The association between maternal exposure to ten neonicotinoid insecticides and preterm birth in Guangxi, China.

Journal: Environmental pollution (Barking, Essex : 1987)
Published Date:

Abstract

Preterm birth (PTB) is a primary cause of mortality among newborns globally. Prenatal exposure to environmental pollutants has been suggested to increase the PTB risk. Studies have shown NEOs may be linked to adverse birth outcomes. However, the impact of maternal NEOs exposure on PTB remains unclear. Therefore, to examine the association between NEOs exposure and PTB risk, we performed a case-control analysis utilizing data from a birth cohort study in Guangxi, China. A total of 157 preterm infants and 471 full-term infants were included. Concentrations of 10 NEOs and their metabolites in maternal serum were quantified using liquid chromatography-tandem mass spectrometry. We employed logistic regression, quantile g-computation, and restricted cubic spline models to evaluate the effects of individual and mixed NEOs exposures. Subsequently, XGBoost machine learning, combined with SHAP, was employed to predict the implications of serum NEOs on PTB. Finally, for 1-standard deviation increment in ln-transformed concentrations of imidacloprid and dinotefuran, significant correlations with higher odds of PTB were observed, showing odds ratios of 1.17 (95 % CI: 1.02, 1.36) and 1.41 (95 % CI: 1.16, 1.72). Similar patterns and higher risks were observed in late preterm birth. In both mixed exposure and machine learning models, dinotefuran and imidacloprid were identified as major predictors of increased PTB risk. Exposure to n-desmethylacetamiprid, sulfoxaflor, thiacloprid, nitenpyram, and thiamethoxam was negatively associated with PTB. Our findings suggested dinotefuran and imidacloprid exposure during pregnancy were risk factors of PTB, particularly among late preterm births. Subsequent research is necessary to illuminate the underlying mechanisms involved.

Authors

  • Dongxiang Pan
    Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Lihong Zhou
    School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Changhui Mu
    Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Mengrui Lin
    Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Xiaogang Wang
    Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Qian Liao
    Department of Oral and Cranio-maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai, China.
  • Lidi Lei
    Department Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China; The Guangxi Zhuang Autonomous Region Brain Hospital, China.
  • Shun Liu
    Department of Child and Adolescent Health & Maternal and Child Health, School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Dongping Huang
    Department of Hematology, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
  • Xiaoqiang Qiu
    Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China; China(Guangxi)-ASEAN Engineering Research Center of Big Data for Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China. Electronic address: xqqiu9999@163.com.
  • Xiaoyun Zeng
    Guilin Medical University, Guilin, 541001, Guangxi, China.