A New Biological Gene Clock Predicts Ageing, Disease, and How Long We Will Live

The Problem of Measuring Age

Chronological age does not accurately reflect biological ageing. Two individuals of the same age can differ greatly in disease burden, functional capacity, and mortality risk. Because of this, scientists have long searched for molecular biomarkers that better capture the true biological state of ageing organisms.

DNA methylation clocks became a major breakthrough following Horvath’s landmark 2013 study. However, these clocks have important limitations. They rely heavily on blood samples, provide limited mechanistic insight, and measure relatively stable epigenetic marks that may not fully capture dynamic cellular damage and physiological decline.

Tyshkovskiy, Kholdina, Davitadze, and colleagues introduced a major advance in Nature (2026) by developing transcriptomic clocks based on RNA-sequencing data from more than 11,000 tissue samples collected across mice, rats, macaques, and humans. Unlike methylation clocks that primarily estimate chronological age, these transcriptomic clocks predict mortality risk, normalized lifespan position, and biological deterioration.

Importantly, the authors also developed pathway-specific module clocks capable of measuring ageing within distinct biological systems such as inflammation, mitochondrial metabolism, chromatin regulation, and extracellular matrix maintenance. The result is a multidimensional gene clock capable of tracking how different cellular systems age across tissues, species, diseases, and interventions.

The Conceptual Architecture of the Transcriptomic Clock- From Chronological to Biological Time

One of the most important conceptual advances in this study is the shift from predicting chronological age to estimating expected mortality. The authors built this framework around Gladyshev’s deleteriome concept, which describes ageing as the progressive accumulation of molecular and cellular damage that gradually increases mortality risk. Within this model, chronological age becomes only an indirect approximation of true biological ageing.

To quantify biological deterioration, the investigators applied Gompertz survival models across multiple strains, sexes, interventions, and experimental cohorts to generate expected hazard estimates for each transcriptomic sample. They also introduced normalized age, calculated as chronological age divided by expected maximum lifespan, allowing ageing trajectories to be compared across species with dramatically different lifespans.

Using these approaches, the authors generated composite mortality scores that reflected not simply how old an organism was, but how biologically damaged it had become relative to its lifespan potential. Importantly, mortality-based transcriptomic clocks outperformed traditional chronological clocks in detecting lifespan-extending and lifespan-shortening interventions and performed comparably to advanced second-generation DNA methylation clocks in predicting mortality risk within the Framingham Heart Study.

Transcriptomic Signatures of Longevity and Mortality Revealed Across Multiple Tissues

To identify major molecular signatures associated with ageing, lifespan, and mortality, the authors analyzed liver gene expression from mice exposed to multiple interventions tested in the National Institute on Aging Interventions Testing Program (ITP). The study included beneficial, harmful, and neutral interventions, allowing investigators to compare molecular patterns linked to both lifespan extension and accelerated ageing.

Using survival data and Gompertz mortality models, the researchers generated biological ageing measures that integrated chronological age, mortality risk, and lifespan effects. Across thousands of rodent transcriptomes collected from multiple tissues and interventions, the investigators identified highly conserved transcriptomic signatures associated with ageing and survival.

The analysis showed that inflammatory and stress-response pathways — including interferon signaling, interleukin pathways, p53 signaling, complement activation, and coagulation pathways — consistently increased with ageing and mortality risk while showing negative associations with lifespan. In contrast, pathways linked to mitochondrial metabolism, oxidative phosphorylation, fatty acid metabolism, xenobiotic metabolism, and cellular energy production were associated with healthier ageing and longer lifespan.

Several important genes also emerged repeatedly as ageing biomarkers. Genes such as Igf1 and Ddost were associated with shorter lifespan and biological deterioration, while genes such as Fmo3 and Nmrk1 were linked to longevity, improved metabolism, inflammation control, and NAD+ biosynthesis. Overall, the findings demonstrate that ageing and mortality are strongly associated with increasing inflammation and declining mitochondrial and metabolic function, whereas lifespan extension is linked to preservation of energy metabolism, stress resistance, and damage-clearance pathways.

