Dataset Information

Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer: A Prospective Study

ABSTRACT: Abstract

Background

We previously investigated the association between 5 “first-generation” measures of epigenetic aging and cancer risk in the Melbourne Collaborative Cohort Study. This study assessed cancer risk associations for 3 recently developed methylation-based biomarkers of aging: PhenoAge, GrimAge, and predicted telomere length.

Methods

We estimated rate ratios (RRs) for the association between these 3 age-adjusted measures and risk of colorectal (N?=?813), gastric (N?=?165), kidney (N?=?139), lung (N?=?327), mature B-cell (N?=?423), prostate (N?=?846), and urothelial (N?=?404) cancer using conditional logistic regression models. We also assessed associations by time since blood draw and by cancer subtype, and we investigated potential nonlinearity.

Results

We observed relatively strong associations of age-adjusted PhenoAge with risk of colorectal, kidney, lung, mature B-cell, and urothelial cancers (RR per SD was approximately 1.2-1.3). Similar findings were obtained for age-adjusted GrimAge, but the association with lung cancer risk was much larger (RR per SD?=?1.82, 95% confidence interval [CI]?=?1.44 to 2.30), after adjustment for smoking status, pack-years, starting age, time since quitting, and other cancer risk factors. Most associations appeared linear, larger than for the first-generation measures, and were virtually unchanged after adjustment for a large set of sociodemographic, lifestyle, and anthropometric variables. For cancer overall, the comprehensively adjusted rate ratio per SD was 1.13 (95% CI?=?1.07 to 1.19) for PhenoAge and 1.12 (95% CI?=?1.05 to 1.20) for GrimAge and appeared larger within 5?years of blood draw (RR?=?1.29, 95% CI?=?1.15 to 1.44 and 1.19, 95% CI?=?1.06 to 1.33, respectively).

Conclusions

The methylation-based measures PhenoAge and GrimAge may provide insights into the relationship between biological aging and cancer and be useful to predict cancer risk, particularly for lung cancer.

SUBMITTER: Dugue P

PROVIDER: S-EPMC7791618 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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