Machine learning-based dna methylation score for fetal exposure to maternal smoking: Development and validation in samples collected from adolescents and adults

Sebastian Rauschert, Phillip E. Melton, Anni Heiskala, Ville Karhunen, Graham Burdge, Jeffrey M. Craig, Keith M. Godfrey, Karen Lillycrop, Trevor A. Mori, Lawrence J. Beilin, Wendy H. Oddy, Craig Pennell, Marjo Riitta Järvelin, Sylvain Sebert, Rae Chi Huang

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

BACKGROUND: Fetal exposure to maternal smoking during pregnancy is associated with the development of noncommunicable diseases in the off-spring. 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 pe-ripheral 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 pro-posed 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.

Original languageEnglish
Article number097003
Pages (from-to)1-11
Number of pages11
JournalEnvironmental Health Perspectives
Volume128
Issue number9
DOIs
Publication statusPublished - Sept 2020

Fingerprint

Dive into the research topics of 'Machine learning-based dna methylation score for fetal exposure to maternal smoking: Development and validation in samples collected from adolescents and adults'. Together they form a unique fingerprint.

Cite this