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Associations between Individual Exposure to Fine Particulate Matter Elemental Constituent Mixtures and Blood Lipid Profiles: A Panel Study in Chinese People Aged 60-69 Years.

Jiaonan WangTiantian LiJianlong FangSong TangYi ZhangFuchang DengChong ShenWanying ShiYuanyuan LiuChen ChenQinghua SunYanwen WangYanjun DuHaoran DongXiaoming Shi
Published in: Environmental science & technology (2022)
Dyslipidemia may be a potential mechanism linking fine particulate matter (PM 2.5 ) to adverse cardiovascular outcomes. However, inconsistent associations between PM 2.5 and blood lipids have resulted from the existing research, and the joint effect of PM 2.5 elemental constituents on blood lipid profiles remains unclear. We aimed to explore the overall associations between PM 2.5 elemental constituents and blood lipid profiles and to identify the significant PM 2.5 elemental constituents in this association. Sixty-nine elderly people were recruited between September 2018 and January 2019. Each participant completed a survey questionnaire, 3 days of individual exposure monitoring, health examination, and biological sample collection at each follow-up visit. Bayesian kernel machine regression (BKMR) models were used to identify the joint effects of the 17 elemental constituents on blood lipid profiles. Total cholesterol, low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) levels were significantly increased in older adults when exposed to the mixture of PM 2.5 elemental constituents. Copper and titanium had higher posterior inclusion probabilities than other constituents, ranging from 0.76 to 0.90 (Cu) and 0.74 to 0.94 (Ti). Copper and titanium in the PM 2.5 elemental constituent mixture played an essential role in changes to blood lipid levels. This study highlights the importance of identifying critical hazardous PM 2.5 constituents that may cause adverse cardiovascular outcomes in the future.
Keyphrases
  • particulate matter
  • air pollution
  • fatty acid
  • essential oil
  • healthcare
  • physical activity
  • mental health
  • deep learning
  • polycyclic aromatic hydrocarbons
  • adverse drug
  • ionic liquid
  • heavy metals
  • social media
  • drug induced