The research team, led by investigators at Yale University including Daniel Borrus and Albert Higgins-Chen, set out to answer a deceptively simple question: when should you actually trust an epigenetic clock result? The problem stems from the sheer number of clock models now in circulation. First-generation clocks such as those developed by Hannum and Horvath were trained to predict chronological age from DNA methylation patterns at CpG sites. Second-generation clocks like PhenoAge and GrimAge were trained instead on mortality risk, while pace-of-aging measures such as DunedinPoAm38 and DunedinPACE were built to capture how quickly a person is aging. Each clock reads a different slice of the methylome, and they do not always agree.

That disagreement matters because a single significant result from one clock can be produced by three very different things: a real, systemic shift in biological aging; a clock-specific effect tied to that particular model’s training data; or plain technical noise. The researchers’ central hypothesis was that a genuine change in biological aging should be visible across multiple clock models, while a lone significant clock is more likely to reflect noise or model quirks. To test this, they assembled twelve clock models, including high-reliability principal component versions of the classics, and re-analyzed longitudinal DNA methylation datasets spanning interventions, stressful events, and control cohorts.

The first target was the published intervention literature itself. The team identified three datasets that had previously reported a significant drop in biological age on a single clock, including an eight-week diet, exercise, and supplement program and a two-year folic acid and vitamin B12 trial, both originally scored with the Horvath multi-tissue clock. When they computed eleven additional clocks on the same data, not one changed significantly in the same direction as the original finding. In one dataset, ten of the eleven additional clocks actually trended in the opposite direction. The sobering implication is that some headline-grabbing claims of epigenetic age reversal may not survive contact with a broader panel of clocks.

To establish what a trustworthy signal looks like, the researchers turned the problem on its head and examined positive controls: events that should accelerate biological aging. They analyzed longitudinal datasets capturing intensive surgery, radiotherapy and chemotherapy for breast cancer, and SARS-CoV-2 infection. If clocks can detect a decrease in biological age, they should certainly detect an increase after a major physiological insult. That is exactly what they found, but with a telling pattern. In all three datasets, at least four of the twelve clocks registered significant age acceleration, and three mortality-linked measures, PCPhenoAge, PCGrimAge, and DunedinPACE, increased significantly in every single one.

From these positive controls, the team distilled three criteria for confidence in an epigenetic clock result. First, the reliable PC-based clocks should respond, since their principal component architecture suppresses the noise from individual CpGs. Second, the mortality-trained and pace-of-aging clocks should respond, because interventions target health outcomes rather than the passage of calendar time. Third, multiple clocks should move in the same direction, reflecting the shared aging signal they all carry. Notably, the original first-generation chronological clocks, Hannum, Horvath multi-tissue, and Horvath skin-and-blood, failed to detect a significant increase in any of the three stressor datasets, and the skin-and-blood clock even suggested a significant decrease after chemo-radiation, the opposite of what was biologically happening.

Why do reliable clocks outperform? The answer lies in a statistical property called the intraclass correlation coefficient, or ICC, which measures test-retest consistency. Earlier work had shown that re-testing the same person with standard clocks can produce swings of up to 2.4 years, while PC clocks cut that deviation to between 0.3 and 0.8 years. The team ran simulation studies of hypothetical clinical trials with 200 virtual participants and a known true effect, showing that high-ICC clocks detect genuine changes at much smaller effect sizes, while noisy clocks drown real signals in measurement error. Longitudinal control datasets confirmed the corollary: changes in reliable clocks correlate strongly with one another, with a mean pairwise correlation of 0.61 among reliable clocks versus just 0.20 among unreliable ones, meaning noisy clocks drift in spurious directions while reliable clocks move together.

Armed with these criteria, the researchers hunted for intervention datasets that passed all three tests, and found three. A two-year Mediterranean diet trial with two arms, one standard and one a green Mediterranean diet enriched with plants and polyphenols, and a three-week alcohol abstinence program in chronic drinkers all showed significant decreases in at least five clocks, with seven responding in the alcohol and green-Mediterranean datasets. Across these true-positive interventions, fourteen of eighteen reliable clock tests reached significance compared with five of eighteen for unreliable clocks, and the per-subject average across all twelve clocks fell significantly in every case. That twelve-clock average, the authors suggest, is not a new biomarker but a simple, interpretable filter for whether the clocks as a group agree.

The study also probes a subtler confound: cell composition. Blood methylation measurements reflect not just the aging of cells but shifts in the mixture of cell types present, and the team used EpiDISH deconvolution to adjust for twelve immune cell fractions. The results were strikingly heterogeneous. Surgery and chemo-radiotherapy effects shrank by roughly three-quarters after adjustment, indicating those signals were largely driven by compositional changes, while the SARS-CoV-2 effect was essentially unchanged and the Mediterranean diet arms retained most of their effect. The authors caution that this does not invalidate clocks trained on bulk blood, whose predictive power partly depends on cell fractions, but it does mean researchers should report cell-composition adjustments to interpret what a clock change actually represents.

