@article {12923, title = {Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality.}, journal = {PNAS}, volume = {120}, year = {2023}, pages = {e2215840120}, abstract = {

Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.

}, keywords = {Acceleration, Aging, Biomarkers, Cross-Sectional Studies, DNA Methylation, Epigenesis, genetic}, issn = {1091-6490}, doi = {10.1073/pnas.2215840120}, author = {Jessica Faul and Jung K Kim and Levine, Morgan E and Bharat Thyagarajan and David R Weir and Eileen M. Crimmins} } @article {11926, title = {Age-related differences in T cell subsets in a nationally representative sample of people over age 55: Findings from the Health and Retirement Study.}, journal = {The Journals of Gerontology, Series A }, volume = {77}, year = {2022}, pages = {927-933}, abstract = {

Though T cell immunosenescence is a major risk factor for age-related diseases, susceptibility to infections, and responses to vaccines, differences in T cells subset counts and representation by age and sex have not been determined for a large sample representative of the national population of the US. We evaluated the counts of T cell subsets including total, CD4+ and CD8+ T cells, and their na{\"\i}ve (Tn), effector memory (Tem) and effector subsets, in the context of age, sex and exposure to cytomegalovirus (CMV) infection among 8,848 Health and Retirement Study (HRS) participants, a nationally representative study of adults over 55 years. Total T cells (CD3+) and CD4+ cells declined markedly with age; CD8+ T cells declined somewhat less. While CD4+ T cell declines with age occurred for both CMV seropositive and CMV seronegative groups, total T cells and CD8+ cells were both substantially higher among the CMV seropositive group. Numbers of Tn CD4+ and CD8+ cells were strongly and inversely related to age, were better conserved among women, and were independent of CMV seropositivity. By contrast, accumulation of the CD8+ and CD4+ Tem and effector subsets was CMV-associated. This is the first study to provide counts of T cell subsets by age and sex in a national sample of older US adults over the age of 55 years. Understanding T cell changes with age and sex is an important first step in determining strategies to reduce its impact on age-related diseases and susceptibility to infection.

}, keywords = {Aging, CMV Seropositivity}, issn = {1758-535X}, doi = {10.1093/gerona/glab300}, author = {Bharat Thyagarajan and Jessica Faul and Vivek, Sithara and Jung K Kim and Nikolich-{\v Z}ugich, Janko and David R Weir and Eileen M. Crimmins} } @article {13514, title = {Socioeconomic status and immune aging in older US adults in the health and retirement study.}, journal = {Biodemography and Social Biology}, volume = {67}, year = {2022}, pages = {187-202}, abstract = {

Socioeconomic and demographic factors including educational attainment, race and ethnicity, and childhood socioeconomic status (SES) are powerful predictors of inequalities in aging, morbidity, and mortality. Immune aging, including accumulation of late-differentiated, senescent-like lymphocytes and lower levels of na{\"\i}ve lymphocytes, may play a role in the development of the age-related health inequalities. This study used nationally representative data from more than 9,000 US adults from the Health and Retirement Study to investigate associations between educational attainment, race and ethnicity, and childhood SES and lymphocyte percentages. Respondents with lower educational attainment, Hispanic adults, and those who had a parent with less than a high school education had lymphocyte percentages consistent with more immune aging compared to those with greater educational attainment, non-Hispanic White adults, and respondents who had parents with a high school education, respectively. Associations between education, Hispanic ethnicity, and parents{\textquoteright} education and late differentiated senescent-like T lymphocytes (TemRA) and B cells were largely driven by cytomegalovirus (CMV), suggesting it is a factor in observed SES inequalities in immunosenescence. Na{\"\i}ve T lymphocytes may be particularly affected by socioeconomic position and may therefore be of particular interest to research interested in inequalities in health and aging.

}, keywords = {Child, Educational Status, ethnicity, Hispanic or Latino, Retirement, Social Class}, issn = {1948-5573}, doi = {10.1080/19485565.2022.2149465}, author = {Klopack, Eric T and Bharat Thyagarajan and Jessica Faul and Meier, Helen C S and Ramasubramanian, Ramya and Jung K Kim and Crimmins, Eileen M} } @article {11422, title = {Quest for a summary measure of biological age: The Health and Retirement Study.}, journal = {Geroscience}, volume = {43}, year = {2021}, pages = {395-408}, abstract = {

Measures of biological age and its components have been shown to provide important information about individual health and prospective change in health as there is clear value in being able to assess whether someone is experiencing accelerated or decelerated aging. However, how to best assess biological age remains a question. We compare prediction of health outcomes using existing summary measures of biological age with a measure created by adding novel biomarkers related to aging to measures based on more conventional clinical chemistry and exam measures. We also compare the explanatory power of summary biological age measures compared to the individual biomarkers used to construct the measures. To accomplish this, we examine how well biological age, phenotypic age, and expanded biological age and five sets of individual biomarkers explain variability in four major health outcomes linked to aging in a large, nationally representative cohort of older Americans. We conclude that different summary measures of accelerated aging do better at explaining different health outcomes, and that chronological age has greater explanatory power for both cognitive dysfunction and mortality than the summary measures. In addition, we find that there is reduction in the variance explained in health outcomes when indicators are combined into summary measures, and that combining clinical indicators with more novel markers related to aging does best at explaining health outcomes. Finally, it is hard to define a set of assays that parsimoniously explains the greatest amount of variance across the range of health outcomes studied here. All of the individual markers considered were related to at least one of the health outcomes.

}, keywords = {Biological age, Biomarkers, Phenotypic age, TAME markers}, issn = {2509-2723}, doi = {10.1007/s11357-021-00325-1}, author = {Eileen M. Crimmins and Bharat Thyagarajan and Jung K Kim and David R Weir and Jessica Faul} }