Nightshift work, chronotype, and genome-wide DNA methylation in blood. Issue 10 (3rd October 2017)

• Record Type: Journal Article
• Authors: Adams, Charleen D.; Jordahl, Kristina M.; Copeland, Wade; Mirick, Dana K.; Song, Xiaoling; Sather, Cassandra L.; Kelsey, Karl; Houseman, Andres; Davis, Scott; Randolph, Timothy; Bhatti, Parveen
• Is Part Of: Epigenetics. Volume 12:Issue 10(2017)
• Page Start: 833
• Page End: 840
• Publication Date: 2017-10-03
• Subjects: Shift work -- chronotype -- DNA methylation; Epigenesis -- Periodicals; Epigenetica; 572.86505
• DOI: 10.1080/15592294.2017.1366407
• Languages: English
• ISSNs: 1559-2294

Abstract

ABSTRACT: Molecular mechanisms underlying the negative health effects of shift work are poorly understood, which remains a barrier to developing intervention strategies to protect the long-term health of shift workers. We evaluated genome-wide differences in DNA methylation (measured in blood) between 111 actively employed female nightshift and 86 actively employed female dayshift workers from the Seattle metropolitan area. We also explored the effect of chronotype (i.e., measure of preference for activity earlier or later in the day) on DNA methylation among 110 of the female nightshift workers and an additional group of 131 male nightshift workers. Methylation data were generated using the Illumina Infinium HumanMethylation450 BeadChip (450K) Array. After applying the latest methylation data processing methods, we compared methylation levels at 361, 210 CpG loci between the groups using linear regression models adjusted for potential confounders and applied the false-discovery rate (FDR) ≤ 0.05 to account for multiple comparisons. No statistically significant associations at the genome-wide level were observed with shift work or chronotype, though based on raw P values and absolute effect sizes, there were suggestive associations in genes that have been previously linked with cancer (e.g., BACH2, JRK, RPS6KA2 ) and type-2 diabetes (e.g., KCNQ1 ). Given that our study was underpowered to detect moderate effects, examining these suggestive results in well-powered independent studies or in pooled data sets may improve our understanding of the pathways underlying the negative health effects of shift work and the influence of personal factors such as chronotype. Such an approach may help identify potential interventions that can be used to protect the long-term health of shift workers.