Biological Age Test: How DNA Methylation Reveals Your True Rate of Aging?

A biological age test analyzes DNA methylation patterns to estimate how your body is aging compared with your chronological age. Depending on the method, the result may be reported as an estimated biological or epigenetic age, an age-acceleration measure, or a pace-of-aging score. These results are research-based biomarkers, not a diagnosis or a guaranteed prediction of lifespan.

A biological age test estimates how your body is aging compared with your chronological age. While chronological age is the number of years since your birth, biological age reflects measurable changes in your cells and tissues that may vary from person to person.

• Chronological age: Your age based on your date of birth.

• Biological age: An estimate of how your body is aging based on biological markers.

Two people with the same chronological age may have different biological ages because aging process is influenced by genetics, lifestyle, environmental exposures, and overall health.

DNA methylation is an epigenetic process in which chemical tags called methyl groups attach to specific regions of DNA. These tags help regulate gene activity without changing the underlying DNA sequence.

As we age, methylation patterns at certain DNA sites change in relatively predictable ways. Lifestyle factors and environmental exposures can also influence these patterns. A biological age test DNA methylation analysis uses these age-associated patterns to estimate biological age or the pace of aging.

DNA methylation is useful for aging research because age-associated changes at specific DNA sites can be measured and combined into mathematical models called epigenetic clocks. These models provide a reproducible way to estimate certain aspects of biological aging, although they do not capture every aspect of aging.

Blood is commonly used because it provides accessible DNA for methylation analysis. Some testing services also offer at-home saliva or cheek-swab kits, depending on the laboratory’s validated testing method. The sample is analyzed to measure methylation patterns, which are then used to calculate a biological age estimate.

Looking for a DNA methylation-based biological age test? 

MyDiagnostics' Biological Age Test uses a blood sample collected at home and reports Biological Age along with DunedinPACE, telomere length, mitotic clock and other longevity-related markers. The current listed turnaround time is 7–10 days.

How to measure biological age accurately depends on the testing method, the biological process being assessed, and the quality of the evidence behind the test. A reliable biological-age assessment should do more than estimate your age from a birthdate, it should provide meaningful information about aging-related health outcomes.

Among these approaches, DNA methylation clocks are among the most extensively studied for predicting health outcomes. However, accuracy depends on the specific clock and outcome being assessed; no single method is universally the most accurate for every purpose.

Accuracy is not simply about how closely a test matches your chronological age. A meaningful biological-age measure should also demonstrate criterion validity—the ability to predict relevant health outcomes in validation studies.

Researchers therefore examine whether a test is associated with outcomes such as mortality, disease onset, disability, or functional decline. For example, DunedinPACE was developed using longitudinal measurements of organ-system integrity and subsequently evaluated in additional cohorts, where faster aging was associated with morbidity, disability, and mortality.

• Which epigenetic clock is used: Horvath, PhenoAge, GrimAge, and DunedinPACE measure different aspects of aging. Their results should not be treated as interchangeable.

• Laboratory and testing standardization: Consistent analytical methods and quality-control procedures help reduce measurement variation.

• Sample handling: Collection, storage, and processing can affect the quality and consistency of DNA methylation measurements.

• Validation in large longitudinal cohorts: Studies that follow people over time and compare results with health outcomes provide stronger evidence than studies based only on chronological age.

• Relying on a single test snapshot: One result cannot establish a long-term aging trend.

• Comparing different clocks as if they were identical: A PhenoAge score and a DunedinPACE score answer different questions.

• Inconsistent testing conditions: Changes in sample collection, handling, or laboratory methods can introduce variation.

• Using unregulated consumer kits: Be cautious when a test does not clearly disclose its laboratory methods, validation studies, or the clock used.

For people tracking lifestyle changes, periodic retesting is more useful than frequent testing. Ideally, repeat testing should use the same clock, laboratory, and collection method under comparable conditions.

DunedinPACE is a useful example of why test-retest reliability matters. In its original validation study, it showed high reliability in replicate blood samples, with an intraclass correlation coefficient of 0.96 in one technical-replicate dataset. This supports its use as a reproducible measure, but it does not mean every biological-age test has the same reliability.

