Abstract
BackgroundIn the first study of its kind, we examine the association between growth and development in early life and DNAm age biomarkers in mid-life.MethodsParticipants were from the Medical Research Council National Survey of Health and Development (n = 1376). Four DNAm age acceleration (AgeAccel) biomarkers were measured when participants were aged 53 years: AgeAccelHannum; AgeAccelHorvath; AgeAccelLevine; and AgeAccelGrim. Exposure variables included: relative weight gain (standardised residuals from models of current weight z-score on current height, and previous weight and height z-scores); and linear growth (standardised residuals from models of current height z-score on previous height and weight z-scores) during infancy (0–2 years, weight gain only), early childhood (2–4 years), middle childhood (4–7 years) and late childhood to adolescence (7–15 years); age at menarche; and pubertal stage for men at 14–15 years. The relationship between relative weight gain and linear growth and AgeAccel was investigated using conditional growth models. We replicated analyses from the late childhood to adolescence period and pubertal timing among 240 participants from The National Child and Development Study (NCDS).ResultsA 1SD increase in relative weight gain in late childhood to adolescence was associated with 0.50 years (95% CI 0.20, 0.79) higher AgeAccelGrim. Although the CI includes the null, the estimate was similar in NCDS [0.57 years (95% CI − 0.01, 1.16)] There was no strong evidence that relative weight gain and linear growth in childhood was associated with any other AgeAccel biomarker. There was no relationship between pubertal timing in men and AgeAccel biomarkers. Women who reached menarche ≥ 12 years had 1.20 years (95% CI 0.15, 2.24) higher AgeAccelGrim on average than women who reached menarche < 12 years; however, this was not replicated in NCDS and was not statistically significant after Bonferroni correction.ConclusionsOur findings generally do not support an association between growth and AgeAccel biomarkers in mid-life. However, we found rapid weight gain during pubertal development, previously related to higher cardiovascular disease risk, to be associated with older AgeAccelGrim. Given this is an exploratory study, this finding requires replication.
Highlights
The demographic shift towards an ageing population is a recognised public health challenge
These studies suggest that higher AgeAccelHorvath in early life is associated with more rapid growth and earlier development [28,29,30]. It is not known if this association is similar for other AgeAccel biomarkers, if it tracks across adulthood, or if growth and development in childhood has additional effects on AgeAccel that persist across the life course. In this exploratory study using data from a subsample of a nationally representative British birth cohort, we investigate the impact of birth weight and physical growth during infancy, early childhood (2–4 years), middle childhood (4–7 years) and late childhood to adolescence (7–15 years) and pubertal timing on four DNA methylation (DNAm)-based biomarkers of ageing in mid-life
There were no major differences in body size, pubertal timing, smoking status or Socioeconomic position (SEP) among participants included in our main analysis versus all other NSHD participants responding to the 53 year data collection (n = 1659, Additional file 1: Table 1)
Summary
The demographic shift towards an ageing population is a recognised public health challenge. Despite increases in life expectancy, compression of morbidity is not evident and there is significant heterogeneity in the occurrence of age-related disease and functional capability among people of the same chronological age [1]. Maddock et al Clin Epigenet (2021) 13:155 of healthy ageing include survival to old age, delaying the onset of age-related diseases and maintaining function [2]. Ageing is a complex process involving changes at the molecular, cellular, physiological and functional level over time [3]. Biomarkers of ageing, which combine information from one or more of these processes, have been proposed as tools to capture healthy ageing [4]. A suitable biomarker of ageing should be better at predicting survival, onset of age-related disease and functional capability at later ages than chronological age alone. In the first study of its kind, we examine the association between growth and development in early life and DNAm age biomarkers in mid-life
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