Sobering work from Shanahan et al.:
Significance
The analysis of gene expression in peripheral whole blood of US young adults in their late 30s revealed socioeconomic status-based inequalities in the molecular underpinnings of the most common chronic conditions of aging. Associations involved immune, inflammatory, ribosomal, and metabolic pathways, and extra- and intra-cellular signaling. Body mass index was a plausible, sizable mediator of many associations. Results point to new ways of thinking about how social inequalities “get under the skin” and also call for renewed efforts to prevent chronic conditions of aging decades before diagnoses.Abstract
Many common chronic diseases of aging are negatively associated with socioeconomic status (SES). This study examines whether inequalities can already be observed in the molecular underpinnings of such diseases in the 30s, before many of them become prevalent. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a large, nationally representative sample of US subjects who were followed for over two decades beginning in adolescence. We now have transcriptomic data (mRNA-seq) from a random subset of 4,543 of these young adults. SES in the household-of-origin and in young adulthood were examined as covariates of a priori-defined mRNA-based disease signatures and of specific gene transcripts identified de novo. An SES composite from young adulthood predicted many disease signatures, as did income and subjective status. Analyses highlighted SES-based inequalities in immune, inflammatory, ribosomal, and metabolic pathways, several of which play central roles in senescence. Many genes are also involved in transcription, translation, and diverse signaling mechanisms. Average causal-mediated effect models suggest that body mass index plays a key role in accounting for these relationships. Overall, the results reveal inequalities in molecular risk factors for chronic diseases often decades before diagnoses and suggest future directions for social signal transduction models that trace how social circumstances regulate the human genome.
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