Relationship Between Accelerated Biological Aging, Race, Perceived Discrimination, and Limitations in Activities of Daily Living
by Maria Teresa Brown1*, Miriam Mutambudzi2, Nai-Wei Chen3
1School of Social Work, Syracuse University, New York, USA
2Department of Public Health, Syracuse University, New York, USA
3Biomedical Informatics, Biostatistics and Medical Epidemiology, School of Medicine, University of Missouri, New York, USA
*Corresponding author: Maria Teresa Brown, School of Social Work/Aging Studies Institute/Syracuse University, 314 Lyman Hall, Syracuse, New York, 13244, USA
Received Date: 17 January, 2025
Accepted Date: 24 January, 2025
Published Date: 31 January, 2025
Citation: Brown MT, Mutambudzi M, Chen NW (2025) Relationship Between Accelerated Biological Aging, Race, Perceived Discrimination, and Limitations in Activities of Daily Living. Int J Geriatr Gerontol 9:199. https://doi.org/10.29011/2577-0748.100099
Abstract
This cross-sectional study aimed to evaluate the relationship between accelerated aging (PhenoAge) and limitations in Activities of Daily Living (ADLs) in White, Black, and Hispanic older adults (≥ 50 years). We further aimed to explore how perceived discrimination may differentially impact this association. We ran multivariable logistic regression models to evaluate the strength of the association between accelerated aging, ADLs, and perceived discrimination using 2014/2016 Health and Retirement Study (HRS) data for White (n=2107), Black (n=435), and Hispanic (n=351) adults aged 50 and older who contributed epigenetic clock data during the 2016 HRS Venous Blood Study. We hypothesized that accelerated aging would be associated with greater levels of functional limitations, and that perceived discrimination would impact this relationship with evident disadvantage among Blacks and Hispanics relative to Whites. We found that more Blacks and Hispanics reported ADLs (23.68% and 23.08% respectively) than Whites (12.48%, p<0.01). Blacks reported more discrimination (M 3.71, SD 4.26, p<0.01) than Hispanics (M 2.69, SD 3.84) or Whites (M 2.86, SD 3.52). ADLs were associated with accelerated aging (OR=1.42, 95% confidence interval = 1.13,1.78) and discrimination (OR=1.07, 95% confidence interval = 1.04,1.10). Our research examining how exposure to discrimination differentially affects the biological aging-ADL association across racial and ethnic groups contributes to efforts addressing health disparities associated with functional decline. This work is part of a broader body of research aiming to understand the impact of discrimination on biological outcomes and their consequences for health and loss of independence.
Key words: epigenetic aging; epigenetic clocks; activities of daily living
Introduction
Biological aging, defined based on patterns of DNA methylation (DNAm) in the human genome [1], is a relatively new field of study. Based on biological differences in aging, adults who are the same chronological age may have different levels of vulnerability to age-related health concerns and mortality [2]. Disparities between chronological and biological aging may contribute to nuanced variations in health outcomes, with cumulative effects across the life course. Advanced chronological age is associated with increased risk of chronic health outcomes [3], functional and cognitive decline [4], and reduced quality of life [5]. When a person’s biological age is greater than their chronological age, they can be considered as experiencing accelerated aging [6].
Previous research has linked biological aging with various indicators of adversity across the life span, and with various health behaviors [6]. Social scientists have not identified all effects of biological aging on health in later life or fully explored the life course experiences and social determinants that contribute to variations in biological aging. This cross-sectional study aimed to evaluate the relationship between accelerated aging (PhenoAge) and limitations in Activities of Daily Living (ADLs) in White, Black, and Hispanic older adults (≥ 50 years. We further aimed to explore how perceived discrimination may differentially impact this association and assess whether this impact varies by race or ethnicity.
Second-Generation Epigenetic Clocks
Rather than being trained on chronological age, as were the first generation of epigenetic clocks, second-generation epigenetic clocks were constructed using clinical measures of phenotypic age and their relation to novel CpG sites, which may make them better predictors of biological aging [2]. GrimAge and PhenoAge are two commonly used second generation epigenetic clocks, which have different developmental backgrounds. GrimAge was derived from data of the predominantly white Framingham Heart Study Offspring sample. [7]. In contrast, PhenoAge was developed using clinical data from the third National Health and Nutrition Examination Survey (NHANES III), which represents a racially diverse, nationwide probability sample (NHANES III; Centers for Disease Control and Prevention, 2024). Consequently, PhenoAge is more suitable for analyzing racially diverse populations.