When choosing a biological age test, consider the testing method, laboratory standards, epigenetic clock analysis used, and available scientific validation. MyDiagnostics provides biological age testing designed to help individuals understand their biological aging profile using DNA methylation-based analysis.

Epigenetic age acceleration describes the difference between a person’s epigenetic age, estimated from DNA methylation patterns, and their actual chronological age. It helps researchers assess whether biological aging appears faster or slower than expected for someone of the same chronological age.

• Positive epigenetic age acceleration: Epigenetic age is higher than chronological age, suggesting faster biological aging. Higher acceleration has been associated in research with increased risks of certain diseases, poorer health outcomes, and mortality.

• Negative epigenetic age acceleration: Epigenetic age is lower than chronological age, suggesting a slower biological aging trajectory and, in some studies, better health outcomes.

Research has linked several lifestyle and environmental exposures with patterns of faster epigenetic aging, including:

• Smoking history

• Chronic psychological stress

• Poor-quality diet

• Sedentary behavior and low physical activity

• Insufficient or poor-quality sleep

• Air pollution and certain environmental toxicant exposures

• Childhood adversity and prolonged early-life stress

These associations do not necessarily mean that a particular factor directly causes epigenetic age acceleration. Epigenetic aging is influenced by multiple interacting genetic, behavioral, and environmental factors.

Research has also associated healthier aging patterns with:

• Regular physical activity and exercise

• Plant-forward, nutrient-dense dietary patterns

• Adequate, consistent sleep

• Strong social connections

• Avoiding tobacco smoking

However, lifestyle changes should not be interpreted as guaranteed ways to lower an epigenetic age score. Individual responses vary, and evidence differs between specific epigenetic clocks and interventions.

Importantly, epigenetic age acceleration is not calculated in exactly the same way for every epigenetic clock. Traditional clocks may compare predicted epigenetic age with chronological age, while newer measures are designed to capture other dimensions of aging, such as physiological risk or the pace of biological aging.

This distinction is important when interpreting results from clocks such as Horvath, PhenoAge, GrimAge, and DunedinPACE, which were developed for different purposes.

First-generation epigenetic clocks are DNA methylation-based models developed primarily to estimate a person’s chronological age. They were trained to predict age from methylation patterns rather than to directly predict disease, mortality, or other health outcomes.

The first generation of epigenetic clocks established that specific patterns of DNA methylation change consistently with age. By analyzing methylation at selected sites across the genome, researchers could develop mathematical models that estimate chronological age from a biological sample.

Two pioneering examples are:

• Horvath clock (2013): Developed by Steve Horvath using DNA methylation measurements from multiple human tissues and cell types. Its ability to estimate chronological age across diverse tissue types made it particularly influential.

• Hannum clock (2013): Developed by Gregory Hannum and colleagues using whole-blood DNA methylation data. The model used 71 methylation markers to predict chronological age and demonstrated that blood methylation patterns could provide accurate age estimates.

The Horvath and Hannum clocks were a major breakthrough in aging research because they demonstrated that DNA methylation contains a measurable biological signal of aging. Large datasets showed that methylation-based models could estimate chronological age with relatively high accuracy.

This established the foundation for the field of epigenetic aging and led researchers to ask a more important question: Can DNA methylation tell us not only how old someone is, but also how healthy or quickly they are aging?

The primary limitation is their training objective. First-generation clocks were optimized to predict chronological age, essentially how closely a person's methylation profile corresponds to their birthdate.

As a result, being older or younger according to a first-generation clock does not necessarily translate directly into a higher or lower risk of disease or mortality. Later-generation clocks were specifically developed to incorporate health-related outcomes and therefore may provide greater information about disease risk, mortality, and biological aging.

