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Impact of early postnatal nutrition on the NMR urinary metabolic profile of infant Flaminia Cesare Marincola, Sara Corbu, Milena Lussu, Antonio Noto, Angelica Dessì, Stefania Longo, Elisa Civardi, Francesca Garofoli, Beatrice Grenci, Elisa Mongini, Andrea Budelli, Alessia Grinzato, Francesca Fasano, Vassilios Fanos, and Mauro Stronati J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00537 • Publication Date (Web): 21 Sep 2016 Downloaded from http://pubs.acs.org on September 26, 2016

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Journal of Proteome Research

Impact of early postnatal nutrition on the NMR urinary metabolic profile of infant

Flaminia Cesare Marincola†*, Sara Corbu†, Milena Lussu‡, Antonio Noto‡, Angelica Dessì‡, Stefania Longo§, Elisa Civardi§, Francesca Garofoli§, Beatrice Grenci§, Elisa Mongini§, Andrea Budelli∥, Alessia Grinzato⊥, Francesca Fasano⊥, Vassilios Fanos‡, Mauro Stronati§,#

†

Department of Chemical and Geological Sciences, Cittadella Universitaria, University of Cagliari

Cagliari, Italy ‡

Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, Azienda Ospedaliera

Universitaria, University of Cagliari, Cagliari, Italy §

Neonatal Unit and Neonatal Intensive Care Unit, Fondazione IRCCS Policlinico San Matteo,

Pavia. ∥

Research & Development, HJ Heinz B.V., Nijmegen, the Netherlands

⊥

Research & Development, Heinz Italia SpA, Latina, Italy

#

Neonatal Immunology Laboratory, Neonatal Unit and Neonatal Intensive Care Unit, Fondazione

IRCCS Policlinico San Matteo, Pavia, Italy

Address for correspondence: Flaminia Cesare Marincola Department of Chemical and Geological Sciences, University of Cagliari, Cittadella Universitaria, SS 554, km 4.5, 09042 Monserrato, Cagliari, Italy Tel +39 070/6754389 Tel +39 070/6754388 E-mail: [email protected]

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Abstract NMR-based metabolomics was used to compare the metabolic urinary profiles of exclusively breast-fed term infants (n=11) with those of a double-blinded controlled trial with 49 formula-fed term newborns randomized to receive either an infant formula enriched by functional ingredients (n=24) or a standard formula (n=25). Anthropometric measurements and urine samples were taken at enrollment (within the first month of life), at around 60 days of life and at the end of study period (average age of 130 days). The metabolic profiles were examined in relation to time and diet strategy. A common age-dependent modification of urine metabolome was observed for the three types of nutrition, mainly characterized by similar temporal trends of choline, betaine, myo-inositol, taurine, and citrate. Contrariwise, differences in the metabolic profiles were identified according to the type of diet (human versus formula milk), while no significant difference was observed between the two formulas. These modifications are discussed mainly in terms of the different composition between milks. Despite the low number of enrolled infants (n=60), these findings pointed out the potential of the metabolomics approach for neonatal nutritional science, and in particular in providing important contributions to the optimization of formula milk.

