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Associations of Fine Particulate Matter Constituents with Metabolic Syndrome and the Mediating Role of Apolipoprotein B: A Multicenter Study in Middle-Aged and Elderly Chinese Adults.

Weizhuo YiFeng ZhaoRubing PanYi ZhangZhiwei XuJian SongQinghua SunPeng DuJianlong FangJian ChengYingchun LiuChen ChenYifu LuTiantian LiHong SuXiao-Ming Shi
Published in: Environmental science & technology (2022)
Fine particulate matter (PM 2.5 ) was reported to be associated with metabolic syndrome (MetS), but how PM 2.5 constituents affect MetS and the underlying mediators remains unclear. We aimed to investigate the associations of long-term exposure to 24 kinds of PM 2.5 constituents with MetS (defined by five indicators) in middle-aged and elderly adults and to further explore the potential mediating role of apolipoprotein B (ApoB). A multicenter study was conducted by recruiting subjects ( n = 2045) in the Beijing-Tianjin-Hebei region from the cohort of Sub-Clinical Outcomes of Polluted Air in China (SCOPA-China Cohort). Relationships among PM 2.5 constituents, serum ApoB levels, and MetS were estimated by multiple logistic/linear regression models. Mediation analysis quantified the role of ApoB in "PM 2.5 constituents-MetS" associations. Results indicated PM 2.5 was significantly related to elevated MetS prevalence. The MetS odds increased after exposure to sulfate (SO 4 2- ), calcium ion (Ca 2+ ), magnesium ion (Mg 2+ ), Si, Zn, Ca, Mn, Ba, Cu, As, Cr, Ni, or Se (odds ratios ranged from 1.103 to 3.025 per interquartile range increase in each constituent). PM 2.5 and some constituents (SO 4 2- , Ca 2+ , Mg 2+ , Ca, and As) were positively related to serum ApoB levels. ApoB mediated 22.10% of the association between PM 2.5 and MetS. Besides, ApoB mediated 24.59%, 50.17%, 12.70%, and 9.63% of the associations of SO 4 2- , Ca 2+ , Ca, and As with MetS, respectively. Our findings suggest that ApoB partially mediates relationships between PM 2.5 constituents and MetS risk in China.
Keyphrases
  • particulate matter
  • air pollution
  • metabolic syndrome
  • heavy metals
  • type diabetes
  • insulin resistance
  • risk factors
  • water soluble
  • neural network
  • aqueous solution