The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data.

Area of Science:

• Gerontology
• Biostatistics
• Genomics

Background:

• Aging is a complex process with individual variations, making biological age a crucial metric.
• Current biological age predictors lack standardized operationalization and robust statistical foundations.
• Existing methods for evaluating biological age predictors are often insufficient.

Purpose of the Study:

• To propose a comprehensive operationalization of biological age.
• To introduce the AccelerAge framework for predicting biological age.
• To develop novel evaluation measures for biological age predictors.

Main Methods:

• Developed the AccelerAge framework utilizing Accelerated Failure Time (AFT) models.
• Modeled the direct effect of aging predictors on survival time.
• Compared AccelerAge predictors against the GrimAge predictor using simulated and real-world data (UK Biobank, Leiden Longevity Study).

Main Results:

• The AccelerAge framework provides a statistically sound approach to biological age prediction.
• Novel evaluation measures offer a more thorough assessment of predictor performance.
• Comparisons demonstrate the potential of AccelerAge in accurately assessing biological age.

Conclusions:

• The AccelerAge framework establishes a robust statistical foundation for biological age clocks.
• This approach enables more accurate and interpretable assessments of individual aging.
• The study advances the field of aging research by improving biological age prediction and evaluation.