Transcriptomic Clocks Accurately Measure Biological Age and Mortality Risk

To develop transcriptomic biomarkers capable of measuring biological ageing and mortality across multiple tissues, the authors trained machine learning models using thousands of mouse and rat RNA sequencing samples collected from 26 different tissues. The models were designed to predict chronological age, normalized lifespan position, and expected mortality risk.

Initially, the investigators developed chronological ageing clocks that predicted age with high accuracy across tissues. To further improve performance, they created “relative” transcriptomic clocks by comparing each sample to matched control samples from the same tissue and dataset. This approach reduced technical variation and dramatically improved predictive accuracy, producing transcriptomic ageing predictions comparable to advanced DNA methylation clocks.

The clocks remained highly accurate even when entire tissues or datasets were excluded during training, demonstrating that ageing-associated transcriptomic signatures are broadly shared across organs rather than being strictly tissue-specific.

The investigators also developed mortality-based transcriptomic clocks that integrated ageing and lifespan information into a single biological ageing measure. These mortality clocks performed especially well in distinguishing lifespan-shortening conditions from lifespan-extending interventions. While simple chronological clocks could detect harmful ageing conditions, they were less effective at identifying beneficial longevity interventions.

In contrast, mortality clocks consistently predicted higher transcriptomic age in short-lived models and lower transcriptomic age in long-lived models. Several genes repeatedly emerged as major ageing biomarkers, including GPNMB, CST7, and CDKN1A, all of which are associated with inflammation, immune activation, lysosomal stress, and cellular senescence. Conversely, genes linked to tissue repair and regeneration, such as NREP, COL1A1, and COL3A1, progressively declined during ageing and mortality acceleration (Table 1).

Table 1. Key Gene Actors in the Transcriptomic Ageing Clock

Transcriptomic Clocks Reveal Conserved Molecular Ageing Across Mammals

To determine whether ageing-associated transcriptomic signatures are conserved across mammals, the authors expanded their analysis to include thousands of transcriptomes from crab-eating macaques and humans in addition to mouse and rat samples. Altogether, the dataset included more than 11,000 tissue samples collected from multiple organs across four mammalian species.

Using these datasets, the investigators developed universal multi-species transcriptomic clocks capable of predicting chronological age, normalized lifespan position, and expected mortality. Remarkably, the clocks performed well across all species, including species not used during training, demonstrating that core molecular ageing patterns are highly conserved throughout mammalian evolution.

Several important genes consistently increased with age across rodents and primates, including Gpnmb, Vsig4, Cdkn1a, and Eda2r, all of which are strongly associated with inflammation, stress responses, immune activation, and cellular senescence. In contrast, genes involved in tissue repair and extracellular matrix maintenance, such as Nrep, Col1a1, and Col3a1, progressively declined with age across species.

At the pathway level, ageing was consistently associated with increased inflammatory, interferon, TNF, p53, hypoxia, and coagulation signaling, while mitochondrial metabolism, oxidative phosphorylation, DNA repair, and collagen formation steadily declined. Overall, the findings demonstrate that mammals share a highly conserved molecular architecture of ageing despite millions of years of evolutionary divergence, suggesting that many core ageing mechanisms are universal across species.

Single-Cell Transcriptomics Reveals a Universal Molecular Signature of Ageing

To determine whether ageing-associated changes observed in whole organs also occur within individual cell types, the authors analyzed the Tabula Muris Senis single-cell RNA sequencing dataset, which contains cells collected from mice at different ages.

Because single-cell sequencing often captures only limited RNA information from each cell, the investigators used a metacell strategy in which cells from the same tissue and animal were pooled together to improve signal quality. This approach allowed transcriptomic clocks to predict age with high accuracy across tissues.

