Abstract

BACKGROUND AND AIM: Telomere length (TL) at birth determines TL in a later life and has been linked to midlife risk of cardiovascular disease, but its own determinants remain unclear. We aim to examine the association and to identify the sensitive window of pre-conception and prenatal exposures to fine particulate matter (PM2.5) with cord blood TL in a heavily air-polluted area in China. METHODS: In 2017, 107 pregnant women admitted for delivery at Anzhen Hospital in Beijing, China, were interviewed for demographics, addresses, and lifestyle factors. Weekly levels of ambient PM2.5 exposure from the three months before pregnancy to the end of pregnancy were estimated from a validated spatiotemporal model. PM2.5 exposure levels based on home and work addresses were combined by work routines. Cord blood leukocyte TL was measured using quantitative polymerase chain reaction. We used distributed lag models (DLMs) to analyze the association and to identify the sensitive window of ambient PM2.5 exposure with cord blood TL with adjusting for potential confounders. DLM analyses were also conducted in stratified subsamples by newborn sex. RESULTS:PM2.5 levels (median: 85 μg/m3) in the three months before LMP was positively (r=0.16) correlated with cord blood TL, while PM2.5 levels (median 78 μg/m3) in the whole pregnancy was inversely (r=-0.15) correlated with cord blood TL, although none of the correlation was significant (P0.05). From DLM, we found PM2.5 exposure was significantly associated with cord blood TL in three sensitive windows: 6-12 weeks before pregnancy (positive association), 6-18 weeks during pregnancy (inverse association), and 39-40 weeks during pregnancy (inverse association). These associations were more evident in female than male newborns. CONCLUSIONS:Preconception and prenatal exposure to ambient PM2.5 exposure may shorten cord blood TL, especially for female fetuses. Future studies with larger sample size are needed to replicate our findings. KEYWORDS: Particulate matter, Birth outcomes, Environmental epidemiology, Modeling

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