Andres Cardenas | PubMed Health

PubMed2025

DNA methylation age deviation and cognitive status among older adults in the US, NHANES 1999-2002.

Biological aging, measured using DNA methylation, is a potential biomarker for cognitive health outcomes. We evaluated associations between DNA methylation measures of aging and cognition in a nationally representative sample of adults aged 60+ in the National Health and Nutrition Examination Survey (NHANES), 1999-2002. Genome-wide DNA methylation data were used to create 13 measures of biological aging trained on different aging phenotypes. Cognition was assessed with the Digit Symbol Substitution Test (DSST). To evaluate associations between each DNA methylation measure and DSST score, survey-weighted linear regression models adjusted for age, sex, race/ethnicity, education, smoking, serum cotinine, and BMI were run. We assessed effect modification by sex, education, and race and ethnicity. Included participants (N=1,463) were an average of 70.5 years old and 82.7% non-Hispanic White. The average DSST score was 46.9 (SD 17.6). Ten of 13 DNA methylation measures were associated with DSST (adjusted p<0.05). One year of GrimAge2 accelerated aging was associated with -0.41 points lower DSST score (95% CI: -0.61, -0.21; adjusted p=5×10-4). In stratified analyses, higher magnitudes of association were observed among male and non-Hispanic White participants across multiple aging measures. DNA methylation may be a useful biomarker of cognitive status among older adults.

DNA methylation↗

PubMed2025

A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits.

BACKGROUND: DNA methylation (DNAm) provides a window to characterize the impacts of environmental exposures and the biological aging process. Epigenetic clocks are often trained on DNAm using penalized regression of CpG sites, but recent evidence suggests potential benefits of training epigenetic predictors on principal components. METHODOLOGY/FINDINGS: We developed a pipeline to simultaneously train three epigenetic predictors; a traditional CpG Clock, a PCA Clock, and a SuperLearner PCA Clock (SL PCA). We gathered publicly available DNAm datasets to generate i) a novel childhood epigenetic clock, ii) a reconstructed Hannum adult blood clock, and iii) as a proof of concept, a predictor of polybrominated biphenyl exposure using the three developmental methodologies. We used correlation coefficients and median absolute error to assess fit between predicted and observed measures, as well as agreement between duplicates. The SL PCA clocks improved fit with observed phenotypes relative to the PCA clocks or CpG clocks across several datasets. We found evidence for higher agreement between duplicate samples run on alternate DNAm arrays when using SL PCA clocks relative to traditional methods. Analyses examining associations between relevant exposures and epigenetic age acceleration (EAA) produced more precise effect estimates when using predictions derived from SL PCA clocks. CONCLUSIONS: We introduce a novel method for the development of DNAm-based predictors that combines the improved reliability conferred by training on principal components with advanced ensemble-based machine learning. Coupling SuperLearner with PCA in the predictor development process may be especially relevant for studies with longitudinal designs utilizing multiple array types, as well as for the development of predictors of more complex phenotypic traits.

DNA Methylation↗