Longevity Science Singapore Conference & Opening of NUHS Centre for Healthy Longevity | AM Session

Scientific Concepts and Discoveries:

• Biological Age vs. Chronological Age: Biological Age is defined as the risk of dying, which increases exponentially with Chronological Age. This relationship is characterized by a mortality rate doubling time, which in humans is approximately 8 years.
• Resilience: The concept of Resilience refers to the ability of an organism to recover from perturbations. As humans age, Resilience decreases, leading to increased fluctuations in Biological Age and ultimately to frailty and death.
• Principal Component Analysis (PCA): PCA is used to identify correlated features within biological data, helping to reduce dimensionality and extract significant aging-related variables.
• Aging Clocks: Aging Clocks can be constructed using various biological markers (e.g., lipids, DNA methylation) to predict Biological Age and assess the health status of an individual.
• Impact of Lifestyle on Aging: Factors such as smoking have a measurable impact on Biological Age, demonstrating the potential for lifestyle interventions to influence healthspan.

Methodology:

• Data Collection: Large datasets from model organisms (e.g., C. elegans, mice) and humans were used to analyze biological aging.
• Regression Models: Auto-regressive models were applied to measure the Resilience of biological systems and predict Biological Age based on various omics data.
• Longitudinal Studies: Following individuals over time allows for the assessment of how Biological Age changes and how Resilience is affected by aging and lifestyle factors.

Key Findings:

• Aging in mice is characterized by rapid disintegration and loss of Resilience, while humans exhibit a more stable aging process with a gradual decline in Resilience.
• The Principal Component Analysis of biological data reveals that most features are correlated, and aging can be described by a few dominant variables.
• The Resilience of biological systems can be quantified, and its loss is a significant predictor of healthspan and lifespan.

Featured Researchers and Sources:

• Andrea Meyer
• Brian Kennedy
• Professor Chong Yap Singh
• Professor Felipe Sierra
• Jan Gruber
• Matt Caberlein
• Tom Randall
• Various researchers from the National University of Singapore and the National University Health System.