Keywords: Metabolomics, NMR, neonatology, human milk, formula milk, urine


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Introduction The evidence of an association between nutrition in early life and health in adulthood nowadays forms a keystone of health promotion and public health nutrition programs. For this reason, while previously the focus of nutrition in early life was on meeting nutritional needs, the new frontier in nutritional sciences is mainly addressed to short and long-term health.1 Human milk (HM) is widely acknowledged as the most complete form of nutrition for infants. It is a complex mixture of a wide range of health-promoting compounds including oligosaccharides, nucleotides, fatty acids, immunoglobulins, cytokines, immune cells, lysozyme, lactoferrin, growth factors, and other immune-modulating factors.2 Benefits of breastfeeding begin at birth and continue throughout the child's life,3 so that exclusive breastfeeding for the first six months of life is now considered a global public heath goal to achieve optimal growth, development, and health of infant.4 In circumstances when breastfeeding is not possible or contraindicated, formula milk (FM) represents a safe and nutritionally adequate substitute to HM. Infant formula is a product based on milk of cows or other animals and/or other ingredients which have been proven to be suitable for infant feeding. The composition of FM should serve to meet the particular nutritional requirements of infants for whom they are intended and to promote normal growth and development. Although the progress in improving formulas to replicate HM more closely has evolved over many years, research in their quantitative formulation still continues. To this aim, the knowledge of the influence of postnatal nutrition on infant metabolism may represent an important point of reference to the understanding of fundamental mechanisms of nutrition and to formulate future nutritional concepts. In food science and nutrition research, metabolomics, one of the “omics” sciences in system biology, offers a promising analytical tool. Metabolomics can be defined as an approach based on the systematic study of the complete set of metabolites (metabolome) present in a given biological system (fluids, cells, or organisms) which are the end products of gene expression.5 By measuring the metabolome, metabolomics allows us to photograph the genome in its interaction with the 3 ACS Paragon Plus Environment


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environment and, thus, to investigate the metabolic status of an organism in determined physiological and/or pathological conditions, for instance as a consequence of drug treatment, environmental influences, nutrition, lifestyle, genetic effects, etc. Mainly applied in clinical research,6 more recently metabolomics has been used in the nutrition field for assessing metabolic responses of humans or animals to dietary interventions and for the definition of metabolic phenotypes.7 This approach appears also a promising technique in neonatology and pediadrics.8 Research in these fields is evolving continuously, providing contributions to the improvement of pediatric and neonatological practices in hospitals and clinics9,10 as well as to a deeper understanding of infant nutrition and growth.11–15 Recently, we have investigated the impact of two infant formulas, in terms of safety and growth ability, on healthy full term newborns during a study period of 135 days.16 The composition of the two formulas were identical except for the presence of functional ingredients. In addition, the effect of formulas on the infant fecal microbiota was evaluated. When compared with the standard formula, the one enriched with functional ingredients supported satisfactory growth and a positive effect on the neonatal gut microbiota, leading to higher counts of Bifidobacteria in feces. In order to deeper explore the nutritional effects of both formulas, in the present work we extended the investigation by applying NMR-based metabolomics to characterize the urinary metabolome of infants and to compare it with that of exclusively breast-fed infants. Comparing the urinary metabolic fingerprints in terms of both time (i.e. infant age) and nutritional type provided information on the influence of both formulas on neonatal metabolism.

MATERIALS AND METHODS Study population


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This double-blinded randomized, controlled trial took place at Neonatal Unit and Neonatal Intensive Care Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, after the approval of the Bioethics Committee of our Institute and once informed consent was obtained from both the parents. Inclusion criteria were: infants of both sexes born to natural or caesarean delivery; gestational age within 37 and 42 weeks; birth weight between 10th and 90th percentile according to the WHO Child Growth Standard;17 single birth; Caucasian race. Exclusion criteria were: infants with genetic and/or congenital diseases, receiving antibiotic therapy, requiring hospitalisation for longer than 7 days, having familial history for atopy, metabolic or chronic diseases, parents refusing to sign a written informed consent. The group of enrolled infants consisted of 60 healthy subjects among which six were excluded during data analysis (see the Results section): 24 were fed with the formula milk enriched with functional ingredients (ENR group), 25 with a standard formula (ST group) and 11 were exclusively fed with human milk (HM group). Heinz Italia S.p.A. (Latina, Italy) supplied ENR and ST formulas. At the time of enrolment, ENR and STD infants had received a mixed human milk and formula feeding. The composition of the two formulas was identical, with the exception of the functional ingredients, galacto-oligosaccharides (7 g/L), beta-palmitate (palmitic acid is 60% of total fatty acids, whose 39% are esterified at the sn-2 position) and acidified milk (which represents 50% of the whole milk in the formula). Nutritional details are reported in Table 1. Both formulas were provided in powdered form and could not be differentiated by smell, consistency, or any other characteristics. Neither the investigators nor the parents knew which product the infant was receiving.

