Relationships Between Socioeconomic Status, Life Expectancy, and Epigenetic Age
As yet the life sciences have provided no way to definitively, robustly measure biological age in an individual. In part this stems from a lack of consensus as to a useful definition of biological age, or indeed of aging more broadly. Researchers have long agreed upon sensible definitions at the high level, such as that aging is an increase with time in the risk of mortality due to intrinsic causes. That definition is validated, measurable over populations, but helps little when it comes to assessing the mortality risk or age of any given individual. At the low level, there are many specific forms of damage and dysfunction that can be measured, albeit not always without invasive sampling. Burden of senescent cells, loss of mitochondrial function, reduced average telomere length, slowed pace of cell replication, reduced grip strength, changes in a thousand biomarkers relating to immune function, and so forth. We have the general sense of trends, but again one cannot use these measures to say definite things about biological age and mortality risk for any given individual.
We live in a world in which measurements and algorithmic combinations of measurements that reflect aging in populations are proliferating alongside the interest in treating aging as a medical condition. This is particularly true for the aging clocks, such as epigenetic clocks, derived from machine learning techniques applied to large bodies of biological data. A slow, incremental ongoing process is underway to find out whether this landscape forms a suitable foundation for the discovery and development of a true consensus measure of biological age that can be applied usefully to individuals. At present that largely involves assessing as many people as possible using as many different measurement approaches as possible, and searching for patterns in the data. Data informs the way in which researchers think about definitions of aging, which inspires new approaches to measurement of biological age, and use of those approaches produces new data. It is a circular road.
Today's open access paper is a snapshot of part this ongoing dialog between theory and data. It is well known that socioeconomic status correlates with life expectancy across populations. Does this mean that low socioeconomic status produces accelerated aging? By what mechanisms, and how does the relative importance of these mechanisms inform our definitions of aging? Looking at epigenetic clock data derived from study populations with different socioeconomic circumstances doesn't answer these questions, but having that data is one step further towards a future in which those answers do exist.
For most documented contexts and time periods, there is a strong association between lower socioeconomic position and risk of higher mortality. The theory of social stratification posits that social stratification caused by a combination of factors, particularly race, ethnicity, and socioeconomic position, would influence health outcomes through differential access to resources, power, and opportunities. These adverse effects even can undermine the beneficial effects from other social exposures such as social cohesion and social resistance. These health disparities are reflected in key social stratification factors such as race and ethnicity, educational attainment, income, and occupation. Studies report notable differences in life expectancy across these dimensions. For instance, according to recent estimates, White Americans who reach age 15 have a life expectancy of 63 years, compared to 59 years for Black Americans and 66 years for Hispanic Americans. Likewise, individuals with an income at or above 400% of the poverty threshold have a life expectancy of 60 years at age 18, while those living below the poverty line have just 49 years. Similar disparities are also observed across different education levels and occupational groups
Aiming to systematically examine the mediating role of DNA methylation clocks in the associations between race, ethnicity, education, income, and occupation and mortality, this study uses nationally representative data to demonstrate that DNA methylation clocks, particularly GrimAge2 and DunedinPoAm, mediate a substantial proportion of racial/ethnic and socioeconomic disparities in mortality. GrimAge2 exhibited significant mediation on all-cause mortality disparities, accounting for 21% of the difference between participants with a high school diploma or GED and those with a college degree or higher, up to 52% of the difference between individuals in high-skilled blue-collar occupations and those in white-collar and professional positions. Similarly, the DunedinPoAm pace of aging mediated 11% of the mortality disparity between high school graduates and individuals with a college degree or above, and 28% of the disparity between Hispanic and White participants. Notably, these mediation results, particularly for GrimAge2, were greater than those observed for traditional clinical biomarkers. These findings suggest that DNA methylation clocks and biomarkers could serve as valuable tools for future research investigating the mechanisms underlying health disparities.