SEARCH · PubMed Health

Results for “DNAm predictors”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

4 recordsLinked to original sources

PubMed2024

Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts.

Chronic inflammation is a hallmark of age-related disease states. The effectiveness of inflammatory proteins including C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N = 17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic-net-regression-based predictor outperformed competing methods and explained 18% of phenotypic variance in the Lothian Birth Cohort of 1936 (LBC1936) cohort, doubling that of existing DNAm predictors. DNAm predictors performed comparably in four additional test cohorts (Avon Longitudinal Study of Parents and Children, Health for Life in Singapore, Southall and Brent Revisited, and LBC1921), including for individuals of diverse genetic ancestry and different age groups. The best-performing predictor surpassed assay-measured CRP and a genetic score in its associations with 26 health outcomes. Our findings forge new avenues for assessing chronic low-grade inflammation in diverse populations.

Humans↗

PubMed2022

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies.

BACKGROUND: DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors. RESULTS: Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations. CONCLUSIONS: We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

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↗

PubMed2024

The effect of low birth weight as an intrauterine exposure on the early onset of sarcopenia through possible molecular pathways.

Sarcopenia, a musculoskeletal disease characterized by the progressive loss of skeletal muscle mass, strength, and physical performance, presents significant challenges to global public health due to its adverse effects on mobility, morbidity, mortality, and healthcare costs. This comprehensive review explores the intricate connections between sarcopenia and low birth weight (LBW), emphasizing the developmental origins of health and disease (DOHaD) hypothesis, inflammatory processes (inflammaging), mitochondrial dysfunction, circadian rhythm disruptions, epigenetic mechanisms, and genetic variations revealed through genome-wide studies (GWAS). A systematic search strategy was developed using PubMed to identify relevant English-language publications on sarcopenia, LBW, DOHaD, inflammaging, mitochondrial dysfunction, circadian disruption, epigenetic mechanisms, and GWAS. The publications consist of 46.2% reviews, 21.2% cohort studies, 4.8% systematic reviews, 1.9% cross-sectional studies, 13.4% animal studies, 4.8% genome-wide studies, 5.8% epigenome-wide studies, and 1.9% book chapters. The review identified key factors contributing to sarcopenia development, including the DOHaD hypothesis, LBW impact on muscle mass, inflammaging, mitochondrial dysfunction, the influence of clock genes, the role of epigenetic mechanisms, and genetic variations revealed through GWAS. The DOHaD theory suggests that LBW induces epigenetic alterations during foetal development, impacting long-term health outcomes, including the early onset of sarcopenia. LBW correlates with reduced muscle mass, grip strength, and lean body mass in adulthood, increasing the risk of sarcopenia. Chronic inflammation (inflammaging) and mitochondrial dysfunction contribute to sarcopenia, with LBW linked to increased oxidative stress and dysfunction. Disrupted circadian rhythms, regulated by genes such as BMAL1 and CLOCK, are associated with both LBW and sarcopenia, impacting lipid metabolism, muscle mass, and the ageing process. Early-life exposures, including LBW, induce epigenetic modifications like DNA methylation (DNAm) and histone changes, playing a pivotal role in sarcopenia development. Genome-wide studies have identified candidate genes and variants associated with lean body mass, muscle weakness, and sarcopenia, providing insights into genetic factors contributing to the disorder. LBW emerges as a potential early predictor of sarcopenia development, reflecting the impact of intrauterine exposures on long-term health outcomes. Understanding the complex interplay between LBW with inflammaging, mitochondrial dysfunction, circadian disruption, and epigenetic factors is essential for elucidating the pathogenesis of sarcopenia and developing targeted interventions. Future research on GWAS and the underlying mechanisms of LBW-associated sarcopenia is warranted to inform preventive strategies and improve public health outcomes.

Humans↗