The interplay of childhood maltreatment and lifetime stress with epigenetic age acceleration in psychiatric disease

Yusupov, Natan; Sauer, Susann; Ködel, Maik; Rex‐Haffner, Monika; Dieckmann, Linda; Kopf‐Beck, Johannes; group, T. BeCOME working; group, T. OPTIMA working; Brückl, Tanja; Binder, Elke

doi:10.1016/j.nsa.2022.100068

Cited by 3 publications

(3 citation statements)

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“…Such differences may be due to a significant bias between the data on which the clock was trained and the test data on which age acceleration is investigated. This approach to determine age acceleration may be useful in the relative comparison of several different groups [47][48][49]. From a machine learning perspective, it is methodologically correct to consider the error -the direct difference between predicted and chronological age, so in this study we defined age acceleration in this way.…”

Section: Preprocessing and Harmonizationmentioning

confidence: 99%

Map of epigenetic age acceleration: a worldwide meta-analysis

Yusipov,

Kalyakulina,

Franceschi

et al. 2024

Preprint

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This study is the first systematic meta-analysis of epigenetic age acceleration of the largest publicly available DNA methylation data for healthy samples (93 datasets, 23K samples), focusing on geographic and ethnic aspects of different countries (25 countries) and populations (31 ethnicities) around the world. The most popular epigenetic tools for assessing age acceleration were examined in detail, their quality metrics were analyzed, and their ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models was explored. In most cases, the models are not consistent with each other and show 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. Although GEO is the largest open-access epigenetic database, most countries and populations are not represented, and 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. However, the DunedinPACE metric, which measures aging rate, adequately reflects the standard of living and socioeconomic indicators in countries, although it can be applied only to blood methylation data. When comparing epigenetic age acceleration, males age faster than females in most of the countries and populations considered.

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Section: Preprocessing and Harmonizationmentioning

confidence: 99%

Map of epigenetic age acceleration: a worldwide meta-analysis

Yusipov,

Kalyakulina,

Franceschi

et al. 2024

Preprint

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“…Studies have also shown associations between accelerated epigenetic agen and stress during childhood and postnatal early life, while exposure to adverse childhood experiences were reported to be associated with accelerated epigenetic age in middle-aged adults [22]. Such accelerated epigenetic aging has been further linked to negative mental and physical health outcomes [23]. For example, one study found that children diagnosed with an internalizing disorder who also had experienced maltreatment showed an accelerated epigenetic age compared to children with no internalizing disorder [24]; however the the association was moderated by the degree of malnutrition.…”

Section: Introductionmentioning

confidence: 99%

Prenatal stress and gestational epigenetic age: No evidence of associations based on a large prospective multi-cohort study

Murgatroyd,

Salontaji,

Smajlagic

et al. 2024

Preprint

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Psychological stress during pregnancy is known to have a range of long-lasting negative consequences on the development and health of offspring. Here, we tested whether a measure of prenatal early-life stress was associated with a biomarker of physiological development at birth, namely epigenetic gestational age, using foetal cord-blood DNA-methylation data. Longitudinal cohorts from the Netherlands (Generation R Study [Generation R], n = 1,396), the UK (British Avon Longitudinal Study of Parents and Children [ALSPAC], n = 642), and Norway (Mother, Father and Child Cohort Study [MoBa], n1 = 1,212 and n2 = 678) provided data on prenatal maternal stress and genome-wide DNA methylation from cord blood and were meta-analysed (pooled n = 3,928). Measures of epigenetic age acceleration were calculated using three different gestational epigenetic clocks: “Bohlin”, “EPIC overlap” and “Knight”. Prenatal stress exposure, examined as an overall cumulative score, was not significantly associated with epigenetically-estimated gestational age acceleration or deceleration in any of the clocks, based on the results of the pooled meta-analysis or those of the individual cohorts. No significant associations were identified with specific domains of prenatal stress exposure, including negative life events, contextual (socio-economic) stressors, parental risks (e.g., maternal psychopathology) and interpersonal risks (e.g., family conflict). Further, no significant associations were identified when analyses were stratified by sex. Overall, we find little support that prenatal psychosocial stress is associated with variation in epigenetic age at birth within the general paediatric population.

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“…Several algorithms have been developed to estimate biological aging from DNA methylation data, including six that we used in the current study: Horvath (Horvath, 2013), Hannum (Hannum et al, 2013), GrimAge (Lu et al, 2019), GrimAge2 (Lu et al, 2022), PhenoAge (Levine et al, 2018) and DunedinPACE (Belsky et al, 2022). The EAA estimates derived from each of these clocks have been widely used in studies targeting on phenotypes such as lifestyle (Drouard et al, 2023a), environmental exposures (de Prado-Bert et al, 2021) and diseases (Lundgren et al, 2022; Yusupov et al, 2023; Joyce et al, 2021; Foster et al, 2023; Carbonneau et al, 2024), all of which have demonstrated adverse health outcomes in individuals with greater EAA. However, studies investigating associations between EAA and multiple omics data remain scarce.…”

Section: Introductionmentioning

confidence: 99%

Multi-omic associations of epigenetic age acceleration are heterogeneously shaped by genetic and environmental influences

Drouard,

Suhonen,

Heikkinen

et al. 2024

Preprint

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Connections between the multi-ome and epigenetic age acceleration (EAA), and especially whether these are influenced by genetic or environmental factors, remain underexplored. We therefore quantified associations between the multi-ome comprising four layers – the proteome, metabolome, external exposome, and lifestyle – with six different EAA estimates. Two twin cohorts were used in a discovery-replication scheme, comprising respectively young (N=642; mean age= 22.3) and older (N=354; mean age=62.3) twins. Within-pair twin designs were used to assess genetic and environmental effects on associations. We identified 40 multi-omic factors, of which 28 were proteins, associated with EAA in the young twins while adjusting for sex, smoking, and body mass index. Within-pair analyses showed that genetic confounding heterogeneously affected these associations, with six multi-omic factors remaining significantly associated with EAA independent of genetic effects. Replication in older twins showed that some of these associations persist across adult generations.

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The interplay of childhood maltreatment and lifetime stress with epigenetic age acceleration in psychiatric disease

Cited by 3 publications

References 0 publications

Map of epigenetic age acceleration: a worldwide meta-analysis

Map of epigenetic age acceleration: a worldwide meta-analysis

Prenatal stress and gestational epigenetic age: No evidence of associations based on a large prospective multi-cohort study

Multi-omic associations of epigenetic age acceleration are heterogeneously shaped by genetic and environmental influences

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