Brain Scan Tool Can Predict Aging Pace and Dementia Risk
A new tool uses brain MRI scans to estimate an individual's pace of aging and predict future risks for diseases.
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Researchers from Duke University, Harvard University and the University of Otago have developed a new tool that uses a brain MRI scan to estimate a person’s pace of aging. Called DunedinPACE Neuroimaging (DunedinPACNI), the tool can forecast the likelihood of developing age-related diseases, including dementia, while individuals are still in midlife and relatively healthy.
The study, published July 1 in Nature Aging, provides evidence that patterns found in midlife brain scans are linked to health outcomes decades later. DunedinPACNI, which is available to researchers at no cost, was trained using data from a longitudinal birth cohort study and then validated across multiple international datasets.
A biological clock tuned to brain structure
Unlike most aging clocks, which rely on data collected from individuals of different ages at a single time point, DunedinPACNI was developed from a study that followed the same 1,037 individuals from birth. These participants, all born in Dunedin, New Zealand, between 1972 and 1973, have been assessed regularly for health markers, including blood pressure, lung function and metabolic indicators, for over 50 years.
“The way we age as we get older is quite distinct from how many times we’ve traveled around the sun.”
Dr. Ahmad Hariri.
Using this long-term health data, researchers derived a pace-of-aging score that reflects physiological decline. DunedinPACNI was trained to predict this score using only a single brain MRI collected at age 45. The team then applied the tool to brain imaging data from diverse populations in the United States, United Kingdom, Canada and Latin America.
Linking brain aging to cognitive decline
Participants with faster aging scores showed more pronounced changes in brain structure, particularly in the hippocampus, a region important for memory. These individuals also scored lower on cognitive assessments and were more likely to experience accelerated decline in thinking and memory functions.
Hippocampus
The hippocampus is a part of the brain that plays a central role in memory formation and spatial navigation. It is one of the first regions affected in Alzheimer’s disease and often shrinks as part of normal aging or in response to neurological disease.
In a dataset of 624 older adults, aged 52 to 89, those identified by DunedinPACNI as fast agers were 60% more likely to be diagnosed with dementia over the following years compared to those with average aging scores. They also exhibited earlier onset of cognitive symptoms.
Brain aging reflects systemic health
Higher DunedinPACNI scores were also associated with greater overall frailty and increased risk for age-related diseases such as cardiovascular and respiratory conditions. The individuals with the fastest aging rates were 18% more likely to develop a chronic illness and 40% more likely to die during the follow-up period.
Frailty
Frailty is a clinical syndrome characterized by decreased strength, endurance and physiological function.
Importantly, these associations were consistent across groups that differed by race, ethnicity and socioeconomic background, suggesting that the tool may have broad applicability.
Implications for early detection and research
While DunedinPACNI is currently a research tool, its developers suggest it could help scientists identify individuals who are at higher risk for dementia or other chronic illnesses before clinical symptoms appear. Early identification could enable more effective testing of interventions and lifestyle changes intended to slow age-related decline.
“We really think of it as hopefully being a key new tool in forecasting and predicting risk for diseases, especially Alzheimer's and related dementias, and also perhaps gaining a better foothold on progression of disease.”
Dr. Ahmad Hariri.
The tool may also be useful in studying how behavioral and environmental factors, such as mental health and sleep patterns, influence the biological aging process.
Reference: 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. Nature Aging. 2025. doi: 10.1038/s43587-025-00897-z
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