Testing Black-White Disparities in Biological Aging Among Older Adults in the United States: Analysis of DNA-Methylation and Blood-Chemistry Methods

Abstract

Biological aging is a proposed mechanism through which social determinants drive health disparities. We conducted proof-of-concept testing of eight DNA-methylation and blood-chemistry quantifications of biological aging as mediators of disparities in healthspan between Black and White participants in the 2016 wave of the United States Health and Retirement Study (HRS; n=9005). We quantified biological aging from four DNA-methylation “clocks” (Horvath, Hannum, PhenoAge, and GrimAge), a DNA-methylation Pace of Aging (DunedinPoAm), and three blood-chemistry measures (PhenoAge, Klemera-Doubal method Biological Age, and homeostatic dysregulation). We quantified Black-White disparities in healthspan from cross-sectional and longitudinal data on physical-performance tests, self-reported activities of daily living (ADL) limitations and physician-diagnosed chronic diseases, self-rated health, and survival. DNA-methylation and blood-chemistry quantifications of biological aging were moderately correlated (Pearson-r range 0.1-0.4). GrimAge, DunedinPoAm and all three blood-chemistry measures were associated with healthspan characteristics (e.g. mortality effect-size range HR=1.71-2.32 per SD of biological aging) and showed evidence of more advanced/faster biological aging in Black compared with White participants (Cohen’s d=.4-.5). These measures accounted for 13-95% of Black-White differences in healthspan-related characteristics. Findings suggest that reducing disparities in biological aging can contribute to building health equity.

DunedinPACE, a DNA methylation biomarker of the pace of aging

“…Follow-up of DunedinPACE in more diverse samples is needed to establish cross-population validity. Importantly, our original DunedinPoAm measure has been followed-up in diverse cohorts and has showed evidence of consistent validity across race/ethnic subgroups ( Crimmins et al, 2021 ; Graf et al, 2021 ; Raffington et al, 2021 ; Schmitz et al, 2021 ). Criterion validity analyses conducted in this article do not consider specific diseases or causes of death because the cohorts used for follow-up are relatively small.…”

“…DunedinPoAm was designed to measure Pace of Aging biological change over time from a single blood sample. Like the original Pace of Aging, people with faster DunedinPoAm scores more often experienced declines in cognitive and physical functioning by midlife and showed more rapid facial aging Belsky et al, 2020 ; in older adults, faster DunedinPoAm predicted increased risk of disease and death ( Belsky et al, 2020 ; Graf et al, 2021 ); in young people, experiences of early-life adversity were linked to faster DunedinPoAm ( Belsky et al, 2020 ; Raffington et al, 2021 ).…”

Association of Pace of Aging Measured by Blood-Based DNA Methylation With Age-Related Cognitive Impairment and Dementia

“…Second, the majority of participants are white because of the lack of ethnic diversity of the participants enrolled in ADNI and Framingham. Initial evidence shows that an earlier version of a methylation Pace of Aging algorithm, DunedinPoAm, is associated with poorer physical health among both Black and White participants 19 , but more research is needed on this front. Third, we were able to report only cross-sectional associations between DNA-methylation measures of aging and cognitive impairment and AD in ADNI because the number transitioning to a new diagnosis was too small for statistical power among ADNI participants who had methylation data.…”

“…But the literature evaluating them is fragmented. Although all of these algorithms purport to measure aging, they have surprisingly low agreement 18,19 ; articles often report promising findings from one (or more) DNA methylation algorithms, but often in different samples, and many algorithms show inconsistent associations with outcomes 11,[20][21][22][23] .…”

Epigenetic clocks and research implications of the lack of data on whom they have been developed: a review of reported and missing sociodemographic characteristics

“…For example, inconsistent associations have been reported with education (Hughes et al, 2018, Dugue et al, 2018, Fiorito et al, 2019, Simons et al, 2021, Stevenson et al, 2019, Zhao et al, 2019, socioeconomic status (Fiorito et al, 2017, Robinson et al, 2020, Simons et al, 2016, Simons et al, 2021, Austin et al, 2018, Lawn et al, 2018, Ryan et al, 2018, Stevenson et al, 2019, Hughes et al, 2018, Miller et al, 2015, Schmitz et al, 2021 and racialized group (Crimmins et al, 2021, Horvath et al, 2016, Tajuddin et al, 2019, Graf et al, 2022; as well as inconsistent associations between epigenetic age acceleration and health and social outcomes when analyses are stratified by sociodemographic characteristics, such as education level (Lu et al, 2019a), country of birth (Dugue et al, 2018), and racialized group (Lu et al, 2019a, Zhao et al, 2019. One recent study reported that when testing association between epigenetic clocks and healthspan-related characteristics, smaller effect sizes were found for Black American participants in comparison to white American participants (Graf et al, 2022), suggesting it is possible that associations could be biased toward the null in some study populations. These inconsistencies may be due in part to some clocks including loci known to be differentially methylated by country of birth (Dugue et al, 2018) and racialized group (Philibert et al, 2020, Simons et al, 2016.…”