Anthropometric data and urine samplings Infants were visited at the enrolment (T0) as baseline, at around 60 days of age (T1), and at the end of the study period, i.e ca 130 days of life (T2). Body weight, length, and head circumference 5 ACS Paragon Plus Environment


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were assessed within routinely pediatric visits. Adverse events, in particular inquiring the parents about minor gastrointestinal issues, were also registered. Information was obtained by diaries filled in by the parents, after they were trained to recognize and measure them with the aid of semiquantitative and/or illustrated scales. Gastrointestinal symptoms included stool frequency and consistency, bowel cramps, intestinal gas. At each visit, urine samples were collected using a noninvasive method with a ball of cotton inserted into the disposable diaper, then aspired with a syringe and transferred to a sterile 2 mL vials. The vials were then stored at −80°C until the NMR analysis. Overall, 41 infants (14 from ENR group, 18 from ST group and 9 from HM group) completed the trial.

1

H NMR spectroscopy

Milk sample preparation ST and ENR infant formulas were reconstituted in the laboratory prior NMR analysis. In order to remove residual lipids and proteins, milk samples were centrifuged at 10000 g for 30 min at 4°C using Amicon Ultra 0.5 mL 10 kDa (Millipore, Billerica, MA, USA) spin filters. Each filtered sample (350 µL) was mixed with 350 µL of 0.1 M phosphate buffer solution (pH 7.4) containing sodium 3-trimethylsilyl-(2,2,3,3-2 H4)-1-propionate (TSP) (final concentration 2 mM) and then transferred into 5 mm wide NMR tube.

Urine sample preparation An amount of 1 mL of urine was transferred into 1.5 mL Eppendorf tube together with 10 µL of an aqueous solution of NaN3 (1%), an antibacterial. The sample was centrifuged at 12000 g for 10 min at 4 ° C to remove any solid particles. Then, 630 µL of the supernatant solution were mixed with 70 µL of 1.5 M phosphate buffer solution (pH 7.4) containing TSP (final concentration 1 mM). The mixture was vortexed and 650 µL were transferred into 5 mm wide NMR tube. 6 ACS Paragon Plus Environment

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Data acquisition 1

H NMR experiments were performed at 300 K on a Varian UNITY INOVA 500 spectrometer

(Agilent Technologies, Inc., Santa Clara, CA), operating at the frequency of 499.83 MHz. Onedimensional (1D) 1H NMR spectra were obtained using a standard pulse sequence, 1D NOESY, with presaturation during relaxation and mixing time for water suppression. For each urine spectrum, a total of 128 scans were collected in 64k data points over a spectral width of 6000 Hz, using a relaxation delay of 2 s, an acquisition time of 1.5 s, and a mixing time of 0.1 s. Milk spectra were acquired with 256 transients over a spectral width of 6000 Hz with a total acquisition time of 1.5 s and a mixing time of 0.1 s. After Fourier transformation with 0.3 Hz, line broadening spectra were phased and baseline corrected and the chemical shift scale was set by assigning a value of δ = 0.00 ppm to the signal for the internal standard TSP. 2D NMR 1H-1H correlation spectroscopy (COSY) spectra were acquired using the following acquisition parameters: spectral width, 6000 Hz in both dimensions; acquisition time, 0.171 s; delay time, 1.0 s; number of data points, 2048 (f2) and 256 (f1); number of scans, 128. 2D NMR 1H-1H total correlation spectroscopy (TOCSY) spectra were acquired in phase sensitive mode with a size and number of data points similar to those of the COSY. The 1H-13C heteronuclear single quantum correlation (HSQC) and heteronuclear multiple bond correlation (HMBC) spectra were acquired at 500 MHz for 1H and 125 MHz for

13

C using a

spectral width of 6000 and 28901 Hz, respectively. Experimental parameters for HSQC were as follows: acquisition time, 0.171 s; delay time, 1.0 s; dummy scans, 32; number of scans, 96; 2048 data points in f2 and 256 increments in f1. The experimental parameters for HMBC were as follows: acquisition time, 0.171s; delay time, 1 s; dummy scans, 32; number of scans, 128; 2048 data points in f2 and 256 increments in f1.



