Abstract Fetal growth abnormalities (FGA) affect 5-10% of all pregnancies and predispose offspring to certain medical conditions later on in life. Intrauterine growth restriction and macrosomia are two extreme forms of FGA and result in small for gestational age (SGA; <5th percentile) and large for gestational age (LGA; >95th percentile) fetuses respectively. FGA are difficult to diagnose and therefore challenging to treat, with ultrasound biometry as the current clinical mode of diagnosis. Therefore, there exists a clinical unmet need for robust biomarkers and an improved understanding of the cause of FGA. The human microbiome has recently taken center- stage in explaining associations between health and disease. For example, preterm birth has been associated with dysbiosis caused by classic inflammatory pathways. However, more recently, non-classical pathways have also been attributed to dysbiosis. We have shown previously that in IUGR conditions, the maternal microbiome profile is altered, with an increase in Bacteroidetes. However, the specific mechanisms associated with dysbiosis and the subsequent development of IUGR remain poorly understood. Microbiome- associated metabolites have previously been reported to be essential in maintaining microbial homeostasis. For example, Clostridium species produce butyrate and acetate, types of short-chain fatty acids, which functions to protect against oxidative stress within the gut. Therefore, in this proposal, we aim to probe the microbiome metabolome to further understand the mechanisms governing the development of IUGR and macrosomia, two extreme forms of FGA. We aim to analyze the maternal microbiome through 16S next- generation sequencing and microbial metabolites through mass spectrometry. Using robust bioinformatics pipelines and data integration of the matched maternal vaginal, rectal and oral samples from a multi-ethnic cohort, we expect to discover microbial subpopulations and microbial metabolites which predispose a fetus to developing IUGR and macrosomia. Through pathway enrichment analysis, we aim to unravel the microbial metabolite signature for FGA. The results of this study would bridge the gap between microbiome metabolomics and the development of FGA. It will also provide preliminary data, in the form of novel biomarkers, for an R01 application where the mechanisms of specific microbial subpopulations and microbiome metabolites will be investigated in a larger validation cohort.