Longitudinal Plasma Metabolomics Profile in Pregnancy—A Study in an Ethnically Diverse U.S. Pregnancy Cohort

Abstract

Amino acids, fatty acids, and acylcarnitine metabolites play a pivotal role in maternal and fetal health, but profiles of these metabolites over pregnancy are not completely established. We described longitudinal trajectories of targeted amino acids, fatty acids, and acylcarnitines in pregnancy. We quantified 102 metabolites and combinations (37 fatty acids, 37 amino acids, and 28 acylcarnitines) in plasma samples from pregnant women in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies—Singletons cohort (n = 214 women at 10–14 and 15–26 weeks, 107 at 26–31 weeks, and 103 at 33–39 weeks). We used linear mixed models to estimate metabolite trajectories and examined variation by body mass index (BMI), race/ethnicity, and fetal sex. After excluding largely undetected metabolites, we analyzed 77 metabolites and combinations. Levels of 13 of 15 acylcarnitines, 7 of 25 amino acids, and 18 of 37 fatty acids significantly declined over gestation, while 8 of 25 amino acids and 10 of 37 fatty acids significantly increased. Several trajectories appeared to differ by BMI, race/ethnicity, and fetal sex although no tests for interactions remained significant after multiple testing correction. Future studies merit longitudinal measurements to capture metabolite changes in pregnancy, and larger samples to examine modifying effects of maternal and fetal characteristics.

Plasma Acylcarnitines during Pregnancy and Neonatal Anthropometry: A Longitudinal Study in a Multiracial Cohort

“…In line with previous studies [15,18,28], we observed varying results for some, but not all acylcarnitines between obese women and non-obese women and between GDM cases and non-GDM women. However, our sensitivity analyses showed that the associations of C12, C10, and C10:1 with neonatal anthropometric measures remained significant even after adjustment for GDM and pre-pregnancy BMI.…”

“…Third, concomitant changes in metabolites other than acylcarnitines, such as branched-chain and aromatic amino acids, non-esterified fatty acids, and phosphatidylcholines, might induce collinearity and further complicate the associations of acylcarnitines, which is difficult to disentangle, especially when they might be highly correlated or in the same biological pathways. We previously reported that most of the maternal acylcarnitines (except C8 and C14) appeared to significantly decline in early/mid-pregnancy and rise slightly after GW30, while the sum of BCAA significantly decreased as gestation progressed, driven by changes in leucine and valine [28]. Finally, the results do not prove a causal effect of acylcarnitines on fetal growth and development.…”

Longitudinal associations of pre-pregnancy BMI and gestational weight gain with maternal urinary metabolites: an NYU CHES study

“…Metabolites were measured in maternal urine samples collected at three time points during pregnancy (median GA in weeks (25 th , 75 th percentile): T1, 10 ( 8 , 13 ); T2, 21 ( 19 , 22 ); T3, 29 ( 27 , 31 )), Figure 2 . We used the AbsoluteIDQ® p180 Urine Extension kit (Biocrates Life Sciences AG, Innsbruck, Austria) to perform the targeted mass spectrometry (MS)-based quantitative metabolomic assay that directly measured metabolite concentrations in urine samples.…”

Defining Blood Plasma and Serum Metabolome by GC-MS

“…The ability of the blood metabolome to reflect internal and external perturbations [15,16] of the organism makes it one of the most important information layers for comprising a global picture of the condition of the individual at a specific point in time [17]. The versatility of the blood metabolome has provided fruitful ground for an impressive body of work investigating rapid [18,19] and slow [20,21] changes in the body. Modern metabolomic approaches allow the identification of characteristic patterns of low molecular weight compounds and associate them with the development of pathologies [22][23][24], lifestyle [25,26], and eating habits [27][28][29] in both personalized mode [10,30,31] and large cohorts [32,33].…”