The research focused on 75 patients diagnosed with stage I non-small cell lung cancer between 1998 and 2008, all of whom underwent surgical resection with curative intent. DNA methylation data from blood samples collected at diagnosis were used to calculate epigenetic age using four different epigenetic clocks: Horvath, Hannum, PhenoAge, and GrimAge2. Unlike chronological age, which simply counts years since birth, epigenetic age reflects cumulative molecular changes shaped by genetics, environmental exposures, and lifestyle factors. When epigenetic age exceeds chronological age, the deviation is termed epigenetic age acceleration, a biomarker previously linked to cancer incidence, cardiovascular disease, and all-cause mortality.

The results were striking. Short-term survivors, patients who died within roughly four years of diagnosis, showed consistently positive epigenetic age acceleration across all four clocks, while long-term survivors, those who lived well beyond a decade, showed consistently negative values, indicating decelerated biological aging. The largest gap appeared with GrimAge2: short-term survivors had a mean acceleration of 3.4 years, while long-term survivors showed a mean deceleration of 3.7 years, a difference that was highly statistically significant. This pattern suggests that the pace of biological aging, measured at the moment of diagnosis, carries meaningful information about survival that traditional clinical metrics do not capture.

Using Cox proportional hazards models adjusted for chronological age, sex, and smoking status, the researchers found that every year of epigenetic age acceleration increased mortality risk across all clocks. Hannum epigenetic age acceleration showed the strongest hazard ratio at 1.09, meaning a 9 percent increase in mortality risk per year of acceleration, while GrimAge2 delivered the best overall predictive discrimination, achieving a C-index of 0.73 compared to 0.69 for the base model. Notably, all epigenetic clocks except Horvath outperformed pack-years, the standard measure of cumulative smoking exposure, in predicting survival. GrimAge2 improved the C-index by 0.042 over the base model, compared to just 0.008 for pack-years alone.

The team then turned to random survival forests, a machine learning method for time-to-event data, to confirm these findings with non-parametric modeling. Refitting each model 500 times to obtain robust estimates, they found that GrimAge2 epigenetic age acceleration consistently delivered the lowest out-of-bag prediction error at 33.6 percent, compared to 38.4 percent for the base model. More remarkably, in the GrimAge2 model, epigenetic age acceleration ranked as the most important predictor in 97.6 percent of iterations, surpassing even smoking status, which had dominated all other models. This finding underscores that GrimAge2 captures biological signals relevant to lung cancer survival that extend beyond what smoking history alone can explain.

A critical question was whether GrimAge2’s prognostic value simply reflects cumulative tobacco exposure, since the clock includes a methylation-based proxy for pack-years. The sensitivity analyses answered this decisively. Self-reported pack-years explained only 9.6 percent of the variance in GrimAge2 epigenetic age acceleration, and adjustment for pack-years minimally attenuated the association, which remained statistically significant. GrimAge2 also enhanced model discrimination beyond pack-years alone, and remained the most important predictor even after adjustment. The researchers suggest this residual value reflects both superior measurement, avoiding recall bias, and individual biological variability that cumulative exposure alone cannot capture. GrimAge2 is also unique among clocks in including a surrogate for TIMP-1, a protein secreted by lung tumors and identified as a marker of poor prognosis in lung cancer.

Partial dependence plots revealed a non-linear relationship between GrimAge2 acceleration and mortality risk. For positive values, predicted mortality increased approximately linearly from 39 percent at zero years of acceleration to 50 percent at five years, before plateauing. For negative values, the effect was more modest, with risk decreasing to 34 percent at six years of deceleration. When stratified by smoking status, the shape of the relationship remained consistent across groups, but absolute mortality risk differed substantially. Current smokers showed markedly higher predicted mortality than former smokers at equivalent epigenetic age acceleration levels, such as 61 percent versus 45 percent at five years of acceleration. This finding suggests that ongoing tobacco exposure interacts with biological aging pathways in ways not fully captured by epigenetic age alone.

Kaplan-Meier survival curves stratified by GrimAge2 acceleration status revealed dramatic survival differences. Among the 16 patients with high acceleration, defined as more than five years of GrimAge2 epigenetic age acceleration, 94 percent died within five years of diagnosis. Among the 19 patients with high deceleration, defined as more than six years of deceleration, only 26 percent died within the same period. When the researchers combined acceleration status with smoking status, decelerated former smokers had the highest survival probability across all timepoints, while accelerated current smokers had the lowest, with only a 16 percent survival probability at three years post-diagnosis and no survivors beyond 15 years.