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NMR data preparation The NMR urine spectra were processed using MestReNova (Version 8.1, Mestrelab Research SL, Santiago de Compostela, Spain) and corrected for misalignments in chemical shift primarily due to pH-dependent signals. Each spectrum was integrated (binned) using 0.0025 ppm integral regions between 9.5 and 0.5 ppm, excluding the portions with the residual water (δ 4.6-5.2) and urea (δ 5.6-6.0) resonances. Bins were normalized to the sum of total spectral area to compensate for the overall concentration differences. The final data set (147×3207) was automatically reduced to ASCII files and converted into an Excel file.

Multivariate statistical analysis The NMR urine data set was imported in SIMCA 14 (Umetrics, Umeå, Sweden), Pareto scaled and analyzed by Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Unsupervised PCA model was performed to initially examine the structure of data and evaluate the presence of any anomalies,18 while OPLS-DA was used to extract maximum information on discriminant compounds from the data.19,20 The quality and reliability of the OPLS-DA models were assessed by the parameters R2 and Q2. R2 is defined as the proportion of variance in the data explained by the models and indicates goodness of fit, while Q2 is defined as the proportion of variance in the data predictable by the model and indicates predictability. The quality of the OPLS-DA models was further validated by permutation tests, consisting of random permutation class membership and the performance of 200 iterations. Additionally, CV-ANOVA (analysis of variance testing of cross-validated predictive residuals) tests were performed to determine significant differences between groups. The inter-class difference in the relative concentrations of metabolites were interpreted by the OPLS-DA coefficient plot. Here, the OPLS modelled covariance [p(cov)] is plotted against the chemical shift and the plot is colored with the modelled correlation [p(corr)]. The direction of the 1H NMR peaks in this plot represents 8 ACS Paragon Plus Environment

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the relative differences in metabolites between two classes: peaks with positive (negative) phase indicate the most abundant metabolites in the group on the right (left) side of the scores plot. The colors on the coefficient plots contains information on the statistical significance of this change: warmed colors signals, such as red, belong to metabolites that contribute most significantly toward class separation than do the metabolites associated with cool colored signals, such as blue.

Univariate statistical analysis Statistical differences in body weight gain were determined by using an one-way ANOVA (analysis of variance). Post hoc Bonferroni’s multiple-comparison test was carried out where intergroup differences were considered statistically significant at the level of P < 0.05. Statistics were analyzed using GraphPad Prism Statistics software package version 3.00 (GraphPad Prism SoftwareInc., San Diego, CA, USA).

RESULTS A total of 60 infants were enrolled during the study period: 24 subjects were given ENR formula, 25 received ST formula, and 11 were breast-fed. The characteristics of the study population are shown in Figure 1. All infants in the three groups were appropriately matched at birth and had similar baseline characteristics (visit T0), except for 3 ENR and 4 ST infants that were enrolled within the first week of life. These seven newborns dropped out of the study, nevertheless their urine was analyzed by 1H-NMR, providing relevant information as discussed below. The primary outcome measure used for assessing nutritional adequacy was weight gain, quantified as the difference of the body weight between two subsequent visits. The growth rate of infants (age-matched at the time of enrolment) was evaluated in relation to time and milk type. The average weight increments over time were found to be statistically significant (P < 0.05) for all three groups of newborns and within the Italian and WHO normal ranges.17 Differently, the inter9 ACS Paragon Plus Environment


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group differences at T1 and T2 were statistically non-significant (P > 0.05), evidencing a comparable effects of the three type of feeding on the growth rate of infants.

