Research Theme

Multi-omics Cohort of Aging

A comprehensive, Asia-centric Longitudinal Deep Omics (LDO) aging cohort is being established as a foundational platform for aging research. This involves the recruitment and follow-up of Asian participants, with longitudinal collection of clinical data, biological specimens, high-resolution ocular images, and lifestyle metrics from wearable devices. A standardized and deep multi-omics database including genomic, proteomic, metabolomic, lipidomic, oculomic profiling is being developed to capture genetic, molecular, environmental, and behavioral diversity of the aging population. The LDO aims to generate both population-wide and individual-specific insights into aging trajectories through integrated clinical, phenotypic, and omics analyses over time.

Multi-omics Cohort

Biological Mechanism of Aging

A key focus of the LDO is understanding how individuals age differently at the molecular and non-molecular levels under healthy and pathological conditions. Central to this is the concept of ageotypes—distinct biological aging patterns that vary from person to person. By characterizing these patterns across diverse individuals using multi-omics data, the LDO seeks to uncover intrinsic drivers of aging and identify modifiable pathways for healthy aging and delaying age-related diseases.

Biological Mechanism

Personalized Biomarkers of Aging

A major goal of LDO is to identify individual-specific and shared biomarkers that define aging trajectories. By integrating longitudinal multi-omics and clinical data, the study identifies biomarkers remain stable or change within individuals, and examines how these patterns vary across the population. Distinguishing markers of healthy versus pathological aging enables translation into clinically actionable signals for early diagnosis, risk prediction, and personalized intervention. This work supports a precision-aging framework driven by dynamic, data-driven health insights.

Personalized Biomarkers

Predictive Models and Monitoring Tools of Aging

A core objective of the LDO is to develop intelligent systems capable of anticipating and tracking the aging process by leveraging insights from big data. This involves the creation of a real-time health monitoring platform tailored to the specific needs of older adults. The platform will integrate biomedical engineering and AI-driven health analytics to deliver continuous, personalized care. Advanced machine learning models will be developed to synthesize diverse data types—such as physiological signals, medical history, and lifestyle factors—to predict aging trajectories and detect the early onset of age-related conditions. These innovations aim to enable proactive aging care through early detection, continuous monitoring, and data-driven clinical decision support.

Predictive Models and Monitoring Tools