bioRxivSystems BiologyPreprint
Perinatal Shifts in Fecal-Derived Metabolites and Associations with Postpartum Depression
Kayla D. LongoriaiD, Michelle L. Wright, Kimberly LewisiD, Sarina R. GarciaiD, Oscar Y. Franco‐RochaiD, Sara DubeiD, Tien C. NguyeniD, Elena FlowersiD, Elizabeth M. WideniD
Abstract
IntroductionResearch on maternal depression is largely limited to static, blood-derived biomarkers in the postpartum period and mechanistic targets derived from populations outside the physiological contexts of pregnancy and postpartum, resulting in critical gaps in understanding context-specific mechanisms underlying this debilitating condition. ObjectivesTo examine temporal shifts in the maternal gut metabolome and associations between pregnancy-specific shifts and postpartum depression (PPD). MethodsWe applied untargeted metabolomics (UPLC-MS/MS) to fecal samples collected from participants (N= 25) enrolled in the Maternal and Infant NutriTion (MINT) study. Random forest analysis was used to identify key pathways and metabolites contributing to temporal shifts. Fold change analysis and paired t-tests were used to quantify the magnitude and significance of metabolite changes. Associations between pregnancy-specific changes and PPD (Edinburgh Postnatal Depression Scale at 6 weeks) were identified using Pearsons correlation. ResultsLipid, amino acid, and xenobiotic metabolism emerged as core pathways driving temporal changes in the maternal gut metabolome. The most pronounced shifts occurred from 35 weeks gestation to postpartum, with 55 metabolites significantly altered compared to 24 from 24-to 35 weeks gestation and 26 from 24 weeks gestation to postpartum. Of the 29 metabolites associated with PPD; 68.9% were metabolic intermediates, primarily involved in lipid and amino acid metabolism (58.6%). ConclusionsThis study provides some of the first evidence of temporal shifts in the maternal gut metabolome and associations with PPD, highlighting the importance of lipid and amino acid metabolism and laying the groundwork for future multi-omics research.
- organism: human
- assay: metagenomics
- research focus: differential expression
- approach: statistical model
- Citations
- 2
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