The authors are candid about limitations: no intervention has yet been definitively proven to extend human lifespan, so there is no gold-standard positive control for rejuvenation, and a robust clock change does not automatically translate into added years of life. Still, the practical prescription is clear. Intervention studies should report multiple clocks, favor reliable PC versions and mortality- or pace-trained models, compute a per-subject clock average, and treat any solitary significant result with suspicion until it is replicated or supported by mechanistic evidence. As epigenetic clocks flood from the laboratory into clinics and consumer testing, this framework offers the field something it badly needs: a shared rulebook for separating genuine biological signal from statistical noise, before the anti-aging literature fills up with findings that cannot be repeated.

Subject of Research: Validation criteria for epigenetic clocks as biomarkers of aging interventions

Article Title: When to Trust Epigenetic Clocks: Avoiding False Positives in Aging Interventions

Article References: Borrus, D. S., Sehgal, R., Armstrong, J. F., Kasamoto, J., Gonzalez, J., & Higgins‐Chen, A. (2026). When to Trust Epigenetic Clocks: Avoiding False Positives in Aging Interventions. Aging Cell, 25(10), Article e70756. https://doi.org/10.1111/acel.70756

Image Credits: AI Generated

DOI: 10.1111/acel.70756

Keywords: epigenetic clocks, biological age, DNA methylation, aging biomarkers, longevity, DunedinPACE, GrimAge, PhenoAge, clinical trials, publication bias, anti-aging interventions, reliability

The study drew on 6,896 adults from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018, with survey weights applied so the findings reflect the broader US adult population. Rather than betting on a single algorithm, the researchers staged a computational tournament: a two-stage machine learning framework evaluated more than 100 candidate survival models, ranging from classical Cox regression to modern ensemble methods. The winner was chosen through Pareto front optimization, a technique borrowed from multi-objective engineering that refuses to sacrifice one performance dimension for another. Instead of picking the model with the best score on a single metric, the approach identifies models that sit on the mathematical frontier where no competitor can improve accuracy without degrading another quality such as calibration or simplicity. The eventual champion was, somewhat surprisingly, a simple Cox model, which achieved a mean concordance index of 0.893, a strikingly high figure for mortality prediction in a free-living population.

From that optimized model, the team distilled CKMAI, a single number that integrates a diverse set of inputs: clinical laboratory measurements, nutritional assessments, lifestyle factors, and social determinants of health. The philosophy is that aging in CKM syndrome is not written in any one biomarker but in the coordinated drift of dozens of variables that together describe how far along the heart-kidney-metabolic axis a person has traveled. The index was then stress-tested against three of the best-known competitors: PhenoAge, the Klemera-Doubal method (KDM), and the cardiometabolic index (CMI). Across every outcome and every time horizon, CKMAI came out ahead. For all-cause mortality, its time-dependent area under the curve reached 0.893 at three years, 0.907 at five years, and 0.890 at ten years. For cardiovascular mortality, the numbers climbed even higher, peaking at 0.937 at both five and ten years, meaning the index could discriminate between those who would and would not die of cardiovascular causes with remarkable precision.

Discrimination is only half the story, however, and the researchers went to considerable lengths to prove the index was not merely ranking people correctly but also assigning them the right absolute risks. Reclassification metrics told a consistent story: net reclassification improvement and integrated discrimination improvement were statistically significant at all time points, confirming that CKMAI moved patients into more accurate risk categories compared with existing indices. Calibration, assessed with the Greenwood-Nam-D’Agostino test, showed no significant departure between predicted and observed mortality, and decision curve analysis demonstrated superior net benefit, a measure of how much real-world clinical value a risk model delivers when used to guide decisions. In practical terms, a physician using CKMAI to decide who needs intensified intervention would make better decisions than one relying on PhenoAge, KDM, or CMI, with fewer false alarms and fewer missed high-risk patients.

One of the most intriguing findings concerns the shape of the relationship between biological aging and death. Using restricted cubic splines and two-piecewise regression, the team found that risk does not climb smoothly with CKMAI. Instead, there are inflection points: 61.587 for all-cause mortality, 60.732 for cardiovascular mortality, and 34.092 for high-risk CKM status. Below each threshold, risk rises steeply; above it, the curve flattens. This counterintuitive pattern suggests that the most dangerous phase of CKM-related aging is the transition zone, the period when a person’s index first crosses into the elevated range. Clinically, this reframes the goal of screening: the patients who most urgently need intervention may be those just approaching the threshold, not those already deep into the high-risk zone, whose trajectory may in some respects already be set.

The index also revealed a startling interaction with conventional biological age. When CKMAI and PhenoAge were analyzed together, the researchers documented a super-additive interaction, quantified by a relative excess risk due to interaction of 15.06 (95% confidence interval 8.14 to 38.44). In plain language, the combined effect of being old by both clocks is far worse than the sum of the two risks separately. A person who is chronologically aged by PhenoAge and additionally accelerated on the CKM-specific axis occupies a biological state that neither measure alone can capture. This finding offers a mechanistic clue about why CKM syndrome progresses so heterogeneously: the syndrome’s damage may compound with, rather than merely parallel, the broader aging process.