PhenoAge has been linked to health behaviors (e.g. smoking) and lifestyle and demographic factors like education, income, and exercise, negatively correlated with “good” HDL cholesterol, and positively correlated with C-reactive protein, insulin, glucose, triglycerides, and waist to hip ratio [2]. PhenoAge has also been linked to chronic health conditions, all-cause mortality, and agingrelated morbidity [2, 8, 9] and is a more accurate predictor of 10- and 20-year survival than first generation epigenetic clocks [10]. DNAm summary measures like PhenoAge have been suggested as potentially improving our understanding of health disparities, disease, and age-associated functional decline [6]. To date however, it is unclear whether accelerated biological age may impact one’s ability to live independently and carry out activities of daily living (ADLs).
Discrimination
Social scientists have only recently begun investigating the role of biological age as a proxy for cumulative inequality, the result of the lifetime accumulation of exposure to structural inequalities that can differentially impact people of color, and a potential predictor of health and social outcomes in epidemiological studies of aging [11]. One experience or range of experiences that may contribute to cumulative inequality and health disparities for non-Whites in the United States is discrimination. Individual experiences of discrimination have been linked to racial differences in quality of education and employment opportunities for individual members of minority groups and in turn adversely affect socioeconomic status for members of minority groups [12].
The association between socioeconomic status and health is welldocumented and there is growing body of research indicating that experiencing multiple forms of discrimination can indirectly affect health regardless of race, but that this impact is worse for Blacks [12-14]. Results from studies exploring the relationship between perceived discrimination and various health outcomes – body mass index, obesity, poorer health, cardiovascular disease – and between discrimination and indicators of poor health – hypertension, markers of inflammation, allostatic load – provide some evidence of an association, although results can vary by gender, race, and the indicators being studied [13]. Our understanding of these associations would benefit from additional research.
Limitations in Activities of Daily Living
Limitations in Activities of Daily Living (ADLs) in older adults have been associated with onset of disability, loss of independence, greater morbidity, and greater mortality due to cardiovascular disease [15-17]. For example, ADL dependence has been associated with higher rates of 30-day hospital readmissions among older adults undergoing hip or knee replacement [18]. Studies have found that retaining ADL independence may be a critical factor in aging successfully among those 80 and older [19]. As many older adults desire to remain healthy and independent, and to exert control over their individual aging experience, it is important to understand what factors affect functional independence and, conversely, ADL limitations [20].
The Current Study
The overarching objective of this cross-sectional study was to explore the association between biological age and functional limitations and to assess the differential impact of perceived discrimination on this association in Black, Hispanic, and White older adults. To achieve this objective, the project used the 2014 and 2016 Health and Retirement Study (HRS) data on adults aged 50 and older including PhenoAge epigenetic clock data from the 2016 HRS Venous Blood Study (VBS) [21].
This study attempted to answer the research questions: 1) How is accelerated aging, as measured by PhenoAge, associated with functional limitations in older adults? and 2) Is this association differentially impacted by perceived discrimination or race/ ethnicity? We hypothesized that: 1) accelerated aging would be associated with greater levels of functional limitations, and 2) perceived discrimination would impact this relationship with evident disadvantage among Blacks and Hispanics relative to Whites.
Materials and Methods
Data
The Health and Retirement Study (HRS) is a longitudinal cohort study of retirement and health in non-institutionalized Americans, which uses a mixed-mode design of telephone and face-to-face interviews, to collect data on a wide range of factors including sociodemographic, social, behavioral, and physical and mental health. Hispanics and non-Hispanic Blacks were oversampled at a 2:1 rate and maintained a high follow-up rate [22]. During the 2016 interview, 78.5% of all HRS respondents consented to have their blood drawn for the HRS VBS [21], and collection was completed for 82.9% or 9,934 cases (65% of eligible HRS cases). Thirteen epigenetic clocks were constructed using the 2016 VBS DNA methylation data (n = 4,018, 40.4% of 2016 respondents), and 3,966 of these participants provided all data needed for regression analyses to estimate epigenetic clocks. Specific details of the processing of these DNA methylation data can be found in [6].