The clocks were then applied separately to different cell populations, and almost all cell types showed clear increases in transcriptomic age as the animals became older. Importantly, even stem cells — including mesenchymal and hematopoietic stem cells — displayed strong ageing-related molecular changes, suggesting that ageing affects not only mature cells but also the body’s regenerative cell populations.

Across multiple cell types, ageing was consistently associated with increased expression of genes linked to inflammation, senescence, and cellular stress, including Cdkn1a, Lgals3, Casp1, S100a8, and S100a4, whereas Sparc, a gene involved in tissue repair and wound healing, progressively declined.

Overall, the findings demonstrate that ageing produces highly similar molecular damage signatures across many tissues and cell types despite their very different biological functions.

The Modular Architecture of Ageing Revealed by Transcriptomic Clocks

To better understand how different biological pathways contribute to ageing and longevity, the authors analyzed large transcriptomic datasets using weighted gene co-expression network analysis (WGCNA). This method groups genes that behave similarly during ageing and under different interventions.

The analysis identified 28 major gene modules, each representing groups of genes involved in related biological functions such as inflammation, immune responses, mitochondrial metabolism, extracellular matrix organization, and stress signaling. Importantly, these modules were highly consistent between males and females, demonstrating that the observed ageing patterns were robust across sexes.

Immune and inflammatory modules increased strongly with age and mortality risk, whereas mitochondrial metabolism, oxidative phosphorylation, and lipid metabolism modules were associated with healthier ageing and longer lifespan. Interestingly, some pathways, including heat stress responses and extracellular matrix remodeling, appeared to function as adaptive or compensatory responses during ageing.

The investigators then transformed these modules into pathway-specific ageing clocks. Instead of generating only one overall biological age score, the system could now measure ageing within individual biological pathways. Altogether, 23 module-specific clocks were developed.

These clocks preserved the relationships between different ageing pathways, with immune-related clocks clustering together and metabolic pathways forming distinct groups. The researchers then tested the system under different biological conditions.

In mice injected with lipopolysaccharide (LPS), which induces strong inflammation, transcriptomic age increased dramatically, particularly within inflammatory and immune-related modules. In contrast, caloric restriction reduced transcriptomic age across many modules, with the strongest rejuvenating effects occurring in mitochondrial metabolism, oxidative phosphorylation, lipid metabolism, and stress-response pathways.

These findings demonstrate that different interventions influence ageing through distinct biological systems and that module-specific transcriptomic clocks provide a powerful framework for measuring pathway-level ageing and rejuvenation.

Accelerated Ageing in Klotho-Deficient Mice Is Linked to Energy Failure Rather Than Inflammation

To further validate the transcriptomic ageing biomarkers, the authors studied Klotho knockout mice, a well-established model of accelerated ageing. Klotho is considered a major longevity protein involved in regulating metabolism, oxidative stress protection, and cellular maintenance. Mice lacking Klotho age rapidly and typically survive only 4–5 months, whereas increased Klotho expression can extend lifespan.

Using RNA sequencing of kidney and skeletal muscle tissues, the investigators found that both chronological and mortality transcriptomic clocks detected strong increases in biological age in Klotho-deficient mice, especially in the kidney where Klotho is normally highly expressed.

One of the strongest ageing-associated genes was Cdkn1a, a major senescence marker elevated in both tissues. Module-specific clocks revealed that the strongest ageing acceleration occurred in pathways related to mitochondrial metabolism, oxidative phosphorylation, cholesterol metabolism, and NRF2 signaling, whereas inflammatory and interferon pathways remained relatively unchanged.

These findings suggest that Klotho deficiency accelerates ageing primarily through metabolic and mitochondrial dysfunction rather than inflammation. The affected tissues also showed broad suppression of genes involved in energy production, respiration, and fatty acid metabolism, closely resembling transcriptomic patterns observed in naturally aged and short-lived animals.