To map that heterogeneity directly, the team applied unsupervised clustering to the combined aging and metabolic data and uncovered six distinct aging-metabolic phenotypes within the population. The most alarming was a frail elderly cluster whose members faced a hazard ratio of 11.74 for mortality compared with the reference group, an eleven-fold elevation in risk. The existence of these discrete phenotypes suggests that CKM syndrome is not a single disease with a single trajectory but a family of biological states, some of which are far more lethal than others. Identifying which phenotype a patient belongs to could eventually guide how aggressively clinicians pursue kidney protection, cardiovascular risk reduction, or metabolic control.

Perhaps the most unexpected thread in the study is psychological. Using weighted bootstrap mediation analysis, the researchers found that depression partially mediates the link between CKMAI and its outcomes. The proportion of the association explained by depression was 5.6 percent for all-cause mortality, 8.2 percent for cardiovascular mortality, and 11.1 percent for high-risk CKM status. In other words, some of the excess mortality carried by an accelerated CKM aging signature travels through the mind: biological aging in this syndrome appears to foster depressive states, which in turn worsen outcomes, likely through behavioral, inflammatory, and neuroendocrine pathways. The mediation is partial, but its consistency across all three outcomes suggests that mental health may be an underappreciated lever in breaking the cycle of cardiovascular-kidney-metabolic decline.

The findings held up under scrutiny. Prespecified subgroup analyses and multiple sensitivity analyses confirmed robustness, and temporal validation within NHANES supported the model’s stability over time. A preliminary external validation in a hospital-based Chinese cohort of 261 patients also supported the index’s ability to identify high-risk CKM status, hinting at generalizability beyond the US population. The authors are candid about the study’s central limitation: CKMAI has not yet been validated in geographically independent cohorts with complete mortality follow-up, and until that happens, its performance estimates carry some uncertainty. Still, the combination of national representativeness, methodological rigor, and early external signals makes a strong case.

If the results replicate, the implications could be substantial. CKMAI is built from data that clinics already collect, bloodwork, nutrition, lifestyle, and social context, which makes it a practical, implementable tool rather than a laboratory curiosity. A single number that flags patients crossing the mortality inflection point, sorts them into one of six aging-metabolic phenotypes, and even points toward depression screening could reshape how preventive care is delivered to the millions of people whose hearts, kidneys, and metabolisms are failing together. As cardiovascular-kidney-metabolic syndrome becomes one of the defining health challenges of an aging century, an aging clock tuned to its specific biology may prove to be exactly the instrument medicine has been missing.

Subject of Research: A machine learning-derived biological aging index for mortality prediction and risk stratification in cardiovascular-kidney-metabolic syndrome

Article Title: A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study

Article References: Zhu, Z., Ren, K., Wang, D., Lv, Y., Jin, H., Zhang, L., & Wang, Y. (2026). A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study. PLOS Medicine, 23(10), e1005078. https://doi.org/10.1371/journal.pmed.1005078

Image Credits: AI Generated

DOI: 10.1371/journal.pmed.1005078

Keywords: biological aging, machine learning, cardiovascular-kidney-metabolic syndrome, mortality prediction, NHANES, risk stratification, PhenoAge, depression, Cox regression, epidemiology, precision medicine, PLOS Medicine

The research team, led by Mengtong Sun and Qida He of City University of Hong Kong along with colleagues at institutions in China and the United States, took an approach that differs from most nutritional epidemiology. Rather than asking participants to estimate how much of each food they ate, the investigators analyzed food preference questionnaire data, capturing what people actually like to eat. Using a statistical technique known as latent profile analysis, they sorted participants into distinct dietary preference profiles based on the overall pattern of their food likes and dislikes. This method identified three coherent groups: a health-aligned profile, a broad-preference profile in which participants reported liking most foods across the spectrum, and a sweet-tooth profile characterized by strong preferences for sugary items.

Latent profile analysis is a person-centered modeling approach that treats the population as a mixture of unobserved subgroups, each with its own characteristic response pattern. Instead of reducing diet to a single score, the method allows researchers to discover natural clusters of preference that may correspond to real-world eating styles. This is a meaningful departure from the food frequency questionnaires that have long dominated dietary research and that have drawn criticism for their reliance on memory and their limited ability to capture the complexity of habitual intake. Preference data, while not a direct measure of consumption, offers a stable and psychologically grounded window into dietary behavior, and previous work in twin cohorts has shown that food preference patterns are partly heritable and reasonably reproducible over time.

To connect dietary preference with dementia, the team employed Cox proportional hazards models, the standard tool for analyzing time-to-event data in cohort studies. These models estimated the association between each dietary preference profile and the risk of incident dementia while accounting for the interaction between diet and genetic susceptibility. Genetic risk was characterized using polygenic risk scores, which aggregate the effects of thousands of genetic variants into a single measure of inherited predisposition. The results showed not merely an independent effect of the sweet-tooth profile but a significant additive interaction: among individuals with high genetic susceptibility to dementia, the sweet-tooth preference amplified risk beyond what would be expected from either factor alone. In other words, the people least equipped biologically to tolerate a sugar-heavy dietary pattern were the ones most harmed by it.

