Age is one of the strongest predictors of cancer, chronic disease, and mortality, but biological responses to aging differ among people. Epigenetic DNA modifications have been used to estimate “biological age,” which may be a useful predictor of disease risk. We tested this hypothesis for breast cancer. Using a case-cohort approach, we measured baseline blood DNA methylation of 2764 women enrolled in the Sister Study, 1566 of whom subsequently developed breast cancer after an average of 6 years. Using three previously established methylation-based “clocks” (Hannum, Horvath, and Levine), we defined biological age acceleration for each woman by comparing her estimated biological age with her chronological age. Hazard ratios and 95% confidence intervals for breast cancer risk were estimated using Cox regression models. All statistical tests were two-sided. Each of the three clocks showed that biological age acceleration was statistically significantly associated with increased risk of developing breast cancer (5-year age acceleration, Hannum’s clock: hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.00 to 1.21, P = .04; Horvath’s clock: HR = 1.08, 95% CI = 1.00 to 1.17, P = .04; Levine’s clock: HR = 1.15, 95% CI = 1.07 to 1.23, P < .001). For Levine’s clock, each 5-year acceleration in biological age corresponded with a 15% increase in breast cancer risk. Although biological age may accelerate with menopausal transition, age acceleration in premenopausal women independently predicted breast cancer. Case-only analysis suggested that, among women who develop breast cancer, increased age acceleration is associated with invasive cancer (odds ratio for invasive = 1.09, 95% CI = 0.98 to 1.22, P = .10). DNA methylation-based measures of biological age may be important predictors of breast cancer risk.

Healthy eating is associated with lower risks of disease and mortality, but the mechanisms underlying these associations are unclear. Age is strongly related to health outcomes, and biological age can be estimated using the blood methylome. To determine whether healthy eating patterns are associated with methylation-based measures of biological age. Among women in the Sister Study, we calculated scores on 4 recommendation-based healthy eating indexes [Dietary Approaches to Stop Hypertension diet, Healthy Eating Index–2015, Alternative Healthy Eating Index (aHEI-2010), and the Alternative Mediterranean diet] using a validated 110-item Block FFQ completed at enrollment. Genome-wide DNA methylation data were generated using the HumanMethylation450 BeadChip on whole blood samples collected at enrollment from a case-cohort sample of 2694 women and were used to calculate 4 measures of epigenetic age acceleration (Hannum AgeAccel, Horvath AgeAccel, PhenoAgeAccel, and GrimAgeAccel). Linear regression models, adjusted for covariates and cohort sampling weights, were used to examine cross-sectional associations between eating patterns and measures of biological age. All 4 healthy eating indexes had inverse associations with epigenetic age acceleration, most notably with PhenoAgeAccel and GrimAgeAccel. Of these, the strongest associations were for aHEI-2010 [per 1-SD increase in diet quality, PhenoAgeAccel β = −0.5 y (95% CI: −0.8 to −0.2 y) and GrimAgeAccel β = −0.4 y (95% CI: −0.6 to −0.3 y)]. Although effect modification was not observed for most lifestyle factors, in analyses stratified by physical activity, the benefits of a healthy diet on epigenetic age acceleration were more pronounced among women who did not meet physical activity guidelines (reporting <2.5 h/wk of exercise). Higher diet quality is inversely associated with methylation-based measures of biological age. Improving diet could have the most benefits in lowering biological age among women with lower levels of physical activity. This trial was registered at clinicaltrials.gov as NCT00047970.

