Abstract
BACKGROUND AND AIM: Little is known about the effects of chemical constituents of fine particulate matter (particles with aerodynamic diameter ≤ 2.5 μm, PM2.5). This study aims to estimate the associations between constituents of PM2.5 and incident depression. METHODS: This large cohort study recruited the local adults in Ningbo, a southeastern coastal city in China. Depression cases were ascertained from local Health Information System. Daily PM2.5 samples were collected at seven monitoring sites for the 10th-16th seven consecutive days and further measured for ten PM2.5-bound metals. Land-use regression models were conducted to predict the residential PM2.5-bound metals exposure. We conducted three cox proportional hazards models which respectively employed single constituent, constituent adjusting for PM2.5 and constituent residual calculated by PM2.5 as exposures to estimate the effect of constituents on incident depression. Environmental Risk Score (ERS) and Weighted Quintile Score (WQS) were utilized to estimate the overall effect of all metals. RESULTS: In fully adjusted constituent models, the higher concentrations of ambient Lead(Pb), Nickel(Ni), Mercury(Hg), Chromium(Cd) and Beryllium(Be) were significantly associated with higher risk of incident depression. In ERS analyses, one main effect (Tl), three squared terms (Hg, Cd and Al) and twelve pairwise interactions (Tl & Pb, Tl & Be, et al) were selected by adaptive elastic-net (AENET) for construction of ERS of incident depression-related ambient metal mixtures. The ERS index was positively and significantly associated with depression incidence. And similar results were found for WQS index which suggesting that Al, Sb, Hg, Tl, Mn and Be contributed the most to the association. CONCLUISIONS: These findings highlighted the associations of exposure to PM2.5-bound metals with incident depression. Further studies are needed to confirm the findings and examine the underlying mechanisms. KEYWORDS: PM2.5-bound Metal, depression, Cox Proportional Hazardous Regression, ERS, WQS.
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