Institutional Context Shapes the Physical Health of College Graduates Differently for U.S. White, Black, and Hispanic Adults.

Greater educational attainment is generally associated with healthier and longer lives. However, important heterogeneity in who benefits from educational attainment, how much, and why remains underexplored. In particular, in the United States, the physical health returns to educational attainment are not as large for minoritized racial and ethnic groups compared with individuals racialized as White. Yet, our current understanding of ethnoracial differences in educational health disparities is limited by an almost exclusive focus on the quantity of education attained without sufficient attention to heterogeneity within educational attainment categories, such as different institution types among college graduates. Using biomarker data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), we test whether the physical health of college graduates in early adulthood (aged 24-32) varies by institution type and for White, Black, and Hispanic adults. In considering the role of the college context, we conceptualize postsecondary institutions as horizontally stratified and racialized institutional spaces with different implications for the health of their graduates. Finally, we quantify the role of differential attendance at and returns to postsecondary institution type in shaping ethnoracialized health disparities among college graduates in early adulthood.

The fortunes and misfortunes of social life across the life course: A new era of research from field, laboratory and comparative studies.

Social gradients in health and aging have been reported in studies across many human populations, and - as the papers included in this special collection highlight - also occur across species. This paper serves as a general introduction to the special collection of Neuroscience and Biobehavioral Reviews entitled "Social dimensions of health and aging: population studies, preclinical research, and comparative research using animal models". Authors of the fourteen reviews are primarily members of a National Institute of Aging-supported High Priority Research Network on "Animal Models for the Social Dimensions of Health and Aging". The collection is introduced by a foreword, commentaries, and opinion pieces by leading experts in related fields. The fourteen reviews are divided into four sections: Section 1: Biodemography and life course studies; Section 2: Social behavior and healthy aging in nonhuman primates; Section 3: Social factors, stress, and hallmarks of aging; Section 4: Neuroscience and social behavior.

Pathways between a polygenic index for education and years of completed schooling: the presentation of self and assessment of others.

Polygenic scores (PGS) are broadly misconstrued as reflecting direct causal genetic effects on their respective phenotypes. While this assumption might be accurate for some anthropometric traits like height, more complex traits such as educational attainment show very large indirect effects that stem from many sources. One unexplored source of confounding is the possibility of evocative gene-environment correlation (rGE). Using data from the National Longitudinal Study of Adolescent to Adult Health, we examine the relationship between interviewer assessments of respondent appearance as a function of education PGS. We show a bivariate association between educational PGS and 1) perceived grooming, 2) physical attractiveness, and 3) personality. We then regress years of education on the educational PGS and show that very little of the association (~1-2%) is mediated by attractiveness or personality but 7.5% of the baseline association is confounded with how others may perceive grooming. These results highlight the importance of social-behavioral mechanisms that may link specific genotypes to successful transitions through high school and college and continue to bridge research from the social and biological sciences.

Sociodemographic patterns in biomarkers of aging in the Add Health cohort.

Biomarkers in population health research serve as indicators of incremental physiological deterioration and contribute to our understanding of mechanisms through which social disparities in health unfold over time. Yet, few population-based studies incorporate biomarkers of aging in early midlife, when disease risks may emerge and progress across the life course. We describe the distributions of several biomarkers of inflammation and neurodegeneration and their variation by sociodemographic characteristics using blood samples collected during Wave V of the National Longitudinal Study of Adolescent to Adult Health (ages 33-44 years). Higher mean levels of inflammatory and neurodegenerative biomarkers were associated with greater socioeconomic disadvantage. For example, the neurodegenerative markers, Neurofilament Light Chain and total Tau proteins were higher among lower income groups, though the relationship was not statistically significant. Similarly, proinflammatory marker Tumor Necrosis Factor-α (TNF-α) levels were higher among those with lower education. Significant differences in the mean levels of other proinflammatory markers were observed by race/ethnicity, sex, census region, BMI, and smoking status. These descriptive findings indicate that disparities in biomarkers associated with aging are already evident among young adults in their 30s and attention should focus on age-related disease risk earlier in the life course.

Socioeconomic inequalities in early adulthood disrupt the immune transcriptomic landscape via upstream regulators.

Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4543 adults in the United States. Results reveal a network-of differentially expressed genes, transcription factors, and protein neighbors of transcription factors-that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index (BMI) plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life.

The Sociodemographic and Lifestyle Correlates of Epigenetic Aging in a Nationally Representative U.S. Study of Younger Adults.

Importance: Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most of this research is based on older adult samples who already have measurable chronic disease. Objective: To investigate whether and how sociodemographic and lifestyle characteristics are related to biological aging in a younger adult sample across a wide array of epigenetic clock measures. Design: Nationally representative prospective cohort study. Setting: United States (U.S.). Participants: Data come from the National Longitudinal Study of Adolescent to Adult Health, a national cohort of adolescents in grades 7-12 in U.S. in 1994 followed for 25 years over five interview waves. Our analytic sample includes participants followed-up through Wave V in 2016-18 who provided blood samples for DNA methylation (DNAm) testing (n=4237) at Wave V. Exposure: Sociodemographic (sex, race/ethnicity, immigrant status, socioeconomic status, geographic location) and lifestyle (obesity status, exercise, tobacco, and alcohol use) characteristics. Main Outcome: Biological aging assessed from blood DNAm using 16 epigenetic clocks when the cohort was aged 33-44 in Wave V. Results: While there is considerable variation in the mean and distribution of epigenetic clock estimates and in the correlations among the clocks, we found sociodemographic and lifestyle factors are more often associated with biological aging in clocks trained to predict current or dynamic phenotypes (e.g., PhenoAge, GrimAge and DunedinPACE) as opposed to clocks trained to predict chronological age alone (e.g., Horvath). Consistent and strong associations of faster biological aging were found for those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use. Conclusions and Relevance: Our study found important social and lifestyle factors associated with biological aging in a nationally representative cohort of younger-aged adults. These findings indicate that molecular processes underlying disease risk can be identified in adults entering midlife before disease is manifest and represent useful targets for interventions to reduce social inequalities in heathy aging and longevity. Key Points: Question: Are epigenetic clocks, measures of biological aging developed mainly on older-adult samples, meaningful for younger adults and associated with sociodemographic and lifestyle characteristics in expected patterns found in prior aging research?Findings: Sociodemographic and lifestyle factors were associated with biological aging in clocks trained to predict morbidity and mortality showing accelerated aging among those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use.Meaning: Age-related molecular processes can be identified in younger-aged adults before disease manifests and represent potential interventions to reduce social inequalities in heathy aging and longevity.

A Bayesian functional approach to test models of life course epidemiology over continuous time.

BACKGROUND: Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. METHODS: We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. RESULTS: Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. CONCLUSIONS: The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

Sociodemographic and Lifestyle Factors and Epigenetic Aging in US Young Adults: NIMHD Social Epigenomics Program.

