Skip to main content

Metabolomic profiling in blood from umbilical cords of low birth weight newborns



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.


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 < 10th percentile, n = 20) and control (control, birth weight between the 75th-90th percentiles, 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.


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.


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.


The concept of the fetal origin of adult disease suggests that early life conditions can “program” the fetus for a spectrum of adverse health outcomes in adulthood[1, 2]. Low birth weight babies (LBW), typically defined as birth weight below the 10th percentile according to gestational age[3], have an increased risk of rapid postnatal weight gain, later obesity, and diseases in adulthood such as type 2 diabetes, hypertension and ischemic heart disease[46]. Therefore, cardiovascular risk is determined not only by conventional risk factors of importance in adult life, but also by early life programming based on intrauterine fetal growth[7].

The mechanistic pathways underlying the relationship between fetal growth restriction and cardiovascular risk are poorly understood[8]. Although the cause of this relationship is unknown, several hypotheses have been proposed: involvement of the kidney (reduction of nephron number, activation of the renin-angiotensin system, an increase in renal sympathetic nerve activity), the neuroendocrine system (up-regulation of the hypothalamic-pituitary-adrenal axis, altered adaptation to stress), and early abnormalities in the vasculature tree[9, 10].

A previous study has demonstrated that endothelial cells from umbilical cord artery derived from individuals of LBW exhibit a different phenotype when compared to that from individuals of higher birth weight[11]. This observation could imply that it is possible to identify functional and/or structural differences at birth which can contribute to providing better information with respect to the risk of developing disease later in life. A further approach to studying differences between low and normal birth weight newborns is to assess their metabolomic profiles.

Metabolomics, the study of small-molecule metabolites, is a discipline that focuses on the measurement of the relative concentrations of endogenous small molecules in biofluids. This technology may be useful for understanding metabolic imbalances and for diagnosing human diseases. Recently, studies using metabolomics have been used to identify a discriminatory metabolite profile in a number of areas: preeclampsia in early pregnancy[12], diabetes[13], obesity[14],[15], risk for preterm delivery[16]. It has been suggested they predict the presence and severity of cardiovascular disease[1720]. There are no data about its use to identify a metabolite profile in umbilical cord from LBW children. The objective of the present study was to analyse the metabolomic profile at birth in newborns with LBW, and to compare it with those from normal birth weight children. Simultaneously, the metabolomic profiles of their mothers were analysed. The identification of deregulated metabolites would potentially give clues for better understanding the abnormalities associated with LBW.


Subjects and sample collection

Newborns were included if born at term (gestational age ≥37 weeks), after uncomplicated pregnancies ascertained according to the Ballard method[21], and by normal delivery or Cesarean section in the absence of perinatal illness at the Hospital General Universitario of Valencia, Spain. All the mothers were healthy and had no cardiovascular risk factors. Fifty newborns and their mothers of European origin were selected to participate in the study. Umbilical cord blood samples were obtained from the clamped umbilical cord immediately after delivery. The samples of their mothers were drawn at 2–4 h after delivery in non-fasting conditions, but with at least 8 hours having passed since the last meal and blood sampling. Two groups of newborns were created according to birth weight: lower than the 10th percentile (lower than 2.5 kg; LBW group, n = 20) or appropriate birth weight for gestational age (control group, birth weight between the 75th-90th percentiles, n = 30)[22]. Parents gave their consent for the study after they were given informed in writing of the objectives of the research project and of the samples that would be taken. The study was approved by the hospital’s review board and was carried out in accordance with the Declaration of Helsinki. Both the samples (plasma from umbilical cord of the newborns and plasma from peripheral veins from the mothers), as well as the collected data, were stored according to the directives dictated by the law of Biomedical Investigation of 2007 (Law 14/2007) and all applicable rules.

