The composition of human breast milk is highly variable, and it can be influenced by genetics, diet, lifestyle, and other environmental factors. This study aimed to investigate the impact of geographical location and mode of delivery on the nuclear magnetic resonance spectroscopy (NMR) metabolic profile of breast milk and its relationship with the milk microbiome. Human milk metabolic and microbiota profiles were determined using NMR and 16S rRNA gene sequencing, respectively, in 79 healthy women from Finland, Spain, South Africa, and China. Up to 68 metabolites, including amino acids, oligosaccharides, and fatty acid-associated metabolites, were identified in the milk NMR spectra. The metabolite profiles showed significant differences between geographical locations, with significant differences (p < 0.05) in the levels of galactose, lacto-N-fucopentaose III, lacto-N-fucopentaose I and 2-fucosyllactose, 3-fucosyllactose, lacto-N-difucohexaose II, lacto-N-fucopentaose III, 2-hydroxybutyrate, 3-hydroxybutyrate, proline, N-acetyl lysine, methyl-histidine, dimethylamine, kynurenine, urea, creatine and creatine phosphate, formate, lactate, acetate, phosphocholine, acetylcholine, LDL, VLDL, ethanolamine, riboflavin, hippurate, spermidine, spermine and uridine. Additionally, the effect of caesarean section on milk metabolome was dependent on the geographical region. Specific interrelations between human milk metabolites and microbiota were also identified. Proteobacteria, Actinobacteria, and Bacilli were most significantly associated with the milk metabolites, being either positively or negatively correlated depending on the metabolite. Our results reveal specific milk metabolomic profiles across geographical locations and also highlight the potential interactions between human milk’s metabolites and microbes.

Background: Low birth weight has been linked to an increased risk to develop obesity, type 2 diabetes, and hypertension in adult life, although the mechanisms underlying the association are not well understood. The objective was to determine whether the metabolomic profile of plasma from umbilical cord differs between low and normal birth weight newborns.Methods: Fifty healthy pregnant women and their infants were selected. The eligibility criteria were being born at term and having a normal pregnancy. Pairs were grouped according to their birth weight: low birth weight (LBW, birth weight < 10^thpercentile, n = 20) and control (control, birth weight between the 75^th-90^thpercentiles, n = 30). Nuclear Magnetic Resonance (NMR) was used to generate metabolic fingerprints of umbilical cord plasma samples. Simultaneously, the metabolomic profiles of the mothers were analysed. The resulting data were subjected to chemometric, principal component and partial least squares discriminant analyses.Results: Umbilical cord plasma from LBW and control newborns displayed a clearly differentiated metabolic profile. Seven metabolites were identified that discriminate the LBW from the control group. LBW newborns had lower levels of choline, proline, glutamine, alanine and glucose than did the control newborns, while plasma levels of phenylalanine and citrulline were higher in LBW newborns (p < 0.05). No significant differences were found between the two groups of mothers.Conclusions: Low birth weight newborns display a differential metabolomic profile than those of normal birth weight, a finding not present in the mothers. The meaning and the potential utility of the findings as biomarkers of risk need to be addressed in future studies.

To identify the metabolomic differences in the saliva of healthy children versus children with active carious lesions and to estimate the predictive capacity of a model based on the salivary metabolomic profile. A study of cases (n = 31) and controls (n = 37) was designed for children aged between 6 and 12 (mean age of the cases: 8.9; controls: 8.7). The said children attended public health centers in Valencia, Spain. Intraoral examinations were performed by a single examiner using ICDAS II diagnostic criteria. Unstimulated total saliva samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy. The dft index for cases was 2.84 while it was 0.19 for the control group, the DMFT index was 1.13 and 0.11, respectively. The predictive model generated by the multivariate PLS-DA analysis projects a separation between the cases and the controls on the score chart with a predictive capacity and generating an area under the curve of 0.71. The metabolites: 3-methyl-2-oxovalerate, 3-hydroxybutyrate, lactate, acetone, citrate, ornithine, ethanolamine, taurine, proline, glycine, mannose, glucose, 1-6-Anhydro-β-d-glucose and citraconate, are those that show greater significance in the model. In the controls, glycine (Cohen's d = 0.430) and glucose (Cohen's d = 0.560) present higher means compared to the cases. On the contrary, taurine (Cohen's d= -0.474) and mannose (Cohen's d= -0.456) show higher means in cases compared to controls. Our findings show a difference in the salivary metabolomic profiles, specifically in the groups of saccharides and amino acids, suggesting an association of these with the level of caries risk. The results reported in the present study reinforce the use of salivary metabolomics as a research method for the search for salivary biomarkers that allow the evaluation of caries risk in patients. Furthermore, it brings us closer to a personalized medicine that will help in dental caries prevention strategies.