Perhaps the most technically ambitious component of the study was its effort to quantify biological aging. The researchers computed two distinct measures of biological age from blood chemistry and organ function markers. The first, the Klemera-Doubal method biological age, or KDM-BA, is an algorithm that combines multiple biomarkers into an estimate of physiological age, allowing researchers to calculate how much faster or slower an individual is aging relative to their chronological peers. The second, phenotypic age or PhenoAge, was developed using data from the NHANES survey and has been validated as a predictor of morbidity and mortality across diverse populations. As a third marker, the team measured leukocyte telomere length, the length of protective DNA-protein structures at the ends of chromosomes in white blood cells, which typically shortens with cell division and chronic biological stress. Telomere length was available for hundreds of thousands of UK Biobank participants through high-throughput measurement.

The sweet-tooth profile was associated with acceleration on all three markers: faster KDM-BA, faster PhenoAge, and shorter leukocyte telomeres. This convergence across independent measures of aging is notable because each captures a different facet of the aging process. KDM-BA and PhenoAge reflect systemic physiological decline as expressed in blood-based biomarkers, while telomere length reflects cellular replicative history and cumulative inflammatory and oxidative stress. That a single dietary preference pattern correlates with all three suggests that the sweet-tooth profile is not merely a marker of some isolated metabolic quirk but is entangled with the broader biology of aging itself. Prior research has already linked accelerated biological aging, measured with these same tools, to increased dementia incidence in the UK Biobank, providing a plausible mechanistic bridge between diet and disease.

To test that bridge formally, the investigators conducted mediation analysis, a statistical framework for estimating how much of the association between an exposure and an outcome flows through an intermediate variable. The exploratory results indicated that the biological aging markers statistically accounted for between 2.28 and 4.82 percent of the observed association between sweet-tooth preference and dementia risk. The authors are careful about the interpretation here, and appropriately so. The mediation proportion is modest, which means that accelerated biological aging explains only a small fraction of the diet-dementia link, leaving the majority of the association to be explained by other pathways. Candidate mechanisms discussed in the broader literature include chronic neuroinflammation, disturbances of the brain-gut-microbiome axis, glucoregulatory stress, and vascular injury, each of which has been implicated in both high-sugar dietary patterns and neurodegeneration. The researchers also note that mediation analysis in observational data is inherently exploratory and cannot establish causal chains with certainty.

The study’s scale and design lend it credibility. It was conducted within the UK Biobank, a resource of roughly half a million middle-aged and older adults in the United Kingdom who have undergone extensive baseline assessment, including dietary questionnaires, physical measurements, blood sampling, and whole-genome genotyping. The cohort design followed participants forward in time, which reduces but does not eliminate the risk of reverse causation, the possibility that preclinical disease influences dietary preference rather than the other way around. The investigators followed reporting guidelines for observational epidemiology, applied competing-risk methods where appropriate, and adjusted for a range of confounders. Still, as with any observational study, unmeasured confounding remains possible, and food preference data capture liking rather than measured intake, so some uncertainty about the exact dietary behaviors driving the association is unavoidable.

What makes the findings compelling is their consistency with an expanding literature on diet and brain health. Mediterranean, DASH, and MIND dietary patterns have each been associated with lower dementia risk and favorable brain structural markers in prior cohort studies, and adherence to the EAT-Lancet planetary health diet has been linked to slower biological aging and longer life expectancy in UK Biobank analyses. Conversely, high consumption of ultra-processed foods and sugary beverages has been tied to adverse brain outcomes and accelerated aging, with some evidence that genetic risk modifies these relationships. The new study extends this work by shifting the analytical lens from nutrient and food-group intake to the underlying architecture of food preference, and by explicitly modeling the interplay between diet, genetic susceptibility, and biological aging within a single framework.

The practical implications are targeted rather than universal. Because the additive interaction showed that the sweet-tooth profile was most dangerous among genetically susceptible individuals, the authors argue for dietary interventions aimed specifically at people at elevated inherited risk of dementia. For that population, curbing sugar-centric preferences and shifting toward health-aligned eating could represent a modifiable lever at a stage of life when prevention is still possible. The researchers stop short of claiming that changing preferences alone would eliminate risk, and the modest mediation share cautions against viewing biological aging as the sole pathway. But the study adds a vivid and quantifiable dimension to an old piece of advice: what you like to eat may shape not just your waistline but the pace at which your body and brain grow old, and for those carrying genetic risk, a sweet tooth may be a warning worth heeding.

Subject of Research: Association between dietary preference profiles, biological aging markers, and incident dementia risk in a prospective UK Biobank cohort

Article Title: Dietary preference, biological aging, and incident dementia: a prospective cohort study

Article References: Sun, M., Huang, F., Sun, N., Ding, H., Shi, F., Wang, S., Yao, J., & He, Q. (2026). Dietary preference, biological aging, and incident dementia: a prospective cohort study. GeroScience. https://doi.org/10.1007/s11357-026-02567-3

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02567-3

Keywords: dementia, dietary preference, biological aging, UK Biobank, sweet tooth, Alzheimer's disease, vascular dementia, telomere length, PhenoAge, polygenic risk score, cohort study, nutrition

The team, led by ophthalmology researchers at the Eye Institute of Fudan University in Shanghai, quantified systemic aging using three complementary instruments. The first was PhenoAge acceleration, a measure derived from clinical biomarkers that estimates how much faster a person’s physiology is aging compared with their chronological peers. The second was age acceleration calculated by the Klemera-Doubal method, another biomarker-based biological age algorithm with a long track record in aging research. The third was a frailty index, a cumulative score built from deficits across health domains that captures the loss of physiological reserve characteristic of advanced aging. Each of these measures tells a slightly different story about aging, and the researchers wanted to know whether all of them converged on the same retinal signature.