In 2014 and 2016 respectively, in-person interviews were conducted with 50% mutually exclusive study samples, who were also given a mail-in self-administered Psychosocial Leave Behind Questionnaire (PLBQ) (90% response rate). Data on perceived discrimination were obtained from the 2014 and 2016 PLBQs for those respondents with whom VBS collection was completed. Approximately 1,735 participants in 2014 and 1,557 participants in 2016 completed the PLBQ and have epigenetic clock data.
Sample Inclusion and Exclusion: Participants were included in this study if they were (1) 50 years and older in 2016, (2) had 2016 VBS DNA methylation data, and (3) had completed the PLBQ in 2014 or 2016, including perceived discrimination items. Participants were excluded if their HRS interviews were completed via proxy. Our final sample consisted of 2,893 HRS participants: 2,107 non-Hispanic Whites, 435 non-Hispanic Blacks, and 351 Hispanics.
Measures
Outcome Measure: The dependent variable for this pilot was limitations in activities of daily living (ADLs), as measured in the HRS in 2016. The HRS identified functional limitations using a modified version of the Katz Activities of Daily Living (ADL) scale from the Rand files [23], which includes six tasks (i) getting out of bed, (ii) bathing, (iii) dressing, (iv) eating, (v) toileting, and (vi) walking across a room, resulting in a 0–6 point scale of ADL limitations [24]. Consistent with previous studies on biological aging and to facilitate clearer interpretation of results, we opted to use a binary variable in our analysis [8]. While this may sacrifice some nuance, it aligns with established practices in the field and enhances clarity of our results.
Independent Variables: The primary independent variable is accelerated aging, which was derived from the second-generation epigenetic clock “PhenoAge” developed and trained by Levine and colleagues (2018). To assess the presence of accelerated aging for each participant, we regressed PhenoAge on chronological age [6, 25] and calculated the residual from this regression [25]. We then constructed a binary variable with 1 representing older predicted epigenetic age or accelerated aging (positive residual) and 0 representing younger predicted epigenetic age (negative residual). All regressions with the independent variable accelerated aging included a control for chronological age.
Moderating Variables: The moderator of interest, perceived discrimination, is measured by the Everyday Discrimination Scale. This five-item scale measures chronic and routine interpersonal discrimination and unfair treatment in everyday life in the past 12 months on a five-point Likert scale (never to almost every day [26]: (1) being treated with less courtesy or respect, (2) receiving poorer service at restaurants or stores; (3) people acting as if they think you are not smart; (4) people acting as if they are afraid of you; and (5) being threatened or harassed [27]. The scale is made up of the average of the five items (range = 0-25, higher scores reflect greater exposure to discrimination). Using these five items, we generated a dichotomous discrimination variable (none vs. any).
Control Variables: Regression models include measures for chronological age, race (Black, Hispanic, and White), sex (female=1, male=0), and years of education (ranged from 0-17). Household income is an integer variable representing total household income of respondent and spouse. Health controls included currently smoking and self-rated health. Currently smoking was defined by responses to the question “about how many cigarettes or packs do you usually smoke in a day now?” with scores of 1 for one or more and 0 for none; missing cases (n=16) were assigned the modal value of 0. Self-rated health scores ranged from excellent (1) to poor (5), with larger values indicating worse self-rated health.
Analytic Plan
We ran descriptive analyses by race to assess differential distributions of ADLs, PhenoAge, and controls for demographic, behavioral, social, and health factors within the study sample. To test our first hypothesis, we ran bivariate analyses (difference of means tests and cross-tabulations with chi-square tests of significance) to examine the association between ADLs and the PhenoAge clock. Multivariable logistic regression models evaluated the strength of the association between ADLs and PhenoAge. Model 1 regressed the presence of at least one ADL on the binary indicator for accelerated aging while controlling for chronological age and additional demographic, social, behavioral, and health factors. To test our second hypothesis, we added perceived discrimination to Model 2, an interaction between accelerated aging and discrimination to Model 3, and an interaction between accelerated aging and race in Model 4.