Importance: Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most research is based on samples of older adults who already have measurable chronic disease. Objective: To investigate whether and how sociodemographic and lifestyle characteristics are associated with biological aging in a younger adult sample across a wide array of epigenetic clock measures. Design, Setting, and Participants: This cohort study was conducted using data from the National Longitudinal Study of Adolescent to Adult Health, a US representative cohort of adolescents in grades 7 to 12 in 1994 followed up for 25 years to 2018 over 5 interview waves. Participants who provided blood samples at wave V (2016-2018) were analyzed, with samples tested for DNA methylation (DNAm) in 2021 to 2024. Data were analyzed from February 2023 to May 2024. Exposure: Sociodemographic (sex, race and ethnicity, immigrant status, socioeconomic status, and geographic location) and lifestyle (obesity status by body mass index [BMI] in categories of reference range or underweight [<25], overweight [25 to <30], obesity [30 to <40], and severe obesity [≥40]; exercise level; tobacco use; and alcohol use) characteristics were assessed. Main Outcome and Measure: Biological aging assessed from banked blood DNAm using 16 epigenetic clocks. Results: Data were analyzed from 4237 participants (mean [SD] age, 38.4 [2.0] years; percentage [SE], 51.3% [0.01] female and 48.7% [0.01] male; percentage [SE], 2.7% [<0.01] Asian or Pacific Islander, 16.7% [0.02] Black, 8.7% [0.01] Hispanic, and 71.0% [0.03] White). Sociodemographic and lifestyle factors were more often associated with biological aging in clocks trained to estimate morbidity and mortality (eg, PhenoAge, GrimAge, and DunedinPACE) than clocks trained to estimate chronological age (eg, Horvath). For example, the ß for an annual income less than $25 000 vs $100 000 or more was 1.99 years (95% CI, 0.45 to 3.52 years) for PhenoAgeAA, 1.70 years (95% CI, 0.68 to 2.72 years) for GrimAgeAA, 0.33 SD (95% CI, 0.17 to 0.48 SD) for DunedinPACE, and -0.17 years (95% CI, -1.08 to 0.74 years) for Horvath1AA. Lower education, lower income, higher obesity levels, no exercise, and tobacco use were associated with faster biological aging across several clocks; associations with GrimAge were particularly robust (no college vs college or higher: ß = 2.63 years; 95% CI, 1.67-3.58 years; lower vs higher annual income: <$25 000 vs ≥$100 000: ß = 1.70 years; 95% CI, 0.68-2.72 years; severe obesity vs no obesity: ß = 1.57 years; 95% CI, 0.51-2.63 years; no weekly exercise vs ≥5 bouts/week: ß = 1.33 years; 95% CI, 0.67-1.99 years; current vs no smoking: ß = 7.16 years; 95% CI, 6.25-8.07 years). Conclusions and Relevance: This study found that important social and lifestyle factors were associated with biological aging in a nationally representative cohort of younger adults. These findings suggest that molecular processes underlying disease risk may be identified in adults entering midlife before disease is manifest and inform interventions aimed at reducing social inequalities in heathy aging and longevity.

Familial Loss of a Loved One and Biological Aging: NIMHD Social Epigenomics Program.

Importance: The link between familial loss of a loved one and long-term health decline is complex and not fully understood. Objective: To test associations of losing a parent, sibling, child, or partner or spouse with accelerated biological aging. Design, Setting, and Participants: Data from the National Longitudinal Study of Adolescent to Adult Health, a US population-based longitudinal cohort study, were analyzed. Participants were enrolled from 1994 to 1995 for wave 1, while in grades 7 to 12, and followed up through wave 5 in 2018. The study analyzed participant reports of loss collected at each wave from 1 to 5 over 24 years and used a banked wave 5 blood sample for subsequent DNA methylation testing and epigenetic clock calculation from 2018 to 2024. Data were analyzed from January 2022 to July 2024. Exposure: Loss of biological parents or parental figures, partners or spouses, siblings, or children at waves 1 to 3 or during childhood, adolescence (aged <18 years), or adulthood at wave 4 to wave 5 (aged 18-43 years). Main Outcomes and Measures: Biological aging assessed from blood DNA methylation using the Horvath, PhenoAge, GrimAge, and DunedinPACE epigenetic clocks at wave 5. Results: Data from 3963 participants were analyzed, with a weighted mean (range) age of 38.36 (36.78-39.78) years at wave 5; 2370 (50.3%) were male, 720 (15.97%) were Black, 400 (8.18%) were Hispanic, and 2642 (72.53%) were White. Nearly 40% of participants experienced loss by wave 5 when they were aged 33 to 43 years, and participants who were Black (379 participants [56.67%]), Hispanic (152 participants [41.38%]), and American Indian (18 participants [56.08%]) experienced a greater proportion of losses compared with White participants (884 participants [34.09%]). Those who experienced 2 or more losses tended to have older biological ages for several of the clocks (PhenoAge ß = 0.15; 95% CI, 0.02 to 0.28; GrimAge ß = 0.27; 95% CI, 0.09 to 0.45; DunedinPACE ß = 0.22; 95% CI, 0.10 to 0.34) compared with those with no losses. In contrast, there were no associations with 2 or more losses for the Horvath clock (ß = -0.08; 95% CI, -0.23 to 0.06). Conclusions and Relevance: This study reveals associations between various measures of loss experienced from childhood to adulthood and biological aging in a diverse sample of the US population. These findings underscore the potentially enduring impact of loss on biological aging even before middle age and may contribute to understanding racial and ethnic disparities in health and mortality.