Evaluation of different computational methods for DNA methylation-based biological age

Structural racism in primary schools and changes in epigenetic age acceleration among Black and White youth

Structural racism generates racial inequities in U.S. primary education, including segregated schools, inequitable funding and resources, racial disparities in discipline and achievement, and hostile racial climates, which are risk factors for adverse youth health and development. Black youth are disproportionately exposed to adverse school contexts that may become biologically embedded via stress-mediated epigenetic pathways. This study examined whether childhood exposure to adverse school contexts is associated with changes in epigenetic aging during adolescent development. DNA methylation-based epigenetic clocks were calculated from saliva samples at ages 9 and 15 among Black (n = 774) and White (n = 287) youth in the Future of Families and Child Wellbeing Study (2009-2015). We performed latent class analyses to identify race-specific primary school contexts using administrative data on segregation, discipline, achievement, resources, economic disadvantage, and racial harassment. We then estimated change in epigenetic age acceleration from childhood to adolescence across school typologies using GrimAge, PhenoAge, and DunedinPACE epigenetic clocks. Three distinct school contexts were identified for Black youth: segregated and highly-disadvantaged (17.0%), segregated and moderately-disadvantaged (52.1%), and integrated and moderately-disadvantaged (30.8%). Two school contexts emerged for White youth: integrated and unequal (46.5%) and predominantly White & advantaged (53.5%). At age 15, Black youth who attended segregated and highly-disadvantaged primary schools experienced increases in their speed of epigenetic aging with GrimAge and DunedinPACE. Slowed epigenetic aging with GrimAge was observed for Black youth who attended integrated and moderately-disadvantaged schools. School contexts were not associated with changes in epigenetic age acceleration for White youth. Our findings suggest that manifestations of structural racism in primary school contexts are associated with early-life epigenetic age acceleration and may forecast future health inequities.

Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR. Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

Evaluation of pediatric epigenetic clocks across multiple tissues

BACKGROUND

Epigenetic clocks are promising tools for assessing biological age. We assessed the accuracy of pediatric epigenetic clocks in gestational and chronological age determination.

RESULTS

Our study used data from seven tissue types on three DNA methylation profiling microarrays and found that the Knight and Bohlin clocks performed similarly for blood cells, while the Lee clock was superior for placental samples. The pediatric-buccal-epigenetic clock performed the best for pediatric buccal samples, while the Horvath clock is recommended for children's blood cell samples. The NeoAge clock stands out for its unique ability to predict post-menstrual age with high correlation with the observed age in infant buccal cell samples.

CONCLUSIONS

Our findings provide valuable guidance for future research and development of epigenetic clocks in pediatric samples, enabling more accurate assessments of biological age.

Biomarkers of aging through the life course: A Recent Literature Update

Purpose of review

The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course.

Recent findings

The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches.

Summary

Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.