Evaluation of short-term epigenetic age fluctuation

Considerable effort has been spent on lowering and maintaining the epigenetic age. However, the extent to which epigenetic age fluctuates under normal conditions is poorly understood. Therefore, we analyzed methylation data from monocytes and peripheral blood mononuclear cells collected from two Japanese men. The ranges of the Pan-tissue, Skin and blood, and DNAm PhenoAge epigenetic age during 3 months were ≥ 5.62, ≥ 3.04, and ≥ 8.23 years, and the maximum daily changes were 5.21, 3.20, and 6.53 years, respectively. These fluctuations were not suppressed by correcting for cell-type composition. Although the underlying biological mechanism remains unclear, there was a nonnegligible degree of age fluctuation which should inform personalized clinical applications.

Longitudinal DNA methylation dynamics as a practical indicator in clinical epigenetics

BACKGROUND

One of the fundamental assumptions of DNA methylation in clinical epigenetics is that DNA methylation status can change over time with or without interplay with environmental and clinical conditions. However, little is known about how DNA methylation status changes over time under ordinary environmental and clinical conditions. In this study, we revisited the high frequency longitudinal DNA methylation data of two Japanese males (24 time-points within three months) and characterized the longitudinal dynamics.

RESULTS

The results showed that the majority of CpGs on Illumina HumanMethylation450 BeadChip probe set were longitudinally stable over the time period of three months. Focusing on dynamic and stable CpGs extracted from datasets, dynamic CpGs were more likely to be reported as epigenome-wide association study (EWAS) markers of various traits, especially those of immune- and inflammatory-related traits; meanwhile, the stable CpGs were enriched in metabolism-related genes and were less likely to be EWAS markers, indicating that the stable CpGs are stable both in the short-term within individuals and under various environmental and clinical conditions.

CONCLUSIONS

This study indicates that CpGs with different stabilities are involved in different functions and traits, and thus, they are potential indicators that can be applied for clinical epigenetic studies to outline underlying mechanisms.

Jude Lifetime Cohort

BACKGROUND

It is well-established that cancer treatment substantially increases the risk of long-term adverse health outcomes among childhood cancer survivors. However, there is limited research on the underlying mechanisms. To elucidate the pathophysiology and a possible causal pathway from treatment exposures to cardiometabolic conditions, we conducted epigenome-wide association studies (EWAS) to identify the DNA methylation (DNAm) sites associated with cancer treatment exposures and examined whether treatment-associated DNAm sites mediate associations between specific treatments and cardiometabolic conditions.

METHODS

We included 2052 survivors (median age 33.7 years) of European ancestry from the St. Jude Lifetime Cohort Study, a retrospective hospital-based study with prospective clinical follow-up. Cumulative doses of chemotherapy and region-specific radiation were abstracted from medical records. Seven cardiometabolic conditions were clinically assessed. DNAm profile was measured using MethylationEPIC BeadChip with blood-derived DNA.

RESULTS

By performing multiple treatment-specific EWAS, we identified 935 5'-cytosine-phosphate-guanine-3' (CpG) sites mapped to 538 genes/regions associated with one or more cancer treatments at the epigenome-wide significance level (p < 9 × 10-8). Among the treatment-associated CpGs, 8 were associated with obesity, 63 with hypercholesterolemia, and 17 with hypertriglyceridemia (false discovery rate-adjusted p < 0.05). We observed substantial mediation by methylation at four independent CpGs (cg06963130, cg21922478, cg22976567, cg07403981) for the association between abdominal field radiotherapy (abdominal-RT) and risk of hypercholesterolemia (70.3%) and by methylation at three CpGs (cg19634849, cg13552692, cg09853238) for the association between abdominal-RT and hypertriglyceridemia (54.6%). In addition, three CpGs (cg26572901, cg12715065, cg21163477) partially mediated the association between brain-RT and obesity with a 32.9% mediation effect, and two CpGs mediated the association between corticosteroids and obesity (cg22351187, 14.2%) and between brain-RT and hypertriglyceridemia (cg13360224, 10.5%). Notably, several mediator CpGs reside in the proximity of well-established dyslipidemia genes: cg21922478 (ITGA1) and cg22976567 (LMNA).

CONCLUSIONS

In childhood cancer survivors, cancer treatment exposures are associated with DNAm patterns present decades following the exposure. Treatment-associated DNAm sites may mediate the causal pathway from specific treatment exposures to certain cardiometabolic conditions, suggesting the utility of DNAm sites as risk predictors and potential mechanistic targets for future intervention studies.

Blood DNA methylation and breast cancer risk: a meta-analysis of four prospective cohort studies

BACKGROUND

Environmental and genetic factors play an important role in the etiology of breast cancer. Several small blood-based DNA methylation studies have reported risk associations with methylation at individual CpGs and average methylation levels; however, these findings require validation in larger prospective cohort studies. To investigate the role of blood DNA methylation on breast cancer risk, we conducted a meta-analysis of four prospective cohort studies, including a total of 1663 incident cases and 1885 controls, the largest study of blood DNA methylation and breast cancer risk to date.

METHODS

We assessed associations with methylation at 365,145 CpGs present in the HumanMethylation450 (HM450K) Beadchip, after excluding CpGs that did not pass quality controls in all studies. Each of the four cohorts estimated odds ratios (ORs) and 95% confidence intervals (CI) for the association between each individual CpG and breast cancer risk. In addition, each study assessed the association between average methylation measures and breast cancer risk, adjusted and unadjusted for cell-type composition. Study-specific ORs were combined using fixed-effect meta-analysis with inverse variance weights. Stratified analyses were conducted by age at diagnosis (< 50, ≥ 50), estrogen receptor (ER) status (+/-), and time since blood collection (< 5, 5-10, > 10 years). The false discovery rate (q value) was used to account for multiple testing.

RESULTS

The average age at blood draw ranged from 52.2 to 62.2 years across the four cohorts. Median follow-up time ranged from 6.6 to 8.4 years. The methylation measured at individual CpGs was not associated with breast cancer risk (q value > 0.59). In addition, higher average methylation level was not associated with risk of breast cancer (OR = 0.94, 95% CI = 0.85, 1.05; P = 0.26; P for study heterogeneity = 0.86). We found no evidence of modification of this association by age at diagnosis (P = 0.17), ER status (P = 0.88), time since blood collection (P = 0.98), or CpG location (P = 0.98).

CONCLUSIONS

Our data indicate that DNA methylation measured in the blood prior to breast cancer diagnosis in predominantly postmenopausal women is unlikely to be associated with substantial breast cancer risk on the HM450K array. Larger studies or with greater methylation coverage are needed to determine if associations exist between blood DNA methylation and breast cancer risk.