Social regulation of inflammation related gene expression in the multi-ethnic study of atherosclerosis
Brown, Kristen M., Jennifer A. Smith, Belinda L. Needham, Sharon L. R. Kardia, Erin Bakshis Ware, Ana V. Diez-Roux, Bhramar Mukherjee, Yongmei Liu, et al. 2020. "Social regulation of inflammation related gene expression in the multi-ethnic study of atherosclerosis." Psychoneuroendocrinology, 117: 104654.
Background Exposure to adverse social factors has been associated with an altered inflammatory profile, a risk factor for several acute and chronic diseases. Differential gene expression may be a biological mediator in the relationship. In this study, associations between a range of social factors and expression of inflammation-related genes were investigated. Methods Social factor and gene expression data were collected from 1,264 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA). Inflammation-related genes were identified from the Gene Ontology database. The associations between social factors and gene expression were first assessed using the Global Analysis of Covariance (Global ANCOVA) gene set enrichment test. When the global test was significant, linear regression and elastic net penalized regression were employed to identify the individual gene transcripts within each gene set associated with the social factor. Results Loneliness (p = 0.003), chronic burden (p = 0.002), and major or lifetime discrimination (p = 0.045) were significantly associated with global expression of the chronic inflammatory gene set. Of the 20 transcripts that comprise this gene set, elastic net selected 12 transcripts for loneliness, 8 for chronic burden, and 3 for major or lifetime discrimination. Major or lifetime discrimination was also associated with the inflammatory response (p = 0.029), regulation of the inflammatory response (p = 0.041), and immune response (p = 0.025) gene sets in global analyses, and 53, 136, and 26 transcripts were selected via elastic net for these gene sets respectively. There were no significant associations in linear regression analyses after adjustment for multiple testing. Conclusions This study highlights gene expression as a biological mechanism through which social factors may affect inflammation.