Why One Biomarker Cannot Tell You How Fast You Are Aging

Two people born on the same day can sit in the same clinic waiting room while carrying profoundly different biological futures. One may have the arteries, immune system and metabolic profile of someone a decade younger; the other may already be accumulating the molecular wear that precedes heart disease, dementia or frailty. Chronological age, the number celebrated on birthdays, captures almost none of this variation. That gap between calendar time and biological reality is the central problem driving one of the fastest-moving fields in modern medicine, and a new collection of studies published in Frontiers in Aging argues that the answer will not come from a single perfect test, but from a carefully woven combination of molecular, computational and physiological measurements.

The research topic, titled Biomarkers and Aging: New Insights for Precision Medicine, gathers contributions that trace a deliberate arc, from the conceptualization of biological age to its molecular measurement, computational interpretation and eventual clinical relevance. Its editors, Oluwafemi Gabriel Oluwole of Olabisi Onabanjo University and the University of Cape Town and Paulo Adriano Schwingel of the University of Pernambuco in Brazil, argue that aging is a dynamic, multidimensional phenomenon shaped by genetic and epigenetic regulation, inflammation, immune and stress responses, DNA repair and individual exposures. No single biomarker or score, they contend, can capture the whole picture.

The most influential tools in this field have come from an unexpected source: chemical tags on DNA. DNA methylation, the attachment of methyl groups to cytosine bases across the genome, changes in predictable patterns as cells age, and these patterns can be read out as so-called epigenetic clocks. A scoping review by Ziesel and colleagues examined 435 studies in this space, 167 of which used methylation-based approaches, charting the progression from simple age-associated CpG patterns to increasingly sophisticated clock models. A companion review from the same group extended the analysis to 309 non-methylation-based biomarkers, revealing a bewildering diversity of candidate markers and metrics.

Read together, the two reviews signal a decisive shift in how the field thinks about measurement. The era of hunting for the single best biomarker of aging is giving way to a search for combinations that capture the dimensions of aging relevant to a specific individual and a specific clinical outcome. The editors illustrate why this matters with a striking asymmetry: a person may show accelerated epigenetic aging alongside fully preserved cardiovascular function, or conversely display metabolic and immune aging despite a modest global biological-age estimate. A single number, in other words, can inform while simultaneously concealing real and clinically meaningful heterogeneity.

Biological age would remain an academic curiosity if it did not connect to outcomes that patients and physicians care about. Chen and colleagues addressed this directly, drawing on data from 349,456 participants in the UK Biobank. Over roughly 14 years of follow-up, they found that phenotypic age acceleration partially mediated the association between impaired lung function and both mortality and cardiovascular disease, with risk highest in participants showing both exposures. The finding suggests that biological age can capture accumulated vulnerability that chronological age or a single organ measurement misses, acting as an integrative indicator of interacting processes rather than a direct cause of disease.

The UK Biobank analysis also carries an important caveat that the editors emphasize: biological-age measures are best understood as complements to established risk factors, not replacements for them. A clinician who abandons conventional cardiovascular risk assessment in favor of an epigenetic score would be discarding decades of validated evidence. The more defensible vision is a layered one, in which biological-age estimates sit alongside traditional measures to refine prediction, particularly in the gray zone where conventional risk calculators disagree with clinical intuition.

Proteomics adds a functional dimension to this layered picture. While epigenetic marks are regulatory signals, proteins are the functional effectors that actually execute biology, so proteomic biomarkers offer a window into downstream biological activity that DNA methylation cannot provide. Luo and colleagues reviewed proteomic biomarkers spanning the immune, metabolic, endocrine, cardiovascular, musculoskeletal and nervous systems, together with proteomic aging clocks linked to frailty, multimorbidity, organ-specific biological age and mortality. Their synthesis reinforces the emerging consensus that a multidimensional aging profile, rather than a single biological-age estimate, is the realistic goal.

Physiological signals point in the same direction. In their own contribution, Oluwole and Schwingel found that reduced heart-rate-variability indices, a marker of autonomic and parasympathetic modulation, were associated with cognitive decline in community-dwelling older adults. Heart-rate variability can be measured with inexpensive, non-invasive equipment, which makes it an attractive functional complement to expensive molecular assays. The finding illustrates how system-specific functional biomarkers extend, rather than replace, molecular measures of biological age, adding real-time information about how the aging nervous system and cardiovascular system are actually performing.

Moving from measurement to precision medicine also requires linking molecular information to candidate interventions, and here computational methods are proving decisive. The editors describe how transcriptomic analysis of human Achilles tendon tissue, combined with sequence analysis, molecular docking and protein-protein interaction analysis, was used to investigate a small peptide derived from methyl jasmonate esterase and its interaction with human complement factor H. In related work, network pharmacology and molecular docking applied to Sorghum bicolor 3-deoxyanthocyanidins identified inflammation- and redox-related pathways, including NF-κB, Keap1-Nrf2 and PPAR-γ, with experimental support for modulating inflammaging-related processes. Neither example constitutes an established anti-aging therapy, but both illustrate an emerging pathway in which biomarkers and mechanisms help prioritize hypotheses for further, experimentally validated investigation.

Two open questions will determine whether this translational framework delivers on its promise. The first concerns population diversity: whether biomarkers and computational clocks developed in specific populations generalize across genetic backgrounds, environments and socioeconomic circumstances remains unresolved, and precision medicine cannot be precise if its underlying biomarkers under-represent the populations in which they will be applied. The second concerns actionability: a biomarker that predicts disease is scientifically valuable, but its clinical worth depends on whether it identifies modifiable processes and supports interventions that improve outcomes. That will require longitudinal studies tracking whether biological-age measures respond to lifestyle or pharmacological intervention, and whether those changes translate into meaningful gains in healthspan. The editors’ conclusion is measured but ambitious: the field is moving beyond measuring biological age toward understanding biological vulnerability, and the next stage will depend on multi-omics measurement, computational modelling and diverse longitudinal cohorts, with models kept reproducible, interpretable and clinically validated.

Subject of Research: Biomarkers of biological aging and their application in precision medicine

Article Title: Editorial: Biomarkers and aging: new insights for precision medicine

Article References: Oluwole, O. G., & Schwingel, P. A. (2026). Editorial: Biomarkers and aging: new insights for precision medicine. Frontiers in Aging, 7, Article 1987600. https://doi.org/10.3389/fragi.2026.1987600

Image Credits: AI Generated

DOI: 10.3389/fragi.2026.1987600

Keywords: biological age, biomarkers, epigenetic clocks, DNA methylation, proteomics, heart-rate variability, precision medicine, aging, inflammaging, UK Biobank, frailty, computational biology