The blood work you get at a yearly physical reads a surprisingly short list. A cholesterol breakdown, fasting glucose, a few liver and kidney values, a blood count, maybe a thyroid number if your doctor is curious. Add it up and a standard panel covers somewhere around fifty markers.1 Your body, for reference, runs on roughly thirty-seven trillion cells.2
That panel has earned its place. It catches a lot, it’s relatively cheap, and most of modern medicine is built around it. But it has a hard ceiling. Most of the numbers on it only move once something has already gone wrong, and fifty readings can describe only so much of a system this complicated.
Here’s the part that tends to surprise people. The same blood holds millions of additional signals. We just didn’t have a way to read them until fairly recently. These signals sit in the epigenome.
What a biomarker actually is
A biomarker is any measurable signal that tells you something about your health, your risk, or how you’re responding to treatment. Blood pressure is a well-known example. So is your HbA1c, or your resting heart rate. The good ones are reliable, repeatable, and ideally they shift early, before you experience any symptoms.
That last part is where the standard panel struggles. A lot of its markers flag trouble late. By the time fasting glucose tips you into the diabetic range, the underlying metabolic problem has usually been building for years. So the search for better medicine is, at its core, a search for better signals: more of them, and read sooner.
The fifty-marker ceiling
Picture trying to follow a national economy with fifty numbers checked once a year. You’d catch the recessions and the obvious booms. You’d miss almost everything happening underneath: the early slowdowns, the slow drifts, the warning signs that only show up when you can watch thousands of indicators together.
Conventional lab panels offer a similar level of resolution. Each marker is its own isolated reading. They rarely show how different systems are interacting, and they’re designed to confirm disease rather than see it coming. Doing better isn’t a matter of running more of the same kind of test. It takes a fundamentally richer signal.
A second data layer, sitting on top of your DNA
Your DNA gets most of the attention, but plays less of a role in everyday health than you might think. This genetic sequence that you inherit is the same in every cell and doesn’t change across your life. It tells you plenty about inherited risk and almost nothing about how you’re doing this week.
Layered on top of your DNA is your epigenome: a dense set of chemical marks that decide which genes get switched on, which get turned down, and which get shut off completely. The best-studied of those marks is DNA methylation, a small chemical tag that attaches at specific spots on the genome called CpG sites.3
The epigenome, unlike the genome, is constantly changing. It responds to age, sleep, diet, stress, exercise, infection, smoking. It behaves less like a blueprint and more like a running log of what your body has actually been doing. Smoking is a great example of your epigenetics at play. A regular habit of using cigarettes leaves such a consistent methylation mark on a gene called AHRR that researchers can often tell from a blood sample alone whether someone smokes.4
And the resolution is on another level entirely. Where a standard panel reads those fifty biomarkers we discussed, that same blood – when analyzed with epigenetics through DNA methylation – reads about a million signals from a few drops of blood.
Why methylation is such a good place to look
A few things make DNA methylation valuable. First, it’s broad. A million readings spread across the genome capture a great deal of biological activity in one pass, across metabolic pathways, immune signaling, inflammation, and more. Second, it reflects the part of your biology you can actually change. The epigenome answers to behavior and environment, which your fixed DNA does not. By one widely cited estimate, your DNA sequence accounts for only about 20% of long-term health outcomes, with the other 80% being driven by epigenetics.5
Last, the most important health insights live in the pattern of biomarkers read together. No single methylation site tells you much on its own.6 The relationships across thousands of them are where the information actually hides.
Reading a million signals at once
The epigenome’s information density is also why the signal has sat unused for so long. Previous technology hasn’t been able to measure and read a million data points at once. A methylation profile is a sprawling, high-dimensional pattern nearly impossible to decipher — until now. Pulling meaning out of the epigenome is exactly a problem built for machine learning.
A model can learn which combinations of methylation marks line up with a biological age, a disease risk, and a likely response to a drug. The biology supplies the signal and the software does the reading. Neither half works alone, which is part of why this approach only became practical recently, once the science was solid and computing had gotten cheap enough to make it worth doing at scale.
That’s roughly how the Infinite Epigenetics platform runs – it reads the language of our biology. You collect a small blood sample at home. Our CLIA-certified lab reads about a million epigenetic signals from it. Our proprietary model then turns those raw signals into something you and your clinician can use. Compared to standard labs such as a lipid panel, we can detect hundreds of biomarkers with a lot less blood (and a better user experience at that – we don’t require a full phlebotomy like most standard labs do).
Infinite Epigenetics reads the body’s most information-dense signal, the epigenome, to make health measurable in ways it hasn’t been before. Its operating companies, TruDiagnostic (clinical) and Tally Health (consumer), have together collected more than 120,000 epigenetic samples and supported over 50 peer-reviewed publications. Written for general education; not medical advice.
Sources
- Labcorp OnDemand, “Standard Health Test,” Labcorp OnDemand, accessed June 2026, ondemand.labcorp.com/lab-tests/basic-wellness.
- Bianconi et al., “An Estimation of the Number of Cells in the Human Body,” Annals of Human Biology, 2013.
- National Human Genome Research Institute, “Epigenomics Fact Sheet,” NIH, updated August 16, 2020. https://www.genome.gov/about-genomics/fact-sheets/Epigenomics-Fact-Sheet
- Joehanes et al., “Epigenetic Signatures of Cigarette Smoking,” Circulation: Cardiovascular Genetics, 2016.
- Rappaport SM, “Genetic Factors Are Not the Major Causes of Chronic Diseases,” PLoS ONE, 2016.
- Horvath S, “DNA Methylation Age of Human Tissues and Cell Types,” Genome Biology, 2013.