尼泊金甲酯
尼泊金丙酯
对羟基苯甲酸酯
环境卫生
医学
防腐剂
化学
食品科学
作者
Chia-Jung Tung,Mei‐Huei Chen,Ching‐Chun Lin,Pau‐Chung Chen
标识
DOI:10.1016/j.envint.2024.108671
摘要
Parabens are a group of substances commonly employed as antimicrobial preservatives. The effect of parabens on the development of neurotoxicity in children is still controversial. This study aimed to explore the associations between parabens exposure and children's neurodevelopmental performance, emphasizing potential sex differences and the combined effects of parabens. We used the long-term follow-up study of Taiwanese generation, Taiwan Birth Panel Study II (TBPS II). We recruited the group of children at 6–8 years old. And, we measured parabens in children urine, including methylparaben (MP), ethylparaben (EP), propylparaben (PP) and butylparaben (BP). Children's attention-related performance was evaluated using the Conners Kiddie Continuous Performance Test 2nd Edition (K-CPT 2). The study employed both linear regression and mixture analysis quantile g-computation (QGC) methods to discern associations. A stratified analysis by sex and QGC was implemented to delve deeper into the cumulative effects of parabens. A total of 446 subjects completed both the parabens analysis and the K-CPT 2 survey. The overall association between parabens and neurodevelopmental performance was not pronounced, but discernible sex differences emerged. In the single pollutant analysis, elevated PP concentrations were associated with higher K-CPT 2 scores particularly in detectability (d') (β = 0.92 [95 % CI = 0.15 to 1.69]) and commissions (β = 0.95 [95 % CI = 0.12 to 1.78]), among girls. Further, in the mixture analysis, a significant association between PP and detectability (d') was observed in girls (β = 1.68 [95 % CI = 0.11 to 3.26]). This study identified sex-specific associations between parabens and attention performance. Consistent outcomes across single and mixture analysis methods. Further research is crucial to clarify these causal associations.
科研通智能强力驱动
Strongly Powered by AbleSci AI