The KDM-13 methodology.

1Biological age vs chronological age

Chronological age is the easiest number in medicine. You count the birthdays. It is exact, it is universal, and as a description of your health it is very nearly useless.

Two people walk into a clinic on the same morning. Both are fifty. One runs marathons and takes no medication; the other is three years from a heart attack he does not know is coming. The calendar says they are identical. Their bodies are not even close. The calendar cannot tell them apart, and it never will.

Biological age is the attempt to tell them apart. It is an estimate of how old your body is behaving, built from measurable signals in your physiology rather than from the date on your passport. And here is the part that makes it worth measuring at all: unlike the calendar, it can move in both directions. Your chronological age only ever climbs. Your biological age can fall.

The reason we read it from blood is practical. Blood is the most information-dense sample routine medicine ever collects. A single draw captures, at one moment, the state of your inflammation, your metabolism, your kidneys and liver, your oxygen-carrying capacity, and your blood-sugar control. Every one of those signals responds to how you eat, how you move, what you supplement, and how you are treated. That responsiveness is the whole point. A biological age you cannot change would be a curiosity. One you can change is a tool.

2Three ways to measure a body's age

There are three broad families of biological-age test, and they answer slightly different questions.

Blood-biomarker models read your active physiology from a blood sample: what your body is actually doing right now. DNA-based clocks read chemical tags on your DNA, at thousands of sites across the genome. And wearable-based estimates, the "body age" number from a smartwatch or fitness ring, are built mostly from heart rate, activity, and sleep. That last kind is the one most people have already met. It is convenient and genuinely motivating, but it does not measure your blood chemistry at all. It is a helpful nudge rather than a physiological measurement.

There is a second difference that matters more than the marketing admits. Many of these tests, the wearables especially but also a number of blood-based ones, run on proprietary algorithms that are never disclosed. You are given a number and asked to trust it, with no way to see how it was reached or whether the method behind it has ever been independently tested. A biological-age score you cannot interrogate is a brand asking for your faith, not a measurement you can check.

None of these is universally best, and anyone who tells you otherwise is selling something. They trade off against each other on four things: what they measure, how reliably they repeat, how fast they return an answer, and, the one that matters most for a person trying to get healthier, whether you can actually act on the result.

EVA™ is a blood-biomarker platform. Our engine, KDM-13, lives in that first family. We compare it directly and fairly to the DNA-based clocks later in this paper, because that comparison is where most of the marketing in this industry quietly cheats.

3Why we chose the Klemera-Doubal Method

Several methods can turn blood data into a biological age. We chose the Klemera-Doubal Method for one reason that overrode all others: when independent researchers have lined the methods up against each other, it wins.

It is worth understanding how it works, because the logic is elegant and you do not need mathematics to follow it. Two biomathematicians, Petr Klemera and Stanislav Doubal, published it in 2006. It runs in three movements.

First, for each marker, it learns the normal arc: how that marker drifts, on average, as people age across a large reference population. Second, for you specifically, it measures how far each of your markers sits from where it should be for someone your age. Third, and this is the move that separates it from cruder scoring systems, it combines all those deviations into a single estimate, but it does not treat them equally. A marker that tracks age tightly and reliably is given a loud voice. A noisy, unreliable marker is made to whisper. The result is a statistically optimal estimate that no single bad signal can distort.

The decisive evidence arrived in 2013. Morgan Levine took five competing biological-age algorithms and tested them head to head on 9,389 American adults followed for eighteen years. The Klemera-Doubal Method predicted who would go on to die more accurately than any other method tested, and significantly more accurately than chronological age itself.

You will notice a fair question here: what about PhenoAge? PhenoAge is an excellent and widely respected clock, and it is worth explaining why we did not build on it.

The difference comes down to what each method is tuned to do. PhenoAge is tuned to predict mortality; it is, in effect, a finely calibrated long-term risk estimate. That makes it superb at telling you where you stand today, but it moves reluctantly, because a risk estimate is designed to be stable, not sensitive. The Klemera-Doubal Method is tuned differently: it measures how far your body has drifted from its expected trajectory, which makes it far more responsive when that trajectory changes. For a platform whose entire purpose is showing you whether your efforts are working, responsiveness is exactly what you want.

There is a quiet irony worth noting. KDM is almost absent from the consumer market; most direct-to-consumer tests are built on epigenetic clocks or on PhenoAge, because those carry better name recognition. EVA™ chose the method the scientific literature rates most highly and built a consumer platform around it. For a company whose stated creed is evidence before opinion, there was no other honest choice.

And we did not simply take the literature's word for it. We rebuilt the method on the largest modern reference dataset available and tested it ourselves. It held. The numbers are in Section 5.

4Thirteen markers, and the ones we excluded

EVA™ measures more than seventy blood markers across seven body systems. You might assume a biological-age model should use all of them. It should not, and understanding why is the key to understanding the whole method.

