Transcriptomics reveals the mechanisms of population susceptibility to blood glucose associated with short-term exposure to ambient fine and ultrafine particles

Abstract

BACKGROUND AND AIM: Chronic obstructive pulmonary disease (COPD) and air pollution are both the leading causes of death in China. However, their interactive effects and underlying mechanisms remain largely unknown. This study aims to examine whether air pollutant-associated blood glucose differs between COPD patients and healthy participants, and provide the underlying biological mechanisms based on transcriptome. METHODS: Based on a panel study named COPDB (COPD in Beijing), blood glucose and whole blood transcriptome were repeatedly measured in 53 COPD patients and 82 healthy participants at up to four clinical visits. Ambient concentrations of fine particulate matter (PM2.5), ultrafine particles (UFPs), temperature, and relative humidity were continuously monitored at a monitoring station. Linear mixed-effects models were used to compare the associations between ln-transformed blood glucose and average 1–14 d concentrations of air pollutants before the clinical visits. Mediating effect models were used to identify transcripts with significant associations between air pollutant and blood glucose. MetaCore was used to conduct the pathway enrichment analyses. RESULTS:Blood glucose was positively associated with interquartile range (IQR) increases in average concentrations of UFPs in all participants. UFPs-associated blood glucose elevations were consistently higher in COPD patients than in healthy participants. Some transcripts were identified to mediate the associations between exposure to UFPs and blood glucose levels. Pathway analyses showed that these transcripts were enriched in negative regulation of hypoxia inducible factor-1 (HIF1A) function and immune response related pathways. CONCLUSIONS:COPD patients are more susceptible to elevated blood glucose associated with UFPs exposure than healthy participants. KEYWORDS: Air pollution, Short-term exposure, Omics technologies, Particulate matter, Molecular epidemiology, Causal inference