AI Identifies the Genes Involved in Muscle Aging

It is hoped that the findings from the Nottingham Trent University study could be used to help delay the impact of the ageing process.

Muscle ageing is a natural process which occurs in everyone, causing people to lose muscle mass, strength and endurance as they get older – and is linked to increasing falls and physical disabilities.

The work provides new insight and understanding into the genes and mechanisms which drive muscle ageing.

The researchers argue that they may have found new targets for drug discovery, which could spark therapies for muscle ageing and for older people living with the disease sarcopenia, enhanced muscle loss linked to this process.

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

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

Using artificial intelligence the researchers were able to identify the top 200 genes influencing – or being influenced by – ageing 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 ageing 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 utilise to delay the impacts of the ageing 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 ageing 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 ageing 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 ageing. 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 a potential to benefit the field of muscle ageing 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.”

Reference: Tarum J, Ball G, Gustafsson T, Altun M, Santos L. Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging. J cachexia sarcopenia muscle. 2024;15(5):2143-2155. doi: 10.1002/jcsm.13562

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