1

H NMR spectroscopy of formula milk

The 1H NMR spectra of 10-kDa filtered ST and ENR formula milks are shown in Supplementary Figure S1, while the most evident differences in the spectra profiles of the two infant products are depicted in Figure 2. Metabolite assignments were achieved by 2D NMR experiments, by reference to the literature data13,21 and the Human Metabolome Data Base22 (Supplementary Table S1). From the visual inspection of the spectra, it was clear that the spectral profiles of ST and ENR formulas were very similar. In the aliphatic region (0.7-3.0 ppm), peaks arising from pantothenic acid, Nacetyl compounds, and organic acids such as citric, acetic, succinic and lactic were identified. In line with the ENR supplementation of acidified milk (by fermentation using a mixture of L(+) lactic acid producing bacteria), the intensities of lactate resonances were higher in the ENR formula spectrum than in those of the ST milk (Figure 2A). The midfield region (3.0-6.0 ppm) showed the contribution of carbohydrates among which lactose was the component with the highest concentration. Again, difference in the spectral profile of this region for the two formulas evidenced differences in their carbohydrates content (Figures 2B-C). Finally, the signals in the aromatic region (6.0-9.5 ppm) indicated the presence of formic, fumaric, and hippuric acids.

Unsupervised metabolomics data analysis A total of 147 1H NMR spectra of infant urine samples were recorded in this study. The expanded regions of a representative spectrum are depicted in the Supporting Information (Figure S2). Initially, PCA modeling of the overall NMR data set was performed to achieve the natural inter-relationship (clustering or outlier detection) among samples. The scores plot in Figure 3 displays the first two principal components, accounting for ca 20% of the variation in the samples. 10 ACS Paragon Plus Environment

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In order to visualize the data distribution in relation to both the sampling time and diet type, this plot is here colored-coded according to the collection time point, while the symbols denote the milk nature. From a general overview of Figure 3, it can be seen that the scores of breast-fed infants are mainly distributed on the top of the plot, while those relative to milk formula feeding are principally located on the bottom. Moreover, samples collected at the enrolment (T0) are principally separated along the first principal component (PC1, negative axis) from samples taken at ca 60 (T1) and 130 (T2) days of life (PC1, positive axis). Additionally, it was noted that many of the outliers corresponded to samples collected during the first week of life (marked with asterisks in Figure 3). No abnormality was observed in the experimental spectra of these outliers that were characterized, in particular, by high levels of resonances in the δ 3.5-4.0 range and high lactose content. Furthermore, no alteration in the health status of the corresponding infants was recorded over the study period. Therefore, we hypothesized that their deviation from the PCA model may be ascribed to the influence of the pre-natal nutrition on infant metabolism, still persistent during the first days after birth and likely stronger than those of the postnatal power supply within this period. Undoubtedly, this aspect deserves further insights that go beyond the main object of the present investigation. Therefore, since our focus was to compare the effects of different milk feeding type on infant metabolism, urines collected during the first week of life were excluded from the following analysis.

Supervised analysis of the temporal changes of the urine metabolome To maximize the over-time group separation and analyze the dynamic change of the urinary metabolic profiles, OPLS-DA models were built for each type of nutrition in pairwise comparisons, using sampling time points as classifiers: T0 vs T1 and T1 vs T2. The scores plots of the models built for HM, ST, and ENR groups are shown in Supplementary Figure S3. All models exhibited satisfactory performances as pointed out by the values of R2Y and Q2Y used to characterize the 11 ACS Paragon Plus Environment


