Assessing Socioeconomic Correlations with Rate of Aging using the Epigenetic Clock

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Life expectancy, mortality, and risk of age-related disease are well known to correlate with a complicated web of socioeconomic factors. Educational attainment correlates with life expectancy, but so does intelligence. The relationship with intelligence might have underlying genetic causes, in that more intelligent people may be more physically robust. Or it may be that intelligence and education are inextricably linked – smarter people are better educated or better educated people do well on tests of intelligence – and the effect on life expectancy has little to do with genetics.

Further, educational attainment correlates with wealth, both of the region, and of the individual. Is it thus a proxy for greater access to medical technology purely due to greater wealth? What about the education and intelligence needed to use that access well? Or perhaps it has little to do with medical technology for most of the life span, and education and intelligence tend to lead to better lifestyle choices? Trying to peel apart these relationships is a complex task, and one that has not yet succeeded in any meaningful way, I would say.

The various epigenetic clocks are measures of age based on an algorithmic weighting of patterns of DNA methylation on the genome that appear to be a characteristic reaction to the damage and dysfunction of aging, occurring in very similar ways in every individual. The underlying molecular damage that causes aging is, after all, the same for everyone. It is as yet unknown as to exactly which underlying processes correspond to which DNA methylation sites on the genome, but the correlation is quite good overall. People in groups with higher risk of mortality or exhibiting age-related diseases tend to have higher assessed DNA methylation age than their healthier peers, which provides a way to determine pace of aging to some degree. Can this be useful as a tool to start dissecting the complicated relationships between aging, lifestyle, and socioeconomic status in populations? Perhaps.

Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis


Aging is characterized by a gradual and constant increase in health inequalities across socioeconomic groups, an association based on strong epidemiological evidence known as the social gradient in health. On average, individuals with lower socioeconomic position (SEP) have lower life expectancy, higher risk of age-related diseases, and poorer quality of life at older ages compared with less disadvantaged groups. Although lifestyles differ by SEP, unhealthy habits only partially explain this association.

The role of epigenetic mechanisms in response to trauma, and evidence for their involvement in intergenerational transmission of biological impacts of traumatic stress have been proposed to explain how social adversity gets biologically embedded, leading to differences in biological functionalities among individuals in different social conditions, especially at older ages. Epigenetics, specifically DNA methylation (DNAm) has been proposed as one of the most powerful biomarkers of biological aging and as one of the plausible biological mechanisms by which social adversities get ‘under the skin’ and affect physiological and cellular pathways leading to disease susceptibility.

Two measures of epigenetic clocks have gained considerable popularity, and the concept of epigenetic aging acceleration (EAA) has been introduced as the difference between predicted DNAm age and chronological age. EAA has been associated with all-cause mortality, cancer incidence and neurodegenerative disorders, as well as non-communicable disease risk factors such as obesity, poor physical activity, unhealthy diet, cumulative lifetime stress and infections.

Given the above, it can be assumed that the various epigenetic clocks describe different aspects of the biological (epigenetic) aging process. We previously showed a dose-response relationship between SEP and EAA. Further, our results suggest that the effect could be partially reversible by improving social conditions during life. In addition, ours and two more recent studies indicate that childhood SEP might have a stronger effect on EAA than adulthood SEP.

Despite extensive research in the field, to date no studies have compared the effect of SEP on epigenetic aging biomarkers with those of other lifestyle-related risk factors for age-related diseases. We aimed to systematically investigate the association of education level, as a proxy for SEP, with the total number of SEMs and ‘accelerated aging’ as assessed using the three epigenetic clocks, and to compare the independent effect of low education with those of the main modifiable risk factors for premature aging: smoking, obesity, alcohol intake, and physical inactivity, by conducting a meta-analysis including data for more than 16,000 individuals belonging to 18 cohort studies from 12 different countries worldwide.

Epigenetic aging biomarkers were associated with education and different sets of risk factors independently, and the magnitude of the effects differed depending on the biomarker and the predictor. On average, the effect of low education on epigenetic aging was comparable with those of other lifestyle-related risk factors (obesity, alcohol intake), with the exception of smoking, which had a significantly stronger effect. Our study shows that low education is an independent predictor of accelerated biological (epigenetic) aging and that epigenetic clocks appear to be good candidates for disentangling the biological pathways underlying social inequalities in healthy aging and longevity.

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