enzyme substrate Using data gathered from to

Using data gathered from 2003 to 2013, our dose–response analysis identified greater Δage in cancer survivors and participants who died from cancer (Δage=0.5 and 2.2years, respectively) relative to cancer-free participants (Δage=−0.4years, test for trend P=0.02) (Fig. 1); Kaplan–Meier (KM) curves comparing Δage also suggested participants with greater Δage had a higher risk of cancer incidence (log-rank P=0.002) and cancer mortality (log-rank P=0.04) (Supplementary material, Fig. S6). We observed weaker, non-significant results in analyses using samples before 2003 and using all samples combined. Adjusted for covariates, the 2003–2013 time-dependent Cox models revealed that each one-year increase in Δage was associated with greater risks of cancer incidence (HR: 1.06, 95% CI: 1.02–1.10), and mortality (HR: 1.17, 95% CI: 1.07–1.28). We found no associations between Δage and cancer risk in either all samples combined or the pre-2003 samples (Table 2). Point-wise HR curves of Δage illustrated a J-shaped relationship in cancer incidence for samples taken pre-2003 (P=0.03 for test of nonlinearity) (Fig. 2A). The risk of cancer mortality increased linearly with Δage in the 2003–2013 samples, but this enzyme substrate association was substantially weaker for pre-2003 samples (Fig. 2B). KM survival curves examining Δage rate of change over time showed that participants who were epigenetically young relative to their chronological age (Δage≤0) and had decelerated or stable epigenetic aging over time (Δage slope≤0) had the lowest risk of cancer incidence and death (log-rank P=0.003 and P=0.02, respectively, Fig. 3).
Discussion Our results indicate a strong, possibly dose-responsive, relationship between Δage and cancer incidence and mortality that is independent of telomere shortening and several epidemiological risk factors for cancer. The relationships between Δage and cancer were strongest in DNA samples collected within three to five years of a cancer event and among participants with accelerated epigenetic aging over time. These findings, coupled with our spline showing a nonlinear, J-shaped relationship between Δage and cancer incidence suggest a dynamic and complex relationship between Δage and cancer over the long term. A diagnostic test to detect an individual\'s discrepancies between epigenetic and chronological age could therefore prove useful for cancer risk and prognosis assessment. Epigenetic modifications are potentially central to biological processes of aging (Ben-Avraham et al., 2012), and both global (Ben-Avraham et al., 2012) and gene-specific methylation can be altered substantially by the aging process (Gravina and Vijg, 2010). While the accumulation of epigenetic changes is a hallmark of cancer, few studies have prospectively examined the potential of age-related epigenetic changes to predict cancer. The observed association between Δage and cancer was independent of chronological age, telomere length, and several known lifestyle risk factors for cancer (e.g., body mass index (BMI), and smoking). The method for calculating epigenetic age in our analysis combines age-dependent CpG sites enriched in pathways that are also involved in carcinogenesis (Hannum et al., 2013; Horvath, 2013), and our findings indicate that this may be a viable strategy for developing predictive biomarkers of cancer. In addition, our sensitivity analyses found that the association between Δage and cancer is independent of both telomere length and other comorbidities, suggesting Δage as a specific cancer biomarker as well as the possibility that Δage reflects molecular-level aging or carcinogenic processes that are not captured by telomere measurements. Our findings are intriguing and biologically plausible. A recent meta-analysis of four large longitudinal cohorts revealed that Δage measured in blood DNA correlates well with all-cause mortality rates and is independent of health, lifestyle, and genetic factors (Marioni et al., 2015). Another recent study of lung cancer found and even stronger relationship between blood epigenetic age and cancer mortality using Horvath\'s method (Levine et al., 2015). While blood epigenetic age is already being studied in HIV-1 patients with clinical signs of aging (Horvath and Levine, 2015), it may be even more valuable in studies of cancer. WBC play an important role in carcinogenesis via inflammatory response and pro-apoptotic processes (Ichikawa et al., 2011; Schnekenburger et al., 2008), both of which also affect epigenetic age. The discrepancy between epigenetic and chronological age, i.e. Δage, has also been associated with cancer prognosis in tumor tissue data from The Cancer Genome Atlas (Lehne et al., 2015). However, sampling different tissues is not feasible for cancer screening at the population level, both for practical reasons and because different cancers can have tissue-specific, differential, and even opposing effects on epigenetic age (Horvath, 2013; Horvath, 2015), Therefore, our finding of the predictive value of blood-based epigenetic age for cancer risk and mortality is particularly provocative and potentially valuable for use as a screening tool for cancer as it can offer more stable and reliable predictions (Horvath, 2013).