Oxidative stress forms part of the molecular basis contributing to the development and manifestation of myopia, a refractive error with associated pathology that is increasingly prevalent worldwide and that subsequently leads to an upsurge in degenerative visual impairment due to conditions that are especially associated with high myopia. The purpose of our study was to examine the interrelation of potential oxidative-stress-related metabolites found in the aqueous humor of high-myopic, low-myopic, and non-myopic patients within a clinical study. We conducted a cross-sectional study, selecting two sets of patients undergoing cataract surgery. The first set, which was used to analyze metabolites through an NMR assay, comprised 116 patients. A total of 59 metabolites were assigned and quantified. The PLS-DA score plot clearly showed a separation with minimal overlap between the HM and control samples. The PLS-DA model allowed us to determine 31 major metabolite differences in the aqueous humor of the study groups. Complementary statistical analysis of the data allowed us to determine six metabolites that presented significant differences among the experimental groups (p < 005). A significant number of these metabolites were discovered to have a direct or indirect connection to oxidative stress linked with conditions of myopic eyes. Notably, we identified metabolites associated with bioenergetic pathways and metabolites that have undergone methylation, along with choline and its derivatives. The second set consisted of 73 patients who underwent a glutathione assay. Here, we showed significant variations in both reduced and oxidized glutathione in aqueous humor among all patient groups (p < 0.01) for the first time. Axial length, refractive status, and complete ophthalmologic examination were also recorded, and interrelations among metabolic and clinical parameters were evaluated.

Bone pathologies such as osteoporosis (OTP) and osteoarthritis (OA) are rising in incidence with the worldwide rise in life expectancy. The diagnosis is usually obtained using imaging techniques such as densitometry, but with both being multifactorial diseases, several molecular mechanisms remain to be understood. Metabolomics offers the potential to detect global changes which can lead to the identification of biomarkers and a better insight in the progress of the diseases. Our aim was to compare the metabolic profiles of a cohort of 100 postmenopausal women, including subcapital hip fragility fracture patients, women with severe OA of the hip that required the implantation of a hip prosthesis and controls, to find altered metabolites and networks. Nuclear magnetic resonance (NMR) spectroscopy was used to obtain the metabolomic profiles of peripheral blood derived serum, and statistical analysis was performed using MATLAB V.6.5. 30 of the 73 metabolites analysed showed statistically significant differences in a 3-way ANOVA, and 11 of them were present in the comparison between OA and controls after adjustment by covariates, including amino acids, energy metabolism metabolites and phospholipid precursors. PLS-DA analysis shows a good discrimination between controls and fracture subjects with OA patients, and ROC curve analysis demonstrates that control and fracture subjects were accurately discriminated using the metabolome, but not OA. These results point to OA as an intermediate metabolic state between controls and fracture, and suggest that some metabolic shifts that happen after a fracture are also present at weaker intensity in the OA process.

Pre-pregnancy obesity and excessive gestational weight gain (GWG) appear to affect birth weight and the offspring’s risk of obesity and disease later in life. However, the identification of the mediators of this relationship, could be of clinical interest, taking into account the presence of other confounding factors, such as genetics and other shared influences. The aim of this study was to evaluate the metabolomic profiles of infants at birth (cord blood) and 6 and 12 months after birth to identify offspring metabolites associated with maternal GWG. Nuclear Magnetic Resonance (NMR) metabolic profiles were measured in 154 plasma samples from newborns (82 cord blood samples) and in 46 and 26 of these samples at 6 months and 12 months of age, respectively. The levels of relative abundance of 73 metabolomic parameters were determined in all the samples. We performed univariate and machine-learning analysis of the association between the metabolic levels and maternal weight gain adjusted for mother‘s age, Body Mass Index (BMI), diabetes, diet adherence and infant sex. Overall, our results showed differences, both at the univariate level and in the machine-learning models, between the offspring, according to the tertiles of maternal weight gain. Some of these differences were resolved at 6 and 12 months of age, whereas some others remained. Lactate and leucine were the metabolites with the strongest and longest association with maternal weight gain during pregnancy. Leucine, as well as other significant metabolites, have been associated in the past with metabolic wellness in both general and obese populations. Our results suggest that the metabolic changes associated to excessive GWG are present in children from early life.