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Additional file 1: Fig. S1. Flowchart of sample selection and analysis steps in this study. Fig. S2. Pairwise Pearson correlation coefficient heat map of fasting glucose, 2-h post-load glucose, fasting insulin, HOMA-IR and HbA1c. Fig. S3. Histograms and pair-wise scatter plots of fasting glucose and 2-h post-load glucose concentrations at preconception, pregnancy and postpartum using trio subjects. Fig. S4. Histograms and pair-wise scatter plots of fasting insulin concentration and HOMA-IR at preconception, pregnancy and postpartum using trio subjects. Fig. S5. Histograms and pair-wise scatter plots of glycated haemoglobin (HbA1c, %) at preconception, pregnancy and postpartum using trio subjects. Fig. S6. Forest plots of fasting glucose concentration at postpartum, 2-h post-load glucose concentration at preconception, and HbA1c level at preconception, pregnancy and postpartum. Fig. S7. Scatter plots of effect sizes at preconception, pregnancy and postpartum in the fasting glucose, 2-h post-load glucose, fasting insulin, HOMA-IR association studies. Fig. S8. Venn diagrams of significant lipid species at preconception, pregnancy and postpartum for fasting glucose concentration, 2-h post-load glucose concentration and GDM status based on nominal p-value cut-off and the profiles of six selected lipid species from the 37 preconception signatures of GDM. Fig. S9. Association results of plasma fasting insulin concentration with plasma lipidomic profiles at preconception, pregnancy and postpartum. Fig. S10. Association results of fasting glucose, 2-h post-load glucose, impaired glucose tolerance status, fasting insulin, HOMA-IR and HbA1c levels at preconception. Fig. S11. Scatter plots of effect sizes in the association results of fasting glucose, 2-h post-load glucose, fasting insulin and HOMA-IR at preconception using trio and all subjects. Fig. S12. Percentage of individual lipid species within phosphatidylcholine, phosphatidylethanolamine and phosphatidylinositol classes. Fig. S13. Lipid ratios for enzyme indices of phosphatidylethanolamine n-methyltransferase (PEMT), lecithin-cholesterol acyltransferase (LCAT) and phospholipase A2 (PLA2).

Additional file 1: Fig. S1. Outlier detection by principal component analysis of the lipidomics data at four time points. Fig. S2. Number of pairs between the participants in antenatal, postnatal, cord blood and child lipidomes. Fig. S3. Comparison of the results of linear regression and paired t-test. Fig. S4. Ratios of PC to PE levels and lysophospholipids (lyso-PL). Fig. S5. Scatter plots of effect sizes and adjusted p-values of plasma lipids associated with mother and child adiposity. Fig. S6. Correlation of pre-pregnancy, antenatal and postnatal maternal body mass indices (BMIs). Fig. S7. Scatter plot of effect sizes in the pre-pregnancy and antenatal BMI studies. Fig. S8. Overlapping plasma lipid signatures associated with mother and child adiposity. Fig. S9. Sex difference in the birth weight and child BMI studies. Fig. S10. Forest plots and volcano plots of the meta-analysis results in GUSTO and BIS for ppBMI and BW studies. Table S1A. Comparison of lipid classes across four sample groups (antenatal, postnatal, cord blood and 6-year-old child) using linear regression model. Table S1B. Comparison of lipids species across four sample groups (antenatal, postnatal, cord blood and 6-year-old child) using linear regression model. Table S1C. Comparison of lipids species across four sample groups (antenatal, postnatal, cord blood and 6-year-old child) using paired t-test. Table S2A. Association of lipid classes with BMI or BW in four sample groups (antenatal, postnatal, cord blood and 6-year-old child) in terms of levels. Table S2B. Association of lipid species with BMI or BW in four sample groups (antenatal, postnatal, cord blood and 6-year-old child) in terms of levels. Table S2C. Association of antenatal lipidomic profiles with antenatal BMI. Table S3A. The overlapping lipid species between the ppBMI and postnatal BMI studies. Table S3B. The overlapping lipid species between the ppBMI and birth weight studies. Table S3C. The overlapping lipid species between the birth weight and 6-year-old child BMI studies. Table S3D. The overlapping lipid species between the 6-year-old child BMI and adult (postnatal mother) BMI studies. Table S3E. Overlapping lipid species in the four lipid-adiposity association studies. Table S4A. Sex difference in the birth weight and child BMI studies in terms of lipid class levels. Table S4B. Sex difference in the birth weight and child BMI in terms of species levels. Table S5. Association studies of lipids with pre-pregnancy BMI and birth weight in Barwon Infant Study (BIS), and the meta-analysis results of GUSTO and BIS.

Additional file 9. Association between BMI (Z-score, log2) and birthweight centile adjusted for each DHA lipid.

Additional file 5. Lipid data.

Additional file 4. LCMS Methodology.

Additional file 1. Clinical characteristics.

Additional file 8. The association of 13C-DHA-lipid amount (Z-score, Log2) with birthweight centile for 17 placenta.

Additional file 6. The association of post-load glycemia with the enrichment of placental 13C-DHA lipids (Z-score, log2 transformed) for 17 placenta.

Additional file 7. Associations (linear regression) between maternal metabolic factors and lipid amount (Z-score, log2) after mutual adjustment

Additional file 2. Lipid extraction.

Additional file 3. Structure of PC 38:6 illustrating the incorporation of 13C-DHA or 12C-DHA (A) and chromatogram illustrating the identical retention times of 13C and 12C PC 38:6 by LCMS (B).

We postulate that myo-inositol, a proposed intervention for gestational-diabetes, affects transplacental lipid supply to the fetus. We investigated the effect of myo-inositol on fatty-acid processing in human placental-explants from uncomplicated pregnancies. Explants were incubated with 13C-labeled palmitic-acid, 13C-oleic-acid and 13C-docosahexaenoic-acid across a range of myo-inositol concentrations for 24 h and 48 h. The incorporation of labeled-fatty-acids into individual lipids was quantified by liquid-chromatography-mass-spectrometry. At 24 h, myo-inositol increased the amount of 13C-palmitic-acid and 13C-oleic-acid labeled lipids (median fold-change relative to control=1). Significant effects were seen with 30 µM myo-inositol (physiological) for 13C-palmitic-acid-lysophosphatidylcholines (1.26) and 13C-palmitic-acid-phosphatidylethanolamines (1.17). At 48 h, myo-inositol addition increased 13C-oleic-acid-lipids but decreased 13C-palmitic-acid and 13C-docosahexaenoic-acid lipids. Significant effects were seen with 30 µM myo-inositol for 13C-oleic-acid-phosphatidylcholines (1.25), 13C-oleic-acid-phosphatidylethanolamines (1.37) and 13C-oleic-acid-triacylglycerols (1.32) and with 100 µM myo-inositol for 13C-docosahexaenoic-acid-triacylglycerols (0.78). Lipids labeled with the same 13C-fatty-acid showed similar responses when tested at the same time-point, suggesting myo-inositol alters upstream processes such as fatty-acid uptake or activation. Myo-inositol supplementation may alter placental lipid physiology with unknown clinical consequences.

Copyright © 2018 Burla et al. Published by The American Society for Biochemistry and Molecular Biology, Inc. Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.