Sarcopenia is an age-related muscle disorder that reduces strength, physical function, and quality of life. However, standardized biomarkers for early diagnosis and personalized treatment are lacking. This study uses a multi-omics approach and AI models to identify novel sarcopenia biomarkers. Leveraging UK Biobank data, we aim to enhance diagnosis, predict disease progression, and develop digital therapeutics.
Primary Objectives of This Study:
Identification and Validation of Sarcopenia Biomarkers
* Conduct cross-analysis of multi-omics data from human clinical cohorts and animal models to explore potential biomarkers related to sarcopenia.
* Assess the reproducibility of these biomarkers and their correlation with physical function indicators using UK Biobank clinical data.
Development of AI-Based Sarcopenia Prediction Models
* Develop AI models for sarcopenia subtype classification and disease onset prediction.
* Validate the models across diverse genetic backgrounds to ensure generalizability.
Establishment of Real-World Digital Biomarkers
* Analyze sensor-derived gait data from UK Biobank participants to establish digital biomarkers.
* Develop a wearable-based digital therapeutic platform to enable personalized interventions.
Development of a Standardized Global Sarcopenia Diagnostic Index
* Integrate UK Biobank and international cohort data to propose a standardized diagnostic index for sarcopenia.
* Conduct a long-term real-world data (RWD) study to evaluate the clinical efficacy of digital therapeutics.
The research findings will be shared through international journal publications and conference presentations, aiding standardized sarcopenia diagnostics. We will comply with UK Biobank’s AI policy, ensuring data safety, transparency, fairness, and accountability. AI-based predictive models will follow TRIPOD-AI, CONSORT/SPIRIT-AI, and DECIDE-AI guidelines for reproducibility and reliability. Generative AI will not be used in this study.