The evolution of epigenetic clocks can be broadly understood as a progression:

• First generation → Predict chronological age

• Second generation → Incorporate health and mortality-related outcomes

• Third generation → Estimate the pace at which biological aging is occurring

The Horvath clock is the flagship example of first-generation epigenetic clocks and remains an important reference point in the field. The next generation introduced clocks such as PhenoAge and GrimAge, which moved beyond simply predicting birthdate-based age. More recently, DunedinPACE shifted the focus toward measuring the rate of biological aging rather than estimating age in years.

The Horvath multi-tissue clock, developed by Steve Horvath and published in 2013, was a landmark development in epigenetic aging research. It demonstrated that DNA methylation patterns could be used to estimate chronological age across many human tissues and cell types.

The original study analyzed 8,000 samples from 82 Illumina DNA methylation datasets covering 51 healthy tissues and cell types and identified 353 CpG sites that together formed the aging clock.

Source: Horvath S. DNA methylation age of human tissues and cell types. Genome Biology. 2013;14.

• The clock uses DNA methylation measurements at 353 specific CpG sites—locations in DNA where methylation can be measured.

• It is called “multi-tissue” because it was developed and validated across approximately 51 different tissue and cell types, rather than being restricted to blood.

• These included tissues and cell types such as blood, skin, brain, liver, and other biological samples.

• This cross-tissue capability was a major innovation because it suggested that certain DNA methylation changes associated with aging are shared across different parts of the body.

• For aging research, this meant researchers could study epigenetic aging beyond blood-based samples and investigate whether similar age-related molecular patterns occur across different tissues.

The Horvath clock uses a statistical method called elastic-net regression to combine methylation levels at 353 CpG sites and generate an estimated age. In simple terms, the model learned which combinations of methylation changes were most useful for predicting a person's chronological age.

Key strengths of the Horvath clock include:

• Broad tissue applicability: It can be applied across multiple tissue and cell types.

• Strong age correlation: It demonstrated a strong relationship between predicted age and chronological age across diverse samples.

• Foundational importance: It provided the framework and benchmark for many subsequent DNA methylation-based aging clocks.

However, the Horvath clock has important limitations:

• Its age-prediction accuracy can be less consistent at the extremes of the age range, particularly among very young and very old individuals.

• Results from blood can be influenced by changes in blood cell-type composition, which may affect measured methylation patterns.

• As a first-generation epigenetic clock, it was primarily trained to predict chronological age rather than disease, mortality, or other health outcomes.

• Therefore, a higher or lower Horvath clock biological age should not be interpreted on its own as a direct measure of disease risk or life expectancy.

Yes. Although newer epigenetic clocks have been developed specifically to capture health risks and the pace of aging, the Horvath clock biological age remains an important research tool and foundational benchmark. It is particularly valuable for studying epigenetic aging across different tissues and for comparing newer biological-age measures with the original multi-tissue approach.

The Horvath clock also provides the historical starting point for the evolution of epigenetic clocks—from predicting chronological age to estimating healthspan, mortality risk, and biological aging rate.

PhenoAge is a biological-aging measure developed by Morgan E. Levine and colleagues and published in 2018 in Aging (Albany NY). It marked an important shift from first-generation epigenetic clocks because DNAm PhenoAge was designed to capture information related to phenotypic aging, healthspan, and mortality-related outcomes rather than simply predicting chronological age.

There are two closely related versions:

• Clinical PhenoAge: Uses chronological age plus nine routinely measured blood biomarkers to calculate phenotypic age.

• DNAm PhenoAge: Uses DNA methylation data to estimate the underlying phenotypic-aging concept from an epigenetic sample.

This distinction is important when comparing PhenoAge vs Horvath clock: Horvath was primarily trained to predict chronological age, whereas PhenoAge was designed to capture physiological aging and mortality-related risk.

The original PhenoAge blood biomarkers include nine clinical measures, together with chronological age:

The original PhenoAge algorithm combines these nine biomarkers with chronological age to generate a single phenotypic age estimate.

One practical advantage is that these biomarkers are generally available through routine blood testing, meaning a PhenoAge calculation can often be performed using laboratory results that may already be part of an annual health assessment.

