TY - JOUR
T1 - Relative contributions of six lifestyle- and health-related exposures to epigenetic aging
T2 - the Coronary Artery Risk Development in Young Adults (CARDIA) Study
AU - Kim, Kyeezu
AU - Zheng, Yinan
AU - Joyce, Brian T.
AU - Jiang, Hongmei
AU - Greenland, Philip
AU - Jacobs, David R.
AU - Zhang, Kai
AU - Liu, Lei
AU - Allen, Norrina B.
AU - Wilkins, John T.
AU - Forrester, Sarah N.
AU - Lloyd-Jones, Donald M.
AU - Hou, Lifang
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Background: DNA methylation-based GrimAge acceleration (GrimAA) is associated with a wide range of age-related health outcomes including cardiovascular disease. Since DNA methylation is modifiable by external and behavioral exposures, it is important to identify which of these exposures may have the strongest contributions to differences in GrimAA, to help guide potential intervention strategies. Here, we assessed the relative contributions of lifestyle- and health-related components, as well as their collective association, to GrimAA. Results: We included 744 participants (391 men and 353 women) from the Coronary Artery Risk Development in Young Adults (CARDIA) study with blood DNA methylation information at CARDIA Exam Year (Y) 20 (2005–2006, mean age 45.9 years). Six cumulative exposures by Y20 were included in the analysis: total packs of cigarettes, total alcohol consumption, education years, healthy diet score, sleep hours, and physical activity. We used quantile-based g-computation (QGC) and Bayesian kernel machine regression (BKMR) methods to assess the relative contribution of each exposure to a single overall association with GrimAA. We also assessed the collective association of the six components combined with GrimAA. Smoking showed the greatest positive contribution to GrimAA, accounting for 83.5% of overall positive associations of the six exposures with GrimAA (QGC weight = 0.835). The posterior inclusion probability (PIP) of smoking also achieved the highest score of 1.0 from BKMR analysis. Healthy diet and education years showed inverse contributions to GrimAA. We observed a U-shaped pattern in the contribution of alcohol consumption to GrimAA. While smoking was the greatest contributor across sex and race subgroups, the relative contributions of other components varied by subgroups. Conclusions: Smoking, alcohol consumption, and education showed the highest contributions to GrimAA in our study. Higher amounts of smoking and alcohol consumption were likely to contribute to greater GrimAA, whereas achieved education was likely to contribute to lower GrimAA. Identifying pertinent lifestyle- and health-related exposures in a context of collective components can provide direction for intervention strategies and suggests which components should be the primary focus for promoting younger GrimAA.
AB - Background: DNA methylation-based GrimAge acceleration (GrimAA) is associated with a wide range of age-related health outcomes including cardiovascular disease. Since DNA methylation is modifiable by external and behavioral exposures, it is important to identify which of these exposures may have the strongest contributions to differences in GrimAA, to help guide potential intervention strategies. Here, we assessed the relative contributions of lifestyle- and health-related components, as well as their collective association, to GrimAA. Results: We included 744 participants (391 men and 353 women) from the Coronary Artery Risk Development in Young Adults (CARDIA) study with blood DNA methylation information at CARDIA Exam Year (Y) 20 (2005–2006, mean age 45.9 years). Six cumulative exposures by Y20 were included in the analysis: total packs of cigarettes, total alcohol consumption, education years, healthy diet score, sleep hours, and physical activity. We used quantile-based g-computation (QGC) and Bayesian kernel machine regression (BKMR) methods to assess the relative contribution of each exposure to a single overall association with GrimAA. We also assessed the collective association of the six components combined with GrimAA. Smoking showed the greatest positive contribution to GrimAA, accounting for 83.5% of overall positive associations of the six exposures with GrimAA (QGC weight = 0.835). The posterior inclusion probability (PIP) of smoking also achieved the highest score of 1.0 from BKMR analysis. Healthy diet and education years showed inverse contributions to GrimAA. We observed a U-shaped pattern in the contribution of alcohol consumption to GrimAA. While smoking was the greatest contributor across sex and race subgroups, the relative contributions of other components varied by subgroups. Conclusions: Smoking, alcohol consumption, and education showed the highest contributions to GrimAA in our study. Higher amounts of smoking and alcohol consumption were likely to contribute to greater GrimAA, whereas achieved education was likely to contribute to lower GrimAA. Identifying pertinent lifestyle- and health-related exposures in a context of collective components can provide direction for intervention strategies and suggests which components should be the primary focus for promoting younger GrimAA.
KW - Accelerated epigenetic age
KW - DNA methylation
KW - Epigenetic aging
KW - Lifestyle- and health-related components
UR - http://www.scopus.com/inward/record.url?scp=85133589128&partnerID=8YFLogxK
U2 - 10.1186/s13148-022-01304-9
DO - 10.1186/s13148-022-01304-9
M3 - Article
C2 - 35799271
AN - SCOPUS:85133589128
SN - 1868-7075
VL - 14
JO - Clinical Epigenetics
JF - Clinical Epigenetics
IS - 1
M1 - 85
ER -