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proportion of variance in Y explained and predicted by the model, respectively (Supplementary Table S2). The reliabilities of the models were further rigorously validated by a random class permutation test (n = 200) (Supplementary Table S2) and additionally, by using internal cross validation (Supplementary Figure S4). Specific metabolites that had major contribution to the metabolic differential pattern were selected from the coefficient loading plots (Figure 4). The absolute values of p(cov) and p(corr) higher than or equal to 0.05 and 0.5, respectively, were defined as cutoff for statistical significance. As can be seen in panels A-C, the temporal modifications of the urine metabolome from the enrolment up to ca 60 days of life exhibited some similar traits for the three types of milk intake, namely the increase in concentration of choline, betaine, taurine, citrate, and the reduction of myo-inositol. The pantothenic acid (vitamin B5) content was found to increase only in the case of formula feeding. Differently, only HM group exhibited increased levels of carnitine. Comparison between the metabolic modifications occurring during the following two months of milk intake, i.e from T1 to T2 (panels D-F in Figures 4) revealed a significant decrease of the myoinositol and betaine contents and an increase in the level of citrate for all three types of milk feeding. Additionally, creatinine increased only in FM groups, while creatine only in ST infants.

Supervised analysis of the infant feeding influence on the urine metabolome In order to examine the differences among the urinary metabolic profiles of infants according to the type of milk intake, pairwise comparisons were carried out based on OPLS-DA models built with samples collected at the same time point (T1 and T2). Since at the time of enrolment STD and ENR infants had received a mixed human milk and formula feeding without following any particular protocol, the inter-class comparison at time T0 was found to be not informative for a discriminant analysis based on the diet types under investigation.


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OPLS-DA models generated for the comparison ST vs HM and ENR vs HM resulted in satisfactory performances, achieving clear separation between the two type of milk intake. The statistical parameters of all models and the cross-validated scores plots are reported in Supplementary Table S3 and Figure S5, respectively. Differently, no significant metabolic differences were highlighted between ST and ENR groups. The differential metabolites contributing to the separation between HM and FM infants were identified by analyzing the coefficient loadings plots (Figure 5). A list of those that most significantly set the differential profile pattern among infant groups is presented in Table 2. The relative contents of these metabolites were further compared by box-whisker plot analysis, showing that the differential levels of the metabolites identified by multivariate statistical analysis remained significantly changed in the univariate statistical analysis (Figure 6). Elevated contents of fucosylate oligosaccharides, N-acetyl compounds, formate, and citrate characterized HM infants, whereas the levels of pantothenic acid, choline, threonate, tartrate, cis-acotinate, and lactate were higher in FM groups. Furthermore, methanol, a metabolite linked to gut microflora,15 was found to contribute to the differentiation between HM and formula-fed group at visit T1.

DISCUSSION Human milk is a dynamic system whose composition is influenced by multiple factors among which maternal diet, duration of pregnancy, or stage of lactation.23 Reproducing exactly the composition of HM that makes it unique for each mother is not an easy task, if at all impossible. Nevertheless, the progress made in the formulation of milk for newborns are reducing more and more the gap between HM and FM. In this contest, investigating the alterations in the neonatal metabolism induced by early nutrition and, consequently, the corresponding metabolic pathways offers a great opportunity to improve the composition of FM to reach the same outcomes as their breast-fed peers. 13 ACS Paragon Plus Environment