The researchers then investigated whether Klotho deficiency caused ageing-associated changes across individual cell types using single-nucleus RNA sequencing of kidney and brain tissues. In the kidney, nearly all cell types displayed accelerated mortality-associated transcriptomic age, confirming that Klotho deficiency produces widespread cellular ageing effects.

Major molecular changes included increased Cdkn1a and ceruloplasmin expression together with suppression of metabolic and detoxification genes. In the brain, neurons, astrocytes, and oligodendrocytes also showed accelerated transcriptomic ageing, although microglia did not display strong inflammatory activation.

Overall, the findings demonstrate that Klotho deficiency drives systemic biological ageing primarily through metabolic collapse and mitochondrial dysfunction while also showing that different tissues and cell types respond to ageing stress in distinct ways.

Transcriptomic Clocks Reveal How Cellular Damage and Rejuvenation Shape Biological Age

To determine whether transcriptomic ageing signatures observed in living animals could also be reproduced in laboratory cell models, the authors applied their transcriptomic clocks to cultured human fibroblasts undergoing stress and senescence.

In normal human fibroblasts, both chronological and mortality transcriptomic age steadily increased over time as the cells aged in culture and gradually lost their proliferative capacity. Similar ageing-associated increases were also observed in WI-38 fibroblast cells undergoing replicative senescence.

Interestingly, transcriptomic age correlated more strongly with time in culture than with the number of cell divisions, suggesting that molecular ageing changes emerge before obvious cellular deterioration becomes visible. One of the strongest ageing-associated genes was CDKN1A, a major marker of cellular stress and senescence.

Importantly, when the cells were engineered to express hTERT, which prevents senescence and extends cellular lifespan, the increase in transcriptomic age almost completely disappeared. Similarly, induced pluripotent stem cells generated from fibroblasts showed dramatically reduced transcriptomic age, indicating that cellular reprogramming can partially reset biological ageing signatures.

The investigators then tested whether acute cellular stress could rapidly accelerate transcriptomic ageing. Human fibroblasts exposed to metabolic stressors such as oligomycin and 2-deoxyglucose showed clear increases in transcriptomic age. Likewise, mouse and naked mole rat fibroblasts exposed to gamma irradiation developed strong ageing-associated transcriptomic changes.

These findings suggest that transcriptomic clocks measure accumulated cellular damage rather than simply the passage of time. Interestingly, DNA methylation clocks did not respond strongly to these short-term stress conditions, indicating that transcriptomic and epigenetic clocks may capture different aspects of biological ageing.

Module-specific transcriptomic clocks further revealed that irradiation strongly affected pathways related to chromatin regulation, inflammation, mitochondrial function, and metabolic stress, whereas metabolic inhibitors mainly altered mitochondrial and energy-related pathways.

Across different stress and rejuvenation models, several genes consistently emerged as universal ageing biomarkers, including CDKN1A, APOD, and EDA2R. Overall, the study demonstrates that transcriptomic clocks can sensitively detect both cellular damage and rejuvenation across multiple biological systems and experimental conditions.

Transcriptomic Mortality Clocks Link Chronic Disease, Inflammation, and Biological Age

To determine whether chronic diseases produce ageing-like molecular changes, the authors analyzed multiple rodent disease models including Alzheimer’s disease, chronic kidney disease, diabetic nephropathy, ischemic stroke, fatty liver disease, and liver cancer.

In nearly all disease models, both chronological and mortality transcriptomic clocks detected strong increases in biological age. In stroke models, transcriptomic age increased rapidly in both the heart and brain, with especially strong ageing signals observed in damaged brain regions and activated microglia. These findings demonstrate that chronic diseases can rapidly accelerate molecular ageing within affected tissues.

