The relationship between socioeconomic status and poverty and mRNA expression in immune cells

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Project Summary/Abstract: The causes of health disparities in inflammatory diseases, including cardiovascular diseases (CVDs) such as hypertension, atherosclerosis, and stroke, are multifactorial and include, but are not limited to, socioeconomic status (SES) and poverty, lifestyle factors, psychosocial stress, genetics, and poor access to health care. However, how environmental factors influence the predisposition to- and pathology of- these conditions remain predominantly unexplored, particularly in minority communities at-risk for these conditions. Our long-term goal is to understand how poverty status influences gene expression in cells immune system and elucidate how this contributes to the biological mechanisms that influence the prevalence of related health disparities. Previous work in our laboratory and elsewhere has identified differential gene expression patterns in immune cells isolated from minorities populations, including African Americans, that are influenced by social factors, including poverty. Importantly, there have been no prior studies that examine the intersection of SES, gender, and race as factors that influence differential gene expression in the immune system. Our objective is to elucidate the effect of SES on gene expression in peripheral blood mononuclear cells and identify the longitudinal impact on inflammatory gene expression due to living in poverty. Our hypothesis is that living in low SES and poverty is associated with differential gene expression profiles within inflammatory-related pathways. This proposal will significantly contribute to our understanding of how the environmental stress of poverty influences the expression of genes in tissues related to the cardiovascular system. Additionally, this proposal will examine gene expression in both males and females of Caucasian and African ancestry in order to further understand the biology of health disparities. The proposed research is innovative because this approach will examine gene expression in a longitudinal cohort and combine both bioinformatic and predictive analysis with rigorous validation studies.