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