Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychology, Fordham University, Bronx, NY, USA.
J Expo Sci Environ Epidemiol. 2024 Jul;34(4):581-590. doi: 10.1038/s41370-023-00535-z. Epub 2023 Mar 25.
Molar sums are often used to quantify total phthalate exposure, but they do not capture patterns of exposure to multiple phthalates.
To introduce an exposure burden score method for quantifying exposure to phthalate metabolites and examine the association between phthalate burden scores and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR).
We applied item response theory (IRT) to data from 3474 adults aged 20-60 years in the 2013-2018 National Health and Examination Survey (NHANES) to quantify latent phthalate exposure burden from 12 phthalate metabolites. We compared model fits of three IRT models that used different a priori groupings (general phthalate burden; low molecular weight (LMW) and high molecular weight (HMW) burdens; and LMW, HMW and DEHP burden), and used the best fitting model to estimate phthalate exposure burden scores. Regression models assessed the covariate-adjusted association between phthalate burden scores and HOMA-IR. We compared findings to those using molar sums. In secondary analyses, we examined how the IRT model could be used for data harmonization when a subset of participants are missing some phthalate metabolites, and accounted for measurement error of the phthalate burden scores in estimating associations with HOMA-IR through a resampling approach using plausible value imputation.
A three correlated factors model (LMW, HMW and DEHP burdens) provided the best fit. One interquartile range (IQR) increase in DEHP burden score was associated with 0.094 (95% CI: 0.022, 0.164, p = 0.010) increase in log HOMA-IR, co-adjusted for LMW and HMW burden scores. Findings were consistent when using log molar sums. Associations of phthalate burden and insulin resistance were also consistent when participants were simulated to be missing some phthalate metabolites, and when we accounted for measurement error in estimating burden scores.
Both phthalate molar sums and burden scores are sensitive to associations with insulin resistance. Phthalate burden scores may be useful for data harmonization.
摩尔总和常用于量化全氟辛酸暴露,但不能捕捉到多种全氟辛酸暴露的模式。
介绍一种量化全氟辛酸代谢物暴露负担的方法,并探讨全氟辛酸负担评分与稳态模型评估的胰岛素抵抗(HOMA-IR)之间的关系。
我们应用项目反应理论(IRT)对 2013-2018 年全国健康与营养调查(NHANES)中 3474 名 20-60 岁成年人的数据进行分析,以量化 12 种全氟辛酸代谢物的潜在全氟辛酸暴露负担。我们比较了三种 IRT 模型的模型拟合度,这三种模型使用了不同的先验分组(一般全氟辛酸负担;低分子量(LMW)和高分子量(HMW)负担;以及 LMW、HMW 和 DEHP 负担),并使用最佳拟合模型估计全氟辛酸暴露负担评分。回归模型评估了全氟辛酸负担评分与 HOMA-IR 之间的协变量调整关联。我们将这些发现与使用摩尔总和的结果进行了比较。在二次分析中,我们研究了当一部分参与者缺少某些全氟辛酸代谢物时,IRT 模型如何用于数据协调,并通过使用似然值插补的重采样方法来估计与 HOMA-IR 的关联,从而考虑了全氟辛酸负担评分的测量误差。
三相关因素模型(LMW、HMW 和 DEHP 负担)提供了最佳拟合。DEHP 负担评分每增加一个四分位间距(IQR),与 log HOMA-IR 增加 0.094(95%CI:0.022,0.164,p=0.010)相关,协调整了 LMW 和 HMW 负担评分。使用对数摩尔总和时,结果一致。当参与者模拟缺失某些全氟辛酸代谢物时,以及当我们在估计负担评分时考虑到测量误差时,全氟辛酸负担与胰岛素抵抗之间的关联也是一致的。
全氟辛酸摩尔总和和负担评分都对与胰岛素抵抗的关联敏感。全氟辛酸负担评分可能有助于数据协调。