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Effect of common pregnancy and perinatal complications on offspring metabolic traits across the life course: a multi-cohort study

  • Ahmed Elhakeem*
  • , Justiina Ronkainen
  • , Toby Mansell
  • , Katherine Lange
  • , Tuija M. Mikkola
  • , Binisha H. Mishra
  • , Rama J. Wahab
  • , Tim Cadman
  • , Tiffany Yang
  • , David Burgner
  • , Johan G. Eriksson
  • , Marjo Riitta Järvelin
  • , Romy Gaillard
  • , Vincent W.V. Jaddoe
  • , Terho Lehtimäki
  • , Olli T. Raitakari
  • , Richard Saffery
  • , Melissa Wake
  • , John Wright
  • , Sylvain Sebert
  • Deborah A. Lawlor
*Corresponding author for this work
  • MRC Integrative Epidemiology Unit at the University of Bristol
  • Bristol Medical School
  • University of Oulu
  • Murdoch Children's Research Institute
  • University of Melbourne
  • Folkhalsan
  • University of Helsinki, Faculty of Medicine
  • Tampere University
  • Fimlab Laboratories
  • University of Copenhagen
  • Bradford Institute for Health Research
  • Monash University
  • University of Helsinki
  • Yong Loo Lin School of Medicine
  • Agency for Science, Technology and Research, Singapore
  • Imperial College London
  • University of Turku
  • Turku University Hospital
  • The University of Auckland
  • NIHR Biomedical Research Centres

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
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Abstract

Background: Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods: We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results: Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of persistence to older ages. LGA was inversely associated with 19 metabolic traits including lower levels of cholesterol, lipoproteins, fatty acids, and amino acids, with associations emerging in adolescence, (e.g. −0.11 SD total fatty acids, 95% CI: −0.18 to −0.05, P=0.0009), and attenuating with older age across adulthood. Conclusions: These reassuring findings suggest little evidence of wide-spread and long-term impact of common pregnancy and perinatal complications on offspring metabolic traits, with most associations only observed for newborns rather than older ages, and for perinatal rather than pregnancy complications.

Original languageEnglish
Article number23
JournalBMC Medicine
Volume21
Issue number1
DOIs
Publication statusPublished - 18 Jan 2023

Bibliographical note

Authors’ contributions:
AE and DAL designed the study and developed the analysis plan with input
from all authors. AE undertook the analysis in ALSPAC, BiB and undertook the
meta-analysis. JR undertook analysis in the NFBC cohorts. TM1 undertook analysis
in the BIS cohort. KL undertook analysis in the CheckPoint cohort. TM2 undertook
analysis in NFBC cohort. BHM undertook analysis in YFS. RW undertook analysis in
Generation R. AE wrote the frst draft of the manuscript. JR TM1, KL, TM2, BHM, RW,
TC, TY, DB, JGE, MRJ, RG, VWVJ, TL, OTR, RS, MW, JW, SS, and DAL provided feedback
on the draft. All authors read and approved the fnal manuscript.

Publisher Copyright:
© 2023, The Author(s).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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