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Objective: 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.
Methods: 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.
Results: 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]).
Conclusions: 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.
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http://dx.doi.org/10.1016/j.envint.2024.108671 | DOI Listing |
Biomed Environ Sci
August 2025
Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan 523808, Guangdong, China;Maternal and Child Research Institute, Shunde Women and Children's Hospital, Guangdong Medical University, Foshan 528300, Guangdong, China.
Objective: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects.
Methods: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures.
Biomed Environ Sci
August 2025
Clinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
Objective: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study.
Methods: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants.
J Eval Clin Pract
September 2025
Health Technology Assessment Unit, Acute and Hospital-Based Care Portfolio, Ontario Health, Toronto, Ontario, Canada.
Rationale: Systematic reviews are essential for evidence-based healthcare decision-making. While it is relatively straightforward to quantitatively assess random errors in systematic reviews, as these are typically reported in primary studies, the assessment of biases often remains narrative. Primary studies seldom provide quantitative estimates of biases and their uncertainties, resulting in systematic reviews rarely including such measurements.
View Article and Find Full Text PDFJ Appl Stat
February 2025
Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA.
When analyzing real data sets, statisticians often face the question that the data are heterogeneous and it may not necessarily be possible to model this heterogeneity directly. One natural option in this case is to use the methods based on finite mixtures. The key question in these techniques often is what is the best number of mixtures or, depending on the focus of the analysis, the best number of sub-populations when the model is otherwise fixed.
View Article and Find Full Text PDFEnviron Epidemiol
October 2025
Department of Psychiatry and Behavioral Health, The Ohio State University, Ohio.
Background: Prospective studies suggest that prenatal exposure to chemical neurotoxicants and maternal stress increase risk for psychiatric problems. However, most studies have focused on childhood outcomes, leaving adolescence-a critical period for the emergence or worsening of psychiatric symptoms-relatively understudied. The complexity of prenatal coexposures and adolescent psychiatric comorbidities, particularly among structurally marginalized populations with high exposure burdens, remains poorly understood.
View Article and Find Full Text PDF