Results
As seen in Table 1, there were racial differences in any ADL limitations (12.5% of Whites compared to 23.1% of Hispanics and 23.7% of Blacks, p<0.01) and in the mean number of ADL limitations (0.50, SD 1.10 for Blacks, 0.50, SD 1.16 for Hispanics, and 0.23, SD 0.76 for Whites, p<0.001). Whites had higher mean chronological age (71.05, SD 9.34, p<0.01) compared to Blacks (66.67, SD 7.97) or Hispanics (66.39, SD 8.35), and their epigenetic age, as indicated by the PhenoAge clock, was older (58.29, SD 0.86, p<0.01) compared to Blacks (55.18, SD 9.36) and Hispanics (55.36, SD8.85). The proportions of Whites (46.18%), Blacks (48.74%), and Hispanics (50.71%) with accelerated aging were similar (p=0.221) (Table 1).
Full Sample |
Black (n=435) |
Hispanics (n=351) |
White (n=2107) |
|||||||
Characteristic |
N / Mean |
% / SD |
N/Mean |
% / SD |
N / Mean |
% / SD |
N / Mean |
% / SD |
Sign. |
|
Chronological Age (mean, SD) |
69.83 |
9.25 |
66.67 |
7.97 |
66.39 |
8.35 |
71.05 |
9.34 |
*** |
|
PhenoAge (mean, SD) |
57.47 |
9.76 |
55.18 |
9.36 |
55.36 |
8.85 |
58.29 |
0.86 |
*** |
|
Accelerated Aging (N, %) |
1363 |
47.11% |
212 |
48.74 |
178 |
50.71 |
973 |
46.18 |
||
Female (N, %) |
1719 |
59.42% |
300 |
68.97 |
208 |
59.26 |
1211 |
57.48 |
*** |
|
Household Income (mean, SD) |
$73,474 |
$101,623 |
$45,064 |
$53,109 |
$39,515 |
$48,043 |
$84,996 |
$112,813 |
*** |
|
Years of Education (mean, SD) |
13.10 |
3.02 |
12.77 |
2.73 |
9.95 |
4.47 |
13.69 |
2.37 |
*** |
|
Self-Rated Health (mean, SD) |
2.81 |
1.00 |
3.03 |
1.00 |
3.25 |
1.01 |
2.69 |
0.97 |
*** |
|
Health Behaviors |
||||||||||
Currently Smoking (N, %) |
288 |
9.96% |
66 |
15.17% |
34 |
9.69% |
188 |
8.92% |
*** |
|
Body Mass Index (mean, SD) |
28.84 |
6.10 |
31.05 |
6.88 |
30.32 |
6.55 |
29.01 |
6.15 |
*** |
|
Functional Limitations |
||||||||||
Number of ADLs (mean, SD) |
0.306 |
0.88 |
0.499 |
1.10 |
0.504 |
1.16 |
0.233 |
0.755 |
*** |
|
Any ADLs (N, %) |
447 |
15.45% |
103 |
23.68% |
81 |
23.08% |
263 |
12.48% |
** |
|
Perceived Discrimination (mean, SD) |
2.97 |
3.69 |
3.71 |
4.26 |
2.69 |
3.84 |
2.86 |
3.52 |
** |
|
Table 1: Sample Characteristics by Race, 2014/2016 Health and Retirement Study
A larger proportion of Blacks in this sample were female (68.97%, p<0.0001) relative to Hispanics (59.26%) and Whites (57.48%). Whites had significantly greater incomes on average ($85,000) compared to $45,000 and $40,000 for Blacks and Hispanics respectively. Whites had significantly better self-rated health (2.69 – good to very good) relative to Blacks (3.03 - good) and Hispanics (3.25 - fair to good), lower body mass index (29.10 vs 31.05 and 30.32), and were less likely to be current smokers (8.92% vs 15.17% and 9.69%). Unexpectedly, Whites had higher perceived discrimination scores (2.86) than Hispanics (2.69), and lower than Blacks (3.71).
