Objectives

We applied three statistical approaches for evaluating associations between prenatal urinary concentrations of a mixture of phthalate metabolites and birth weight.

Methods

We included 300 women who provided 732 urine samples during pregnancy and delivered a singleton infant. We measured urinary concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, di-n-butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear regressions; 2) classification methods [principal component analysis (PCA) and structural equation models (SEM)]; and 3) Bayesian kernel machine regression (BKMR), to evaluate associations between phthalate metabolite mixtures and birth weight adjusting for potential confounders. Data were presented as mean differences (95% CI) in birth weight (grams) as each phthalate increased from the 10th to the 90th percentile.

Results

When analyzing individual phthalate metabolites using linear regressions, each metabolite demonstrated a modest inverse association with birth weight [from −93 (−206, 21) to −49 (−164, 65)]. When simultaneously including all metabolites in a multivariable model, inflation of the estimates and standard errors were noted. PCA identified two principal components, both inversely associated with birth weight [−23 (−68, 22), −27 (−71, 17), respectively]. These inverse associations were confirmed when applying SEM. BKMR further identified that monoethyl and mono(2-ethylhexyl) phthalate and phthalate concentrations were linearly related to lower birth weight [−51(−164, 63) and −122 (−311, 67), respectively], and suggested no evidence of interaction between metabolites.

Conclusions

While none of the methods produced significant results, we demonstrated the potential issues arising using linear regression models in the context of correlated exposures. Among the other selected approaches, classification techniques identified common sources of exposures with implications for interventions, while BKMR further identified specific contributions of individual metabolites.

中文翻译：

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评估产前接触邻苯二甲酸酯混合物对出生体重的影响：三种统计方法的比较

 目标

我们应用三种统计方法来评估邻苯二甲酸酯代谢物混合物的产前尿液浓度与出生体重之间的关联。

 方法

我们纳入了 300 名妇女，她们在怀孕期间提供了 732 份尿液样本，并生下了一名单胎婴儿。我们测量了邻苯二甲酸二(2-乙基己基)酯、邻苯二甲酸二异丁酯、邻苯二甲酸二正丁酯、丁基苄基酯和邻苯二甲酸二乙酯代谢物的尿浓度。我们应用了1）线性回归； 2）分类方法【主成分分析（PCA）和结构方程模型（SEM）】； 3) 贝叶斯核机器回归 (BKMR)，用于评估邻苯二甲酸酯代谢物混合物与调整潜在混杂因素的出生体重之间的关联。数据以出生体重（克）的平均差异（95% CI）表示，每种邻苯二甲酸盐从第 10 个百分位数增加到第 90 个百分位数。

 结果

当使用线性回归分析单个邻苯二甲酸酯代谢物时，每种代谢物都表现出与出生体重适度的负相关性[从-93 (-206, 21)到-49 (-164, 65)]。当同时将所有代谢物纳入多变量模型时，会注意到估计值和标准误差的膨胀。 PCA 确定了两个主成分，均与出生体重呈负相关[分别为−23 (−68, 22)、−27 (−71, 17)]。这些反向关联在应用 SEM 时得到了证实。 BKMR 进一步发现，邻苯二甲酸单乙酯和单（2-乙基己基）酯以及邻苯二甲酸酯浓度与较低出生体重呈线性相关[分别为−51(−164, 63) 和−122 (−311, 67)]，并表明没有证据表明代谢物之间的相互作用。

 结论

虽然这些方法都没有产生显着的结果，但我们证明了在相关暴露的背景下使用线性回归模型产生的潜在问题。在其他选定的方法中，分类技术确定了对干预措施有影响的常见暴露来源，而 BKMR 进一步确定了个体代谢物的具体贡献。