Background: Chronological age is a prominent risk factor for many types of cancers including colorectal cancer (CRC). Yet, the risk of CRC varies substantially between individuals, even within the same age group, which may reflect heterogeneity in biological tissue aging between people. Epigenetic clocks based on DNA methylation are a useful measure of the biological aging process with the potential to serve as a biomarker of an individual's susceptibility to age-related diseases such as CRC.
Methods: We conducted a genome-wide DNA methylation study on samples of normal colon mucosa (N = 334). Subjects were assigned to three cancer risk groups (low, medium, and high) based on their personal adenoma or cancer history. Using previously established epigenetic clocks (Hannum, Horvath, PhenoAge, and EpiTOC), we estimated the biological age of each sample and assessed for epigenetic age acceleration in the samples by regressing the estimated biological age on the individual's chronological age. We compared the epigenetic age acceleration between different risk groups using a multivariate linear regression model with the adjustment for gender and cell-type fractions for each epigenetic clock. An epigenome-wide association study (EWAS) was performed to identify differential methylation changes associated with CRC risk.
Results: Each epigenetic clock was significantly correlated with the chronological age of the subjects, and the Horvath clock exhibited the strongest correlation in all risk groups (r > 0.8, p < 1 × 10). The PhenoAge clock (p = 0.0012) revealed epigenetic age deceleration in the high-risk group compared to the low-risk group.
Conclusions: Among the four DNA methylation-based measures of biological age, the Horvath clock is the most accurate for estimating the chronological age of individuals. Individuals with a high risk for CRC have epigenetic age deceleration in their normal colons measured by the PhenoAge clock, which may reflect a dysfunctional epigenetic aging process.
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http://dx.doi.org/10.1186/s13148-019-0801-3 | DOI Listing |
Alzheimers Dement
December 2024
German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
Background: While some memory decline in old age is "normal", there are some older individuals with maintained high cognitive performance. Using a multimodal approach including neuroimaging, fitness, genetic and questionnaire data (Figure 1A), we aimed to identify factors that are related to successful cognitive aging and whether these differ between sexes.
Method: We analyzed 165 cognitively normal older adults age ≥ 60 years from an ongoing study (SFB1436) (age=71±8years, 43% female).
Alzheimers Dement
December 2024
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Background: There is increasing need for noninvasive biomarkers of Alzheimer's Disease (AD) neuropathologic change for early detection and intervention through risk-factor modification and disease-modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Centre for Brain Research (CBR), Indian Institute of Science, Bengaluru, Karnataka, India.
Background: Data-driven methods, particularly deep learning, are transforming neuroimaging by accurately estimating Brain Age using diverse modalities. Discrepan- cies between predicted and actual age unveil potential health risks. Utilizing a training set of healthy subjects, a regression algorithm correlates brain features to age, allowing inference for unseen patients.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Background: Brain age (BA) prediction models have emerged as valuable tools for understanding individual differences in trajectories of brain aging. These models aim to estimate overall brain health by predicting BA based on structural MRI data. To enhance the specificity of existing BA models, we introduce a deep learning-based BA prediction model.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Human Genetics Center, School of Public Health, University of Texas Health Science Center, Houston, TX, USA.
Background: Epigenetic clocks are biomarkers of biological age based on DNA methylation (DNAm) patterns and are widely used as predictors of health and aging outcomes. Multiple epigenetic clocks have been developed and reflect different aspects of the multidimensional aging process, above and beyond chronological age. To date, no study has examined the relationship of epigenetic aging with circulating biomarkers of Alzheimer's Disease (AD).
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