Some individuals age gracefully, maintaining their cognitive and physical abilities well into their senior years, while others encounter health challenges much earlier. A groundbreaking study has identified 408 genes associated with distinct patterns of accelerated aging, commonly referred to as frailty. This research reveals that these patterns emerge from varying biological processes.
Rather than relying on a single metric, the research team developed a model incorporating a general genetic factor alongside six specific factors. These factors track shared signals across 30 aging deficits, offering a more nuanced understanding than a one-size-fits-all approach.
Isabelle Foote, the study’s lead author and a postdoctoral researcher at the University of Colorado Boulder’s Institute for Behavioral Genetics, spearheaded this research. The team analyzed genetic and health records from the UK Biobank, a resource comprising 500,000 individuals, which is designed for large-scale population studies. This extensive dataset enabled the researchers to identify subtle genetic effects that smaller studies often overlook.
Understanding Frailty: Beyond a Single Score
Clinicians typically use a frailty index, which counts 30 to 40 deficits across symptoms, diseases, and functions, to assess risk in older adults. While valuable in clinical settings, this approach can obscure why two individuals with the same score may require different care strategies.
Previous research has demonstrated that frailty reflects the accumulation of deficits and varies significantly among individuals of the same age. By dividing the score into distinct domains, researchers can create a clearer intervention map.
Genes Linking Aging and Frailty
Some genetic signals are found in well-known biological areas, such as the transcription factor SP1. This factor regulates genes linked to amyloid and tau in Alzheimer’s disease and exhibits altered levels in patient brain tissue. SP1 also plays a role in immune and stress-response pathways, suggesting a potential link between inflammation and cognitive decline.
Other findings intersect with metabolic control, including the FTO gene, which is strongly associated with higher body mass index and obesity throughout life. These connections align with frailty subtypes centered on cognition, mobility, metabolism, and social context, without assuming a single cause.
Targeting Aging: A Geroscience Approach
The study’s findings lend support to geroscience, which posits that targeting the biological processes of aging could reduce the risk of multiple conditions simultaneously, rather than addressing each disease individually.
“To be able to identify treatments to stop or reverse accelerated biological aging, you need to know what the underlying biology is,” noted Foote. “This is the largest study yet to use genetics to try to do that.”
Andrew Grotzinger, a study co-author, added,
“This paper suggests that it’s probably not going to be a single magic pill to address all the diseases that come with aging, but maybe it doesn’t need to be hundreds anymore.”
Experts recommend incorporating subtypes into frailty evaluations, enabling doctors to target cognitive frailty with dementia prevention and metabolic frailty with measures to reduce risks for heart disease and diabetes. Researchers can begin testing these strategies now, while genetics continues to fuel the search for new targets.
Genetic Scores as a Preventive Tool
One promising tool is the polygenic risk score, which consolidates thousands of small genetic effects into a single number estimating predisposition for traits like frailty. Early validation efforts indicate that these scores can predict frailty in independent cohorts, explaining about two percent of the variance in older adults. This tool could identify individuals who might benefit from earlier screening or lifestyle coaching.
However, scores are not definitive, and their effectiveness depends on factors such as ancestry, sample size, and context. Experts caution against overinterpreting a single score outside well-studied populations. As datasets diversify and clinical studies progress, the precision of these scores is expected to improve.
Genes, Context, and the Future of Aging Science
The study utilized a large genome-wide association study to explore the connections between genes and traits. The research team applied genomic structural equation modeling to uncover hidden genetic factors that help explain why certain traits cluster together.
This modeling method tests whether specific sets of genetic variants tend to occur alongside particular constellations of deficits, providing crucial insights when aging is not a singular process. Genomic SEM distinguishes shared genetic effects across areas such as cognition, movement, metabolism, and disease risk, while also accounting for overlapping datasets.
The authors suggest adding subtypes to frailty evaluations, allowing doctors to target cognitive frailty with dementia prevention and metabolic frailty with strategies to reduce heart disease and diabetes risks. The path forward involves recruiting broader cohorts and developing tools that perform equitably across diverse groups.
While genes play a role in aging, life experiences and environmental factors are equally important. Care plans must consider social and behavioral factors, as well as support networks, alongside genetic information. The genetic map enhances resolution but does not overshadow the broader picture.
The study is published in the journal Nature Genetics.
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