Abstract
OBJECTIVE: This study was aimed at clarifying the unique metabolic alterations in preterm neonates, distinct from full-term neonates, between the first 24 and 48 h postnatally. METHODS: A cohort of 60 preterm and 60 full-term neonates was analyzed. The metabolomic profiles of plasma samples were determined using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Multivariate statistical analyses were employed to discern metabolic differences. Multiple machine learning models were constructed to further select key metabolites. Spearman's correlation analysis was performed to assess the correlation between neonatal immune cell subsets and key metabolites. RESULTS: The study revealed 70 specific metabolic alterations in preterm neonates during the early postnatal period. Then, 32 of these metabolites were jointly selected by the Top 5 machine learning models, which exhibited high predictive performance with an AUC > 0.9. Subsequent analyses including multivariable linear regression and ROC curve revealed 12 key metabolites significantly associated with gestational age. Correlation analyses exposed significant associations between immune cells and these metabolites. Integrated pathway analysis identified 10 key metabolic pathways involved in preterm neonates. NMR-based validation confirmed two of the 12 prioritized metabolites and six additional metabolites from the broader panel, supporting the robustness of our findings. CONCLUSION: Our findings provide insights into the metabolic and immune adaptation processes in preterm neonates during the early life stage. The correlations between immune cell subsets and the key metabolites highlight a potential effect of metabolism on immune adaptation in preterm neonates. These key metabolites and pathways could serve as potential biomarkers for early diagnosis and therapeutic strategies to enhance immune function and health outcomes in preterm infants.