That signature turned out to be strikingly consistent. Across all three aging metrics, higher biological age acceleration and greater frailty were associated with reduced macular thickness, with the effect spanning every inner retinal subfield measured by optical coherence tomography. The standardized effect sizes ranged from −1.386 to −0.421, and all associations remained highly significant after correction for multiple comparisons, with false discovery rate adjusted P values below 0.001. In practical terms, people whose bodies were aging faster than their birthdays implied tended to have measurably thinner retinas, and the relationship held whether aging was defined by molecular biomarkers, composite algorithms, or the clinical syndrome of frailty.

Optical coherence tomography, the imaging technology behind these measurements, deserves a moment of explanation. Originally described in Science in 1991, OCT uses low-coherence interferometry to generate cross-sectional images of biological tissue with micrometer-scale resolution. In the UK Biobank, tens of thousands of participants underwent retinal OCT scanning, producing an enormous, standardized dataset of retinal layer thicknesses. Because the retina is embryologically part of the central nervous system and shares vascular and metabolic characteristics with the brain, changes in its structure have been proposed as noninvasive biomarkers for neurological and systemic disease. Previous work has linked retinal thinning to cardiovascular risk, Alzheimer disease, and early age-related macular degeneration, but the broader question of how whole-body aging states map onto retinal structure had remained poorly characterized.

The most technically ambitious part of the new study involved the plasma metabolome. Blood samples collected from participants between 2006 and 2010 had been profiled using nuclear magnetic resonance spectroscopy, a platform that quantifies hundreds of circulating metabolites, including lipoprotein subclasses, fatty acids, amino acids, and glycolysis-related markers. From this high-dimensional data, the researchers derived metabolic signatures of aging using elastic net regression, a machine learning technique that selects sparse, predictive combinations of variables while guarding against overfitting. The resulting metabolomic aging signatures were then tested as statistical mediators of the relationship between biological aging and retinal thinning.

The mediation results were the study’s headline finding. Metabolic signatures accounted for 81.14 percent of the association between PhenoAge acceleration and macular thickness, 19.92 percent of the association for Klemera-Doubal method age acceleration, and 32.27 percent of the association for the frailty index, with all mediation effects significant after FDR correction. These proportions are remarkable, particularly for PhenoAge, and they suggest that circulating metabolites are not merely passive bystanders in the aging process but plausible conduits through which systemic senescence reaches the neurosensory retina. The retina is among the most metabolically demanding tissues in the body, with photoreceptors and the retinal pigment epithelium locked in a tightly coupled metabolic ecosystem that depends on glucose, lactate shuttling, and mitochondrial oxidative metabolism. Disruption of systemic metabolic homeostasis, the authors argue, is therefore well positioned to leave structural fingerprints in retinal tissue.

The study also incorporated the exposome, the concept introduced by cancer epidemiologist Christopher Wild in 2012 to describe the totality of environmental exposures an individual experiences across a lifetime. The researchers assembled exposome factors from phenotypic data covering lifestyle, diet, air pollution, and mental health. Their integrative analyses showed that adverse exposome profiles, including tobacco exposure, poor diet, air pollution, and negative psychosocial states such as loneliness and depression, were each correlated with reduced macular thickness. More importantly, systemic aging and metabolic dysregulation emerged as significant statistical intermediaries within these multidimensional pathways, meaning that environmental burdens appear to translate into retinal change at least partly by accelerating biological aging and reshaping the circulating metabolome.

This framing has implications that extend well beyond ophthalmology. If the retina reflects the convergence of environmental stress, metabolic dysregulation, and biological aging, then a routine retinal scan could in principle serve as a rapid, noninvasive readout of an individual’s cumulative aging trajectory. The findings align with a growing body of work on retinal oculomics, including phenome-wide analyses of UK Biobank OCT images that have linked ocular measurements to systemic health, and epidemiological studies showing that ambient air pollution is associated with retinal thinning and age-related macular degeneration. The new study unifies these threads by proposing an explicit causal architecture in which exposures act on aging biology, aging biology acts on metabolism, and metabolism acts on the retina.

The authors are careful about what their statistics can and cannot show. Mediation analysis in observational data identifies statistical intermediaries, not proven causal mechanisms, and the cross-sectional design of the UK Biobank baseline assessments means that temporal ordering cannot be fully established. The metabolomic platform used, while comprehensive for lipids and small molecules, does not capture every biologically relevant compound, and the elastic net signatures are predictive composites rather than single causal metabolites. Residual confounding by socioeconomic factors, which shape both exposome and health outcomes, remains a persistent challenge in cohort studies of this kind. Nevertheless, the sheer scale of the cohort, the consistency of the associations across three independent aging metrics, and the rigorous multiple-comparison correction lend considerable weight to the central conclusion.

