It is a common observation that some elderly people appear to be significantly younger in terms of their physical and mental abilities relative to their chronological age, while the decline in physical and mental abilities is much more advanced in other people of the same age. The determination of this biological age is a field of research that has gained considerable attention, as the availability of appropriate biomarkers is of great importance beyond basic research also with regard to clinical application. Promising markers of biological age are based on DNA methylation age (epigenetic clock), as well as its deviation from chronological age (DNA methylation age acceleration). First-generation epigenetic clocks (7-CpG, Horvath, Hannum clock) trained to predict chronological age showed only isolated significant associations with age-associated phenotypes in our analyses. Second-generation epigenetic clocks trained to predict mortality-associated laboratory parameters and health-related risk factors (PhenoAge, GrimAge) showed associations with significantly more of the phenotypes studied. This preliminary work will be extended to longitudinal analyses in this project proposal. For this purpose, we will mainly use data from the Berlin Age Study II (BASE-II), from which phenotypic data are available that were collected at mean intervals of approximately 7.5 (N=1,100) and 11 years (N~700) from the baseline survey (N=1,671). Genome-wide methylation profiles are available for two of these time points at the start of the project, and the third data point (N~700) will be generated as part of this project. Based on the experience gained in preliminary work in the extensive BASE-II dataset, the generation and (external) validation of a new epigenetic biomarker (MultiAge) is planned. For the development of this multidimensional marker for "biological age", the model of "hierarchical aging" will be used conceptually. Overall, the analyses planned here are suitable for further exploring the potential of the various epigenetic biomarkers in terms of their ability to predict age-associated phenotypes at an early stage, thus possibly paving the way for their clinical application.
|Effective start/end date
|01.01.21 → 31.12.25
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):