Chronological age is the time elapsed since birth. Biological age is an estimate that summarizes selected age-related measurements under a specific model. The two can be compared as an age gap or ratio, but the estimate is not a second birth certificate and should not be interpreted as a literal number of years gained or lost.
Editorial note
Published 2026-07-16. Last reviewed 2026-07-16. Evidence, interpretation, and Metric’s implementation are identified separately below.
The core definitions
- Chronological age: elapsed time since birth. It advances at the same calendar rate for everyone.
- Biological age: an age-like output estimated from a model’s chosen biomarkers or functional signals.
- Age gap: biological age minus chronological age. A negative gap means the estimate is younger under that model.
- Age ratio: biological age divided by chronological age. A value below 1.00 means the estimate is lower than calendar age.
- Pace of aging: a model intended to estimate biological change per unit of chronological time, often scaled around 1.00 per year.
- Age acceleration: a model-specific measure of being older or changing faster than expected after accounting for calendar age.
Why biological age exists as a concept
People with the same birth year can differ in fitness, function, biomarkers, disease burden, and future risk. Biological-age research tries to summarize part of that heterogeneity. Some methods combine routine clinical biomarkers; others use DNA methylation or functional data. The output is useful only insofar as the selected inputs and target capture the question being asked.
A worked example
Suppose someone is 40 years old and a model returns 36. Their age gap is −4 years and their age ratio is 0.90. Those statements mean the model placed the current input pattern below the reference expected at age 40. They do not mean the person will live four years longer, that every organ resembles age 36, or that the person has reversed four calendar years.
Why two biological ages can disagree
- A DNA clock and a wearable model observe different biological layers.
- One model may optimize calendar-age prediction while another optimizes mortality-related risk.
- Reference populations, age ranges, sex handling, and health status can differ.
- Missing values, source quality, and preprocessing can change the final estimate.
- One model may react quickly to behavior while another changes slowly.
What the evidence says
Klemera and Doubal explicitly noted the lack of an exact universal definition. Subsequent work found that some clinical-biomarker age algorithms added information about mortality beyond chronological age in particular cohorts. DNA-methylation clocks and pace measures opened different questions. This supports careful model-specific use, not the claim that biological age replaces chronological age.
How Metric relates the two
Metric converts available Apple Health contributors into age-equivalent values, combines their weighted deviations around the user’s chronological age, and smooths the result over a rolling history. Its current “aging rate” display divides rolling biological age by chronological age. Therefore 0.90× means the estimate is 90% of calendar age under Metric’s model; it does not mean the body aged exactly 0.90 biological years during the last year.
How to use the comparison
- Inspect contributors before interpreting the headline age.
- Treat small changes inside ordinary sensor and model variability as noise unless a trend persists.
- Compare a model with itself over consistent data, not casually with an unrelated test.
- Use the estimate to identify measurable habits or inputs worth reviewing—not to forecast a death date.
- Keep conventional care, symptoms, and standard measurements separate from a wellness-age display.
Limitations
Chronological age is exact for its purpose; biological age is uncertain by design. It depends on what was measured, when it was measured, which population informed the model, and what outcome the model represents. A single age value necessarily compresses disagreement among systems and should be read as a summary, not an identity.
Sources and further reading
- Mechanisms of Ageing and Development (2006): A new approach to the concept and computation of biological ageExplains why biological age lacks a single exact definition and proposes a multibiomarker estimation method.
- The Journals of Gerontology: Series A (2013): Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?Compared five biological-age algorithms in NHANES III and their associations with mortality.
- Genome Biology (2013): DNA methylation age of human tissues and cell typesIntroduced a multi-tissue DNA-methylation age estimator, illustrating a molecular-clock approach to biological age.
- Aging (2018): An epigenetic biomarker of aging for lifespan and healthspanDeveloped DNAm PhenoAge from a mortality-related clinical phenotype and evaluated associations with aging outcomes.
- eLife (2022): DunedinPACE, a DNA methylation biomarker of the pace of agingDefines a pace measure scaled around one biological year per chronological year and reports test-retest and outcome analyses.
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Health information disclaimer
Metric is a wellness product. This article is educational and does not provide medical advice, diagnosis, or treatment. Wearable measurements and app-generated scores are estimates; discuss symptoms, unusual readings, medication effects, and changes to a care or training plan with an appropriate healthcare professional.