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By linking prospective cohort studies with epigenetics and CVD outcomes, it may be possible to gain insight into the complex mechanisms underlying CVD. This study aims to evaluate the impact of air pollution on CVD and investigate whether DNA methylation (DNAm) mediates the association between air pollution and CVD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In the prospective cohort study, the relationship between air pollutants and CVD incidence was analyzed using Cox regression. Dose\u2013response was assessed by the restricted cubic spline model, and multiple pollutants\u2019 impact was evaluated by the weighted quantile sum model. The link between genetically predicted DNAm sites related to air pollutants and CVD risk was explored through epigenetic Mendelian randomization (MR), with further evidence provided by gene colocalization analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      For every 10\u00a0\u03bcg\/m\n                      <jats:sup>3<\/jats:sup>\n                      increase, particulate matter with diameters less than 2.5\u00a0\u03bcm (PM\n                      <jats:sub>2.5<\/jats:sub>\n                      ), particulate matter with diameter less than 10\u00a0\u03bcm (PM\n                      <jats:sub>10<\/jats:sub>\n                      ), nitrogen dioxide (NO\n                      <jats:sub>2<\/jats:sub>\n                      ), and sulfur dioxide (SO\n                      <jats:sub>2<\/jats:sub>\n                      ) increased the risk of CVD by 6.2%, 4.4%, 9.3%, and 6.1%, respectively, with all showing a linear association. Of the four air pollutants, PM\n                      <jats:sub>10<\/jats:sub>\n                      and PM\n                      <jats:sub>2.5<\/jats:sub>\n                      were identified as the most significant contributors to the CVD risk, accounting for 61% and 20%, respectively. Genetically predicted methylation at the PM\n                      <jats:sub>2.5<\/jats:sub>\n                      -related CpG site cg01065697 was linked to a higher risk of myocardial infarction (MI) and coronary heart disease (CHD), the NO\n                      <jats:sub>2<\/jats:sub>\n                      -related CpG site cg07091220 was associated with increased MI risk, the NO\n                      <jats:sub>2<\/jats:sub>\n                      -related sites cg15474579, cg16348358, and cg19869422 were linked to a higher risk of heart failure (HF).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study confirms a significant association between air pollution, DNAm and CVD risk, and provides new insights into the pathogenic effects of air pollution on CVD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13148-025-02016-6","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T12:34:28Z","timestamp":1766147668000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dissecting the pathogenic effects of ambient air pollution exposure and its blood DNA methylation markers on cardiovascular disease risk"],"prefix":"10.1186","volume":"17","author":[{"given":"Weipeng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiya","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaonan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunli","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"issue":"25","key":"2016_CR1","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","volume":"76","author":"GA Roth","year":"2020","unstructured":"Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. 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