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Updated: Jan 8, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
Published on: May 6, 2022
Age-varying DNA methylation patterns associated with blood pressure across the lifespan
Gang Xu1, Xiaowei Zhuang1,2, Amei Amei3
1Laboratory of Neurogenetics and Precision Medicine, College of Sciences, University of Nevada Las Vegas, Las Vegas, NV. USA.
Background:
Epigenome-wide association studies (EWAS) have identified associations between DNA methylation and blood pressure, yet most rely on single-time-point data and cannot capture how methylation and blood pressure relationships change with age.
Methods:
We conducted a longitudinal EWAS of 1,945 blood samples from 976 participants in the Multi-Ethnic Study of Atherosclerosis using a spline-based varying-coefficient model to detect age-dependent associations between DNA methylation in blood and blood pressure traits. Findings were evaluated for replication in 1,187 samples from the Framingham Heart Study. Models were adjusted for sex, ancestry, and leukocyte composition to account for cellular heterogeneity.
Results:
Six CpG sites showed significant age-dependent associations with systolic or pulse pressure after correction for multiple testing. These included loci within STIP1, CSRP1, and KDM6A that replicated in the Framingham cohort. Several CpG sites demonstrated a reversal of effect direction with advancing age, where higher methylation was associated with higher systolic pressure in younger adults but lower pressure later in life. Pathway enrichment analyses identified focal adhesion, actin cytoskeleton remodeling, and Wnt/β-catenin signaling, which are processes relevant to vascular aging. Drug target mapping identified 23 FDA-approved agents interacting with genes at these loci.
Conclusions:
Blood-derived DNA methylation shows dynamic age-related associations with blood pressure that likely reflect systemic or vascular aging processes rather than direct cellular mediation. Longitudinal analytical frameworks can reveal temporal patterns in epigenetic variation that are not detectable in single time point studies and may inform the discovery of biomarkers for age related cardiovascular risk.
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