Bivariate analyses of associations between ADL limitations and the PhenoAge clock are presented in Table 2. A greater proportion of participants with accelerated aging (19.15%) reported any ADL limitations relative to those with no accelerated aging (12.16%, p<0.0001). The association between specific numbers of ADL limitations and the experience of accelerated aging was also significant and varied based on the number of ADL limitations. For example, participants with accelerated aging were more likely to report one limitation than those without it (10.56% vs 5.82%, p<0.0001), but the differences between participants with and without accelerated aging were much smaller when looking at four or more ADL limitations (1.89% vs 2.65%). Finally, the mean number of ADL limitations significantly varied based on whether a participant had experienced accelerated aging (0.36, SD 0.03, p<0.0001) or not (0.26, SD 0.84) (Table 2).
No Accelerated Aginga |
Accelerated Aginga |
Sig. |
|
Number of ADL Limitations – mean (SD) |
0.26 (0.84) |
0.36 (0.030) |
*** |
Any ADL Limitations |
12.16% |
19.14% |
*** |
One ADL Limitation |
6.20% |
10.41% |
*** |
Four or more ADL Limitations |
2.20% |
2.30% |
*** |
*p<0.05, **p<0.01, ***p<0.001 aAccelerated aging indicated by a binary variable representing the presence (1) or absence (0) of PhenoAge residual values exceeding chronological age. |
Table 2: Bivariate Associations Between ADL Limitations and Accelerated Aging, 2014/2016 Health and Retirement Study
In Model 1 of the multivariate logistic regressions, accelerated aging was associated with greater odds of having at least one ADL limitation. Each year of accelerated aging increased the risk of ADLs by 37% (OR 1.37, 95% CI 1.10-1.71). Other factors associated with greater risk of at least one ADL were older chronological age (OR 1.03, 95% CI 1.02-1.04), being Black (OR 1.86, 95% CI 1.392.48), and poorer self-rated health (OR 2.70, 95% CI 2.38-3.07). Being Hispanic was not associated with greater risk of ADLs (OR 1.33, 95% CI 0.93-1.90) (Table 3).
Model 1 |
Model 2 |
Model 3 |
Model 4 |
||||||
Variable |
OR |
95% CI |
OR |
95% CI |
OR |
95% CI |
OR |
95% CI |
|
Accelerated Aginga |
1.37 |
[1.10,1.71] |
1.42 |
[1.13,1.78] |
1.41 |
[1.05,1.89] |
1.41 |
[1.07,1.88] |
|
Chronological Age |
1.03 |
[1.02,1.04] |
1.03 |
[1.02,1.05] |
1.03 |
[1.02,1.045 |
1.03 |
[1.02,1.05] |
|
Demographic Characteristics |
|||||||||
Female |
1.24 |
[0.98,1.56] |
1.30 |
[1.03,1.65] |
1.30 |
[1.03,1.65] |
1.30 |
[1.03,1.65] |
|
Household Income |
1.00 |
[1.00, 1.00] |
1.00 |
[1.00, 1.00] |
1.00 |
[1.00, 1.00] |
1.00 |
[1.00, 1.00] |
|
Years of Education |
0.97 |
[0.94,1.01] |
0.97 |
[0.94,1.01] |
0.97 |
[0.94,1.01] |
0.97 |
[0.94,1.01] |
|
Race / Ethnicity (Ref: Non-Hispanic White) |
|||||||||
Non-Hispanic Black |
1.86 |
[1.39,2.48] |
1.83 |
[1.37,2.45] |
1.83 |
[1.37,2.45] |
1.99 |
[1.31,3.04] |
|
Hispanic |
1.33 |
[0.93,1.90] |
1.43 |
[1.00,2.04] |
1.43 |
[1.00,2.05] |
1.26 |
[0.76,2.10] |
|
Health Behaviors |
|||||||||
Currently Smoking |
1.00 |
[0.71,1.43 |
1.01 |
[0.71,1.43] |
1.01 |
[0.71,1.43] |
1.00 |
[0.70,1.43] |
|
Self-Rated Health |
2.70 |
[2.28,2.07] |
2.62 |
[2.30,2.97] |
2.62 |
[2.30,2.97] |
2.62 |
[2.30,2.98] |
|
Perceived Discrimination |
1.07 |
[1.04,1.10] |
1.07 |
[1.03,1.11] |
1.07 |
[1.04,1.10] |
|||
Interaction Effects |
|||||||||
Accelerated Aging with Discrimination |
1.00 |
[0.95,1.06] |
|||||||
Accelerated Aging with Race |
|||||||||
Non-Hispanic White |
(omitted) |
||||||||
Non-Hispanic Black |
0.85 |
[0.49,1.50] |
|||||||
Hispanic |
1.25 |
[0.67,2.32] |
|||||||
Intercept |
-6.83 |
-7.42 |
-7.42 |
-7.43 |
|||||
aAccelerated aging indicated by a binary variable representing the presence (1) or absence (0) of PhenoAge residual values exceeding chronological age. |
|||||||||
Table 3: Presence of at Least One ADL Limitation in 2016 (N=2,869), 2014/2016 Health and Retirement Study
