In this talk, Dr. Varun Dwaraka discusses epigenetic clocks and their applications in clinical investigations. He covers the philosophy of treating aging as a disease and introduces DNA methylation as a biomarker. Dwaraka highlights the work of TruDiagnostic, a specialist in epigenetic methylation, and their collaborations with top research institutes. The talk also delves into the importance of developing tools like epigenetic clocks for objective age measurements and their associations with disease risk. Dwaraka explains the concept of DNA methylation and its significance in diagnostics for various disease states

He also explains different generations of epigenetic clocks. The first-generation clocks, like the Horvath clocks, train against CpG sites highly associated with chronological age. The second-generation clocks, including GrimAge and Levine's PhenoAge, train against biological variables and phenotypic scores. Third-generation clocks train against aging phenotypes and other measurements. An example of this is the DunedinPACE clock, which uses longitudinal data from a study in New Zealand.

In conclusion, Dwaraka highlights the power of DNA methylation as a stable biomarker for understanding epigenetic age, protein estimations, and immune changes, all from a single blood test. He briefly discusses future directions for the development of an epigenetic clock incorporating a multi-omic approach, which could facilitate deeper insights into an individual's longevity.

About Dr. Dwaraka:
Dr. Varun Dwaraka is a principal investigator in aging and longevity specializing in epigenetics and bioinformatics. He has co-authored numerous publications relating to genetics, epigenetic clocks, DNA methylation, and tissue regeneration. In 2020, Dr. Dwaraka was elected as a full member to the Sigma Xi, The Scientific Research Honor Society, and currently serves as a 2023 Foresight Fellow in Biotechnology and Health Expansion, awarded by the Foresight Institute.

Dr. Dwaraka is passionate about implementing machine learning methods to advance predictive medicine as well as our understanding of the biology of aging.

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