综合金属和全氟辛烷磺酸暴露与膳食模式的关系:初步研究

Augustina Odediran, Emmanuel Obeng-Gyasi
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引用次数: 0

摘要

背景:全球慢性病负担日益加重,有证据表明,饮食和暴露于环境污染物(如全氟和多氟烷基物质(PFAS)和重金属)可能会导致慢性病的发生。膳食炎症指数(DII)可评估个人膳食的炎症潜能。然而,PFAS、重金属和 DII 之间复杂的相互作用在很大程度上仍未得到研究。研究目的本横断面研究的目的是利用 2017-2018 年美国国家健康与营养调查(NHANES)的数据,调查以 DII 表示的膳食与个人及组合铅、镉、汞、全氟辛酸(PFOA)和全氟辛烷磺酸(PFOS)暴露之间的关联。研究方法首先使用描述性统计、相关分析和线性回归来评估相关变量之间的关系。随后,我们采用贝叶斯核机器回归(BKMR)分析数据,以评估全氟辛烷磺酸和金属与 DII 之间的非线性、非加成、暴露-反应关系和相互作用。结果:多变量线性回归显示,DII 与镉和汞之间存在显著关联。我们的 BKMR 分析表明,PFAS、金属暴露和 DII 之间存在复杂的关系。在我们的单变量暴露-反应函数图中,镉和汞分别呈现出正负线性关系,这表明在整个暴露范围内,镉和汞与 DII 之间存在正负关系。此外,混合物中两种接触物之间的二元接触-反应函数显示,在铅、汞、全氟辛烷磺酸和全氟辛烷磺酸的不同量值下,镉与 DII 呈稳健的正相关关系,表明镉含量的增加与 DII 有关。汞的双变量图显示,在所有污染物的所有量纲中,汞都与 DII 呈负相关。此外,后纳入概率(PIP)结果表明,镉和汞与个人饮食中的炎症潜能值(在我们的研究中被操作为 DII)具有一致的重要性,两者的 PIP 值均为 1.000。其次是全氟辛烷磺酸(PIP 值为 0.8524)、全氟辛酸(PFOA)(0.5924)和铅(PIP 值为 0.5596),铅在五种环境污染物中的影响最小。结论我们的研究表明,暴露于环境金属和全氟辛烷磺酸,尤其是汞和镉,与 DII 有关。这些研究结果还证明了全氟辛烷磺酸、重金属和 DII 之间错综复杂的关系。这些发现强调了考虑多种污染物暴露的累积效应的重要性。未来的研究应侧重于阐明这些关联背后的机理途径和剂量-反应关系,并对因果关系进行研究,从而更深入地了解与环境污染物相关的膳食风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Association between Combined Metals and PFAS Exposure with Dietary Patterns: A Preliminary Study
Background: The global burden of chronic diseases has been increasing, with evidence suggesting that diet and exposure to environmental pollutants, such as per- and polyfluoroalkyl substances (PFAS) and heavy metals, may contribute to their development. The Dietary Inflammatory Index (DII) assesses the inflammatory potential of an individual’s diet. However, the complex interplay between PFAS, heavy metals, and DII remains largely unexplored. Objective: The goal of this cross-sectional study was to investigate the associations between diet operationalized as the DII with individual and combined lead, cadmium, mercury, perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS) exposures using data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018. Methods: Descriptive statistics, a correlational analysis, and linear regression were initially used to assess the relationship between the variables of interest. We subsequently employed Bayesian kernel Machine regression (BKMR) to analyze the data to assess the non-linear, non-additive, exposure–response relationships and interactions between PFAS and metals with the DII. Results: The multi-variable linear regression revealed significant associations between the DII and cadmium and mercury. Our BKMR analysis revealed a complex relationship between PFAS, metal exposures, and the DII. In our univariate exposure–response function plot, cadmium and mercury exhibited a positive and negative linear relationship, respectively, which indicated a positive and negative relationship across the spectrum of exposures with the DII. In addition, the bivariate exposure–response function between two exposures in a mixture revealed that cadmium had a robust positive relationship with the DII for different quantiles of lead, mercury, PFOA, and PFOS, indicating that increasing levels of cadmium are associated with the DII. Mercury’s bivariate plot demonstrated a negative relationship across all quantiles for all pollutants. Furthermore, the posterior inclusion probability (PIP) results highlighted the consistent importance of cadmium and mercury with the inflammatory potential of an individual’s diet, operationalized as the DII in our study, with both showing a PIP of 1.000. This was followed by PFOS with a PIP of 0.8524, PFOA at 0.5924, and lead, which had the lowest impact among the five environmental pollutants, with a PIP of 0.5596. Conclusion: Our study suggests that exposures to environmental metals and PFAS, particularly mercury and cadmium, are associated with DII. These findings also provide evidence of the intricate relationships between PFAS, heavy metals, and the DII. The findings underscore the importance of considering the cumulative effects of multi-pollutant exposures. Future research should focus on elucidating the mechanistic pathways and dose–response relationships underlying these associations in a study that examines causality, which will enable a deeper understanding of the dietary risks associated with environmental pollutants.
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