Breast cancer formation is associated with frequent changes in DNA methylation but the extent of very early alterations in DNA methylation and the biological significance of cancer-associated epigenetic changes need further elucidation. Pyrosequencing was done on bisulfite-treated DNA from formalin-fixed, paraffin-embedded sections containing invasive tumor and paired samples of histologically normal tissue adjacent to the cancers as well as control reduction mammoplasty samples from unaffected women. The DNA regions studied were promoters (BRCA1, CD44, ESR1, GSTM2, GSTP1, MAGEA1, MSI1, NFE2L3, RASSF1A, RUNX3, SIX3 and TFF1), far-upstream regions (EN1, PAX3, PITX2, and SGK1), introns (APC, EGFR, LHX2, RFX1 and SOX9) and the LINE-1 and satellite 2 DNA repeats. These choices were based upon previous literature or publicly available DNA methylome profiles. The percent methylation was averaged across neighboring CpG sites. Most of the assayed gene regions displayed hypermethylation in cancer vs. adjacent tissue but the TFF1 and MAGEA1 regions were significantly hypomethylated (p ≤0.001). Importantly, six of the 16 regions examined in a large collection of patients (105 – 129) and in 15-18 reduction mammoplasty samples were already aberrantly methylated in adjacent, histologically normal tissue vs. non-cancerous mammoplasty samples (p ≤0.01). In addition, examination of transcriptome and DNA methylation databases indicated that methylation at three non-promoter regions (far-upstream EN1 and PITX2 and intronic LHX2) was associated with higher gene expression, unlike the inverse associations between cancer DNA hypermethylation and cancer-altered gene expression usually reported. These three non-promoter regions also exhibited normal tissue-specific hypermethylation positively associated with differentiation-related gene expression (in muscle progenitor cells vs. many other types of normal cells). The importance of considering the exact DNA region analyzed and the gene structure was further illustrated by bioinformatic analysis of an alternative promoter/intron gene region for APC. We confirmed the frequent DNA methylation changes in invasive breast cancer at a variety of genome locations and found evidence for an extensive field effect in breast cancer. In addition, we illustrate the power of combining publicly available whole-genome databases with a candidate gene approach to study cancer epigenetics.

Epigenetic age, as defined by DNA methylation, may be influenced by air pollution exposure. To evaluate the relationship between NO2, particulate matter (PM), PM components and accelerated epigenetic age. In a sample of non-Hispanic white women living in the contiguous U.S. (n = 2747), we estimated residential exposure to PM2.5, PM10 and NO2 using a model incorporating land-use regression and kriging. Predictive k-means was used to assign participants to clusters representing different PM2.5 component profiles. We measured DNA methylation (DNAm) in blood using the Illumina's Infinium HumanMethylation450 BeadChip and calculated DNAm age using the Hannum, Horvath and Levine epigenetic clocks. Age acceleration was defined based on residuals after regressing DNAm age on chronological age. We estimated associations between interquartile range (IQR) increases in pollutants and age acceleration using linear regression. For PM2.5, we stratified by cluster membership. We examined epigenome-wide associations using robust linear regression models corrected with false discovery rate q-values. NO2 was inversely associated with age acceleration using the Hannum clock (β = -0.24, 95% CI: -0.47, -0.02). No associations were observed for PM10. For PM2.5, the association with age acceleration varied by PM2.5 component cluster. For example, with the Levine clock, an IQR increase in PM2.5 was associated with an over 6-year age acceleration in a cluster that has relatively high fractions of crustal elements relative to overall PM2.5 (β = 6.57, 95% CI: 2.68, 10.47), and an almost 2-year acceleration in a cluster characterized by relatively low sulfur fractions (β = 1.88, 95% CI: 0.51, 3.25). In a cluster distinguished by lower relative nitrate concentrations, PM2.5 was inversely associated with age acceleration (β = -1.33, 95% CI: -2.43, -0.23). Across the epigenome, NO2 was associated with methylation at 2 CpG sites. Air pollution was associated with epigenetic age, a marker of mortality and disease risk, among certain PM2.5 component profiles.

α-Klotho (klotho) is a protein involved in suppressing oxidative stress and inflammation. In animal models, it is reported to underlie numerous aging phenotypes and longevity. Among a nationally representative sample of adults aged 40–79 years in the United States, we investigated whether circulating concentrations of klotho is a marker of mortality risk. Serum klotho was measured by ELISA on 10 069 individuals enrolled in the National Health and Nutrition Examination Survey between 2007 and 2014. Mortality follow-up data based on the National Death Index were available through December 31, 2015. After a mean follow-up of 58 months (range: 1–108), 616 incident deaths occurred. Using survey-weighted Cox regression models adjusted for age, sex, and survey cycle, low serum klotho concentration ( 985 pg/mL, hazard ratio [HR]: 1.31, 95% confidence interval [CI]: 1.00, 1.71, p = .05). Associations were consistent for mortality caused by heart disease or cancer. Associations of klotho with all-cause mortality did not appear to differ by most participant characteristics. However, we observed effect modification by physical activity, such that low levels of serum klotho were more strongly associated with mortality among individuals who did not meet recommendation-based physical activity guidelines. Our findings suggest that, among the general population of American adults, circulating levels of klotho may serve as a marker of mortality risk.