AI Unlocks Secret Genes behind Muscle Aging

This study, which analyzed gene expression in younger and older adults, highlights USP54 as a key gene in muscle degradation. Findings suggest potential new drug targets and exercise-based interventions to preserve muscle mass and prevent age-related disabilities. The study, which also involved Sweden’s Karolinska University Hospital and Karolinska Institute, and Anglia Ruskin University, is reported in the Journal of Cachexia, Sarcopenia and Muscle.

Researchers at Nottingham Trent University hope their new research findings will help delay the effects of aging.

Muscle aging is a natural process everyone experiences, leading to a gradual loss of muscle mass, strength, and endurance over time. This decline is closely tied to a rise in falls and physical disabilities in older adults.

This study provides new insights into the genes and mechanisms that drive muscle aging. The researchers believe they have identified potential drug targets, paving the way for therapies that could slow muscle aging and aid people with sarcopenia, a condition marked by accelerated muscle loss associated with aging.

Physical exercise is currently the only recommended treatment for muscle aging and sarcopenia, showing benefits in improving life expectancy and delaying the onset of age-associated disorders.

The new study involved analyzing gene expression datasets of both younger (aged 21-43) and older (63-79) adults related to both muscle aging and resistance exercise.

Using artificial intelligence the researchers were able to identify the top 200 genes influencing – or being influenced by – aging or exercise, along with the strongest interactions between them.

They found that one gene in particular – USP54 – appears to play a key role in the advancement of muscle aging and muscle degradation in older people.

The significance of the findings was then further confirmed via muscle biopsy in older adults, where the gene was found to be highly expressed.

They also discovered several potential resistance exercise-associated genes. While further research is required, the team argues these could aid development of more informed exercise-based interventions targeting the preservation of muscle mass in older people, which would be key to mitigating against falls and disabilities.

“We want to identify genes that we can utilize to delay the impacts of the aging process and extend the health span,” said Dr. Lívia Santos, an expert in musculoskeletal biology at Nottingham Trent University.

She said: “We have used AI to identify the genes, gene interactions, and molecular pathways and processes associated with muscle aging that until now have remained undiscovered. The data was analysed in 20 different ways and every time the significant genes were found to be the same.

“Muscle aging is a huge challenge. As people lose muscle mass and strength we see changes in their gait which makes them more prone to falls, but they are also at increased risk of developing a range of physical disabilities making it a major public health concern.

“We urgently need to understand the mechanisms regulating muscle aging. This is crucial in helping to prevent and treat sarcopenia and enable a greater level of dependency among older people.”

Researcher Dr. Janelle Tarum said: “This study suggests that AI has the potential to benefit the field of muscle aging and sarcopenia.

“AI has not previously not been used in the field of skeletal muscle mass regulation. This motivated us to apply it to discover new genes to better understand and predict sarcopenia, or be used as targets for therapies that could benefit research on sarcopenia.”

4155/v