Aims Maternal metabolic disorders place the mother at risk for negative pregnancy outcomes with potentially long‐term health impacts for the child. Metabolic syndrome, a cluster of features associated with increased risk of metabolic disorders, such as cardiovascular disease, diabetes and stroke, affects roughly one in five Canadians. Metabolomics is a relatively new technique that may be a useful tool to identify women at risk of metabolic disorders. This study set out to characterize urinary metabolic biomarkers of pregnant women with obesity and of pregnant women who later developed gestational diabetes mellitus (pre‐GDM), compared to controls. Methods and Materials Second trimester urine samples were collected through the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort and examined with 1H nuclear magnetic resonance (NMR) spectroscopy. Multivariate analysis was used to examine group differences, and machine learning feature selection tools identified the metabolites contributing to separation. Results Obesity and pre‐GDM metabolomes were distinct from controls and from each other. In each comparison, the glycine, serine and threonine pathways were the most impacted. Pantothenate, formic acid and glycine were downregulated by obesity, while formic acid, dimethylamine and galactose were downregulated in pre‐GDM. The three most impacted metabolites for the comparison of obesity versus pre‐GDM groups were upregulated creatine/caffeine, downregulated sarcosine/dimethylamine and upregulated maltose/sucrose in individuals who later developed GDM. Conclusion These findings suggest a role for urinary metabolomics in the prediction of GDM and metabolic marker identification for potential diagnostics and prognostics in women at risk.
Analysis of enteric microbiota function indirectly through the fecal metabolome has the potential to be an informative diagnostic tool. However, metabolomic analysis of feces is hampered by high concentrations of macromolecules such as proteins, fats, and fiber in samples. Three methods—ultrafiltration (UF), Bligh–Dyer (BD), and no extraction (samples added directly to buffer, vortexed, and centrifuged)—were tested on multiple rat (n = 10) and chicken (n = 8) fecal samples to ascertain whether the methods worked equally well across species and individuals. An in silico baseline correction method was evaluated to determine if an algorithm could produce spectra similar to those obtained via UF. For both rat and chicken feces, UF removed all macromolecules and produced no baseline distortion among samples. By contrast, the BD and no extraction methods did not remove all the macromolecules and produced baseline distortions. The application of in silico baseline correction produced spectra comparable to UF spectra. In the case of no extraction, more intense peaks were produced. This suggests that baseline correction may be a cost-effective method for metabolomic analyses of fecal samples and an alternative to UF. UF was the most versatile and efficient extraction method; however, BD and no extraction followed by baseline correction can produce comparable results.
The effects of ancestral prenatal stress can propagate across generations to alter the well-being of directly and indirectly exposed descendants via epigenetic mechanisms. Prenatal stress has been shown to alter the function of the gut-brain axis, a bi-directional signaling pathway between the gut microbiome and the enteric and central nervous systems. There has been no study investigating the impact of remote prenatal stress in ancestors on the gut-microbiome connection. Here we investigated if exposure to transgenerational ancestral stress affects the gut-brain axis through changes in the microbiome and microbiota. A multigenerational rat cohort consisting of a F0, F1, F2, and F3 generation was utilized in this study. Pregnant dams in the F0 generation were exposed to repeated restraint stress and overnight social isolation from gestational days 12-18. Breeding of three successive generations occurred in the absence of gestational stress along with a lineage of yoked controls. Fecal collection occurred for males and female in each generation at the age of 30 days, 90 days, and 115 days. Fecal samples were analyzed using 1H-NMR spectroscopy to examine the metabolome. The data are being analysed using supervised and unsupervised machine learning approaches. The data are expected to reveal that the fecal metabolome is characteristically altered by ancestral prenatal stress in each generation, resulting in a biomarker signature that is linked to the behavioural phenotype. We predict changes in the gut metabolome and microbiome to be most significant in the F3 generation. These findings could lead to further understanding of intestinal dysbiosis and its impact on the brain, and sex-specific metabolic biomarkers that are predictive of stress-associated adverse health outcomes.
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