An international team of researchers has identified common transcriptomic “signatures” of aging and mortality across different mammalian species, according to a study published in the journal Nature. Based on these findings, the scientists developed molecular “clocks” capable of estimating biological age, disease risk, and even predicting life expectancy.

The research focused on how gene activity changes during the aging process. Scientists noted that previous biological clocks — such as those based on DNA methylation — often failed to explain the underlying biology of aging and were usually limited to specific tissues or species. The new approach instead relies on the transcriptome, the complete set of active RNA signals within cells.

The study included more than 11,000 transcriptomes from humans, mice, rats, and crab-eating macaques. Researchers also analyzed data from genetically diverse mice exposed to various interventions, including rapamycin, canagliflozin, captopril, 17-alpha-estradiol, calorie restriction, and high-fat diets.

Using survival models, including the Gompertz model, along with machine learning methods, scientists created transcriptomic “aging clocks” and “mortality clocks” designed to measure the body’s real molecular condition rather than simple chronological age.

The results revealed remarkably similar aging patterns across different mammalian species: • aging increases the activity of genes linked to inflammation and immune responses; • activity decreases in genes associated with mitochondrial energy production, tissue repair, and the extracellular matrix.

In other words, molecular aging appears to be strongly connected to chronic inflammation and declining cellular energy metabolism.

Researchers also examined single-cell RNA sequencing data and confirmed that age-related changes affect immune cells, blood vessel endothelium, liver cells, muscle tissue, and stem cells.

Particular attention was given to factors that influence aging. The study found that: • rapamycin and calorie restriction slow molecular aging; • inflammatory conditions and progeroid models accelerate it; • some aging-related processes may be partially reversible through interventions such as cellular reprogramming.

Scientists additionally identified several universal biomarkers associated with mortality risk in humans, including the proteins CDKN1A, LGALS3, and GPNMB, whose levels correlate with multiple diseases and mortality rates in large population databases.

The study’s main conclusion is that aging is not a random process, but a systemic biological phenomenon driven by shared molecular mechanisms across species and tissues. Key processes include inflammation, mitochondrial dysfunction, metabolic disruption, and reduced tissue regeneration capacity.

According to the authors, these transcriptomic “aging clocks” could eventually help detect early signs of aging long before disease symptoms appear and may be used to evaluate the effectiveness of anti-aging therapies. However, they stress that significant additional validation is still required before such tools can be applied clinically in humans.