Sarcopenia, characterized by the age-related loss of muscle mass and function, is closely associated with increased risks of falls, hospitalizations, and mortality in older adults. Early detection of sarcopenia is crucial to mitigate its progression and minimize its impact on health outcomes.
The etiology of sarcopenia is both multifactorial and complex. Numerous factors, including malnutrition, physical inactivity, age-related cellular changes, oxidative stress, inflammation, and hormonal imbalances, contribute to the age-related decline in muscle mass and strength with age. This complexity, along with its association with various other health-related conditions, has driven growing interest in identifying reliable biomarkers to monitor its progression.
To date, most reported studies have primarily focused on identifying single biomarkers for sarcopenia. However, given the “multifactorial” nature of sarcopenia involving multiple biological pathways, relying on a single marker is unlikely to provide sufficient or reliable information. Instead, a multifaceted approach that integrates clinical data, biological insights, and multi-omics analysis is essential for accurately classifying and assessing older adults with sarcopenia.
Despite growing interest, a significant research gap persists, particularly in studies combining clinical data and multi-omics analysis through artificial intelligence (AI). Addressing this gap, our three-year project aims to uncover novel risk factors for sarcopenia by utilizing clinical data, multi-omics insights, and advanced AI methodologies.
