Telomere Length and Neighborhood Circumstances
Evaluating Biological Response to Unfavorable Exposures
Published in: Cancer Epidemiology Biomarkers & Prevention, Volume 26, Issue 4 (April 2017), pages 553-560. doi: 10.1158/1055-9965.EPI-16-0554
Posted on RAND.org on May 31, 2017
Read MoreAccess further information on this document at Cancer Epidemiology Biomarkers & Prevention
This article was published outside of RAND. The full text of the article can be found at the link above.
Multilevel frameworks suggest neighborhood circumstances influence biology; however, this relationship is not well studied. Telomere length (TL) shortening has been associated with individual-level and neighborhood-level exposures and disease and may provide insights into underlying biologic mechanisms linking neighborhood with biology. To support neighborhood-biology investigations, we sought to determine the independent effect of neighborhood exposures on TL using standard multilevel linear regression models and quantile regression, a nonlinear, social science method applicable for testing the biologic hypothesis that extremes of the TL distribution are related to poor outcomes.
In a multicenter, cross-sectional study, blood TL was measured in 1,488 individuals from 127 census tracts in three U.S. regions using terminal restriction fragment assays. Multilevel linear and quantile regression models were adjusted for individual-level race, education, perceived stress, and depression. Neighborhood exposures included population density, urban/residential crowding, residential stability/mobility, and socioeconomic status.
TL was not associated with any neighborhood variable using linear models, but quantile regression revealed inverse associations between population density and urban crowding at the lower tails of the TL distribution [5th (population density P = 0.03; urban crowding P = 0.002), 50th (both P < 0.001), 75th percentiles (both P < 0.001)]. TL was related to residential stability at the upper tail (95th percentile P = 0.006).
Findings support the use of nonlinear statistical methods in TL research and suggest that neighborhood exposures can result in biological effects.
TL may serve as an underlying example of a biologic mechanism that can link neighborhood with biology, thus supporting multilevel investigations in future studies.