Systems Age: A Practical Shift in Measuring Biological Aging

 


Measuring aging isn’t as simple as counting birthdays. Biological age often moves faster or slower than the calendar, and it doesn’t unfold evenly across the body. A person’s heart may show signs of accelerated decline while their brain or muscles remain relatively resilient. This unevenness, although central to how health and disease emerge, has been difficult to capture with existing tools.

Traditional epigenetic clocks, based on DNA methylation patterns, have been valuable for estimating overall biological age. Yet they tend to compress the complexity of aging into a single score. Such measures can reveal who appears biologically older or younger than their years, but they leave unanswered the crucial question of where the aging is happening. This limits their usefulness for predicting system-specific diseases or guiding interventions.

The Systems Age framework was designed to move past this limitation. From a single blood test, it produces eleven system-specific scores spanning the heart, lungs, brain, immune system, metabolism, and more and integrates them into a composite measure. The appeal here is practical: it avoids the need for a battery of medical tests while still uncovering the heterogeneity of aging within a single individual.

The evidence supporting this approach is striking. In large population studies, the heart score showed strong associations with coronary heart disease and heart attack risk, while the brain score aligned closely with cognitive decline. The musculoskeletal score, in turn, predicted functional impairment, arthritis, and diabetes. When directly compared with clocks like GrimAge and PhenoAge, the system scores often proved more precise. In fact, across fourteen disease categories, the most relevant system score outperformed traditional clocks in ten of them.

What makes these results even stronger is how well they hold up under real-world conditions. Smoking is one of the biggest factors that can distort aging studies, because it affects so many parts of the body. When researchers adjusted their analyses to account for smoking, the predictive ability of Systems Age remained largely intact. The heart score, for example, still predicted cardiovascular risk with only a small drop in accuracy, while older clocks lost much more of their strength. This shows that Systems Age is capturing deeper biological changes, not just signals linked to lifestyle habits.

The framework also revealed patterns in how different systems age together. Some systems tended to decline side by side for instance, heart and lung aging often went hand in hand, while inflammation was closely tied to musculoskeletal deterioration. When researchers grouped people based on their system scores, nine distinct “aging subtypes” emerged. Two of these groups faced a higher risk of cardiovascular disease, but for very different reasons: one showed accelerated lung aging linked to smoking, while the other showed faster metabolic and inflammatory aging despite relatively healthy lungs. In both cases the outcome was the same greater heart risk but the biological paths leading there were not alike.

These insights raise practical questions. If aging can take such different routes, should prevention be tailored not only to a person’s overall biological age but also to their system-specific vulnerabilities? Would risk management become more effective if it focused less on averages and more on identifying the weak points in an individual’s physiological profile? And might future check-ups include an “aging panel” alongside cholesterol or glucose levels, offering a clearer picture of which systems are holding up and which are in decline?

The larger lesson is that Systems Age does not replace older clocks so much as extend them. Instead of stopping at the question, “How fast am I aging compared to others?” it adds a deeper layer: “Which parts of me are aging the fastest, and what does that mean for my health?”

Link To Study




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