639 - Common and Divergent Dynamic Changes of the Human Plasma Metabolome Across the First Week of Life in The Gambia and Papua New Guinea

Publication Number: 1616.639

Annmarie Hoch, Boston Children's Hospital, Cambridge, MA, United States; Caitlin Syphurs, Boston Children's Hospital, Boston, MA, United States; Jing Chen, Boston Children's Hospital, Boston, MA, United States; Rym Ben-Othman, RAN BIOLINKS, Toronto, ON, Canada; Asimenia Angelidou, Harvard Medical School, Boston, MA, United States; Kinga K. Smolen, Boston Children's Hospital, Boston, MA, United States; Oludare (Dare) Odumade, University of Minnesota Medical School, Minneapolis, MN, United States; Kerry McEnaney, Boston Children's Hospital, Boston, MA, United States; Olubukola T. Idoko, London School of Hygiene and Tropical Medicine, Francheville, Rhone-Alpes, France; Rebecca Ford, Papua New Guinea Institute of Medical Research, Goroka, Eastern Highlands, Papua New Guinea; Jessica A. Lasky-Su, Brigham and Women's Hospital, Bston, MA, United States; Al Ozonoff, Harvard Medical School, Boston, MA, United States; William Pomat, Papua New Guinea Institute of Medical Research, Goroka, Eastern Highlands, Papua New Guinea; Beate Kampmann, Charite Centre for Global Health, Charite Universitatsmedizin, berlin, Berlin, Berlin, Germany; Tobias R. Kollmann, Dalhousie University, Halifax, NS, Canada; Ofer Levy, Precision Vaccines Program, Boston Children's Hospital, Boston, MA, United States; Joann Diray-Arce, Boston Children's Hospital, Boston, MA, United States

Background: The plasma metabolome changes greatly over the first week of life, but there are still many unknowns about the specific trajectory and how it differs across geographically distinct cohorts. The Expanded Program on Immunization Consortium (EPIC) studied markers of immune ontogeny in newborns in The Gambia (GAM; West Africa) and Papua New Guinea (PNG; Oceania).

Objective: Determine how the newborn plasma metabolome differs based on day of life and geographic site.

Design/Methods: To gain insight into potential differences in ontogeny between geographic sites, newborn participants were enrolled in GAM (n=45) and PNG (n=45). All participants had samples collected at birth and were randomized to have their second sample collected on Day of Life (DOL) 1, 3, or 7. All participants were breastfed. Global untargeted plasma metabolomics employed ultra-performance liquid chromatography mass spectrometry (Metabolon) to identify 1253 metabolite features. Principal Component Analysis (PCA), regression methods, and enrichment analysis were used to identify significant differences in metabolite profiles between time points, using a significance level of p< 0.01 and adjusting for multiple comparisons.

Results: Plasma metabolites clustered both by DOL and by geographic site (Figure 1). Over 60% of metabolite features showed a significant difference based on DOL. The most common of these were lipids (46.0%), then amino acids (17.1%), with strong post-natal upregulation for both (Figure 2). Temporal dynamics revealed dramatic changes during the first week of life, with the most pronounced shifts occurring between DOL1 and DOL3. The most significant temporal changes were consistent across the two geographic sites, suggesting robust underlying biological processes. Additionally, 305 of the 1,253 metabolite features showed a significant difference between sites, most commonly lipids (28.2%). Xenobiotics such as food metabolites were distinct in GAM vs. PNG, suggesting differences in maternal diet and environmental factors.

Conclusion(s): The plasma metabolome undergoes marked changes during the first week of human life, with both common and distinct features across geographic locations. These observations warrant further exploration with respect to potential correlations to immune status and clinical outcomes. The prominence of changes in lipid pathways over the first week of life suggests an important role in early life development. Differences in exogenous xenobiotic exposure may contribute to metabolic differences between populations, indicating the importance of capturing and considering environmental exposures.

Figure 1: Principal Component Analysis highlights distinct age-dependent clustering of plasma metabolome in first week of life in both GAM and PNG.


Figure 2: Up and down bar plots show strong upregulation in lipid (green rows) and amino acid (pink rows) pathways in later timepoints compared. Both xenobiotic (red rows) and lipid (green rows) pathways also feature marked downregulation.
Upregulation is indicated by the red bars, and downregulation is indicated by the blue bars, with birth as the reference level.