SFEBES2017 Poster Presentations Reproduction (24 abstracts)
Pregnancy, pre-eclampsia and vitamin D: a multi-scale mathematical approach
Casper Beentjes 1 , Jennifer Tamblyn 2, , Anahita Bayani 4 , Christopher Davis 5 , Joe Dunster 6 , Gary Mirams 7 , Jake Taylor-King 1, , Sara Jabbari 9 , Mark Kilby 2, & Martin Hewison 2
1Mathematical Institute, University of Oxford, Oxford, UK; 2Institute of Metabolism and Systems Research (IMSR), College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; 3Birmingham Women’s Foundation Hospital, Edgbaston, Birmingham, UK; 4Department of Physics and Mathematics, Nottingham Trent University, Nottingham, UK; 5Centre for Complexity Science, University of Warwick, Warwick, UK; 6Institute for Cardiovascular and Metabolic Research, University of Reading, Reading, UK; 7Centre for Medicine and Biology, University of Nottingham, Nottingham, UK; 8Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research institute, Tampa, Florida, USA; 9School of Mathematics and Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
Vitamin D deficiency during pregnancy has been linked to adverse pregnancy outcomes such as pre-eclampsia (PET), but continues to be defined by serum measurement of a single metabolite, 25-hydroxyvitamin (25(OH)D). To identify broader changes in vitamin D metabolism during normal and PET pregnancies we developed a mathematical model using a reduced reaction network for vitamin D metabolism that allows for complete parametrisation by multiple vitamin D metabolites. Serum vitamin D metabolites were analysed for a cross-sectional cohort of women from the West Midlands (n=88); which included normal pregnant women at 1st (NP1, n=25) and 3rd trimester (NP3, n=21) and pregnant women with PET (n=22), as well as non-pregnant female controls (n=20). Conventional statistical analysis showed no significant difference between NP3 and PET for serum concentrations of 25(OH) Dand 24, 25(OH) 2D3. However, the reaction network mathematical model revealed clear differences in vitamin D metabolic pathways between NP3 and PET groups. To assess the possible predicative value of this model, further studies were carried out using serum vitamin D metabolome data (n=50) from an early 2nd trimester pregnancy cohort (SCOPE Ireland study), of which 25 women went on to develop PET later in pregnancy. However, the mathematical model showed no significant difference between NP3 and PET cases at 15 weeks of gestation. These data indicate that mathematical modelling offers a novel strategy for defining the impact of vitamin D metabolism on human health. This is particularly relevant in the context of pregnancy, where major changes in vitamin D metabolism occur across gestation, and dysregulated metabolism was clearly evidenced in women with established PET. Further studies are required to determine the efficacy of mathematical modelling as a predictive tool for other adverse events in pregnancy, and for the broader impact of vitamin D on human health.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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