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To the best of our knowledge, only a few studies in the literature has used metabolomics for elucidating the metabolic effects of postnatal nutrition. Martin et al.12 have analyzed the impact of breast-feeding and high- and low-protein formulas on the metabolism and growth of infants from overweight and obese mothers by collecting urine and fecal samples at 3, 6, and 12 months of life. Chiu et al.11 have investigated the age-related metabolic changes in early childhood starting from birth (6 months) to 4 years of age. Dessì et al.24 have explored the role of HM and FM feeding during the first week of life of newborns. In the present study, we have focused our attention on the postnatal nutrition effects during the first 4 months of life, comparing the influence of three types of milk diet (HM vs two different formulas) on the dynamic change of the urine metabolic profile. By using 1H NMR, several metabolites were identified to play a discriminant role in the class separation in relation to both lactation time and diet type. Choline metabolism. One of the most significant time-dependent metabolic perturbations in all groups of newborns was the change in the choline and betaine levels. In particular, choline contents increased between study entry and visit T1 and then no significant differences were observed among samples collected at visit T1 and T2. Differently, betaine levels increased significantly from T0 to T1, whereas it decreased during the second two months. Choline and betaine have a variety of biological effects and are closely connected metabolically.25 Choline is obtained from the diet or by sequential methylation of phosphatidylethanolamine. Being precursor of membrane and lipoprotein phospholipids and the neurotransmitter acetylcholine, it is important for the integrity of cell membranes, lipid metabolism, and cholinergic nerve function and plays a vital role in the human neonate for the developing brain. Betaine is the major metabolite of choline excreted in urine and can acts as an osmolyte or methyl group donator to homocysteine to form methionine and S-adenosylmethionine. The similar temporal trend of choline and betaine for the thee infant groups suggested a comparable choline metabolism. In particular, the changes in the betaine levels were consistent 14 ACS Paragon Plus Environment

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with the results of other experimental studies indicating in healthy infants a substantial excretion of betaine from birth26,27 and a progressive decrease from 2-3 months of age,27 likewise due to an imbalance of a choline dietary supply. It was interesting to note that at visits T1 and T2 the choline excretion was substantially higher in FM than HM newborns (P < 0.05) (Figure 6). It is well known from the literature that the choline content of human breast milk differs considerably from those of infant formulas.28,29 Therefore, our findings may reflect differences in the choline supply (in terms of amount and composition) between the types of milk under investigation, probably higher in formulas than human milk. Accordingly, the similar content of choline in both formulas (Table 1 and δ 3.20 in Figure 2) may explain the absence of significant differences between the urinary level of this metabolite in ENR and ST groups. These hypotheses seem to be also compatible with the higher levels of tartaric acid in FM than HM infant urines (P < 0.05) (Figure 6). Tartaric acid is not a mammalian metabolite, mainly destroyed in the intestinal tract by microbial action, and only partially (about 15-20% of consumed tartaric acid) secreted in the urine unchanged.30 The higher concentrations of both tartrate and choline in the urine of FM groups compared with HM infants could be linked to the supplementation of both formulas by choline tartrate. Taurine. The temporal dynamic change of taurine level was another common feature for the three groups of infants, the excretion at T1 being significantly higher than that at T0 and not significantly different from that at T2. Taurine is a β-amino acid, not incorporated into any proteins and mainly free in intracellular water. It has different vital biologic functions, including neuromodulation, cell membrane stabilization, antioxidation, detoxification, bile acid conjugation, and osmoregulation.31 In most mammals, part of taurine is derived from diet and part from the metabolism of cysteine. In human milk, taurine is the second most abundant free amino acid, while cow’s milk contains just small amounts of this compound. Absence from the diet of a conditionally essential nutrient does not produce immediate deficiency disease but, in the long term, can cause problems. For this reason, most formulas are enriched with taurine as a measure of prudence to 15 ACS Paragon Plus Environment


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provide improved nourishment with the same margin of safety for its newly identified physiologic functions as that found in human milk.31 In the present study, no significant differences were observed between the urinary taurine contents of HM and FM-fed newborns, thus suggesting that the supplementation of taurine in ENR and STD formulas is close enough to the neonatal nutritional requirement for this metabolite. TCA cycle. Citrate is an important intermediate in the tricarboxylic acid (TCA) cycle, the link of metabolic pathways responsible for the metabolism of carbohydrates, fats, and proteins. It is continuously being formed and broken down in tissue cells. Different factors can play an important role in regulating urinary citrate excretion. Principally, the level of citrate can be affected by diet and particularly by alteration in the acid-base balance.32 In our study, citrate urinary content increased over time in the order T0

Impact of Early Postnatal Nutrition on the NMR ... - ACS Publications

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