Socioeconomic Inequalities and Molecular Risk for Aging in Young Adulthood.

Diverse manifestations of biological aging often reflect disparities in socioeconomic status (SES). In this paper, we examine associations between indicators of SES and an mRNA-based aging signature during young adulthood, before clinical indications of aging are common. We use data from wave V (2016-2018) of the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study of adults aged 33-43 years, with transcriptomic data from a subset of 2,491 participants. Biological aging is measured using 1) a composite transcriptomic aging signature previously identified by Peters et al.'s out-of-sample meta-analysis (Nat Commun. 2015;6:8570) and 2) 9 subsets that represent functional pathways of coexpressed genes. SES refers to income, education, occupation, subjective social status, and a composite measure combining these 4 dimensions. We examine hypothesized mechanisms through which SES could affect aging: body mass index, smoking, health insurance status, difficulty paying bills, and psychosocial stress. We find that SES-especially the composite measure and income-is associated with transcriptomic aging and immune, mitochondrial, ribosomal, lysosomal, and proteomal pathways. Counterfactual mediational models suggest that the mediators partially account for these associations. The results thus reveal that numerous biological pathways associated with aging are already linked to SES in young adulthood.

An integrative approach for the analysis of risk and health across the life course: challenges, innovations, and opportunities for life course research.

Life course epidemiology seeks to understand the intricate relationships between risk factors and health outcomes across different stages of life to inform prevention and intervention strategies to optimize health throughout the lifespan. However, extant evidence has predominantly been based on separate analyses of data from individual birth cohorts or panel studies, which may not be sufficient to unravel the complex interplay of risk and health across different contexts. We highlight the importance of a multi-study perspective that enables researchers to: (a) Compare and contrast findings from different contexts and populations, which can help identify generalizable patterns and context-specific factors; (b) Examine the robustness of associations and the potential for effect modification by factors such as age, sex, and socioeconomic status; and (c) Improve statistical power and precision by pooling data from multiple studies, thereby allowing for the investigation of rare exposures and outcomes. This integrative framework combines the advantages of multi-study data with a life course perspective to guide research in understanding life course risk and resilience on adult health outcomes by: (a) Encouraging the use of harmonized measures across studies to facilitate comparisons and synthesis of findings; (b) Promoting the adoption of advanced analytical techniques that can accommodate the complexities of multi-study, longitudinal data; and (c) Fostering collaboration between researchers, data repositories, and funding agencies to support the integration of longitudinal data from diverse sources. An integrative approach can help inform the development of individualized risk scores and personalized interventions to promote health and well-being at various life stages.