Interestingly, liver cancer behaved differently. Certain tumour-related pathways appeared biologically younger because cancer cells become highly proliferative and dedifferentiated, resembling embryonic-like states. However, tumours simultaneously displayed strong inflammatory and metabolic ageing signatures. This suggests that cancer contains both youthful and pro-ageing molecular features at the same time.

Across multiple diseases, several genes consistently emerged as major markers of biological deterioration, particularly Gpnmb, Cdkn1a, and Lgals3. These genes are strongly linked to inflammation, immune activation, cellular stress, and chronic tissue damage.

Module-specific transcriptomic clocks revealed that inflammatory and interferon-related pathways showed the strongest and most consistent ageing acceleration across diseases, further supporting the central role of chronic inflammation in ageing-associated pathology.

Importantly, similar findings were also observed in human diseases including Crohn’s disease, ulcerative colitis, chronic kidney disease, heart failure, and Alzheimer’s disease. In the Framingham Heart Study, transcriptomic mortality clocks successfully predicted risk of death and performed similarly to advanced DNA methylation ageing clocks.

Finally, large UK Biobank proteomics datasets showed that proteins such as GPNMB, CDKN1A, and LGALS3 were strongly associated with mortality, cardiovascular disease, diabetes, kidney failure, obesity, hypertension, depression, and several other chronic conditions.

Together, these findings identify GPNMB, CDKN1A, and LGALS3 as highly conserved biomarkers of ageing, mortality, and chronic disease across tissues, species, and molecular systems.

Exposure to Young Blood Reverses Inflammatory and Ageing Signatures in Old Animals

To identify molecular signatures associated with slowing or reversing biological ageing, the authors analyzed liver gene expression from mice subjected to heterochronic parabiosis, a procedure in which old mice share blood circulation with young mice.

Previous studies have shown that exposure to young blood can improve stem cell activity, tissue repair, neurogenesis, blood vessel formation, and overall physiological function in old animals. Earlier work from the same group also demonstrated that heterochronic parabiosis reduces epigenetic age and can even extend lifespan in old mice.

In this study, transcriptomic clocks showed that old mice exposed to young blood developed significantly lower transcriptomic age, particularly after the animals were separated and allowed to recover for an additional two months. In contrast, young mice temporarily displayed slightly increased transcriptomic age after sharing circulation with old animals, although this effect disappeared following recovery.

One of the strongest rejuvenation-associated genes was Nrep, which increased markedly in old mice exposed to young blood and is linked to tissue regeneration and repair. At the same time, important ageing and inflammatory genes such as Cdkn1a and Vcam1 were reduced.

Module-specific transcriptomic clocks demonstrated that rejuvenation effects occurred across multiple biological systems, indicating a broad systemic anti-ageing effect rather than changes limited to a single pathway.

Old mice exposed to young blood also showed reduced inflammatory, interferon, p53, and apoptosis-related signaling together with increased oxidative phosphorylation and lipid metabolism, effectively reversing many ageing-associated molecular patterns.

Overall, the findings suggest that young circulation can partially reset ageing-associated transcriptomic signatures and improve multiple biological systems simultaneously.

Embryos Undergo a Natural Molecular Rejuvenation Before Ageing Begins

To understand how biological age changes during early development, the authors analyzed gene expression patterns in mouse embryos from fertilization to birth.

Previous DNA methylation studies had suggested that embryos undergo a natural rejuvenation phase early in development before ageing resumes later in life. Using transcriptomic ageing clocks, the investigators observed a very similar U-shaped pattern of biological age.

Transcriptomic age steadily decreased during early embryonic development, reached its lowest point around embryonic day 10, and then began increasing again afterward. This finding suggests that embryos experience a natural molecular rejuvenation event during development, creating a biological “ground zero” before lifelong ageing begins (Fig. 1).

Fig. 1 U-shaped transcriptomic aging during embryogenesis

Several important ageing-associated genes, including Cdkn1a, Lgals3, S100a8, and S100a9, were strongly reduced during this rejuvenation phase and subsequently increased again after embryonic day 10.

