Sex-Specific Associations between Particulate Matter Exposure and Gene Expression in Independent Discovery and Validation Cohorts of Middle-Aged Men and Women

Background: Particulate matter (PM) exposure leads to premature death, mainly due to respiratory and cardiovascular diseases.

Objectives: Identification of transcriptomic biomarkers of air pollution exposure and effect in a healthy adult population.

Methods: Microarray analyses were performed in 98 healthy volunteers (48 men, 50 women). The expression of 8 sex-specific candidate biomarker genes (significantly associated with PM10 in the discovery cohort and with a reported link to air pollution-related disease) was measured with qPCR in an independent validation cohort (75 men, 94 women). Pathway analysis was performed using Gene Set Enrichment Analysis. Average daily PM2.5 and PM10 exposures over 2-years were estimated for each participant’s residential address using spatiotemporal interpolation in combination with a dispersion model

Results: Average long-term PM10 was 25.9 (± 5.4) and 23.7 (±2.3) µg/m3 in the discovery and validation cohorts, respectively. In discovery analysis, associations between PM10 and the expression of individual genes differed by sex. In the validation cohort, long-term PM10 was associated with the expression of DNAJB5 and EAPP in men and ARHGAP4 (p=0.053) in women. AKAP6 and LIMK1 were significantly associated with PM10 in women, although associations differed in direction between the discovery and validation cohorts. Expression of the 8 candidate genes in the discovery cohort differentiated between validation cohort participants with high vs low PM10 exposure (area under the receiver operating curve = 0.92; 95% CI: 0.85, 1.00; p=0.0002) in men, 0.86; 95% CI: 0.76, 0.96; p=0.004 in women).

Conclusions: Expression of the sex-specific candidate genes identified in the discovery population predicted PM10 exposure in an independent cohort of adults from the same area. Confirmation in other populations may further support this as a new approach for exposure assessment, and may contribute to the discovery of molecular mechanisms for PM-induced health effects.

Publication year:2017

Keywords:Air Pollution/statistics & numerical data, Cardiovascular Diseases/epidemiology, Environmental Exposure/statistics & numerical data, Female, Gene Expression, Humans, Male, Middle Aged, Models, Theoretical, Particulate Matter, Respiratory Tract Diseases/epidemiology, Sex Factors

CSS-citation score:1

Authors:Regional

Accessibility:Open