GrimAge is a DNA methylation-based biological-aging measure developed by Ake Lu, Steve Horvath, and colleaguesand published in 2019. It represented another major evolution in epigenetic clocks because it was designed to predict time to death and other major health outcomes, rather than simply estimate chronological age.

Unlike a conventional age-prediction clock, GrimAge is technically a mortality-risk estimator that is expressed in years. Its strong associations with lifespan and healthspan have made it one of the most extensively studied epigenetic measures of aging.

A GrimAge biological age test produces an estimated age-like score, while its age-adjusted form, commonly referred to as GrimAge acceleration (AgeAccelGrim), indicates whether an individual's GrimAge is higher or lower than expected for their chronological age.

Research has found that higher GrimAge and GrimAge acceleration are associated with increased risks of all-cause mortality, cardiovascular disease, cancer, and other age-related health outcomes. In the original validation research, GrimAge showed strong associations with time-to-death, time-to-coronary-heart-disease, and time-to-cancer across large cohorts.

Rather than interpreting a GrimAge result as a prediction of an individual's lifespan, it is more accurate to view it as a population-validated biomarker associated with mortality risk. The strength of these associations can vary between populations and study designs.

GrimAge2 is an updated version that adds DNA methylation-based estimates of high-sensitivity C-reactive protein (CRP) and hemoglobin A1C (HbA1c). In multi-cohort analyses, GrimAge2 showed improved mortality prediction compared with the original GrimAge.

GrimAge is a composite epigenetic clock, not a single methylation score. Its original model combines a DNA-methylation-based estimate of smoking exposure with methylation-based surrogate measures for several circulating plasma proteins.

The original GrimAge components include:

• DNAm PACKYRS — estimated smoking pack-years

• DNAm ADM — adrenomedullin

• DNAm B2M — beta-2-microglobulin

• DNAm Cystatin C

• DNAm GDF-15 — growth differentiation factor 15

• DNAm Leptin

• DNAm PAI-1 — plasminogen activator inhibitor-1

• DNAm TIMP-1 — tissue inhibitor of metalloproteinase-1

Why is smoking included? Smoking is strongly associated with both accelerated biological aging and mortality. Incorporating a DNA-methylation-based estimate of smoking exposure therefore adds information relevant to mortality prediction.

An important distinction is that GrimAge does not directly measure these proteins from a blood sample. Instead, DNA methylation patterns are used to create surrogate estimates of their circulating levels. GrimAge2 expands this model by adding methylation-based surrogates for CRP and HbA1c.

DunedinPACE is a DNA methylation-based measure designed to estimate the pace of biological aging, rather than simply assigning someone an age. It was developed from the Dunedin Multidisciplinary Health and Development Study and published in 2022 in eLife.

The measure was developed using longitudinal data tracking changes in 19 indicators of organ-system integrity across four time points spanning approximately two decades. The researchers then distilled this information into a single-time-point DNA-methylation blood test and evaluated DunedinPACE in additional datasets.

Research found that DunedinPACE had high test-retest reliability and was associated with morbidity, disability, and mortality.

• DunedinPACE represents a distinct approach to biological aging because it measures how quickly a person's biological systems are aging at present rather than estimating a fixed biological age.

• The measure was developed using 173 CpG sites and machine-learning methods trained on approximately 20 years of longitudinal change across 19 biomarkers.

• These biomarkers represented multiple physiological systems, including cardiovascular, metabolic, renal, hepatic, immune, dental, and pulmonary health.

• One notable advantage is its high test-retest reliability, meaning repeated measurements under comparable conditions tend to produce consistent results. This is an important distinction when tracking changes in the pace of aging over time.

The DunedinPACE rate-of-aging score is interpreted relative to an expected aging pace:

• 1.0: Aging at the expected rate of approximately one biological year per calendar year.

• Above 1.0: Aging faster than the expected pace, indicating a faster biological-aging trajectory.

• Below 1.0: Aging more slowly than the expected pace, indicating a slower biological-aging trajectory.

Research has associated faster DunedinPACE with higher risks of morbidity, disability, and mortality in midlife and older adults. Subsequent research has also examined associations between DunedinPACE and factors such as early-life adversity and cognitive decline.

