The study, led by Raphaël Waziry of Columbia University Irving Medical Center together with colleagues including Sara Hägg of the Karolinska Institutet, tackled a question that has long hovered over the booming field of geroscience: do the different biomarkers of aging, measured in different tissues, actually tell the same story about the same person? Aging researchers currently rely on a patchwork of measures. Telomere length tracks the protective caps on chromosomes, which erode with each cell division. Epigenetic clocks read chemical tags called methyl groups scattered across the genome, whose patterns shift predictably as we age. Physiology-based measures combine routine blood chemistry values, such as albumin, creatinine, glucose, and C-reactive protein, into a composite estimate of biological age. Whether these domains of aging biology converge within a single individual, and whether a measurement in one tissue reflects measurements in another, had remained largely unresolved.

To answer it, the team turned to the Health and Retirement Study, a nationally representative longitudinal survey of more than 37,000 Americans aged 50 and older, coordinated by the University of Michigan’s Institute for Social Research. Participants who provided saliva samples during the 2008 interview wave had their average telomere length measured using quantitative PCR, a technique that compares the copy number of telomere sequences to a single-copy gene to produce a ratio proportional to mean telomere length. Eight years later, in 2016, many of the same participants contributed venous blood samples, which were processed within 24 to 48 hours and analyzed at a CLIA-certified laboratory at the University of Minnesota. In total, 2,406 individuals had complete data on both salivary telomere length and blood-based physiology measures, and 1,029 also had DNA methylation data, allowing direct within-person comparisons across tissues.

The DNA methylation analysis was unusually thorough. Rather than relying on a single epigenetic clock, the researchers computed eight of them, spanning the field’s history from first-generation chronological age predictors to second-generation mortality-focused algorithms. These included the original Horvath pan-tissue clock built on 353 CpG sites across 51 healthy tissues and cell types; the Hannum clock, comprising 71 CpG sites developed in whole blood; PhenoAge, based on 513 CpG sites and trained on phenotypic age; GrimAge, which incorporates epigenetic surrogates for a dozen plasma proteins along with smoking pack-years and is considered among the strongest predictors of time to death, coronary heart disease, and cancer; the skin and blood clock built on 391 CpGs; and the more compact Lin, Weidner, and VidalBralo clocks, which use 99, three, and eight CpG sites respectively. On the physiology side, the team used phenotypic age, an algorithm derived from Gompertz proportional hazard models that integrates nine blood biomarkers: albumin, creatinine, glucose, log C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count.

The headline result was strikingly consistent. Across every blood-based measure examined, longer salivary telomeres were associated with slower biological aging, an inverse relationship that held after adjusting for sex, the interaction between sex and telomere length, and the eight-year gap between the saliva and blood collections. When participants were grouped into tertiles of telomere length, those in the highest tertile showed biological ages roughly one to four years younger than those in the lowest tertile, depending on the clock. The largest effects appeared for the compact Weidner clock, with a beta coefficient of minus 3.97 years, followed by Lin at minus 3.45 years and GrimAge at minus 3.33 years, the latter highly statistically significant. The pattern persisted even in sensitivity analyses that excluded telomere measurements with a T/S ratio above 2.0, values more likely to be artifacts in salivary samples, and when the sex interaction term was dropped from the models.

Notably, the cross-tissue correlations, while statistically significant, were modest in magnitude, with coefficients such as minus 0.067 for the Lin clock and minus 0.064 for Weidner. This stands in sharp contrast to the intra-tissue comparisons within blood, where DNA methylation clocks correlated strongly with the physiology-based phenotypic age. GrimAge led the pack with a correlation coefficient of 0.75, followed by Hannum at 0.68 and the skin and blood clock at 0.66, all highly significant. The interpretation is nuanced: within a single tissue, the different domains of aging biology move together robustly, but the signal weakens when it must travel across tissues, time, and measurement technologies. An eight-year interval between samples, which the authors adjusted for but could not eliminate, likely contributes to this attenuation, as does the well-documented fact that saliva and blood differ substantially in the concentration and abundance of their molecular analytes.

One of the most intriguing findings emerged from the finer-grained analysis of individual physiology biomarkers. When each of the nine blood analytes was correlated separately with the DNA methylation clocks, the strength of association varied widely. Creatinine, a marker of kidney function, and lymphocyte percent, a marker of immune status, showed the strongest correlations with epigenetic aging measures, in some cases exceeding their correlations with chronological age itself. Alkaline phosphatase, by contrast, showed little relationship with any of the clocks. The authors suggest this points to potentially prominent roles for renal function and immunity in accelerating or decelerating biological aging at the epigenetic level, a hypothesis that aligns with longstanding observations that immune cell composition shifts and kidney decline are hallmarks of the aging process.

The study also revealed sex-based variation. In models stratified by sex, the effect estimates linking each tertile increase in telomere length to blood-based aging measures differed between males and females across the various clocks. This was anticipated by the researchers, who included a sex-by-telomere interaction term in their primary models based on prior evidence that telomere dynamics differ between the sexes, with women generally maintaining longer telomeres than men of the same chronological age. The precise biological underpinnings of these differences, whether hormonal, inflammatory, or related to differential exposures across the life course, remain an open question for future work.

The practical implications could prove significant. Saliva is among the most accessible human tissues: collection requires no needles, no trained phlebotomist, and minimal equipment, and samples are cheaper and easier to ship and store than blood. It has already proven its worth in therapeutic drug monitoring for conditions such as epilepsy, diabetes, and multiple sclerosis, and in clinically difficult populations where venous access is not feasible. The new findings suggest that a salivary telomere measurement, even taken years before a blood draw, carries real information about a person’s epigenetic and physiological aging status. For vulnerable populations, including frail elderly patients, young children, and people in low-resource settings, this opens the possibility of integrating aging biology monitoring into routine care using whichever tissue is accessible. The authors caution, however, that saliva composition is influenced by circadian rhythms, medications, sympathetic and parasympathetic nervous activity, and conditions ranging from diabetes to infectious disease, and that immune cell contamination can confound salivary DNA methylation measurements.

The researchers are candid about the limitations. The biomarkers were measured at two different time points, introducing potential imprecision despite statistical adjustment. Only telomere length was available in saliva, so no epigenetic clocks could be computed from that tissue. Each individual was measured only twice, precluding analysis of within-person trajectories over long periods. And because participants were aged roughly 59 to 72 at saliva collection, the results cannot be extrapolated to younger adults. Still, the study’s scale, national representativeness, and unusually comprehensive battery of aging measures make it one of the clearest demonstrations to date that the body’s aging clocks are connected across tissues. As epigenetic testing becomes cheaper and repeated measurement becomes more feasible, the convergence of telomere, methylation, and physiology-based measures, whether sampled from blood or saliva, may form the foundation for the surrogate endpoints that geroscience trials have long sought, and for a future in which a routine swab of the cheek helps clinicians track how quickly, or slowly, their patients are growing old.

Subject of Research: Cross-tissue correspondence between salivary telomere length and blood-based DNA methylation and physiology biomarkers of biological aging in older adults

Article Title: Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults

Article References: Waziry, R., Gu, Y., Williams, O., & Hägg, S. (2023). Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults. Epigenetics Communications, 3(1), Article 7. https://doi.org/10.1186/s43682-023-00022-4

Image Credits: AI Generated

DOI: 10.1186/s43682-023-00022-4

Keywords: biological aging, telomeres, DNA methylation, epigenetic clocks, saliva biomarkers, blood biomarkers, GrimAge, phenotypic age, Health and Retirement Study, geroscience, biomarkers, older adults