A Comprehensive Analysis of Real-World Data-Driven Markers Related to Aging

The increasing elderly population and associated multimorbidity have spurred studies to use computational models to explore the roles of epigenetic, cellular, metabolic, clinical, and social aging markers. This research recognizes the gap in a comprehensive predictive model that utilizes vast real-world observational data against these diverse isolated biomarkers from disparate and rare data sources. This research offers a focused exploration of aging biomarkers within electronic health records (EHR), highlighting the potential of digital health data to shed light on the aging process. Our literature review systematically identifies and categorizes key biomarkers of aging available in EHRs, such as vital signs, blood chemistry values, and clinical indicators of organ function and disease prevalence. We present a clear and concise overview of these biomarkers, detailing the available data and emphasizing their relevance to aging research.