Racial/ethnic variations in inflammatory markers: exploring the role of sleep duration and sleep efficiency

Heather R Farmer1, Danica C Slavish2, John Ruiz3

  • 1Department of Human Development and Family Sciences, University of Delaware, Newark, DE, 19716, USA. hfarmer@udel.edu.

Individuals from minoritized racial/ethnic groups have higher levels of circulating inflammatory markers. However, the mechanisms underlying these differences remain understudied. The objective of this study was to examine racial/ethnic variations in multiple markers of inflammation and whether impaired sleep contributes to these racial/ethnic differences. Nurses from two regional hospitals in Texas (n = 377; 71.62% White; 6.90% Black; 11.14% Hispanic, 10.34% Asian; mean age = 39.46; 91.78% female) completed seven days of sleep diaries and actigraphy to assess mean and variability in total sleep time (TST) and sleep efficiency (SE). On day 7, blood was drawn to assess 4 inflammatory markers: C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-1 beta (IL-1β), and tumor necrosis factor-alpha (TNF-α). Results from regression models showed differences in inflammatory markers by race/ethnicity, adjusting for age and gender. The associations between sleep parameters and inflammatory markers also varied by race/ethnicity. Among White nurses, lower mean and greater variability in actigraphy-determined TST and greater variability in diary-determined TST were associated with higher levels of IL-6. Among Black nurses, lower mean diary-determined SE was associated with higher levels of IL-6 and IL-1β. Among Hispanic nurses, greater diary-determined mean TST was associated with higher CRP. Among Asian nurses, greater intraindividual variability in actigraphy-determined SE was associated with lower CRP. Among nurses, we did not find racial/ethnic disparities in levels of inflammation. However, analyses revealed differential relationships between sleep and inflammatory markers by race/ethnicity. Results highlight the importance of using a within-group approach to understand predictors of inflammatory markers.

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