Inflammatory, interferon, p53, and metabolic stress pathways were also broadly suppressed during early embryogenesis and later became activated as development progressed. Interestingly, similar rejuvenation-associated molecular patterns were also observed in caloric restriction and heterochronic parabiosis experiments, suggesting that different anti-ageing interventions may share common biological mechanisms.

Module-specific transcriptomic clocks demonstrated that many pathways, including mitochondrial metabolism, lipid metabolism, and stress-response systems, followed similar U-shaped rejuvenation patterns during development.

The researchers also examined individual embryonic cell lineages using single-cell RNA sequencing. Almost all major developing cell types across the three germ layers showed decreasing transcriptomic age during early embryogenesis, indicating that rejuvenation occurs broadly across the developing embryo. However, some extraembryonic tissues did not display the same rejuvenation pattern, suggesting that the effect may be largely restricted to embryonic tissues themselves.

Overall, the findings strongly support the idea that mammalian embryos naturally undergo a systemic rejuvenation process early in development and that many of the same molecular pathways involved in ageing and rejuvenation later in life are already active during embryogenesis.

Conclusion: A New Grammar of Biological Time

The work of Tyshkovskiy and colleagues represents far more than the development of another ageing biomarker. This study introduces a fundamentally new framework for understanding biological time. Ageing is not portrayed as a single uniform process progressing equally throughout the body. Instead, the study demonstrates that ageing consists of multiple interconnected biological programs, each advancing at different rates across tissues, pathways, and cellular systems depending on genetics, environment, disease burden, and interventions.

The transcriptomic gene clock, particularly in its modular form, provides an unprecedented view into this complexity. Rather than simply estimating chronological age, the clock can identify which biological systems are ageing most rapidly, which molecular pathways are driving damage accumulation, and how different interventions alter these ageing trajectories.

In many ways, the study transforms the ageing organism from a biological “black box” into a partially transparent system in which molecular deterioration, inflammation, mitochondrial dysfunction, regenerative decline, and mortality risk can now be directly observed and quantified.

Several molecular signatures repeatedly emerged throughout the study, particularly CDKN1A, LGALS3, GPNMB, inflammatory signaling pathways, and mitochondrial dysfunction. These pathways appeared consistently across tissues, diseases, species, cellular stress models, rejuvenation experiments, and mortality analyses. Their remarkable conservation across mammals strongly suggests that they represent core biological mechanisms of ageing shaped by deep evolutionary pressures rather than random molecular drift.

Importantly, the study also demonstrates that biological age is not fixed. Young-blood exposure, caloric restriction, embryonic development, and cellular reprogramming all partially reversed transcriptomic ageing signatures. These findings suggest that at least some components of biological ageing remain plastic and modifiable.

The transcriptomic clock therefore becomes not only a biomarker of ageing, but also a powerful platform for measuring rejuvenation and evaluating future anti-ageing interventions.

Ultimately, this work moves the field closer to a future in which ageing can be measured, monitored, dissected into pathway-specific components, and potentially manipulated at the molecular level. The gene clock is therefore not simply reading the biology of ageing — it may eventually help scientists learn how to rewrite it.

Reference

Tyshkovskiy, A., Kholdina, D., Davitadze, M. et al. Universal transcriptomic hallmarks of mammalian ageing and mortality. Nature (2026). https://doi.org/10.1038/s41586-026-10542-3

Image credit: Portions of the figure in this article were generated using ChatGPT (OpenAI) or Google Gemini.

Disclaimer: This blog post is intended solely for educational and scientific informational purposes. Any mention of therapeutic drug names, including FDA-approved medications, is for the purpose of accurate reporting and discussion of biomedical research and does not constitute medical advice, endorsement, or promotion. Readers should not interpret the content as a recommendation for any specific treatment. Always consult a qualified healthcare professional for medical advice or treatment decisions.

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