Why some people age better: Hundreds of genes identified

Some people stay sharp and steady into old age, while others face illness and limits far sooner. A new study maps 408 genes across distinct patterns of accelerated aging, known as frailty, and shows these patterns arise from different biology.

Rather than one score, the team modeled a general genetic factor plus six specific factors that track shared signals across 30 aging deficits, moving beyond a single category for everyone.

Study lead author Isabelle Foote is a postdoctoral researcher at the University of Colorado Boulder’s Institute for Behavioral Genetics.

The researchers analyzed genetic and health records from the UK Biobank – a 500,000-person resource designed for large-scale population research. The scale allowed the team to detect subtle effects that smaller cohorts often miss.

Same score, different health risks

Clinicians often use a frailty index – a count of 30 to 40 deficits across symptoms, diseases, and functions, to estimate risk in older adults.

That approach is useful in clinics but can hide why two people with the same score need different care.

Previous research has shown that frailty reflects the accumulation of deficits and varies widely among people the same age. Dividing the score into distinct domains creates a clearer map for intervention.

Genes link aging and frailty

Some of the signals appear in biologically familiar places, including the transcription factor SP1, which regulates genes tied to amyloid and tau in Alzheimer’s disease and shows altered levels in patient brain tissue.

SP1 also participates in immune and stress-response pathways, suggesting one route by which inflammation might contribute to cognitive decline.

Other findings intersect with metabolic control, such as FTO, a gene whose common variants are strongly associated with higher body mass index and obesity across the life course.

Connections like these align with frailty subtypes that center on cognition, mobility, metabolism, and social context without presuming a single cause.

Targeting aging, not just disease

The findings support geroscience – the idea that targeting the biology of aging could lower the risk for many conditions at once, rather than chasing each disease separately.

“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.”

“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,” said study co-author Andrew Grotzinger.

The experts recommend adding subtypes to frailty evaluations. This would allow doctors to target cognitive frailty with dementia prevention and metabolic frailty with measures to reduce risks for heart disease and diabetes. Researchers can test these steps now, while genetics fuels the search for new targets.

Genetic scores may guide prevention

One near-term tool is the polygenic risk score, which summarizes thousands of small genetic effects into a single number estimating predisposition for a trait like frailty.

Early validation work shows that such scores predict frailty in independent cohorts, explaining about two percent of the variance in older adults. This tool could flag people who may benefit from earlier screening or coaching.

Scores are not destiny, and their performance depends on ancestry, sample size, and context. This is why experts caution against overinterpreting a single score outside well-studied populations. As datasets diversify and clinical studies mature, the numbers should become more precise.

The larger lesson here is that precision matters in aging science, because unhealthy aging is not a single pathway. With better maps, researchers and clinicians can match prevention and treatment to the biology that truly needs attention.

Genes cluster traits in aging science

The study draws on a large genome-wide association study, which searches for links between genes and traits. The team applied a method called genomic structural equation modeling to uncover hidden genetic factors that help explain why certain traits tend to cluster together.

In plain terms, this modeling method tests whether the same sets of variants tend to occur alongside specific constellations of deficits – exactly the kind of insight needed when aging is not a single process.

Genomic SEM distinguishes which genetic effects are shared across areas – such as thinking ability, movement, metabolism, and disease risk – and which are unique.

It also accounts for the fact that some of the same individuals may appear in multiple datasets. That is how the team identified a broad factor and six specific factors that better match what doctors observe in practice.

Genes matter, but context matters more

The authors propose adding subtypes to frailty evaluations. This approach lets doctors target cognitive frailty with dementia prevention and metabolic frailty with strategies to lower heart disease and diabetes risks.

The path forward includes recruiting broader cohorts and building tools that perform equitably across groups.

Genes play a role in aging, but life experiences also matter. Because social and behavioral factors are important, care plans must account for environment, habits, and support networks – not just DNA. The genetic map adds resolution, but it does not erase the rest of the picture.

The study is published in the journal Nature Genetics.

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