Correlations exist between health, life expectancy, lifestyle choices and the web of connections between intelligence, educational achievement, wealth, and status. Higher socioeconomic status and greater intelligence both correlate with a longer life expectancy, but it remains a challenge to move from correlational data to an understanding of the causes and their relative importance. Is it all down to obesity and exercise? Are there genetic factors that link intelligence and the physical robustness needed for greater longevity? Does wealth buy better access to medicine in ways that matter for life expectancy?
It is interesting to ask whether specific choices or life status factors literally accelerate degenerative aging. In the case of being overweight, there is a body of evidence to suggest that, yes, at least some of the known underlying causes of aging are accelerated. The accumulation of senescent cells, for example. For low socioeconomic status it is a little harder to theorize on why there would be a direct mechanistic link to life expectancy and pace of aging. Given present proxy measures for biological age, the accumulated burden of damage and dysfunction, one can show that biological age proceeds faster in people of low socioeconomic status, but that still leaves open the question of why this is the case.
Socioeconomic status (SES) disparities in healthcare have been well documented for decades and have severe implications. Individuals classified as having a lower SES have a shorter life expectancy and are at increased risk for age-related chronic conditions such as chronic pain. Among individuals with chronic low back pain (cLBP), those with a lower SES have greater pain intensity and pain-related disability. This is relevant because low back pain is a leading cause of years lived with disability. Emerging evidence has linked worse pain outcomes to epigenetically induced alterations in pathways involved in neuroinflammation, hormonal dysregulation, impaired immune function, allostatic loads, and poor metabolic control. Interestingly, these major biological pathways overlap with processes that control aging.
We used the Dunedin Pace of Aging Calculated from the Epigenome (DunedinPACE) software to determine the pace of biological aging in adults ages 18 to 85 years with no cLBP (n = 74), low-impact pain (n = 56), and high-impact pain (n = 77). The mean chronological age of the participants was 40.9 years. On average, the pace of biological aging was 5% faster (DunedinPACE = 1.05 ± 0.14) in the sample. Individuals with higher levels of education had a significantly slower pace of biological aging than those with lower education levels (F = 5.546). After adjusting for sex and race, household income level significantly correlated with the pace of biological aging (r = - 0.17), pain intensity (r = - 0.21), pain interference (r = - 0.21), and physical performance (r = 0.20). In mediation analyses adjusting for sex, race, and body mass index (BMI), the pace of biological aging mediates the relationship between household income (but not education) level and cLBP intensity, interference, as well as physical performance.
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