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Chronological age tells us how many years have passed since birth. It does not necessarily tell us how quickly the body is changing.

Two people who are both 50 may have very different patterns of cellular, metabolic, vascular, and immune health. That difference is the premise behind biological age: an attempt to describe the state of aging-related processes in the body, rather than simply the passage of time.

A 2024 review in Frontiers in Aging takes stock of the methods currently used to measure biological age. The field has made meaningful progress, particularly with epigenetic clocks, but there is still no single test or score that captures aging in all its complexity.

Aging is often discussed as though it were a single process moving at a single rate. In reality, it reflects a network of interacting changes across cells, tissues, and organ systems.

These include:

These processes do not move in lockstep. They are influenced by genetics, environment, lifestyle, disease history, and social conditions—and they can unfold differently across tissues within the same person.

This is why a single biological-age number is both compelling and difficult to create. A measure may capture one dimension of aging well while overlooking important changes elsewhere.

Vascular health is one important part of the broader aging picture.

With age, the endothelium—the thin inner lining of blood vessels—often becomes less responsive. Oxidative stress and inflammation can increase, and arteries can gradually stiffen. These changes can affect blood-flow regulation and contribute to cardiovascular risk over time.

Several core features of aging, including mitochondrial dysfunction, oxidative stress, inflammation, and telomere attrition, have been associated with cardiovascular disease and vascular aging. While no single marker defines “vascular age,” maintaining vascular health remains one of the more practical and evidence-supported ways to support healthy aging overall.

There is not yet a universally accepted biological-age test. Instead, current approaches examine different signals associated with aging.

Telomeres are protective structures at the ends of chromosomes. They tend to shorten with cell division, which made them an early and intuitive candidate for a biological-age marker.

But telomere biology is more complicated than a simple countdown clock. Telomere length varies substantially between individuals, is strongly influenced by genetics and lifestyle, and does not decline at a uniform rate throughout life. A single telomere-length measurement may be useful in research settings, but it is not a reliable stand-alone measure of whole-body biological age.

Epigenetic clocks are among the most developed tools currently available.

They measure patterns of DNA methylation—chemical modifications that influence gene activity without changing the DNA sequence itself. Certain methylation sites change in relatively predictable ways with age, allowing models such as the Horvath and Hannum clocks to estimate biological age.

Put simply, these tools ask: based on this methylation profile, how old does this biology appear?

Their performance has made them valuable in aging research, but they should not be treated as a universal scorecard for health. An epigenetic-age estimate captures specific biological patterns; it does not fully represent every component of aging, disease risk, resilience, or function.

The field is also exploring additional biological signals.

The gut microbiome is one area of interest because microbial diversity and composition change across the lifespan and may track with healthier or less healthy aging trajectories.

Exosomes—small vesicles released by cells—are another emerging candidate. Their protein and RNA cargo may carry information about cellular stress, inflammation, and senescence. Both areas are promising, but neither is ready to serve as a stand-alone biological-age measure.

The most useful future approach may not be a better single biomarker, but a more integrated one.

Newer machine-learning models combine multiple data types, including epigenetic, transcriptomic, proteomic, metabolomic, clinical, and imaging data. This multi-omics approach reflects a basic reality: aging is multisystem, so measuring it well will likely require more than one type of signal.

The goal is not simply to assign everyone an age-like score. Ideally, these tools will help identify which systems appear most affected, clarify risk earlier, and make interventions more targeted. But the field has not yet converged on a gold standard, and biological-age tests should be interpreted with appropriate caution.

Check out this weeks youtube video to see our mascot Dr. Angio bringing complex health and research topics to life.

Best wishes,
- The Angiogenesis Foundation

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