Making Sense of Aging with Data Big and Small

Area of Science:

• Gerontology
• Personalized Medicine
• Biomedical Data Science

Background:

• Individual health and aging are shaped by a unique combination of genetic and environmental factors throughout life.
• Cumulative life experiences lead to significant intra-individual variability in aging rates and resilience to disease.
• Standardized group data (e.g., means, medians) often inadequately predict individual clinical status and prognosis.

Purpose of the Study:

• To address the limitations of group-based health data in predicting individual outcomes.
• To explore alternative data-driven approaches for personalized health assessment and management.
• To highlight the potential of leveraging individual temporal data for tailored healthcare.

Main Methods:

• Contrasting the utility of 'big data' (large cohorts) versus 'small data' (individual temporal streams).
• Proposing the analysis of rich, longitudinal data from single individuals ('small data').
• Emphasizing the processing and interpretation of combined individual data streams over time.

Main Results:

• High intra-individual variability necessitates personalized approaches beyond normative group data.
• 'Small data' approaches, focusing on individual temporal patterns, offer a viable alternative.
• Leveraging an individual's unique data streams can improve diagnostic accuracy and prognostic prediction.

Conclusions:

• Relying solely on group data is insufficient for accurate individual health assessment.
• Utilizing an individual's comprehensive temporal data ('small data') enables tailored diagnosis and management.
• This approach supports enhanced patient care, self-care, sustained independence, and research.