A biological-age model does not improve as you add more inputs. It improves when every input carries real signal. A marker that varies for reasons unrelated to ageing adds no information; it adds noise, and noise makes the final number less stable without making it more true. The discipline is not in collecting markers. It is in selecting them.

KDM-13 uses thirteen, each one tested against four criteria. It must carry a genuine, well-documented ageing signal. It must be linked in the published literature to real health outcomes, not merely correlated with the calendar. Together with the others, it must cover the whole body rather than one corner of it. And it must be measurable to a high analytical standard in accredited laboratories, so that a change in your score reflects a change in you rather than a fluctuation in the assay.

The thirteen that qualified read inflammation, metabolism, kidney and liver function, blood-sugar control, and the health of your red and white blood cells. No major axis of ageing is invisible to the model.

Now, the obvious question: why thirteen? Why not five, or sixty? The answer is a balance. Too few markers, and the model is blind to whole regions of the body. Too many, and you begin drowning the signal in noise: every extra marker is another test that can drift, another value that can go missing, another way for your score to wobble for reasons that have nothing to do with you. In our testing, adding markers beyond our set stopped improving the model's real predictive power; the gain flattened, and further markers only added instability. We did not choose thirteen because the number is elegant. We arrived at it because that is where the evidence settled.

But the markers we excluded reveal more about our standards than the ones we kept.

Consider creatinine, a well-established marker of kidney function that we deliberately left out. The reference data that teaches the model what normal ageing looks like was measured decades ago using an older laboratory technique, the Jaffe method. Modern laboratories, including the one EVA™ uses, measure creatinine with a newer enzymatic method that reads systematically lower. The two are not directly comparable, so against a model trained on old-method values every healthy client appears abnormal, not because anything is wrong with them, but because the two measurements sit on different scales. We applied the published correction. It proved insufficient. So we removed creatinine entirely, and the model's accuracy improved: the kidney and protein signal it carries is already captured by blood urea nitrogen, which we retained. This is what accuracy before volume means in practice rather than as a slogan.

We also excluded the hormones, most vitamins, and the minerals. Not because they are unimportant; they matter a great deal, and EVA™ measures them as part of your clinical picture. But an ageing clock imposes a stricter requirement: a marker must change with age, reliably, across a population. Many of these do not. They therefore do their work elsewhere in EVA™, within the clinical interpretation, and remain outside the engine, where they would contribute noise rather than signal.

This is the unglamorous reality of building a trustworthy instrument. The headline number earns confidence not from how many markers went into it, but from how rigorously each one was tested before it was allowed in.

5How we know it works

A methodology is only as good as its evidence. Here is ours, stated plainly.

The reference population behind KDM-13 is NHANES, the US National Health and Nutrition Examination Survey. Run for decades by the American Centers for Disease Control, it has gathered detailed blood work and long-term health outcomes from tens of thousands of people, and almost every serious biological-age clock in the literature is built and tested on it. It is the closest thing medicine has to a definitive picture of how a large population actually ages.

We did not work from the raw survey, but from a cleaned and standardised research version of it, in which the measurements have been harmonised so they are comparable across the many years the survey spans. That groundwork was laid by a research team at Duke University, who assembled this dataset and made openly available reference implementations of the major methods so that others could build on them rigorously. KDM-13 stands on that foundation, and on the researchers who developed and validated these methods before us. Where we have gone further is in adapting the method for the population and the modern laboratory measurements EVA™ works with.

When we rebuilt the Klemera-Doubal Method on this dataset, using nearly thirty-nine thousand adults, and tested it against long-term survival data, KDM-13 predicted health outcomes better than chronological age alone. One measure of a test like this asks a simple question: given two people, one who fared worse and one who fared better, how often does the model correctly tell them apart? A coin toss would be right half the time. Knowing nothing but a person's age gets you surprisingly far, to around eighty percent, because age genuinely does predict a great deal. KDM-13 does better still. It beats the calendar. A biological-age model that could not outperform simply knowing your birthday would not be worth building.

There is one further piece of evidence that shaped our confidence. In the CALERIE trial, the only randomised controlled trial to test whether a biological-age measure actually responds to a deliberate intervention in humans, the Klemera-Doubal Method was the measure that moved. Participants on a sustained metabolic intervention showed a measurable slowing of their KDM-estimated ageing, while a control group aged normally. A clock that predicts outcomes is useful. A clock shown, under trial conditions, to respond when people change their behaviour is the one you want tracking your progress.

Finally, the model is not frozen. The expected ageing trajectories at its core are recalibrated as EVA™'s own regional cohort grows, which matters more than it sounds: most ageing clocks are trained on American and European populations, while the Gulf carries a distinct metabolic profile. And every result, before it reaches you, passes under the review of a qualified clinician. The instrument is held to evidence, and to human judgement, at every step.

