| Summary: | Early life experiences, including mode of delivery and nutrition during the neonatal period, have been proven to have an impact on health in later life. Studying human metabolic development has major implications for understanding the aetiology and risk of disease, including metabolic syndrome. Initially, a sample preparation protocol was developed and optimised using metabonomic procedures for studying urine and faeces from infants, to accommodate for limited sample volume and to take into account the compositional differences between adult and infant biofluids. This primarily indicated that age is an important variable that contributes to the metabolic profile of biofluids. Faecal metabonomics is fast becoming a useful tool for defining interactions among host, microbial communities and nutritional interventions. Infant development trajectory was assessed through analysis of faecal metabolic profiling by 1H NMR. A large non-clinical cohort longitudinal study was obtained; 1802 faecal samples from 524 infants at 6 time points from 4 days to 730 days postpartum. Furthermore, 1H NMR, UPLC-MS and metagenomic phenotyping techniques was performed on urine (n=278) and faecal (n=308) samples from 150 infants born term or preterm (< 37 wks gestational age). This multi-omics approach provided further demonstration of contribution of microbial co-metabolites to infant metabolism early in life and therefore the potential impact on overall health. This PhD project was able to identify certain metabolic pathways which were shown to be different in relation to gestation age as well as postnatal age, mode of delivery, BMI status and nutrition. In particular, choline and methylamine derivatives (e.g. betaine, trimethylamine), short chain fatty acids (SCFA) and amino acids related to nutrition and the gut microbiome functionality as well as metabolites indicating infant renal development from birth (e.g. myo-inositol, 1-N-methylnicotinamide). Overall, these investigations have shown that an understanding of the sources of variation in biofluid metabolite profiles are essential for interpretation of data acquired during normal infant development.
|
|---|
