Long-term exposure to PM and liver cancer mortality: Insights into the role of smaller particulate fractions.

Journal: Ecotoxicology and environmental safety
Published Date:

Abstract

Particulate matter (PM) is a recognized carcinogen, but the effects of PM on liver cancer remain underexplored. This study investigates the long-term association between PM and liver cancer mortality, as well as the contribution of smaller particles relative to larger PM effects. Data on 244,558 liver cancer deaths in Shandong were collected. The 10-year weighted moving average of PM was calculated to assess the long-term effects. Bayesian spatiotemporal models were applied to quantify the long-term associations between PM fractions (PM, PM, PM, PM, PM, and PM), ratios (PM/PM, PM/PM, PM/PM, and PM/PM) and liver cancer mortality while accounting for potential confounders and spatiotemporal effects. Nonlinear effects of PM were further explored using SHapley Additive exPlanations (SHAP) with eXtreme Gradient Boosting (XGBoost). Classification and Regression Tree (CART) models were applied to evaluate the importance of factors in regions with various PM concentrations. The findings showed that PM was credibly associated with an increased risk of liver cancer mortality (Relative risk (RR)= 1.813, 95 % Credible Interval (CrI): 1.647-1.997). Among larger PM, the proportion of smaller PM was associated with liver cancer mortality. (PM/PM: RR=1.135, 95 % CrI: 1.106-1.166; PM/PM: RR= 1.097, 95 % CrI: 1.079-1.115). Nonlinear relationships were identified, with an increasing risk at low PM concentrations. In summary, long-term exposure to PM is associated with liver cancer mortality, highlighting the role of smaller particles in PM-related risks. The integration of multiple models provides a robust approach to analyzing environmental impacts on cancer, aiding tool development in environmental oncology and supporting targeted health interventions.

Authors

  • Ting Gan
    Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia.
  • Jie Chu
    Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China.
  • Hilary Bambrick
    Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia; National Centre for Epidemiology and Population Health, Australian National University, ACT, Australia.
  • Xiaolei Guo
    Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China. Electronic address: guoxiaolei@126.com.
  • Wenbiao Hu
    School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia. w2.hu@qut.edu.au.