Understanding Longitudinal Epigenetic Age Data
Author Information
Author(s): Großbach Anna, Suderman Matthew J., Hüls Anke, Lussier Alexandre A., Smith Andrew D. A. C., Walton Esther, Dunn Erin C., Simpkin Andrew J.
Primary Institution: University of Galway
Hypothesis
How do different modeling strategies impact the estimation of longitudinal epigenetic age?
Conclusion
The study provides guidance on methodological choices for longitudinal epigenetic age studies, emphasizing the importance of using chronological age as a time variable.
Supporting Evidence
- Using chronological age as a time variable provided the most accurate estimates in simulations.
- Models that included categorical time variables led to inflated effect estimates.
- Real-world analyses confirmed the simulation findings regarding the impact of time variables on effect estimates.
Takeaway
This study helps scientists understand how to measure biological aging over time, showing that the way we analyze data can change the results.
Methodology
The study used simulations and real-world data from the Avon Longitudinal Study of Parents and Children (ALSPAC) to evaluate different modeling approaches for longitudinal epigenetic age.
Potential Biases
Potential biases may arise from using different time variables in modeling.
Limitations
The real-world examples were limited to two time points, which may affect the comprehensiveness of findings.
Participant Demographics
The study included a sample of 1162 individuals from the ALSPAC cohort, with a mix of biological sexes and various birthweights.
Statistical Information
P-Value
p<0.05
Confidence Interval
95% CI
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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