TruDiagnostic has developed epigenetic aging analysis of individual organ systems, using validated and system-specific trained clocks.

These algorithms, developed by scientists at Yale University, were previously released in biology journal bioRxiv as a preprint, with peer-reviewed publications expected later this year.

Now known as SYMPHONYAge (System Methylation Proxy of Heterogeneous Organ Years), this collection of system-specific clocks provides a new way of looking at aging by examining how different parts of the body decline independently, and synchronously, over time.

Due to lifestyle and dietary choices, researchers explain that organ systems age differently in a single body, and that disease-related risks can be calculated by examining 11 major systems and their biological effect on one another. This includes: Lung Age, Heart Age, Brain Age, Hormone Age, Metabolic Age, Musculoskeletal Age, Blood Age, Liver Age, Inflammation Age, Kidney Age, and Immune System Age.

While some biological age tests already claim to predict the extent of aging of various organ systems, SYMPHONYAge is notably different in scope, validation, and associations with various health outcomes. It is also the only published clock which gives organ specific aging information. TruDiagnostic has an exclusive license on the use of these algorithms which were previously called Systems Clock during earlier stages of research and development.

Scientists from Yale University and TruDiagnostic stress that our body's systems do not age in isolation, and thus age-related disease and functional decline often do not occur in isolation either.

"Many age-related illnesses stem from issues in various biological systems working together. For instance, arthritis is the result of both musculoskeletal wear and inflammation, whereas stroke can happen due to problems in the cardiovascular system, metabolism, inflammation, and brain function," explains Yale researcher Dr. Albert Higgins Chen.

These interconnected patterns can lead to different aging types, making some people more prone to certain age-related diseases. Understanding these patterns helps in forecasting health outcomes.