For the aging research community, the study adds the retina to the growing list of organs whose structural integrity tracks systemic biological age, and it elevates plasma metabolism to the status of a key correlate of neurosensory retinal health. For clinicians, it hints at a future in which retinal imaging, already fast and inexpensive, might help identify people whose bodies are aging faster than their years, potentially guiding earlier interventions on smoking, diet, air quality, and psychosocial wellbeing. And for the public, the message is a vivid one: the same exposures that wear down the heart, the brain, and the metabolism may also be quietly etched into the tissue that lets you read this page. The eye, it seems, does not only take in the world; it keeps a record of what the world has done to us.

Subject of Research: Associations between systemic biological aging, frailty, exposome factors, plasma metabolomics, and retinal structural changes in the UK Biobank

Article Title: Association of systemic aging and frailty with retinal alterations: insights from an integrated exposome and metabolome framework

Article References: Chen, T., Wang, D., Ma, Y., Ye, Y., Wang, X., Lei, Y., Zhou, X., & Zhao, J. (2026). Association of systemic aging and frailty with retinal alterations: insights from an integrated exposome and metabolome framework. GeroScience. https://doi.org/10.1007/s11357-026-02572-6

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02572-6

Keywords: retinal oculomics, biological aging, frailty, plasma metabolomics, exposome, UK Biobank, macular thickness, optical coherence tomography, PhenoAge, mediation analysis, GeroScience, aging biomarkers

The research drew on 393 adults aged 18 to 79 years enrolled in the Côte d’Ivoire dual burden of disease study, known as CoDuBu, which operates within the Taabo health and demographic surveillance system in the south-central part of the country. Roughly half of the participants were women, 55 percent lived in rural settings, and the vast majority were lifetime non-smokers who reported high levels of physical activity. Blood spots collected during health examinations were shipped to a biobank, where DNA was extracted, chemically converted and analyzed on an Illumina EPIC methylation array covering more than 866,000 genomic positions. After rigorous quality control, 841,953 CpG sites remained for the epigenetic age calculations, making this one of the most comprehensive methylation datasets ever assembled from this region of Africa.

From those methylation patterns, the team computed four of the most widely used epigenetic clocks. The first generation, HorvathAge and HannumAge, were trained purely on chronological age and estimate how old a person’s cells appear to be. The second generation, PhenoAge and GrimAge, incorporate clinical markers of disease and mortality, so they aim to capture not just age but the pace of health decline. For each clock, the researchers calculated epigenetic age acceleration, the residual difference between a person’s estimated biological age and their actual calendar age, adjusted where appropriate for the mix of white blood cells circulating in the sample. This acceleration measure is what researchers believe reflects the cumulative wear of environment, behavior and disease on the genome.

The headline result is reassuring: the clocks work. Correlations between epigenetic age and chronological age were strong across all four measures, with Pearson coefficients ranging from 0.83 to 0.93. Concordance statistics, which are stricter because they penalize systematic bias, were somewhat weaker, between 0.73 and 0.85, and the first-generation clocks outperformed the mortality clocks in raw age prediction. HannumAge was the most accurate, deviating from true age by an average of just under two years, while HorvathAge overestimated age by nearly six years on average. Each clock also showed a characteristic sweet spot where its predictions were sharpest: HannumAge performed best around age 32, HorvathAge around 62, and GrimAge around 78. These patterns mirror what has been reported in Ghanaian, South African and high-income cohorts, suggesting that the underlying biology of methylation aging is broadly conserved across human populations.

Yet the clocks are not interchangeable. Correlations between the acceleration measures themselves were surprisingly low, ranging from 0.24 to 0.66, which means each clock captures a partially distinct facet of biological aging. That distinction became vivid when the researchers examined which social and behavioral factors predicted faster aging. Men showed significantly higher acceleration than women on three of the four clocks, by roughly 1.6 to 2.9 years depending on the measure. Participants from the wealthiest households aged more slowly on the extrinsic and phenotypic clocks, an effect of about two years, consistent with the idea that economic security, healthcare access and nutrition buffer the genome against premature aging. High-risk alcohol consumption and smoking both accelerated the mortality-sensitive clocks, with smokers showing GrimAge acceleration of about 3.5 years.

Two of the most striking findings were non-linear. Physical activity and body mass index both showed U-shaped relationships with GrimAge acceleration, meaning that people at the extremes aged fastest. Underweight participants, who made up 7 percent of the sample, showed even stronger acceleration than those with obesity. In a setting where low body weight can reflect undernutrition, chronic infection and psychosocial stress rather than healthy leanness, this result suggests that the methylation clock registers the biological toll of deprivation just as faithfully as it registers the toll of metabolic excess. The authors also note a physical activity paradox: in populations where most activity comes from repetitive manual labor rather than leisure exercise, very high activity levels may promote chronic exhaustion and cardiovascular strain rather than protection, a pattern previously documented in large European studies.