The key difference between DunedinPACE and an age-estimate clock is the concept of trajectory. A biological-age clock provides a snapshot of where your biological age appears to be at a particular point in time, whereas DunedinPACE attempts to capture the momentum or rate of aging.

This makes the DunedinPACE speed of aging particularly relevant when studying whether biological aging is changing over time. For example, when measured consistently before and after a lifestyle intervention, a change in the score may provide information about whether the underlying aging trajectory has shifted—although a single change should not be interpreted as proof that a particular lifestyle intervention caused the change.

Different epigenetic clocks measure different aspects of biological aging. Comparing them helps clarify why the same person can receive different results depending on the clock used.

There is no single “best” epigenetic clock for every purpose. Each clock was developed using a different training approach and captures a different dimension of aging.

• Horvath: Useful for estimating chronological age from DNA methylation and as a foundational research benchmark.

• PhenoAge: More focused on physiological aging and mortality-related health risk.

• GrimAge: Particularly useful for studying mortality and longevity-related outcomes.

• DunedinPACE: Focuses on the rate at which biological aging is occurring, making it useful for studying changes in aging trajectory.

For this reason, aging researchers often examine multiple epigenetic clocks together rather than relying on one score. Looking across different measures can provide a more complete picture of biological aging, because no single clock captures every mechanism or dimension of aging.

Your biological age is not necessarily fixed. Research suggests that several modifiable lifestyle factors are associated with healthier patterns of DNA methylation and slower epigenetic aging. These include:

• Regular physical activity: Higher physical activity has been associated with lower epigenetic age acceleration in several studies.

• Quality sleep: Consistent, restorative sleep is increasingly linked with healthier aging and DNA methylation patterns.

• A nutrient-dense diet: A balanced, minimally processed diet rich in vegetables, fruits, whole grains, legumes, healthy fats, and adequate protein may support healthy aging.

• Stress management: Chronic psychological stress can influence biological pathways involved in aging, although the relationship between stress reduction and changes in epigenetic age is still being studied.

• Not smoking: Cigarette smoke exposure is consistently associated with accelerated epigenetic aging, making smoking cessation one of the most important modifiable steps for healthy aging.

However, “reversing your biological age” should not be interpreted as a guaranteed or clinically proven outcome. Epigenetic aging is an active area of research, and studies are still determining which lifestyle interventions can produce meaningful, lasting changes in different biological-age measures.

If you use a biological age test based on DNA methylation, it is generally more useful to monitor your trend over time than to focus on a single result. For people using testing as part of a healthy-aging plan, repeating the same test approximately once a year can provide a practical interval for comparing changes, provided the same testing method, laboratory, and epigenetic clock are used where possible.

For people interested in understanding their biological age through DNA methylation, MyDiagnostics can provide testing that helps assess biological aging using validated laboratory methods. The results should be viewed as one part of a broader approach to healthy aging, alongside lifestyle, clinical, and other health assessments.

Explore the MyDiagnostics Biological Age Test

1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biology. 2013;14:R115. DOI: 10.1186/gb-2013-14-10-r115.

2. Hannum G, Guinney J, Zhao L, et al. Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates. Molecular Cell. 2013;49(2):359–367.

3. Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573–591. DOI: 10.18632/aging.101414.

4. Lu AT, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY).2019;11(2):303–327.

5. Lu AT, Binder AM, Zhang J, et al. DNA methylation GrimAge version 2. Aging (Albany NY).2022;14(23):9484–9549. DOI: 10.18632/aging.204434.

6. Belsky DW, Caspi A, Corcoran DL, et al. DunedinPACE, a DNA methylation biomarker of the pace of aging.eLife. 2022;11:e73420. DOI: 10.7554/eLife.73420.

Medical Disclaimer: A biological age test provides an estimate based on specific biomarkers and should not be considered a diagnosis or a definitive prediction of disease, lifespan, or future health. Results should be interpreted in context and discussed with a qualified healthcare professional when appropriate.