全国健康与营养检查调查
医学
萧条(经济学)
逻辑回归
横断面研究
内科学
广义加性模型
病人健康调查表
环境卫生
人口
抑郁症状
精神科
焦虑
病理
数学
统计
宏观经济学
经济
作者
W Yi,Lihui Xuan,Hesham M.H. Zakaly,Vladimir Marković,Justyna Miszczyk,Hua Guan,Ping‐Kun Zhou,Ruixue Huang
标识
DOI:10.1016/j.envres.2023.117188
摘要
Per- and polyfluoroalkyl substances (PFAS) are widespread persistent organic pollutants (POPs) associated with diseases including osteoporosis, altered immune function and cancer. However, few studies have investigated the association between PFAS mixture exposure and Depression in general populations. Nationally representative data from the National Health and Nutrition Examination Survey (NHANES) (2005–2018) were used to analyze the association between PFAS and Depression in U.S. adults. Total 12,239 adults aged 20 years or older who had serum PFAS measured and answered Patient Health Questionnaire-9 (PHQ-9) were enrolled in this study. PFAS monomers detected in all 7 investigation cycles were included in the study. Generalized additive model (GAM) was used to fit smooth curves and threshold effect analysis was carried out to find the turning point of smooth curves. Generalized linear model (GLM) was used to describe the non-linear relationship between PFAS and depression and unconditioned logistic regression was used to risk analysis. The median of total serum PFAS concentration was 14.54 ng/mL. The curve fitting results indicated a U-shaped relationship between total serum PFAS and depression: PFAS< 39.66 ng/mL, A negative correlation between PHQ-9 score and serum PFAS concentration was observed (β 0.047,95%CI -0.059, −0.036). The depression PHQ-9 score decreased with the increase of serum PFAS concentration. PFAS ≥ 39.66 ng/mL, A positive correlation was observed between PFAS and PHQ-9 score (β 0.010,95% CI 0.003, 0.017). The depression PHQ-9 score increased with the increase of serum PFAS concentration. Our study provides new clues to the association of PFAS with depression, and large population-based cohort studies that can validate the causal association as well as toxicological mechanism studies are needed for validation.
科研通智能强力驱动
Strongly Powered by AbleSci AI