Summary
A bioRxiv preprint reports that blood DNA methylation patterns vary across development, chronic disease and aging in humans and other mammals. The researchers also describe epigenetic clocks designed to predict expected mortality across species and tissues.
A bioRxiv preprint reports that changes in blood DNA methylation connect mammalian development, chronic disease, aging and mortality. The analysis combined a meta-analysis of 16 human chronic diseases with comparisons across mammals, and produced epigenetic clocks intended to predict expected mortality across species and tissues.
The study was published as a preprint on September 14, 2026, by Stanislav Tikhonov and Sergey E. Dmitriev of Lomonosov Moscow State University. It is an analysis of molecular data rather than an intervention study: the researchers examined patterns in DNA methylation, a chemical modification that helps regulate whether genes are more or less active without changing the DNA sequence itself.
Methylation patterns changed in different ways across disease and age
The authors examined methylation in blood from people associated with 16 chronic diseases. Their meta-analysis identified heterogeneous signatures—patterns that were not uniform across diseases—and grouped them into two major clusters. The clusters were distinguished by their associations with developmental changes and with methylation changes related to sex.
The study also assessed epigenetic entropy. In this context, entropy is a statistical measure of the heterogeneity or disorder in methylation patterns across DNA sites. The authors report that entropy increased steadily across the lifespan. Several diseases, however, were associated with lower blood DNA methylation entropy, even after the analysis accounted for differences in blood-cell composition. This adjustment matters because a blood sample contains multiple cell types, and changes in the proportions of those cells can alter the overall methylation signal.
A separate cross-species analysis found that many CpG sites followed U-shaped age-related methylation trajectories. CpG sites are locations in the genome where the DNA bases cytosine and guanine occur next to each other and are common sites of methylation measurement. A U-shaped trajectory means that methylation changes in one direction over part of the lifespan and then reverses direction later. The authors report that these patterns paralleled the U-shaped pattern of mortality across age.
This result challenges a simple view of aging as a steady accumulation of molecular damage. It instead points to a mixture of age-related processes, including changes that may reflect both early development and later-life decline. The finding is an association in the molecular data, not evidence that a particular methylation change is itself the cause of aging or disease.
Epigenetic clocks were designed to estimate expected mortality
Epigenetic clocks are statistical models that use methylation measurements to estimate an age-related biological state. In this work, the researchers developed clocks aimed at predicting expected mortality across different mammalian species and tissues. They report that the clocks also detected a range of disease models.
The proposed link between development, aging and mortality could give researchers a common molecular framework for comparing disease processes across mammals. It may also help evaluate disease models by testing whether their methylation patterns resemble age- or mortality-related changes seen in broader datasets. The work is therefore relevant to basic aging biology and to the design of molecular measures for preclinical research.
The findings remain preliminary because the study is a bioRxiv preprint and has not been presented in the supplied source as a peer-reviewed publication. The source abstract does not provide sample sizes, the species and tissues included, or quantitative performance measures for the mortality clocks. Those details will be important for assessing how consistently the patterns and predictions apply across populations, diseases and mammalian models.