Are You Really Your Age?

Image Credit: [1]

Written by: Christopher Perez ‘27

Edited by: Grace Li ‘26

Aging is something we all share. We can mark the passage of time with a calendar, yet the amount of time that has passed since your birth—your chronological age— may not match the age of your body. That deeper measure is called biological age, and it offers a more meaningful glimpse into your health and disease risk.

Biological age reflects how our cells and tissues function at a molecular level. Two people who are both 50 years old may not have the same biological age: one may have the arteries and immune system of someone far younger, while the other may already show signs of premature decline. Understanding why this happens has changed the landscaping of aging.

At the center of aging research is epigenetics, the study of how gene activity is regulated without altering the genetic code—DNA. One type of studied epigenetic modification is DNA methylation, where small chemical tags attach to DNA and influence whether a gene is switched on or off. Because methylation patterns shift in predictable ways as we grow older, scientists have learned to use them as “epigenetic clocks” to estimate biological age.

The first generation of these clocks, such as Horvath’s clock, was trained to predict chronological age across various types of tissues [2]. After the first model, more clocks were developed and refined. For example, PhenoAge integrated clinical data to reflect age-related health changes, while GrimAge and its successor GrimAge2 linked DNA methylation to disease risk and lifespan [3]. Yet even the most advanced clocks lack information on inflammation, a critical alert of aging. As we age, our bodies often settle into a state of chronic, low-level inflammation, a phenomenon researchers call “inflammaging” [4]. Unlike the acute inflammation that heals a cut or fights infection, inflammaging is a smoldering process that gradually erodes tissues and organs. It contributes to many diseases of aging, including diabetes, cardiovascular disease, Alzheimer’s disease, and cancer. Many existing studies have shown that inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6) strongly predict mortality, in some cases more effectively than epigenetic clocks alone [5].

The challenge is that measuring inflammation usually requires repeated blood tests, which can be invasive and costly. Hence, the question for researchers is as follows: could we use non-invasive, epigenetic data like DNA methylation itself to capture both epigenetic aging and inflammation? If so, a single methylation test might offer a more complete and easily obtainable view of biological age.

That is the premise behind a new framework called EpInflammAge [6]. This model integrates epigenetic data with inferred inflammatory profiles to provide a dual measure of aging. Researchers designed it as a two-step system. First, they trained deep learning models to detect patterns in DNA methylation that predict levels of inflammatory proteins in the blood. Each model was tailored to a specific marker, ranging from cytokines like IL-6 to proteins such as CPR. For efficiency, the models used a feature selection method called minimum redundancy maximum relevance to identify the 100 most informative DNA sites for each marker. By comparing predictions against actual blood measurements, the models learned to “read” inflammation directly from methylation data.

The second step combined these inferred inflammation protein profiles with traditional methylation features to predict biological age. In one pilot study, researchers trained 32 separate neural networks on data from 329 individuals, including both healthy participants and those with kidney disease. The result was EpInflammAge, a composite clock capable of detecting both biological age acceleration and inflammation levels in disease states without the need for additional bloodwork.

The promise of EpInflammAge lies in its ability to reveal hidden burdens of aging. It could detect accelerated biological decline in patients with chronic disease before symptoms appear, offer a less invasive way to monitor inflammaging, and improve predictions of health outcomes. Its technology aims to merge epigenetic aging and chronic inflammation into one measure. Perhaps equally important, it provides researchers with new insight into the molecular interplay between DNA methylation and inflammation.

Consequently, deep learning models have limited clinical integration because they are unable to identify which DNA sites or inflammatory markers contribute the most to aging outcomes. Inferring inflammatory profiles from methylation is imperfect, and results may vary across populations. Even though clocks are powerful biomarkers, whether slowing epigenetic or inflammatory aging will extend a person’s healthy lifespan remains uncertain.

As a rule of thumb, chronological age will always count the years, but biological age reveals how those years have been lived. With tools like EpInflammAge on the horizon, we may one day measure age in cellular health, offering a clearer picture of how old we truly are.

References

[1] Epigenetic “Clocks” Predict Animals’ True Biological Age | Quanta Magazine [Internet]. Quanta Magazine. 

2022. Available from: https://www.quantamagazine.org/epigenetic-clocks-predict-animals-true-biological-age-20220817/ 

[2] Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biol 14, 3156. 

https://doi.org/10.1186/gb-2013-14-10-r115 

[3] Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD, Whitsel 

EA, Assimes TL, Ferrucci L, et al. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging, 11(2), 303–327. https://doi.org/10.18632/aging.101684 

[4] Franceschi, C., Garagnani, P., Parini, P. et al. (2018). Inflammaging: a new immune–metabolic viewpoint for 

age-related diseases. Nat Rev Endocrinol 14, 576–590. https://doi.org/10.1038/s41574-018-0059-4 

[5] Liu, R., Liu, M., Wang, C., Tao, Z., & Hu, G. (2025). Association of systemic immune-inflammation index 

(SII) with epigenetic age acceleration in adults: insights from NHANES. Epigenetics, 20(1). https://doi.org/10.1080/15592294.2025.2541248 

[6] Kalyakulina, A., Yusipov, I., Trukhanov, A., Franceschi, C., Moskalev, A., & Ivanchenko, M. (2025). 

EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning. International Journal of Molecular Sciences, 26(13), 6284. https://doi.org/10.3390/ijms26136284