Neighborhood Exposome at Birth and Trajectories of Epigenetic Age Acceleration Across Childhood

Neighborhood poverty duration, timing, and severity from adolescence to early midlife and biological aging.

Adolescent weight gain trajectories and their associations with biological aging: a genetically informed study.

BACKGROUND High body mass index (BMI) in adolescence is associated with accelerated biological aging, which might predict the onset of obesity-related diseases before they develop. Genetic factors may shape both adolescent BMI and weight trajectories. METHODS Participants were from the Young Finns Study (n = 3 596, ages 3-18 at baseline), followed from 1980 to 2018-2020. Biological aging was estimated using DNA methylation based epigenetic clocks DunedinPACE (years/calendar year) and PC-GrimAge (years) at three follow-ups (ages 15-56, n = 2045). Genetic predispositions to BMI and childhood body size were quantified using polygenic risk scores (PRSs) (941 and 286 genetic variants). BMI trajectories were modelled from BMI measured at ages 9, 12, 15 and 18 using latent growth curve modelling. Path analysis was used to examine whether genetic liability to BMI is associated with biological aging and if BMI trajectories in adolescence mediate this association. The causal effect of genetically predicted adolescent BMI on biological aging in adulthood was examined with Mendelian randomisation (MR) using individual-level data. RESULTS Higher level of adolescent BMI partly mediated the association between higher BMI-PRS and accelerated biological aging from late adolescence to middle adulthood. MR analyses supported a positive causal effect from genetically predicted adolescent BMI on biological aging, and the causal effect was more consistent when DunedinPACE was used to measure biological aging in 2011 (causal estimate = 0.020 [95% CI = 0.008, 0.031]) and 2018 (0.019 [0.003, 0.035]). CONCLUSIONS Our findings indicate that high BMI in adolescence may accelerate biological aging, especially in individuals with a genetic predisposition to high BMI. Adolescents with a genetic susceptibility to high BMI and elevated BMI might be prone to obesity-related health risks, highlighting early prevention strategies' importance.

DNA methylation informs adult cardiovascular risk and prevention for low birthweight individuals

The Barker-consistent hypothesis-free discovery approach identified novel and known candidate genes that, with future validation, may serve as therapeutic targets, identify high-risk individuals, and support clinical trial recruitment.

Intergenerational epigenetic signatures of prenatal adversity and their role in emerging child psychopathology

PSD was associated with epigenetic signatures at birth, with a subset of associations persisting across early childhood and converging on cellular stress response biology, suggesting differential biological embedding of structural versus psychological dimensions of adversity.

Early‐life internalizing behavioral trajectories and childhood‐onset asthma: An epigenetic link

Abstract Background Behavioral problems and asthma in childhood are considered related despite the debatable causal relationship and unknown mechanisms. We investigated whether early‐life behavioral problem trajectories affect the risk of childhood asthma and ascertained potential DNA‐methylation mechanisms. Methods Based on two independent birth cohorts, this study included 1041 and 1647 children aged 6–10 and 6–7 years in the Cohort for Childhood Origin of Asthma and Allergic Diseases (COCOA) study and Panel Study on Korean Children (PSKC), wherein asthma was defined by a physician's diagnosis and parental questionnaire, respectively. The Korean version of the Child Behavior Checklist (CBCL) was administered at ages 2–6 years. The CBCL trajectories were identified using a latent generalized mixture model. Blood samples from 7‐year‐old participants were used for DNA‐methylation profiling and quantifying metabolites and proteins. Results Trajectories with high internalizing behavioral problem scores in preschool age were significantly associated with childhood‐onset asthma. Increased CBCL anxiety/depression scores at age 2 (COCOA) and somatization scores at ages 4 and 6 (COCOA and PSKC) significantly increased the risk of asthma symptoms and current asthma. In an exploratory sub‐study, DNA‐methylation analysis suggested hypomethylation of HAL and MAD1L1 in children with co‐occurring asthma and behavioral problems, accompanied by higher HAL protein and lower circulating histidine. Conclusion Early‐life internalizing behavioral problem trajectories precede childhood asthma, which may be mediated by methylation, especially of HAL and MAD1L1.

The Long Half-Life of Neighborhood Poverty: Duration, Timing, and Biological Aging.

Point-in-time measures of neighborhood poverty exposure suggest a link with physiological aging, but it remains unclear how histories and the timing of residential disadvantage connect to weathering. In this issue, D'Alessio et al. (Am J of Epidemiol. 2026) use 25 years of prospective residential data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to link neighborhood poverty across adolescence, early adulthood, and early midlife to three epigenetic clocks. They identify selective dose-response relationships. Each additional life stage in a high-poverty tract is associated with accelerated biological aging, and exposure is distributed with sharp racial inequality. The per-stage effect is modest in individual terms but consequential at population scale. Its unequal allocation makes residential segregation a plausible epigenetic mechanism of population-level racial disparities in aging. We argue that-despite its considerable strengths-the study leaves two questions unresolved. First, does health-selective residential mobility drive part of the association? Second, does the prominence of mid-life exposure reflect a true sensitive period, or the fact that mid-life poverty is measured in close proximity to biospecimen collection? We outline potential follow-up work that could address these questions via structured life course methods and better-powered cohorts with tract-linked data, including Add Health.