The study has important limitations. The extreme phenotype sampling design, which deliberately selected short-term and long-term survivors to maximize statistical power, produces optimistic estimates of model discrimination, and the researchers emphasize relative changes in model performance rather than absolute metrics. The modest sample size of 75 patients limited multivariable adjustment and predictive modeling, and the sample was diagnosed between 1998 and 2008, preceding contemporary advances such as robotic-assisted surgery and perioperative immunotherapy. The population also had limited representation of never smokers, a growing subset of lung cancer patients for whom epigenetic age biomarkers may be particularly valuable.

Despite these constraints, the findings support the potential utility of blood-based GrimAge2 as a readily accessible biomarker that improves survival prediction in early-stage non-small cell lung cancer beyond traditional risk factors. If validated in larger, contemporary cohorts, epigenetic age acceleration could eventually inform personalized risk stratification, helping clinicians identify which patients with seemingly identical early-stage disease face vastly different mortality trajectories, and potentially guiding more informed decisions about adjuvant therapy and follow-up intensity in the quest to close the survival gap that separates lung cancer from other common malignancies.

Subject of Research: Epigenetic age acceleration as a prognostic biomarker for overall survival in early-stage non-small cell lung cancer

Article Title: Epigenetic age acceleration enhances prediction of overall survival in early-stage non-small cell lung cancer

Article References: Gandara, I., Du, M., Li, Y., Su, L., Kothari, J., Lanuti, M., Gainor, J. F., & Christiani, D. C. (2026). Epigenetic age acceleration enhances prediction of overall survival in early-stage non-small cell lung cancer. iScience, 29(11), Article 117843. https://doi.org/10.1016/j.isci.2026.117843

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117843

Keywords: epigenetic age, lung cancer, GrimAge2, DNA methylation, biological aging, survival prediction, Epigenetic, acceleration, enhances, prediction, overall, survival

The centerpiece of the study is a new metric called the Empirical Dietary Index for Slower Epigenetic Aging, or EDISEA. Unlike traditional dietary scores, which are typically constructed from hypotheses about which foods benefit health, EDISEA is data-driven. The researchers began with a derivation sample of 1,711 participants and searched for the dietary features that best predicted lower acceleration of GrimAge2, a second-generation epigenetic clock widely regarded as one of the strongest molecular predictors of mortality and morbidity. Epigenetic clocks work by measuring patterns of DNA methylation, the chemical tags that attach to DNA and influence gene activity without changing the underlying genetic code. As people age, these methylation patterns shift in predictable ways, allowing scientists to estimate biological age from a blood sample with remarkable precision. When a person’s epigenetic age exceeds their chronological age, that gap, known as age acceleration, signals faster biological wear and tear.

The technical elegance of this approach lies in its directionality. Most prior studies asked whether existing diet scores correlate with epigenetic aging; this team inverted the question, asking which foods and nutrients the aging clocks themselves point toward. The resulting index is empirical, meaning it emerges from observed associations in real population data rather than from expert opinion. In the derivation sample, participants with the highest EDISEA scores showed epigenetic aging that was 4.17 years slower than expected, a difference that was highly statistically significant with a P-value below 0.001. Crucially, the finding was not an artifact of the sample used to build the index. In an independent validation sample of 2,311 participants, higher EDISEA was associated with 3.77 years slower epigenetic aging, confirming that the pattern generalizes beyond the population in which it was developed.

Slower epigenetic aging is an intriguing molecular signal, but the question that matters for public health is whether it translates into longer, healthier lives. To address this, the researchers turned to outcome-wide association analyses across multiple large cohorts, including the Health and Retirement Study, the National Health and Nutrition Examination Survey, the English Longitudinal Study of Ageing, and UK Biobank. The mortality results were consistent and substantial: across two US cohorts, higher EDISEA was associated with a pooled hazard ratio of 0.80 for all-cause mortality, with a 95 percent confidence interval of 0.76 to 0.85. In practical terms, that corresponds to roughly a 20 percent lower risk of death from any cause among people whose diets most closely matched the epigenetic aging-informed pattern, compared with those whose diets matched it least.