The study then connected biological age to actual disease. About 22 percent of participants met the criteria for metabolic syndrome, and 56 percent had elevated fasting glucose, a strikingly high figure for a predominantly rural cohort. When the researchers regressed these outcomes on epigenetic age acceleration, they found that extrinsic acceleration and PhenoAge acceleration each increased the odds of metabolic syndrome severity by roughly 23 to 26 percent per standard deviation, and both were linked to high blood pressure and impaired fasting glucose. These associations held up after adjustment for demographic, socioeconomic and lifestyle factors, and for body mass index, indicating that the clocks carry information about cardio-metabolic risk beyond what conventional measures provide.

Perhaps the most mechanistically interesting result concerns what drives GrimAge. This clock is built from methylation surrogates of eight plasma proteins plus cumulative smoking exposure, and the team found that one component in particular, a methylation proxy for plasminogen activator inhibitor-1, or PAI-1, largely explained the clock’s associations with both risk factors and metabolic disease. The PAI-1 surrogate was associated with metabolic syndrome, high blood pressure, high triglycerides, central obesity and elevated glucose, and its links with lifestyle risks were robust to adjustment for blood cell composition. PAI-1 is a well-known player in inflammation, coagulation and metabolic dysregulation, and the authors argue that it may serve as a more reliable molecular marker of aging-related metabolic damage than the composite clock itself, a hypothesis they hope future studies will test.

The study has limitations that the authors acknowledge candidly. It is cross-sectional, so it cannot establish whether accelerated aging precedes disease or follows it, and the sample, though carefully characterized, is modest compared with the mega-cohorts of high-income epigenetics. The cohort was originally assembled to study asymptomatic malaria, although sensitivity analyses showed the results were stable when malaria cases were excluded. Even so, the work fills a conspicuous gap. Previous African validations, in Central African hunter-gatherers, South African men and Ghanaian migrants, covered narrow age ranges or specific subgroups; this study spans nearly six decades of adult life in both urban and rural settings and links methylation age to measured clinical phenotypes rather than self-report alone.

The broader implications are considerable. Populations across sub-Saharan Africa are aging rapidly while non-communicable diseases such as diabetes and hypertension rise in parallel with persistent infectious and environmental burdens, a double burden that could compress healthspan and push disease onset into ever-younger ages. If epigenetic clocks can be validated and eventually calibrated with African methylation data, they could become powerful tools for identifying which exposures, from poverty to alcohol to occupational strain, truly accelerate aging in transitioning societies, and for evaluating interventions before clinical disease emerges. The Côte d’Ivoire team argues that future longitudinal studies should fold in the wider exposome, including pollution, stress and sleep, to pinpoint the public health utility of these molecular hourglasses. For now, the message is clear: the clocks tick the same way in a rural Ivorian village as they do in a European clinic, and in both places, they are listening closely to how people live.

Subject of Research: Validation of DNA methylation epigenetic clocks and their cardio-metabolic risk associations in adults in Côte d'Ivoire

Article Title: Validity and cardio-metabolic risk profiles of DNA methylation clocks among adults in south-central Côte d’Ivoire

Article References: Sanchez, D., Jeong, A., Essé, C., Bassa, F. K., Koné, S., Acka, F., Laubhouet-Koffi, V., Kouassi, D., Utzinger, J., N’Goran, E. K., Bonfoh, B., Imboden, M., Holloway, J. W., & Eze, I. C. (2022). Validity and cardio-metabolic risk profiles of DNA methylation clocks among adults in south-central Côte d’Ivoire. Epigenetics Communications, 2(1), Article 1. https://doi.org/10.1186/s43682-022-00008-8

Image Credits: AI Generated

DOI: 10.1186/s43682-022-00008-8

Keywords: epigenetic clocks, DNA methylation, biological aging, Côte d'Ivoire, metabolic syndrome, GrimAge, PhenoAge, HorvathAge, HannumAge, non-communicable diseases, Africa, PAI-1

The research, led by Hyman B. Muss of the University of North Carolina at Chapel Hill and Mina S. Sedrak of UCLA, together with colleagues at City of Hope and the Mayo Clinic, examined 251 women with early-stage breast cancer and 49 cancer-free controls. The team measured p16INK4a, a gene whose expression rises as cells enter senescence—a state of permanent growth arrest linked to aging and disease—and compared it against five DNA methylation clocks: Horvath, Hannum, PhenoAge, GrimAge, and the Dunedin Pace of Aging, known as mPoA. DNA methylation clocks estimate biological age from characteristic patterns of chemical tags on DNA that shift predictably over a lifetime.

The two biomarker families barely spoke to each other. Across both cancer patients and controls, correlations between T-cell p16 and the methylation clocks were weak, with correlation coefficients generally below 0.3. In the cancer cohort, p16 showed only modest associations with Hannum, PhenoAge, and GrimAge, and none at all with the Horvath clock or the Dunedin pace measure. The pattern held in the control group and in a separate cohort of younger patients, where the strongest relationship—between p16 and GrimAge—reached only a moderate correlation of 0.40. The Dunedin measure, which estimates the rate of aging rather than accumulated age, showed essentially no relationship with p16 anywhere.

