Abstract

Background:

Fetal exposure to maternal smoking during pregnancy is associated with the development of noncommunicable diseases in the offspring. Maternal smoking may induce such long-term effects through persistent changes in the DNA methylome, which therefore hold the potential to be used as a biomarker of this early life exposure. With declining costs for measuring DNA methylation, we aimed to develop a DNA methylation score that can be used on adolescent DNA methylation data and thereby generate a score for in utero cigarette smoke exposure.

Methods:

We used machine learning methods to create a score reflecting exposure to maternal smoking during pregnancy. This score is based on peripheral blood measurements of DNA methylation (Illumina’s Infinium HumanMethylation450K BeadChip). The score was developed and tested in the Raine Study with data from 995 white 17-y-old participants using 10-fold cross-validation. The score was further tested and validated in independent data from the Northern Finland Birth Cohort 1986 (NFBC1986) (16-y-olds) and 1966 (NFBC1966) (31-y-olds). Further, three previously proposed DNA methylation scores were applied for comparison. The final score was developed with 204 CpGs using elastic net regression.

Results:

Sensitivity and specificity values for the best performing previously developed classifier (“Reese Score”) were 88% and 72% for Raine, 87% and 61% for NFBC1986 and 72% and 70% for NFBC1966, respectively; corresponding figures using the elastic net regression approach were 91% and 76% (Raine), 87% and 75% (NFBC1986), and 72% and 78% for NFBC1966.

Conclusion:

We have developed a DNA methylation score for exposure to maternal smoking during pregnancy, outperforming the three previously developed scores. One possible application of the current score could be for model adjustment purposes or to assess its association with distal health outcomes where part of the effect can be attributed to maternal smoking. Further, it may provide a biomarker for fetal exposure to maternal smoking. https://doi.org/10.1289/EHP6076

中文翻译：

---

基于机器学习的胎儿甲基溴暴露的甲基化分数：从青少年和成人收集的样本中的发展和验证

摘要

背景：

怀孕期间胎儿暴露于母体吸烟与后代中非传染性疾病的发展有关。孕妇吸烟可能会通过DNA甲基化组的持续变化而诱发此类长期影响，因此有可能被用作这种早期生命暴露的生物标志物。随着测量DNA甲基化的成本下降，我们的目标是开发可用于青少年DNA甲基化数据的DNA甲基化评分，从而为子宫内香烟烟雾暴露产生一个评分。

方法：

我们使用机器学习方法来创建一个分数，以反映怀孕期间孕妇吸烟的情况。该分数基于DNA甲基化（Illumina的Infinium HumanMethylation450K BeadChip）的外周血测量结果。该分数是在Raine研究中开发并测试的，其中使用了10倍交叉验证，来自995名17岁的17岁白人参与者的数据。该分数在来自北芬兰出生队列1986（NFBC1986）（16岁）和1966（NFBC1966）（31岁）的独立数据中进行了进一步测试和验证。此外，将三个先前提出的DNA甲基化分数用于比较。最终得分是使用弹性网回归得出的204 CpGs。

结果：

先前开发的性能最好的分类器（“里斯评分”）的灵敏度和特异度值分别为：Raine为88％和72％，NFBC1986为87％和61％，NFBC1966为72％和70％；使用弹性净回归方法的相应数字分别为91％和76％（Raine），87％和75％（NFBC1986），以及NFBC1966的72％和78％。

结论：

我们已经开发了用于孕妇孕期吸烟的DNA甲基化评分，优于之前制定的三个评分。当前评分的一种可能应用可能是出于模型调整目的或评估其与远端健康结局的关联，其中部分影响可能归因于孕妇吸烟。此外，它可以为胎儿暴露于孕妇吸烟提供生物标记。https://doi.org/10.1289/EHP6076