期刊
METABOLITES
卷 12, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/metabo12060536
关键词
premature birth; H-1-NMR metabolomics; hypoxia; fecal metagenomics; biomarkers; activity; hypoxia
资金
- Slovenian Research Agency (ARRS) [J3-7536]
- Ljubljana University Medical Centre [TP20140088]
- ARRS [P1-0242, 51867, P2-0095]
Preterm birth has a significant impact on child mortality, and preterm born adults have an increased risk of chronic diseases. This study explored the differences in metabolomes between preterm and full-term male participants during rest and exercise, as well as the overall differences in human gut-microbiomes. The findings revealed a complex set of microbiome-related metabolic biomarkers, indicating distinct host-microbiome interactions between the two groups.
Preterm birth (before 37 weeks gestation) accounts for similar to 10% of births worldwide and remains one of the leading causes of death in children under 5 years of age. Preterm born adults have been consistently shown to be at an increased risk for chronic disorders including cardiovascular, endocrine/metabolic, respiratory, renal, neurologic, and psychiatric disorders that result in increased death risk. Oxidative stress was shown to be an important risk factor for hypertension, metabolic syndrome and lung disease (reduced pulmonary function, long-term obstructive pulmonary disease, respiratory infections, and sleep disturbances). The aim of this study was to explore the differences between preterm and full-term male participants' levels of urine and fecal proton nuclear magnetic resonance (H-1-NMR) metabolomes, during rest and exercise in normoxia and hypoxia and to assess general differences in human gut-microbiomes through metagenomics at the level of taxonomy, diversity, functional genes, enzymatic reactions, metabolic pathways and predicted gut metabolites. Significant differences existed between the two groups based on the analysis of H-1-NMR urine and fecal metabolomes and their respective metabolic pathways, enabling the elucidation of a complex set of microbiome related metabolic biomarkers, supporting the idea of distinct host-microbiome interactions between the two groups and enabling the efficient classification of samples; however, this could not be directed to specific taxonomic characteristics.
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