Low birthweight is associated with epigenetic age acceleration in the first 3 years of life

Unified high-resolution immune cell fraction estimation in blood tissue from birth to old age

Variations in immune-cell fractions can confound or hamper interpretation of DNAm-based biomarkers in blood. Although cell-type deconvolution can address this challenge for cord and adult blood, currently there is no method applicable to blood from other age groups, including infants and children. Here we construct and extensively validate a DNAm reference panel, called UniLIFE, for 19 immune cell-types, applicable to blood tissue of any age. We use UniLIFE to delineate the dynamics of immune-cell fractions from birth to old age, and to infer disease associated immune cell fraction variations in newborns, infants, children and adults. In a prospective longitudinal study of type-1 diabetes in infants and children, UniLIFE identifies differentially methylated positions that precede type-1 diabetes diagnosis and that map to diabetes related signaling pathways. In summary, UniLIFE will improve the identification and interpretation of blood-based DNAm biomarkers for any age group, but specially for longitudinal studies that include infants and children. The UniLIFE panel and algorithms to estimate cell-type fractions are available from our EpiDISH Bioconductor R-package: https://bioconductor.org/packages/release/bioc/html/EpiDISH.html.

Epigenetic signatures of intergenerational exposure to violence in three generations of Syrian refugees

Maternal trauma influences infant and adult health outcomes and may impact future generations through epigenetic modifications such as DNA methylation (DNAm). Research in humans on the intergenerational epigenetic transmission of trauma effects is limited. In this study, we assessed DNAm signatures of war-related violence by comparing germline, prenatal, and direct exposures to violence across three generations of Syrian refugees. We compared families in which a pregnant grandmother versus a pregnant mother was exposed to violence and included a control group with no exposure to war. We collected buccal swab samples and survey data from mothers and 1-2 children in each of 48 families (n = 131 participants). Based on an epigenome-wide association study (EWAS), we identified differentially methylated regions (DMPs): 14 were associated with germline and 21 with direct exposure to violence. Most DMPs showed the same directionality in DNAm change across germline, prenatal, and direct exposures, suggesting a common epigenetic response to violence. Additionally, we identified epigenetic age acceleration in association with prenatal exposure to violence in children, highlighting the critical period of in utero development. This is the first report of an intergenerational epigenetic signature of violence, which has important implications for understanding the inheritance of trauma.

Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance

BACKGROUND

Epigenetic age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional, using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (1) their choice of model; (2) the primary outcome (EA vs. EAA); and (3) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.

RESULTS

Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered advanced GrimAge in individuals assigned male at birth that decelerates over time.

CONCLUSION

Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

Map of epigenetic age acceleration: A worldwide analysis

We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.

Maximizing Insights from Longitudinal Epigenetic Age Data: Simulations, Applications, and Practical Guidance

Background

Epigenetic Age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional - using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (i) their choice of model; (ii) the primary outcome (EA vs. EAA); and (iii) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.

Results

Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered an accelerated EA rate in males and an advanced EA that decelerates over time in children with higher birthweight.

Conclusion

Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

Birth size and the serum level of biological age markers in men

Previous studies showed that intrauterine growth restrictions, resulting in smaller body size at birth, are associated with altered development and the risk of age-related diseases in adult life. Thus, prenatal development may predict aging trajectories in humans. The study aimed to verify if body size at birth is related to biological age in adult men. The study sample consisted of 159 healthy, non-smoking men with a mean age of 35.24 (SD 3.44) years. Birth weight and length were taken from medical records. The ponderal index at birth was calculated. Biological age was evaluated based on serum levels of s-Klotho, hsCRP, DHEA/S, and oxidative stress markers. Pregnancy age at birth, lifestyle, weight, cortisol, and testosterone levels were controlled. The results showed no relationship between birth size and s-Klotho, DHEA/S level, inflammation, or oxidative stress. Also, men born as small-for-gestational-age (N = 49) and men born as appropriate-for-gestational-age (N = 110) did not differ in terms of biological age markers levels. The results were similar when controlled for pregnancy week at birth, chronological age, BMI, testosterone, or cortisol level. The results suggest that there is no relationship between intrauterine growth and biomarkers of aging in men aged 30-45 years from the affluent population.