Any sufficiently complex set of biological data obtained from a population of individuals of varied ages (or even at a single age, provided that later outcomes are known) will exhibit differences that can be used to produce an aging clock. Machine learning approaches are applied to the data to find algorithmic combinations of values that produce a predictor of chronological age, or mortality, or risk of disease. Typically clocks output an age. This is a biological age, distinct from chronological age. A greater clock-predicted biological age than chronological age indicates a greater burden of age-related damage and dysfunction, a person who is aging faster than the average for the population whose data was used to produce the clock.
The growth of interest in the mainstream work of producing ever better epigenetic clocks, derived from data on the methylation status of specific CpG sites on the nuclear genome, this being a measure of changes in gene expression and cell behavior, has led to the creation of a great many other clocks. Clocks have been produced from other omics data, combinations of simple measures of healthy such as grip strength and complete blood count, and imaging data. Retinal imaging is a newly popular area of study in the production of aging clocks, for example.
In today's open access paper, researchers demonstrate that brain scans can also be used to produce potentially interesting aging clocks. This proliferation of different clocks may slow down at some point, or it may be that aging is sufficiently complex than no one or no few clocks will prove to be universally useful, and the future of aging clocks in medicine is that every specialty will have a few clocks to choose from on a case by case basis.
Current neuroimaging-based approaches to measure aging, akin to first-generation epigenetic clocks, involve training models to predict chronological age from variability in MRI measures of brain structure in large multi-age samples. Researchers then typically quantify a "brain age gap," which reflects the difference between a participant's predicted and actual chronological age. A positive brain age gap is interpreted as evidence of accelerated brain aging. As with first-generation epigenetic clocks, these age-deviation approaches unavoidably mix model error (e.g., historical differences in environmental exposures, survivor bias, disease effects, measurement bias) with a person's true rate of biological aging.
Here, using a single T1-weighted MRI scan collected at age 45 in the Dunedin Study, we describe the development and validation of a novel brain MRI measure for the Pace of Aging. We call this measure the Dunedin Pace of Aging Calculated from NeuroImaging or "DunedinPACNI." Using data from the Human Connectome Project we evaluated the test-retest reliability of DunedinPACNI. Exporting the measure to the Alzheimer's Disease Neuroimaging Initiative (ADNI) and UK Biobank, we conducted a series of preregistered analyses designed to evaluate the utility of DunedinPACNI for predicting multiple aging-related health outcomes. To benchmark our findings, we compared effect sizes for DunedinPACNI to those for brain age gap. DunedinPACNI is the first brain-based measure trained to directly estimate longitudinal aging of non-brain organ systems.
Neuroimaging Initiative and UK Biobank neuroimaging datasets revealed that faster DunedinPACNI predicted participants' cognitive impairment, accelerated brain atrophy, and conversion to diagnosed dementia. Underscoring close links between longitudinal aging of the body and brain, faster DunedinPACNI also predicted physical frailty, poor health, future chronic diseases, and mortality in older adults. Furthermore, DunedinPACNI followed the expected socioeconomic health gradient. When compared to brain age gap, an existing MRI aging biomarker, DunedinPACNI was similarly or more strongly related to clinical outcomes. DunedinPACNI is a "next generation" MRI measure that will be made publicly available to the research community to help accelerate aging research and evaluate the effectiveness of dementia prevention and anti-aging strategies.
View the full article at FightAging