That disconnect matters because researchers and clinicians increasingly rely on these tests to gauge whether diseases or treatments are accelerating aging, and to evaluate interventions meant to slow it down. If p16 and methylation clocks captured the same underlying biology, they could be used interchangeably. The new findings suggest they cannot. The authors argue that the two measures reflect fundamentally different aspects of aging: p16 tracks senescence within a specific immune cell population, while methylation clocks integrate epigenetic signals across the heterogeneous mixture of cell types found in whole blood. Differences in biological compartment, measurement scale, and clock design—all calibrated differently, some to chronological age and others to mortality risk—likely all contribute to the weak overlap.

The study also probed whether cancer itself leaves a measurable imprint on these markers. When the researchers plotted biomarker levels against chronological age, women with breast cancer did not differ from controls in p16, Hannum, Horvath, or the Dunedin pace measure. But two of the mortality-informed clocks told a different story: GrimAge and PhenoAge were both significantly higher in the cancer group, and the differences persisted after adjusting for race, ethnicity, and body mass index. The result aligns with a growing body of evidence that cancer is associated with physiological changes consistent with accelerated aging, while suggesting that standard epigenetic clocks are not uniformly sensitive to that signal.

The most striking results came from the longitudinal arm. In a subset of 48 women with early breast cancer who gave blood before and three to six months after adjuvant chemotherapy, p16 expression rose significantly, by an average of 0.7 log2 units—an increase the authors note is equivalent to roughly 10 to 20 years of chronological aging. Yet four of the five methylation clocks—Horvath, PhenoAge, GrimAge, and the Dunedin pace—showed no significant change over the same interval. Only the Hannum clock increased, and only modestly. When the team split patients into those whose p16 rose beyond assay precision and those whose did not, none of the epigenetic clocks changed in either group, underscoring that the chemotherapy signal seen in senescence markers simply was not mirrored in the methylation-based measures.

The findings complicate the interpretation of earlier studies. Some prior work reported epigenetic age acceleration after cancer treatment: one study of breast cancer survivors found those who received chemotherapy were biologically two to three years older than controls two to three years after treatment, and a small study of 18 patients reported acceleration of roughly 3.5 to 8 years after a single anthracycline-containing cycle, though that analysis lacked paired samples. Other research found clock changes only years or decades later, or no change at all depending on regimen and follow-up timing. The new data suggest methylation clocks are not blind to treatment-related aging effects, but may respond on a different timescale or capture different biological consequences than the rapid senescence response registered by p16.

The team also explored senescence-associated secretory phenotype proteins—inflammatory molecules shed by senescent cells that have been linked to morbidity and mortality. In 20 patients treated with doxorubicin-based chemotherapy, chemotherapy-induced increases in p16 correlated with rising levels of PARC, TNFRII, ICAM1, and TNF-alpha. Intriguingly, baseline p16 showed little to no association with baseline levels of these proteins, suggesting the link is driven by the chemotherapy itself. Together with prior work showing increased p16, DNA damage, and inflammatory markers in survivors over two years, the results paint a picture in which cytotoxic chemotherapy provokes a coordinated senescence and inflammatory response that current epigenetic clocks largely miss.

The authors are careful about the limits of their analysis. The longitudinal component was exploratory and small; the cross-sectional and longitudinal cohorts differed in age and sampling protocols; p16 and methylation were measured in different biological compartments; and chemotherapy-related shifts in immune cell composition can confound whole-blood methylation measures. The three-to-six-month follow-up window may also simply be too short for clocks that evolve over years. Treatment regimens were heterogeneous, mixing anthracycline and non-anthracycline approaches, though recent long-term follow-up found persistently elevated p16 regardless of regimen.

Even so, the message is clear and potentially consequential: the most popular biomarkers of biological aging are not interchangeable. Choosing between them requires knowing which aging process—and which timescale—a study actually cares about. For the millions of breast cancer survivors living with the long-term consequences of treatment, that distinction could shape how researchers track accelerated aging, design interventions such as exercise or senolytic drugs, and ultimately judge whether a therapy that cures cancer is also quietly aging the body that carries it.

Subject of Research: Comparison of cellular senescence marker p16INK4a and DNA methylation epigenetic clocks as biomarkers of biological aging in women with early breast cancer treated with chemotherapy

Article Title: p16INK4a and DNA methylation clocks in women treated with chemotherapy for early breast cancer

Article References: Muss, H. B., Sun, C., Ding, Y. C., Ji, J., White, T. A., Nyrop, K., LeBrasseur, N. K., Neuhausen, S. L., Mitin, N., & Sedrak, M. S. (2026). p16INK4a and DNA methylation clocks in women treated with chemotherapy for early breast cancer. GeroScience. https://doi.org/10.1007/s11357-026-02521-3

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02521-3

Keywords: p16INK4a, DNA methylation clocks, biological aging, cellular senescence, breast cancer, chemotherapy, epigenetic age, GeroScience, GrimAge, PhenoAge, biomarkers of aging, cancer survivors