Sarcopenia is one of the major age-related processes and involves the loss of skeletal muscles and its function.
The deep-learning based model can be used to estimate the relevant importance of the genetic and epigenetic factors driving this process within many age groups.
"We believe that the most effective anti-ageing therapy should be tissue-specific, so we focused on the development of tissue-specific biomarkers of ageing. This work is an example of a marker of skeletal muscle tissue," said Polina Mamoshina, senior scientist at Insilico Medicine -- a US-based next-generation artificial intelligence company.
In the study, detailed in the journal Frontiers in Genetics, the team analysed publicly available gene expression profiles of young and old tissues from healthy donors.
Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissues and to preprocess the resulting data for a set of machine learning algorithms.
Then using several machine learning methods they predicted the age of samples based on their transcriptomic signatures.
Ultimately, the trained age predictors were used to identify tissue-specific ageing clocks.
This combined data-driven approach demonstrates that age prediction models can become a powerful tool for identifying prospective targets for geroprotectors, the researchers said.
Age-associated muscle wasting remains an important clinical challenge that impacts hundreds of millions of older adults. It is associated with serious negative health outcomes such as falls, impaired standing balance, physical disability, and mortality.
The many insights into sarcopenia from ageing research suggest that understanding the molecular mechanisms of muscle ageing can reveal novel potentially rejuvenating treatments.
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