The associations extended well beyond survival. Higher EDISEA scores were linked to lower risks of a range of aging-related diseases, fewer functional limitations, and reduced multimorbidity, the co-occurrence of multiple chronic conditions that places the greatest burden on aging populations and health systems. Multimorbidity is one of the most challenging features of human aging because it resists single-disease interventions; a dietary pattern that appears to shift the entire trajectory of aging could, in principle, delay many conditions at once. The breadth of these findings across both US and UK aging cohorts strengthens the case that the pattern captures something fundamental about how nutrition interacts with the biology of growing older.

Perhaps the most visually compelling evidence comes from the molecular and neuroimaging analyses conducted in a large UK population-based cohort. Participants with higher EDISEA scores showed more favorable profiles across vascular, inflammatory, metabolic, and brain structural measures. Neuroimaging signatures are particularly noteworthy because brain structure changes slowly and is difficult to modify through intervention; observing that an epigenetic aging-informed diet is associated with healthier brain structural profiles suggests that the dietary pattern may be tracking genuine biological preservation rather than merely reflecting healthier lifestyles in general. The convergence of molecular blood markers, imaging data, and clinical outcomes provides a multi-layered picture in which diet, epigenetic aging, and downstream health all move together in a coherent direction.

The study’s design deserves careful attention when weighing what these results mean. Because the analyses are observational, they demonstrate association rather than causation. People who eat in ways that slow their epigenetic clocks may also differ in other respects, such as socioeconomic circumstances, physical activity, smoking habits, or access to health care, and the researchers used statistical adjustment across large cohorts to account for many such confounders. Still, residual confounding cannot be fully excluded, and randomized trials will ultimately be needed to establish whether adopting the EDISEA pattern directly causes slower epigenetic aging and better health outcomes. What distinguishes this work from much of nutritional epidemiology, however, is its explicit validation architecture: the index was derived in one sample, tested in an independent sample, and then evaluated against hard outcomes including mortality in multiple additional cohorts spanning two countries.

The implications for research and clinical practice could be considerable. Existing dietary guidelines are built largely around cardiovascular and metabolic endpoints, and indices designed for those outcomes may not be optimal for targeting the aging process itself. EDISEA offers an epigenetic aging-informed framework that researchers can use to study dietary strategies relevant to healthy aging, and it provides a template for a new generation of dietary indices built around molecular biomarkers rather than disease diagnoses alone. If future interventional studies confirm the observational findings, clinicians could eventually use epigenetic clocks both to identify individuals whose biological aging is outpacing their years and to tailor dietary recommendations aimed at slowing that process. The approach also dovetails with a broader movement in geroscience, which seeks to treat aging as a modifiable risk factor underlying many chronic diseases simultaneously.

There are important caveats to keep in view. Epigenetic clocks continue to evolve, and GrimAge2, while among the best-validated predictors, is one of several competing measures of biological age. The study populations were drawn from the United States and the United Kingdom, and dietary patterns, food environments, and population genetics differ across the world, so generalizability to other settings remains to be demonstrated. The index is also empirical, which means its specific food components reflect the populations used to derive it and may be refined as more data accumulate. The authors note that the work is intended as a framework for studying dietary strategies rather than a prescription, and the open-access publication, released on 25 September 2026 with full supplementary data and a transparent peer-review file, allows other research groups to scrutinize and extend the analysis.

Even with those qualifications, the study represents a conceptual milestone. It demonstrates that the molecular machinery of aging can serve as a design target for nutrition science, and that a diet built around that target is associated with slower epigenetic aging, lower mortality, less disease, and healthier brains and blood vessels across thousands of people in multiple nations. As epigenetic testing becomes faster and cheaper, and as the science of biological age advances from the laboratory toward the clinic, the idea that what we eat can be tuned to the ticking of our methylation clocks may move from an intriguing correlation to a practical strategy for extending not just lifespan, but healthspan, the years of life spent free of disease and disability. For now, the message is one of cautious excitement: a data-driven eating pattern informed by epigenetic aging is associated with a spectrum of better aging outcomes, and the research community now has both the tool and the motivation to test whether that association can be turned into cause and effect.

