Background: Little is known about the extent to which ageing trajectories of different body systems share common causes. We present a large longitudinal twin study investigating the trajectories of change in 5 systems: respiratory, cardiovascular, skeletal, body composition, and metabolic.
Methods: Longitudinal clinical data was collected on 4487 twins in the TwinUK registry (mean follow-up 10.3 ± 2.6 yrs, range 4-7.5 years). Trajectories of change in 5 organ systems were generated using mixed effects models. Multivariate structural equation modelling was used to estimate the contribution of genes and environment to variance in each trajectory and the correlation of these factors between different body systems.
Results: Ageing trajectories had remarkably low heritabilities, ranging from 8% in respiratory ageing to 22% in metabolic ageing. However, we found significant effect of environmental factors shared between twins (which includes family environment, early socio-economic status), explaining the variation in ageing in cardiac (48%), skeletal (33%) body composition (53%) and metabolic systems (32%). The remainder was due to environmental factors unique to each person. There were significant and substantial correlations between the unique environmental latent factors between all organ systems ranging from 0.11 between metabolic and skeletal systems to 0.57 in skeletal and cardiac systems. Significant correlations were also found between shared (family) environmental factors.
Discussion: This study, the first of its kind in ageing, found that diverse organ systems shared common aetiology, which is not genetic. There is an important contribution of family environmental factors to ageing in diverse systems, which may, in part reflect the lifespan hypothesis of ageing. Importantly as ‘unique environment’ also includes measurement error, it is often overlooked. This study shows there to be significant correlation between unique environmental aetiology of different systems ageing, unlikely to be due to error because multiple data-points contribute to each trajectory. Epigenetic modifications may account for this.