Scientists at Moscow State University and Harvard Develop a Universal Tool for Measuring Biological Age
An international team that included researchers from Moscow State University and Harvard has developed a fundamentally new transcriptomic aging clock, tAge. The mathematical model analyzes gene-expression patterns to measure biological age and accurately predict the risk of age-related diseases in mammals.
Science has moved closer to solving the puzzle of how aging works. An international group of researchers that included scientists from Lomonosov Moscow State University, the Belozersky Institute of Physico-Chemical Biology, and Harvard Medical School has introduced a new bioinformatics tool: the universal transcriptomic aging clock tAge. The work, which has attracted significant attention from the scientific community, was published in Nature.
What Are Aging Clocks, and Can Aging Be Slowed?
So-called aging clocks play an important role in the biology of aging. These clocks are typically implemented as sophisticated mathematical models. Using biomarkers such as DNA chemical marks, physical-activity data, blood-test results, or even a person’s appearance, such models can estimate an organism’s chronological, or calendar, age with varying degrees of accuracy.
The researchers made a twofold discovery. They not only identified key marker genes that act as universal indicators of aging but also organized them into clusters with synchronized activity patterns. Many of these genetic modules proved to be functionally interconnected. This framework creates a direct path toward identifying so-called master regulators, the principal biological switches that control specific aging mechanisms, as well as pathways involved in adaptation and resistance to age-related decline.
Large-Scale Data and a Bioinformatics Achievement
A key advantage of tAge lies in both its universality and the scale of the training dataset. The machine-learning model was trained on more than 11,000 samples spanning 25 tissue and organ types across four mammalian species. Importantly, part of the dataset came from animals whose lifespans had been experimentally altered. As a result, tAge can detect subtle changes associated with health status, disease processes, and the effects of potential geroprotective interventions.
For Russia’s technology sector, the achievement carries particular significance. It demonstrates how globally competitive scientific products can emerge at the intersection of information technology, artificial intelligence, and genomics. In this case, the software model is more than a supporting analytical script. It represents a sophisticated scientific product in its own right, capable of processing vast biomedical datasets.
From the Laboratory to Preventive Medicine
tAge is neither a pharmacy-based rapid test nor a predictor of an individual’s date of death. However, the analytical framework could eventually transform preventive medicine.
In 2026, biological age is already beginning to enter practical healthcare discussions. Russian health centers are introducing screening programs in which physicians investigate potential causes of accelerated aging when biological age exceeds chronological age by more than five years. tAge could make such assessments substantially more precise by helping evaluate the risk of age-associated diseases, monitor treatment effectiveness, and even guide lifestyle interventions.
This aligns closely with national priorities. By 2030, relevant Russian government agencies have been tasked with organizing at least four large-scale studies focused on biological age assessment and the prevention of chronic diseases.
Four Directions for Development
The technology is likely to advance along several tracks. The first is fundamental research. tAge makes it possible to measure how specific factors affect particular aging modules. For example, researchers can examine how calorie restriction influences metabolic pathways or how chronic diseases affect inflammatory pathways.
The second area is pharmaceuticals. The clock could become a valuable tool for identifying and testing geroprotectors, while also helping monitor safety when experimentally activating rejuvenation programs in order to minimize cancer-related risks.
The third area is the development of digital services. Researchers have already launched a web application called TACO that allows users to calculate the biological age of their own samples.
However, the scientific community remains appropriately cautious. As researchers at Skoltech have previously noted, existing aging clocks can produce substantial errors when evaluating cellular rejuvenation, particularly after radical reprogramming, if a model is applied outside the scope of its training data. For that reason, tAge remains a biomarker that requires further validation across diverse populations.
Looking Back and Looking Ahead
The development of tAge represents the latest stage in the rapid growth of aging research over the past several years. In 2023, Pirogov Russian National Research Medical University introduced a neural network capable of estimating the biological age of the heart using 13 parameters. During 2024 and 2025, Skoltech and AIRI focused on the standardization and benchmarking of epigenetic models. Today, the field is moving toward more comprehensive transcriptomic approaches.
Biology is increasingly becoming a computational discipline. Age and disease risk are now being evaluated through mathematical models and algorithms. For Russia, participation in the creation of tAge represents a significant contribution to the global race to develop technologies for healthy longevity. In the coming years, researchers are expected to focus on validating the model, identifying new geroprotectors, and creating personalized digital health profiles.
