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Epigenetic age oscillates during the day (Neu 30 yr old)

ABSTRACT: Since their introduction, epigenetic clocks have been extensively used in aging and human disease research. In this study, we reveal an intriguing pattern: epigenetic age predictions display a 24-hour periodicity. These paradoxical age oscillations can be attributed to variations in blood cell type composition and epigenomes, both of which demonstrate circadian rhythmicity. This discovery emphasizes the significance of factoring-in the time of day to obtain accurate estimates of epigenetic age.

ORGANISM(S): Homo sapiens

PROVIDER: GSE247193 | GEO | 2024/04/19

REPOSITORIES: GEO

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Project description:Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variation in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation, and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (P < 0.0001), and were significant predictors of tissue origin (P < 0.0001). In solid tissues (n=119) we found striking, highly significant CpG island dependent correlations between age and methylation; loci in CpG islands gained methylation with age, loci not in CpG islands lost methylation with age (P < 0.001), and this pattern was consistent across tissues and in an analysis of blood-derived DNA. Our data clearly demonstrate age and exposure related differences in tissue-specific methylation, and significant age associated methylation patterns which are CpG island context dependent. This work provides novel insight into the role of aging and the environment in susceptibility to diseases such as cancer, and critically informs the field of epigenomics by providing evidence of epigenetic dysregulation by age-related methylation alterations. Collectively we reveal key issues to consider both in the construction of reference. The causes and extent of tissue-specific interindividual variation in human epigenomes are underappreciated and hence, poorly characterized. We surveyed over 200 carefully annotated human tissue samples from ten anatomic sites at 1413 CpGs for methylation alterations to appraise the nature of phenotypically, and hence potentially clinically important epigenomic alterations. Within tissue types, across individuals, we found variation in methylation that was significantly related to aging and environmental exposures such as tobacco smoking. Individual variation in age and exposure-related methylation may significantly contribute to increased susceptibility to several diseases. As the NIH-funded HapMap project is critically contributing to annotating the human reference genome defining normal genetic variability, our work raises key issues to consider in the construction of reference epigenomes. It is well recognized that understanding genetic variation is essential to understanding disease. Our work, and the known interplay of epigenetics and genetics, makes it equally clear that a more complete characterization of epigenetic variation and its sources must be accomplished to reach the goal of a complete understanding of disease. Additional research is absolutely necessary to define the mechanisms controlling epigenomic variation. We have begun to lay the foundations for essential normal tissue controls for comparison to diseased tissue, which will allow the identification of the most crucial disease-related alterations and provide more robust targets for novel treatments.