Aging is a gradual process that does not occur overnight and involves a progressive decline in the efficiency of all body systems. However, individuals born in the same year can be aging significantly faster or slower, and researchers are working to understand why. A newly developed brain-based imaging biomarker called DunedinPACE Neuroimaging (DunedinPACNI) offers a novel approach to estimating biological age. Designed by a research team, this tool can estimate a person’s whole-body biological age (not just the brain) from a single magnetic resonance imaging (MRI) scan.
Conventional methods of biological aging typically target genetic markers in DNA known as epigenetic clocks. These have proved useful but are overly reliant on blood samples and are not easily integrated with brain imaging techniques. To fill this gap, the researchers have sought out the Dunedin Study, a unique, decades-long birth cohort study that has followed the health of more than 1,000 individuals born in Dunedin, New Zealand, between 1972 and 1973. These participants have undergone regular medical examinations and laboratory analyses for over 40 years, providing rare and valuable insights into the realities of aging.
Based on this enriched data, the team used a machine learning model to infer DunedinPACNI by analyzing structural brain features, such as cortical thickness and brain volume. The rate is measured based on fluctuations in 19 established biomarkers, which determine the health of the cardiovascular, immune, kidney, lung, and dental systems. By correlating brain structure with systemic aging, DunedinPACNI has the potential to expand on the type of “cognitive age gap”. This often lacks consistency and fails to reliably predict health outcomes.
To demonstrate the efficiency of their tool, the scientists tested the biomarker in large-scale brain-related studies, including the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the UK Biobank, and the Human Connectome Project. The results were alarming; people who had MRI scans indicated faster biological aging also showed some of the classic signs of brain degeneration, such as thinner cortex, reduced gray matter, enlarged ventricles, diminished thinking skills, increased memory loss, and an increased risk for dementia.
Importantly, the implications extended beyond the brain. Rapid aging in terms of DunedinPACNI was also associated with increased frailty, poorer self-perceived health, more chronic diseases such as heart disease and stroke, and even advanced mortality. It was also socioeconomically significant: less educated participants or those with lower income had a higher chance of displaying accelerated brain-based aging, emphasizing the social health inequality.
Unlike DNA-based aging clocks, DunedinPACNI can be applied to existing MRI datasets that are being collected by many researchers, which makes it a feasible means of extending aging research. The tool consistently showed itself robust in various groups, including the Latin American cohort, implying that it can be used outside the original New Zealand sample. Notably, DunedinPACNI was not a duplicate of the existing aging measures. Unlike traditional brain age gap models, it explained distinct and meaningful variations in overall health, offering insights not reflected in other biomarkers.
Although not yet available for routine clinical use, DunedinPACNI is a powerful research tool. It enables scientists to study how lifestyle, environment, and genetics affect the aging process, as well as to test interventions aimed at slowing it. Its greatest strength is that it is based on a long-term population-based study, where the same individuals were followed over decades, thus minimizing biases and enhancing reliability.
Now freely available, DunedinPACNI can be utilized by scientists worldwide to explore the association between brain structure and systemic aging, ultimately advancing efforts to delay aging and improve lifelong health.
References: Whitman ET, Elliott ML, Knodt AR, et al. DunedinPACNI estimates the longitudinal Pace of Aging from a single brain image to track health and disease. Nat Aging. 2025. doi:10.1038/s43587-025-00897-z
