Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-07
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04

    • br Funding Sources This work was supported

    2018-10-30


    Funding Sources This work was supported by an Applied Research Support Fund Award from the NYU Office of Industrial Liaison, center of excellence funding from NYU School of Medicine to the Dept of Urology, and a translational pilot project award from the NYU Cancer Institute center grant from NIH (5P30CA016087) to IM. IM was also supported in part by NIH Grants AI073898 and GM056927. AP was supported in part by funds from the Philippe Foundation. SD was supported by grants from the USA Department of Defense (DOD) Breast Cancer Research Program (BCRP) (W81XWH-11-1-0532), and The Chemotherapy Foundation. ABF was supported by NIH grant R01 CA108573. Work at Benevir was supported in part by NCI SBIR Phase 1 grant 1R43CA168172-01A1. External funding sources did not have any involvement with study design, data collection, data interpretation, preparing the manuscript or the decision to submit the manuscript for publication.
    Conflict of Interest Statement
    Author Contributions
    Acknowledgments
    Introduction As the population continues to age in the coming decades, the need for biological measures of age and more precise screening tests for age-related diseases will become increasingly urgent. Genetic and epigenetic studies have added to the potential clinical utility of this subject area, with researchers identifying elements associated with age-related processes (e.g., telomeres) that nonetheless have unverified predictive power in human populations (Brooks-Wilson, 2013). Epigenetics serves as an intersection between genetic and environmental risk factors for aging processes and age-related diseases, holding great promise for constructing biological age measures that will provide clinical diagnostic tools for aging-related diseases such as cancer. Blood-based epigenetic markers are particularly well-suited to these purposes due to their minimally invasive method of collection and cost-effectiveness on the population scale. Epigenetic age is a recently developed algorithm that uses DNA methylation measurements to describe biological age at the level of human tissues, cells, and endothelin receptor (Hannum et al., 2013; Horvath, 2013; Weidner et al., 2014). Epigenetic age does not always parallel chronological age, particularly in tumor samples (Hannum et al., 2013; Horvath, 2013), a discrepancy we refer to here as Δage. Furthermore, since the methods for measuring epigenetic age incorporate loci in pathways related to both cancer development and aging in general (e.g., DNA damage, cellular proliferation, and oxidative stress) (Hannum et al., 2013; Horvath, 2013), it is highly possible that Δage can be a predictive biomarker for cancer risk, metastasis, and mortality in addition to serving as an indicator of aging. With further study and refinement, the concept of epigenetic age may also be useful for improving our understanding of mechanisms by which age and cancer are related. However, no longitudinal analysis has yet evaluated how blood epigenetic age changes over time prior to cancer diagnosis or cancer-related death, and whether blood Δage can predict future risk of cancer incidence and mortality. Our objective is to assess whether white blood cell (WBC) Δage can predict cancer incidence and mortality, and to evaluate these predictions over time. We will achieve our goals by comparing multiple estimates of Δage obtained using blood DNA samples collected prior to cancer incidence and death in: 1) individuals who developed cancer relative to cancer-free individuals and 2) individuals who died of cancer relative to both cancer survivors and cancer-free individuals.
    Materials and Methods
    Results Characteristics of participants at first blood draw were similar to previous reports (Hou et al., 2015; Zhu et al., 2011), indicating successful randomization. No participant characteristics were associated with Δage (Supplementary material, Table S2). Mean epigenetic age was almost identical to chronological age but with greater variance (Table 1). There were 422 observations of 370 participants during the first half of the follow-up period (pre-2003) and 412 observations of 306 participants during the second half (2003–2013). In the pre-2003 stratum the median times to cancer diagnosis and death were 5.1 (IQR (interquartile range) 2.7–8.6) and 7.0years (IQR 5.3–10.1), respectively. In the 2003–2013 stratum the median times to cancer diagnosis and death were 3.0 (IQR 0.6–5.7) and 4.6years (IQR 2.4–7.0), respectively. The pre-2003 analysis included 370 and 52 samples from participants at their first and second time-point of measures, respectively. The 2003–2013 analysis included 72, 247, and 93 samples from participants at their first, second, and third time-point of measures, respectively.