In Model 2 we added perceived discrimination, which increased the odds of having at least one ADL limitation by 7% (OR 1.07, 95% CI 1.04-1.10). Including discrimination in the models increased the association between accelerated age and ADLs (OR 1.42, 95% CI 1.13-1.78). At the same time, it slightly reduced the risk of ADLs based on being Black (by 3%, OR 1.83, 95% CI 1.372.45), and increased the risk based on being Hispanic (by 10%, OR 1.43, 95% CI 1.00-2.04). The risk of ADLs because of self-rated health also decreased (by 8%, OR 2.62, 95% CI 2.30-2.97). In Models 3 and 4 we added interactions between accelerated aging and discrimination and accelerated aging and race. None of these interactions were significant, nor did they change the relationship between accelerated aging and the odds of having ADL limitations.
Discussion
This study contributes to the literature on biological aging by exploring the relationship between biological aging and the presence of ADL limitations in older adults. We found evidence that accelerated aging, as measured by Levine’s PhenoAge epigenetic clock, was associated with greater risk of ADL limitations at age 50 or older. Our first hypothesis was supported by the significant association between accelerated aging and risk of one or more ADL limitations and between race and ethnicity and risk of ADL limitations. Our second hypothesis was partially supported by evidence that perceived discrimination was significantly associated with ADLs and accounting for discrimination increased the odds of having at least one ADL limitation by 5% for individuals with accelerated aging. The addition of discrimination to the model partially explained the odds of ADL limitations associated with being Black. Counterintuitively, being Hispanic was not initially associated with the risk of ADL limitations, and accounting for discrimination increased the odds of ADL limitations among Hispanics. However, the cross-sectional design of the study precludes any determination of causation between discrimination and other factors. Finally, we found no significant effects for interactions between accelerated aging and discrimination or between accelerated aging and race.
Experiences of racial discrimination at different points in the life course have previously been linked with accelerated or biological aging [28-30]. The weathering hypothesis posits that social inequity, and the repeated exposure to socially structured stressors resulting from that inequality, leads to health deterioration early in the life course for African Americans but not for Caucasians [31], and that this repeated exposure can lead to life-course accumulation of disease vulnerability in marginalized groups [32]. This process of physical weathering leads to disparities in allostatic load [33], biological aging [34], risk of hypertension [35,36], and in combination with genomics leads to racial disparities in breast cancer risk [37].
Our findings provide partial support for this pattern among Black respondents in our sample, in that we have linked perceived discrimination with greater risk of ADL limitations among Black respondents. However, our results do not show a similar pattern for Hispanics in our sample, as accounting for discrimination increased the odds of ADL limitations among Hispanics, rather than explaining that association. Contrary to prior research, Hispanics in our sample also reported lower exposure to discrimination than Black or White respondents [38]. Our findings related to the Hispanic subgroup in our sample indicate a broader diversity in Hispanic experiences of ADL limitations and discrimination, and a need to disaggregate Hispanic samples to capture their range of experiences [39]. Future research should incorporate larger samples of Hispanics to enable disaggregation of this population.