Subject of Research: An empirically derived dietary index targeting epigenetic aging and its associations with aging-related health outcomes

Article Title: An epigenetic aging-informed dietary pattern is associated with a spectrum of aging-related health outcomes

Article References: Lai, S., Yu, J., Zhang, L., Wu, M., Peng, G., Zong, G., Ma, H., Yuan, C., Luo, B., & Chen, H. (2026). An epigenetic aging-informed dietary pattern is associated with a spectrum of aging-related health outcomes. Nature Communications. https://doi.org/10.1038/s41467-026-77790-9

Image Credits: AI Generated

DOI: 10.1038/s41467-026-77790-9

Keywords: epigenetic aging, DNA methylation, GrimAge2, dietary patterns, EDISEA, biological age, healthy aging, mortality, multimorbidity, neuroimaging, nutritional epidemiology, Nature Communications

The study, published in Aging Cell, is among the first to benchmark eleven proteomic organ clocks against molecular, imaging, cognitive and physical biomarkers within a single population. The organ clocks were originally developed by mapping thousands of plasma proteins to their tissues of origin and training mathematical models to predict chronological age for organs including the brain, heart, liver, kidney, immune system and arteries. When the researchers applied these clocks to the Scottish cohort, they found substantial heterogeneity: organs within the same person appeared to age at strikingly different rates, and the organ age gaps were only mildly correlated with one another, with a mean pairwise correlation of 0.32. Extreme ageing, defined as an organ age gap exceeding two standard deviations from the population mean, most commonly affected just a single organ rather than several at once.

Despite this biological richness, the organ clocks delivered only modest prognostic power. Each standard deviation of accelerated organ ageing corresponded to a 16 to 43 percent higher mortality hazard over the follow-up period. The strongest effects emerged for the liver, with a hazard ratio of 1.43, followed by the immune system at 1.42 and the heart at 1.38. The proteomic brain age gap, at a hazard ratio of 1.31, performed almost identically to a brain age gap derived from magnetic resonance imaging, which scored 1.30. These findings replicate the original organ clock study, which reported a 17 to 53 percent mortality increase per standard deviation in a separate American cohort, confirming that the approach generalises across populations.

Yet several other biomarkers left the organ clocks behind. The clear champion was GrimAge2, a second-generation epigenetic clock that estimates biological age from DNA methylation patterns in blood. A single standard deviation of GrimAge2 acceleration conferred a 62 percent higher mortality hazard, the largest effect of any biomarker tested. Close behind came structural brain measures: smaller total brain volume raised mortality risk by 52 percent per standard deviation, and smaller grey matter volume by 44 percent. Reduced lung function also proved formidable, with a one-second forced expiratory volume deficit carrying a 51 percent higher hazard, and poorer general cognitive function adding 46 percent. All of these surpassed every proteomic organ clock in effect size.

The researchers then asked which biomarkers carried independent information when all were considered simultaneously. In a multivariable Cox model including 21 ageing measures among the 460 participants with complete data, only four survived: total brain volume, white matter hyperintensity volume, general cognitive function and walking time. GrimAge2, despite its stellar univariable performance, lost significance, not because it lacks biological relevance but because much of its prognostic signal overlaps with correlated measures. Together, the four independent biomarkers explained roughly 19 percent of the variance in mortality risk, while all 21 biomarkers combined raised that figure only to 23 percent, suggesting diminishing returns from adding further measures.

The variance decomposition was equally revealing. Chronological age and sex accounted for just 2 percent of mortality risk, with lifestyle factors adding 4 percent. GrimAge2 contributed a further 6 percent, proteomic organ clocks 8 percent and physical function measures 9 percent. Brain-related measures, combining neuroimaging and cognition, accounted for around 16 percent, roughly double any other single domain. For the authors, this underscores that brain health, encompassing atrophy, cerebrovascular damage and cognitive capacity, is a particularly powerful window on survival in old age, likely because the brain regulates essential physiological functions and because atrophy captures neurodegenerative processes long before clinical diagnosis.

Beyond benchmarking clocks, the team mined the full plasma proteome for mortality signals. Using the SomaScan 11K platform, which quantifies nearly 10,000 protein targets from a small plasma sample, they identified 368 proteins significantly associated with all-cause mortality after adjustment for age and sex. The strongest positive associations belonged to GDF15, a stress- and inflammation-induced cytokine, with a hazard ratio of 1.56, followed by WFDC2 at 1.47 and TIMP1 at 1.45. Notably, GDF15 ranked as the second strongest mortality predictor overall, behind only GrimAge2, and three of the nine proteins whose DNA methylation surrogates are built into GrimAge2 appeared among the top twenty. On the protective side, higher circulating levels of neuropeptide S, BAGE3 and ABCC6 each corresponded to roughly 41 to 42 percent lower mortality risk per standard deviation.

