O., Zheng, W., & Guo, X. (2020) · Figshare · doi:10.6084/m9.figshare.12955312
Bruzzone, S. E. P., Ozenne, B., Fisher, P. M., Ortega, G., Jensen, P. S., Dam, V. H., Svarer, C., Knudsen, G. M., Lesch, K., & Frokjaer, V. G. (2024) · Figshare · doi:10.6084/m9.figshare.26726360
Huang, J., Bai, L., Cui, B., Wu, L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097362
Additional file 1 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.26571940
Additional file 2 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.26571943
Additional file 3 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.26571946
Hu, W., Chen, S., Cai, J., Yang, Y., Yan, H., & Chen, F. (2024) · Figshare · doi:10.6084/m9.figshare.26728942
Song, M.-A., Mori, K. M., McElroy, J. P., Freudenheim, J. L., Weng, D. Y., Reisinger, S. A., Brasky, T. M., Wewers, M. D., & Shields, P. G. (2023) · Figshare · doi:10.6084/m9.figshare.24295676
Scherer, M., Gasparoni, G., Rahmouni, S., Shashkova, T., Arnoux, M., Louis, E., Nostaeva, A., Avalos, D., Dermitzakis, E. T., Aulchenko, Y. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16632876
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.12177885
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.
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