BACKGROUND Maternal cigarette smoking during pregnancy (MSDP) is associated with numerous adverse health outcomes in infants and children with potential lifelong consequences. Negative effects of MSDP on placental DNA methylation (DNAm), placental structure, and function are well established. OBJECTIVE Our aim was to develop biomarkers of MSDP using DNAm measured in placentas (N=96), collected as part of the Vitamin C to Decrease the Effects of Smoking in Pregnancy on Infant Lung Function double-blind, placebo-controlled randomized clinical trial conducted between 2012 and 2016. We also aimed to develop a digital polymerase chain reaction (PCR) assay for the top ranking cytosine-guanine dinucleotide (CpG) so that large numbers of samples can be screened for exposure at low cost. METHODS We compared the ability of four machine learning methods [logistic least absolute shrinkage and selection operator (LASSO) regression, logistic elastic net regression, random forest, and gradient boosting machine] to classify MSDP based on placental DNAm signatures. We developed separate models using the complete EPIC array dataset and on the subset of probes also found on the 450K array so that models exist for both platforms. For comparison, we developed a model using CpGs previously associated with MSDP in placenta. For each final model, we used model coefficients and normalized beta values to calculate placental smoking index (PSI) scores for each sample. Final models were validated in two external datasets: the Extremely Low Gestational Age Newborn observational study, N=426; and the Rhode Island Children's Health Study, N=237. RESULTS Logistic LASSO regression demonstrated the highest performance in cross-validation testing with the lowest number of input CpGs. Accuracy was greatest in external datasets when using models developed for the same platform. PSI scores in smokers only (n=72) were moderately correlated with maternal plasma cotinine levels. One CpG (cg27402634), with the largest coefficient in two models, was measured accurately by digital PCR compared with measurement by EPIC array (R2=0.98). DISCUSSION To our knowledge, we have developed the first placental DNAm-based biomarkers of MSDP with broad utility to studies of prenatal disease origins. https://doi.org/10.1289/EHP13838.

中文翻译：

---

ECHO 项目中母亲怀孕期间吸烟的新型胎盘 DNA 甲基化生物标志物的开发和验证。


背景 母亲在怀孕期间吸烟（MSDP）与婴儿和儿童的许多不良健康结果相关，并可能造成终生后果。 MSDP 对胎盘 DNA 甲基化 (DNAm)、胎盘结构和功能的负面影响已得到充分证实。目的 我们的目标是使用胎盘 (N=96) 中测量的 DNAm 开发 MSDP 生物标志物，胎盘 (N=96) 是作为维生素 C 的一部分收集的，旨在减少妊娠期吸烟对婴儿肺功能的影响，进行双盲、安慰剂对照随机临床试验2012 年至 2016 年间。我们还旨在开发一种针对顶级胞嘧啶-鸟嘌呤二核苷酸 (CpG) 的数字聚合酶链式反应 (PCR) 检测方法，以便能够以低成本筛查大量样品的暴露情况。方法 我们比较了四种机器学习方法 [逻辑最小绝对收缩和选择算子 (LASSO) 回归、逻辑弹性网络回归、随机森林和梯度增强机] 根据胎盘 DNAm 特征对 MSDP 进行分类的能力。我们使用完整的 EPIC 阵列数据集和 450K 阵列上的探针子集开发了单独的模型，以便两个平台都存在模型。为了进行比较，我们使用先前与胎盘中 MSDP 相关的 CpG 开发了一个模型。对于每个最终模型，我们使用模型系数和归一化 beta 值来计算每个样本的胎盘吸烟指数 (PSI) 分数。最终模型在两个外部数据集中进行了验证：极低胎龄新生儿观察性研究，N=426；以及罗德岛儿童健康研究，N=237。结果 Logistic LASSO 回归证明了在交叉验证测试中输入 CpG 数量最少的情况下具有最高性能。 当使用为同一平台开发的模型时，外部数据集的准确性最高。仅吸烟者 (n=72) 的 PSI 评分与母亲血浆可替宁水平呈中度相关。两种模型中系数最大的一个CpG（cg27402634）与EPIC array的测量结果相比，数字PCR的测量结果更准确（R2=0.98）。讨论 据我们所知，我们开发了第一个基于胎盘 DNAm 的 MSDP 生物标志物，可广泛用于产前疾病起源的研究。 https://doi.org/10.1289/EHP13838。