Gene set enrichment analysis revealed a striking functional dichotomy among these proteins. Those whose higher levels predicted death were overwhelmingly enriched for immune processes, including antimicrobial humoral immune responses, chemokine and cytokine activity, and interleukin-4 and interleukin-13 signalling, painting a picture of inflammaging and immunosenescence, the chronic low-grade inflammation and immune decline that characterise ageing. Proteins associated with survival, by contrast, were enriched for genomic integrity: nucleosome assembly, regulation of the G2/M cell cycle transition, damaged DNA binding and ribosome biogenesis. Among the protective candidates were Sirtuin-2 and Caspase-2, both involved in DNA damage response and tumour suppression, with higher levels linked to 18 and 32 percent lower mortality risk respectively. An elastic net penalised regression across all 9,703 protein targets distilled the signal to just 37 proteins, with GDF15 carrying the largest positive coefficient and strong concordance with the univariate results.

The study has limitations the authors acknowledge. The cohort consists of relatively healthy, ethnically homogeneous Scots born in 1936, so validation in more diverse populations is needed. The analyses relied on single baseline measurements rather than longitudinal trajectories, examined all-cause rather than cause-specific mortality, and depended on one proteomics platform. The findings are observational and cannot establish causality. Still, the practical implications are considerable: simple, inexpensive measures such as walking speed, lung function and cognition rival or exceed costly molecular assays, while integrative biomarkers trained directly on mortality outcomes, like GrimAge2, currently offer the strongest single-molecule performance. Whether organ clocks add value for cause-specific mortality, such as distinguishing cardiac from neurological deaths, remains an open question.

What emerges is a clear message for the booming biological-ageing industry: no single number captures how fast someone is ageing. Organs age at their own pace, blood proteins tell one story, brain scans another, and the most informative forecast comes from combining complementary measures across modalities. As the world’s population over 60 doubles to a projected 2.1 billion by 2050, choosing the right combination of biomarkers could determine who benefits from early intervention, and the new results suggest that protecting the brain, preserving lung capacity and taming chronic inflammation may matter more than any single molecular clock.

Subject of Research: Multimodal ageing biomarkers and plasma proteomic signatures associated with all-cause mortality in older adults

Article Title: Multimodal Ageing Biomarkers and Plasma Proteomic Signatures Associated With All‐Cause Mortality

Article References: Pyrgioti, M., Eguiagaray, I. M., Redmond, P., Corley, J., Bastin, M. E., Hernández, M. V., Russ, T. C., Wardlaw, J. M., Hannon, E., Deary, I. J., Walker, K. A., Tucker‐Drob, E. M., Cox, S. R., Marioni, R. E., & Harris, S. E. (2026). Multimodal Ageing Biomarkers and Plasma Proteomic Signatures Associated With All‐Cause Mortality. Aging Cell, 25(10), Article e70747. https://doi.org/10.1111/acel.70747

Image Credits: AI Generated

DOI: 10.1111/acel.70747

Keywords: biological age, proteomic organ clocks, epigenetic clock, GrimAge2, all-cause mortality, plasma proteomics, GDF15, brain atrophy, cognitive function, lung function, immunosenescence, Lothian Birth Cohort 1936

The study, led by Yeonju Kim and Habyeong Kang of Hanyang University in Seoul, drew on data from 1,911 adults aged 50 to 84 who participated in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2002. NHANES is a long-running program run by the National Center for Health Statistics that combines interviews, physical examinations, and laboratory testing on a sample designed to represent the entire US population. For this analysis, the researchers took advantage of a relatively recent addition to the survey: DNA methylation measurements, which capture patterns of methyl groups — small chemical modifications — attached to cytosine bases across the genome.

DNA methylation is central to what scientists call epigenetic aging clocks. These are mathematical models, trained on large datasets, that read methylation patterns at specific sites in the genome and convert them into an estimate of biological age. Because methylation patterns shift in predictable ways with stress, disease, and lifestyle exposures, the estimated epigenetic age can diverge from chronological age. When it runs ahead, researchers call the difference epigenetic age acceleration, or EAA — a measure that has repeatedly been linked to elevated risks of cardiovascular disease, cancer, and earlier death.

