Associations of annual ambient PM2.5 components with DNAm PhenoAge acceleration in elderly men: The Normative Aging Study

• Published in: Environmental pollution (1987) Vol. 258; p. 113690
• Main Authors: Wang, Cuicui, Koutrakis, Petros, Gao, Xu, Baccarelli, Andrea, Schwartz, Joel
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2020
• Subjects: air quality; Bayesian theory; biomarkers; blood sampling; body mass index; calcium; chronic exposure; cigarettes; Components; confidence interval; DNAm PhenoAge; elderly; epigenetics; Fine particulate matter; lead; long term effects; Long-term exposure; men; monitoring; mortality; particulates; phenotype; silicon; smoking (habit); Components; DNAm PhenoAge; Long-term exposure; Fine particulate matter
• ISSN: 0269-7491; 1873-6424; 1873-6424
• DOI: 10.1016/j.envpol.2019.113690

Current studies indicate that long-term exposure to ambient fine particulate matter (PM2.5) is related with global mortality, yet no studies have explored relationships of PM2.5 and its species with DNAm PhenoAge acceleration (DNAmPhenoAccel), a new epigenetic biomarker of phenotypic age. We identified which PM2.5 species had association with DNAmPhenoAccel in a one-year exposure window in a longitudinal cohort. We collected whole blood samples from 683 elderly men in the Normative Aging Study between 1999 and 2013 (n = 1254 visits). DNAm PhenoAge was calculated using 513 CpGs retrieved from the Illumina Infinium HumanMethylation450 BeadChip. Daily concentrations of PM2.5 species were measured at a fixed air-quality monitoring site and one-year moving averages were computed. Linear mixed-effect (LME) regression and Bayesian kernel machine (BKM) regression were used to estimate the associations. The covariates included chronological age, body mass index (BMI), cigarette pack years, smoking status, estimated cell types, batch effects etc. Benjamini-Hochberg false discovery rate at a 5% false positive threshold was used to adjust for multiple comparison. During the study period, the mean DNAm PhenoAge and chronological age in our subjects were 68 and 73 years old, respectively. Using LME model, only lead and calcium were significantly associated with DNAmPhenoAccel. For example, an interquartile range (IQR, 0.0011 μg/m3) increase in lead was associated with a 1.29-year [95% confidence interval (CI): 0.47, 2.11] increase in DNAmPhenoAccel. Using BKM model, we selected PM2.5, lead, and silicon to be predictors for DNAmPhenoAccel. A subsequent LME model showed that only lead had significant effect on DNAmPhenoAccel: 1.45-year (95% CI: 0.46, 2.46) increase in DNAmPhenoAccel following an IQR increase in one-year lead. This is the first study that investigates long-term effects of PM2.5 components on DNAmPhenoAccel. The results demonstrate that lead and calcium contained in PM2.5 was robustly associated with DNAmPhenoAccel. [Display omitted] •The first study investigates long-term effects of PM2.5 species on DNAm PhenoAge acceleration.•The mean DNAm PhenoAge was 5-year less than the chronological age.•An IQR increment in lead was associated with a 1.3-year increase in DNAm PhenoAge acceleration.