6Blood versus DNA: what "accuracy" actually means

Much of this industry sells accuracy by the dollar. Nine hundred thousand DNA sites. A million sites. The implication is that more sites mean a more accurate test. It is worth stating clearly, because almost no one selling these tests will: that is a measure of resolution, not of accuracy.

Resolution is how finely an instrument reads. Accuracy is whether it tells you the truth. They are not the same thing, and confusing them is the central sleight of hand in consumer longevity testing. A camera with more megapixels is not a more honest photographer.

A biological-age test is accurate to the degree that it does three things: it predicts real health outcomes, it returns the same answer when you take it again, and it reflects something you can actually change. Judged on those three, a well-built blood-based model is not the poor relation of the epigenetic clock. On the third count in particular, it is the stronger instrument.

Epigenetic clocks read the tags on your DNA, a layer that is real and meaningful, but slow to shift and difficult to move deliberately. Blood markers read your living physiology: your inflammation this month, your metabolism this quarter, your blood sugar as it stands today. When you change how you eat, train, supplement, and are cared for, it is the blood that responds first and most visibly. An epigenetic clock can tell you, with great resolution, how you have aged. A blood-based clock can tell you whether what you are doing right now is working, and then, six months later, tell you again.

The two approaches are complementary, not enemies. But for the specific task of guiding what a person does next and confirming that it worked, a modifiable, fast, whole-body blood model is simply the more useful instrument. That is the precise and bounded ground on which EVA™ describes KDM-13 as the most accurate blood-based biological age test: not by dismissing the epigenetic clocks, but by refusing to accept resolution as a substitute for truth.

7How to judge any biological age clock

The category is crowded and the marketing is loud, so here is a plain rubric that anyone can apply to any biological-age clock, including this one. A trustworthy clock should be able to answer yes to all five questions.

1. What markers is it built on? They should reflect how you actually age, and meaningfully change with age. A number built on signals that barely shift over a lifetime cannot tell a younger body from an older one, however sophisticated it looks.

2. Is it transparent? Is the method named and the reasoning published, as it is in this paper, or is it a black box you are asked to trust on brand alone?

3. Is it actionable? Can the inputs actually change, so that retesting shows genuine progress rather than a fixed verdict?

4. Is it validated against outcomes? The method should predict real health outcomes, not merely correlate with the calendar. Correlating with age is easy. Predicting health is the hard, meaningful thing.

5. Is it reproducible? Ask whether the clock returns the same answer on retest. A clock that cannot state its own reliability is asking for trust it has not earned.

We offer this rubric knowing it will be applied to us. KDM-13 is built to answer yes to all five: a thirteen-marker panel, measured under laboratory quality control, built on the outcome-validated Klemera-Doubal Method, using markers you can move, and documented openly in the pages you are reading.

8What KDM-13 is, and what it is not

In keeping with the transparency this method is built on, the boundaries should be as clear as the claims.

KDM-13 is a directional, blood-based estimate of physiological ageing, designed to be tracked over time and read by clinicians. It is a well-validated instrument for a specific purpose.

It is not a medical diagnosis. It is not a screening test for any particular disease. It is not a prediction of anyone's lifespan, and it should never be read as one. Its power lies not in any single reading but in the trend across many; the direction of travel over repeated measurements matters far more than the figure returned on any one morning. A single biological-age score is a photograph. The trend is the film, and the film is what you are actually here to change.

9In summary

If you take one idea from this paper, let it be this: a biological-age number is only as good as the method behind it, and most tests never show you that method. This paper has shown you ours.

EVA™ measures your biological age from a single blood draw, using KDM-13. It rests on three things. The first is the method: KDM-13 is built on the Klemera-Doubal Method, which predicted health outcomes more accurately than every other method tested, and more accurately than age alone, in independent research. The second is careful selection: thirteen markers that cover your whole body, each chosen on evidence, with several well-known markers left out because the evidence did not support them. The third is checking and oversight: we use real, published figures, we keep the model tuned to our own regional population as it grows, and a qualified doctor reviews every result before it reaches you.

We are careful about what we claim. We do not say KDM-13 is the best possible measure of ageing ever devised. We say something more precise, and more useful to you: it is the best blood-based test, built on the best-validated method, for the one job that matters most, showing you how your body is ageing and helping you change it. A single score is a photograph. The trend over time is the film. We built KDM-13 to help you change how that film ends.

•References

• Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mechanisms of Ageing and Development. 2006;127(3):240-248.
• Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? The Journals of Gerontology: Series A. 2013;68(6):667-674.
• Liu Z, et al. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: a cohort study. PLOS Medicine. 2018;15(12):e1002718.
• Kwon D, Belsky DW. A toolkit for quantification of biological age from blood-chemistry and organ-function test data: BioAge. GeroScience. 2021;43(6):2795-2808.
• Waziry R, et al. Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. Nature Aging. 2023;3:248-257.