Chronological aging occurs at the same rate for everyone and has been identified as a strong risk factor for various aging-related diseases and death. However, due to genetic variation and the accumulation of lifetime exposures, experiences, and health factors, adults of similar chronological age may be experiencing different rates of biological aging [40-42]. Enhancing our comprehension of the efficient utilization of biological age as a potential indicator for health and social consequences that contribute cumulative inequality over the life course [11] is necessary.
Numerous policies rely on chronological age, posing a latent challenge for adults experiencing health or independent living declines before becoming eligible for specific benefits or assistance. Assessing health and social needs based on biological age could enable the fine-tuning of population-level health and social policies. Further, identifying health disparities related to biological age can contribute to targeted public health interventions. This refinement is crucial for mitigating aging related health challenges, dependency, and diminished quality of life, in particular for those with accelerated aging. By exploring the relationship between biological aging and functional limitations, the findings from this study can improve our understanding of the potential need for services and supports among older adults who would benefit, but may currently be excluded, from age-tested retirement, social support, income support, or health and long-term care policies.
Limitations
These findings should be evaluated in the context of several limitations. First, the first-generation epigenetic clocks were derived using primarily white European samples of older adults, which restricts their usability with non-White and non-Western populations. PhenoAge was developed using NHANES III, which was a more diverse sample and included oversampling of Mexican Americans and Non-Hispanic Blacks [43,44]. However, as a second-generation clock, it does have roots in first generation clock data and may still be limited in its ability to explain variation in health among non-white older adults. Second, the data employed in this analysis were cross-sectional, and therefore we do not know how functional limitation may change over time based on biological aging. Future studies should incorporate ADL measures from subsequent waves of the HRS to evaluate differences in trends over time. Third, measures for perceived discrimination in these data only account for experiences in the prior 12 months, and therefore do not account for discrimination experienced across the life course; further research should include lifetime measures of discrimination. Fourth, the use of a binary measure for accelerated aging may introduce threshold effects and eliminate the ability to detect the different associations with ADL limitations that we might find between individuals with a biological age ten years older than their chronological age and those with a biological age only one year older than chronological age. However, the distribution of the residual values for PhenoAge was so broad (-27.83 to 42.29), and the mean value so small (8.19838E-15, SD 6.74), that using those residual values to capture the rate of accelerated aging resulted in nonsignificant results. Finally, the HRS data did not capture within-person factors that may influence functional limitations over time. Other data should be incorporated to assess the influence of intrapersonal factors that may explain some of the variation in functional limitation seen in these data.
Implications
Our findings contribute to knowledge about how biological age, in spite of chronological age, may be associated with functional limitations differently for those exposed to any discrimination compared to none, in mid to later life. Research examining how discrimination affects the biological aging-functional limitations association for each racial group can better inform efforts to address disparities in health and well-being associated with loss of independence due to functional decline. The findings from this study can inform the design and use of targeted structured interventions to prevent or reduce ADL limitations among higherrisk older adults, such as older adults who have been exposed to discrimination or have experienced accelerated aging.
Conclusions
The ability to measure biological age provides a significant opportunity to rethink processes of aging and policy and health interventions to address health disparities across the life course [45]. Previous research has shown how social factors can result in accelerated aging, with subsequent unfavorable health outcomes [29,46]. It is important to understand whether and how chronological and biological age may be incongruent, as this may provide important insights into which adults may need supports earlier than expected and are often unable to access resources reserved for adults beyond a certain chronological age.
The current study identified accelerated aging and perceived discrimination as risk factors for ADL limitations. Understanding differing levels of risk among older adults based on history of discrimination or biological age can inform preventive measures and design and implementation of interventions to address these risks. This knowledge can be used to evaluate and modify existing social and economic support programs to acknowledge the different levels of needs older adults may experience when their biological age is greater than their chronological age.
Acknowledgements
This work was supported by grant, P30AG66583, Center for Aging and Policy Studies, awarded to Syracuse University, in consortium with Cornell University and the University at Albany, by the National Institute on Aging at the National Institutes of Health.
Ethical Guidelines
This study analyzed secondary, de-identified data, and was determined to qualify for an exemption by the Syracuse University Institutional Review Board.
Conflict of Interest
We have no conflicts of interest to disclose.
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