Participants in the study were classified as never smokers, former smokers, or current smokers, with former smokers further divided by how long they had quit: less than 10 years, 10 to just under 20 years, or 20 or more years. Rather than relying on a single clock, the team evaluated six validated DNA methylation clocks spanning three generations of development. The first-generation clocks — HorvathAge, HannumAge, and SkinBloodAge — were the original models introduced in 2013 and 2018 that estimate chronological age from methylation patterns. The second-generation clocks, PhenoAge and GrimAge2, were designed to predict clinical outcomes such as mortality and disease rather than birthday count alone. The third-generation clock, DunedinPoAm, estimates the pace of biological aging itself, capturing how quickly a person’s body is deteriorating relative to peers.

The results were strikingly consistent. Across all six clocks, current smokers showed the highest epigenetic age acceleration of any group, confirming that active smoking is associated with a measurably faster biological aging profile. But the most important finding concerned former smokers. The researchers observed a dose–response relationship: the longer a person had been quit, the lower their epigenetic age acceleration. This pattern held across the panel of clocks but was strongest for GrimAge2 and DunedinPoAm, the two clocks most closely tied to mortality risk and the pace of aging.

The magnitude of the effect among long-term quitters was remarkable. Former smokers who had abstained for 20 years or more showed a reduction in GrimAge2 acceleration of 12.66 years compared with current smokers, with a 95 percent confidence interval spanning 14.56 to 10.75 years. That figure approached the level observed among never smokers, whose GrimAge2 acceleration was 13.88 years lower than current smokers, with a confidence interval of 15.70 to 12.05 years. In other words, after two decades of cessation, the epigenetic aging profile of former smokers was statistically close to that of people who had never picked up the habit.

To understand the timing of this recovery, the researchers used restricted cubic spline analysis, a statistical technique that models the relationship between cessation duration and epigenetic aging without assuming a straight-line pattern. The splines revealed a rapid initial decline in epigenetic age acceleration during the first decade after quitting, followed by continued, more gradual improvement beyond the 20-year mark for the GrimAge2 and DunedinPoAm clocks. This shape suggests that the body begins unwinding the epigenetic damage from smoking relatively quickly, but that full recovery is a long-term process measured in decades rather than months.

The findings fit with a broader body of molecular evidence. Cigarette smoke leaves a well-documented signature on the methylome, including changes at sites in the AHRR gene that are among the most robustly replicated epigenetic markers of smoking exposure. Previous work has shown that some of these methylation changes begin to revert after cessation, and studies have linked accelerated epigenetic aging to cardiometabolic abnormalities, kidney dysfunction, and other hallmarks of biological decline. What the new study adds is a nationally representative, survey-weighted analysis that quantifies how the reversal unfolds over time across multiple generations of aging clocks.

The technical rigor of the analysis matters for interpreting the results. Because NHANES uses a complex sampling design, the researchers applied survey weights in their regression models, ensuring that the estimates reflect the US population rather than the specific sample. The use of six independent clocks guards against the possibility that the association is an artifact of any single algorithm, and the consistency of the dose–response pattern across first-, second-, and third-generation clocks strengthens the biological interpretation. Still, as with any cross-sectional study, the data capture a single moment in time; the researchers measured different people at different cessation durations rather than following the same individuals over the years after they quit.

That limitation leaves open some questions that longitudinal studies will need to address, including whether individual trajectories of epigenetic recovery mirror the population-level pattern and how factors such as smoking intensity, duration of prior smoking, sex, and genetic background modify the pace of reversal. Even so, the implications are significant. Tobacco smoking remains one of the leading modifiable risk factors for age-related disease and premature mortality worldwide, and health campaigns have long emphasized the clinical benefits of cessation. This study adds a biological dimension to that message: quitting does not merely halt further damage, but appears to set in motion a measurable molecular recovery that brings the aging clock back toward baseline. For the millions of former smokers wondering whether the harm is already done, the answer, at the level of DNA methylation, appears to be that much of it can be undone — given enough time smoke-free.

Subject of Research: The relationship between smoking cessation duration and DNA methylation-based epigenetic age acceleration in US adults

Article Title: Duration of smoking cessation and DNA methylation aging clocks among US adults: potential reversibility of smoking-induced biological aging

Article References: Kim, Y., & Kang, H. (2026). Duration of smoking cessation and DNA methylation aging clocks among US adults: potential reversibility of smoking-induced biological aging. GeroScience. https://doi.org/10.1007/s11357-026-02535-x

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02535-x

Keywords: smoking cessation, epigenetic clocks, DNA methylation, biological aging, epigenetic age acceleration, NHANES, GrimAge2, DunedinPoAm, tobacco smoking, GeroScience, healthy aging, epigenetics