Associations of Socioeconomic Disparities With Buccal DNA-Methylation Measures Of Biological Aging

Abstract

Background: Individuals who are socioeconomically disadvantaged are at increased risk for aging-related diseases and perform less well on tests of cognitive function. The Weathering Hypothesis proposes that these disparities in physical and cognitive health arise from an acceleration of biological processes of aging. Theories of how life adversity is biologically embedded identify epigenetic alterations, including DNA methylation (DNAm), as a mechanistic interface between the environment and health. Consistent with the Weathering hypothesis and theories of biological embedding, recently developed DNAm algorithms have revealed profiles reflective of more advanced aging and lower cognitive function among socioeconomically-at-risk groups. These DNAm algorithms were developed using blood-DNA, but social and behavioral science research commonly collect saliva or cheek-swab DNA. This discrepancy is a potential barrier to research to elucidate mechanisms through which socioeconomic disadvantage affects aging and cognition. We therefore tested if social gradients observed in blood-DNAm measures could be reproduced using buccal-cell DNA obtained from cheek swabs. Results: We analyzed three DNAm measures of biological aging and one DNAm measure of cognitive performance, all of which showed socioeconomic gradients in previous studies: the PhenoAge and GrimAge DNAm clocks, DunedinPACE, and Epigenetic-g. We first computed blood-buccal cross-tissue correlations in n=21 adults (GEO111165). Cross-tissue correlations were low-to-moderate across (r=.25 to r=.48). We next conducted analyses of socioeconomic gradients using buccal DNAm data from SOEP-G (n=1128, 57% female; age mean=42 yrs, SD=21.56, range 0-72). Associations of socioeconomic status with DNAm measures of aging were in the expected direction, but were smaller as compared to reports from blood DNAm datasets (r=-.08 to r=-.13). Conclusions: Our findings are consistent with the hypothesis that socioeconomic disadvantage is associated with DNAm indicators of worse physical and cognitive health. However, relatively low cross-tissue correlations and attenuated effect-sizes for socioeconomic gradients in buccal DNAm compared with reports from analysis of blood DNAm suggest that, in order to take full advantage of buccal-DNA samples, DNAm algorithms customized to buccal DNAm are needed.

Educational Mobility, the Pace of Biological Aging, and Lifespan in the Framingham Heart Study

“…First, DunedinPACE is predictive of diverse aging related outcomes, including disease, disability, and mortality (14,28,30,32,35). Second, DunedinPACE is associated with social determinants of healthy aging in young, midlife, and older adults (14,15,31,33,34,58). Third, DunedinPACE shows evidence of being modified by calorie restriction (59), an intervention that modifies the basic biology of aging in animal experiments (60).…”

“…First, DunedinPACE is predictive of diverse aging related outcomes, including disease, disability, and mortality (14,28,30,32,35). Second, DunedinPACE is associated with social determinants of healthy aging in young, midlife, and older adults (14,15,31,33,34,58). Third, DunedinPACE…”

“…Isolating effects of education on biological aging is challenging because the level of education people complete is strongly influenced by familial factors and other features of early life environment that are associated with the pace of biological aging (31,58,(63)(64)(65). We addressed this potential confound in two ways.…”

Self-control is associated with health-relevant disparities in buccal DNA-methylation measures of biological aging in older adults

“…Based on the availability of funds, residual frozen DNA samples of n=1128 of the SOEP-G sample were selected for DNA-methylation analyses. The inclusion criteria were as following: 1) samples from children and adolescents with residual DNA samples holding at least 50ng of DNA, 2) adults with extending age distribution past 18 years, that had at least 250ng of DNA left, had a call rate of at least 0.975, and did not have participating children in the dataset to maximize number of households, 3) match between genetic sex and self-reported sex (see Raffington et al, 2022 for more details). This resulted in the availability of DNA-methylation data for n=1058 participants (M age = 42.42, SD age =21.17, 58% female), for whom polygenic scores are also available (see above).…”

“…Third, we corrected for background noise and color dye bias (with ‘PreprocessNoob’ in minfi, Triche et al, 2013), accounted for probe-type differences (with ‘BMIQ’ in minfi, Teschendorff & Widschwendter, 2012) and estimated cell composition using robust partial correlations (with ‘HEpiDisch’ in EpiDISH). In order to call the sample a ‘buccal sample’ we set a threshold of 0.5 for epithelial cell proportions (Raffington et al, 2022).…”