Fetal Exposure to Maternal Smoking and Neonatal Metabolite Profiles

Kim N. Cajachagua-Torres, Sophia M. Blaauwendraad, Hanan El Marroun, Hans Demmelmair, Berthold Koletzko, Romy Gaillard, Vincent W.V. Jaddoe*

*Corresponding author for this work

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Abstract

Fetal tobacco exposure has persistent effects on growth and metabolism. The underlying mechanisms of these relationships are yet unknown. We investigated the associations of fetal exposure to maternal smoking with neonatal metabolite profiles. In a population-based cohort study among 828 mother-infant pairs, we assessed maternal tobacco use by questionnaire. Metabolite concentrations of amino acids, non-esterified fatty acids, phospholipids and carnitines were determined by using LC-MS/MS in cord blood samples. Metabolite ratios reflecting metabolic pathways were computed. Compared to non-exposed neonates, those exposed to first trimester only tobacco smoking had lower neonatal mono-unsaturated acyl-alkyl-phosphatidylcholines (PC.ae) and alkyl-lysophosphatidylcholines (Lyso.PC.e) 18:0 concentrations. Neonates exposed to continued tobacco smoking during pregnancy had lower neonatal mono-unsaturated acyl-lysophosphatidylcholines (Lyso.PC.a), Lyso.PC.e.16:0 and Lyso.PC.e.18:1 concentration (False discovery rate (FDR) p-values < 0.05). Dose-response associations showed the strongest effect estimates in neonates whose mothers continued smoking ≥5 cigarettes per day (FDR p-values < 0.05). Furthermore, smoking during the first trimester only was associated with altered neonatal metabolite ratios involved in the Krebs cycle and oxidative stress, whereas continued smoking during pregnancy was associated with inflammatory, transsulfuration, and insulin resistance markers (p-value < 0.05). Thus, fetal tobacco exposure seems associated with neonatal metabolite profile adaptations. Whether these changes relate to later life metabolic health should be studied further.

Original languageEnglish
Article number1101
JournalMetabolites
Volume12
Issue number11
DOIs
Publication statusPublished - 11 Nov 2022

Bibliographical note

Funding Information:
This research was supported by European Research Council (Consolidator Grant, ERC-2014-CoG-648916) (V.W.V.J.); Stichting Volksbond Rotterdam, the NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (grant number 27853), the Netherlands Organization for Health Research and Development (Aspasia grant No. 015.016.056) and the European Union’s Horizon 2020 Research and Innovation Program (LifeCycle, grant agreement 733206) (H.E.M.); Peruvian Scholarship (grant agreement 547-2018-SERVIR) and KNAW Ter Meulen Grant from the KNAW Medical Sciences Fund of the Royal Netherlands Academy of Arts and Sciences (grant agreement KNAWWF/1085/TMB406) (K.N.C.-T.); Dutch Heart Foundation (grant number 2017T013), the Dutch Diabetes Foundation (grant number 2017.81.002) and the Netherlands Organization for Health Research and Development (ZonMW, grant number 543003109) (R.G.). The metabolomics analyses were financially supported in part by the European Research Council (Advanced Grant META-GROWTH ERC-2012-AdG–no.322605), EU Joint Programming Initiative JPI HDL BiomaKids, the German Ministry of Education and Research (Grant Nr. 01EA2203A), and German Research Council (DFG INST 409/224-1 FUGG). B.K. is the Else Kröner Seniorprofessor of Pediatrics at LMU—University of Munich, financially supported by Else Kröner-Fresenius-Foundation, LMU Medical Faculty and LMU University Hospitals. The study sponsors had no role in the study design, collection, analysis and interpretation of data, writing of the report, or in the decision to submit the paper for publication.

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© 2022 by the authors.

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