Metabolomic profile

Storage, preparation and 1 H Nuclear magnetic resonance spectroscopic analysis of blood plasma

Umbilical venous cord blood and maternal peripheral venous blood were collected in EDTA-tubes, centrifuged to yield plasma, stored at −80 °C and thawed before use. For Nuclear Magnetic Resonance (NMR) analysis, 500 μl of plasma were mixed with 50 μl of D2O (as a field lock). A total of 500 μl of the mixture of each sample was then individually transferred into a 5 mm high quality NMR tube. All 1 H NMR spectra were acquired using a standard one-dimensional pulse sequence with water suppression (Bruker Avance 600 spectrometer operating at 600.13 MHz with a 1 mm 1 H/13 C/15 N TXI probe). A total of 256 FIDs (free induction decay) were collected into 64 k data points with a spectral width of 14 ppm and a recycle delay (RD) of 1 s. The water signal was saturated with weak irradiation during the recycle delay. Before Fourier transformation, the free induction decay was multiplied by a 0.3 Hz exponential line broadening.

Spectral chemical shift referencing on the Alanine CH3 doublet signal at 1.475 ppm was performed in all spectra. Resonances in these spectral regions were assigned using the literature[23] and two-dimensional spectra were selected (2D NMR, especially for long chain metabolites). Spectral regions between 0.5 and 4.5 ppm and between 5.5 and 9.5 ppm were binned in segments of 0.01 ppm width (6 Hz) for multivariate analysis. Data binning in NMR spectra for multivariate analysis is a common pre-processing practice[24, 25], which, although it may decrease the effect of minor observations, allows for the detection of relevant regions, decreases the risk of model overfitting and artificially increases the signal to noise-ratio. Data binning reduces the impact of read noise on the processed data at the cost of a lower resolution. We used binning only for the pattern recognition. There are two benefits associated with the use of pattern recognition approaches in the binning of spectral data. First, the signal-to-noise ratio increases. Second, there is a reduction in the possibility of overfitting. Once the bucket with the relevant peaks in each spectra were identified, they were quantified. Therefore, the values reported are quantified peaks by spectral integration. The binned data was normalised to the total spectral area. Thus, the individual peak intensities were normalized to total metabolite content for better a comparison between samples. Spectral regions belonging to the EDTA resonances (2.52 to 2.57 ppm and 3.06 to3.17 ppm) were removed from the spectra for subsequent analysis. The available spectral databases and 2D NMR experiments were used to aid in structural identification of the relevant metabolites. All spectra were processed using Topspin 1.3 (Bruker Biospin GmbH, Germany) and transferred to MATLAB® (MathWorks Inc, 2006) using in-house scripts for data analysis. Signals belonging to selected metabolites were integrated and quantified using semi-automated in-house MATLAB peak-fitting routines. These fitting routines were based on Levenburg-Marquard optimization procedures. The target function for the optimization included experimental spectra measured for standard solutions of selected metabolites with complex multiplet patterns and theoretically generated Lorentzian-shape signals for those metabolites with simpler spectral patterns. One way analysis of variance (ANOVA) was used for the determination of statistical significance between group means of the corresponding integrals.

Multivariate analysis of nuclear magnetic resonance spectra

In the present study, binned spectral regions for blood plasma were mean-centered chemometric analyses. Relevant bins were identified in our chemometric models. The relevant spectral regions within these bins were identified and quantified by spectral integration. Chemometric and statistical analyses were performed using in-house MATLAB scripts and the PLS Toolbox (Eigenvector Research, Inc.). Principal Component Analysis (PCA) and Projection to Latent Structures for Discriminant Analysis (PLS-DA) were applied to NMR spectra data sets. PCA is able to find low dimensional embeddings of multivariate data in a way that optimally preserves the structure of the data. The PCA technique transforms the variables of a data set into a smaller number of new latent variables called principal components (PCs) which are uncorrelated to each other and account for decreasing proportions of the total variance of the original variables. Each new PC is a linear combination of the original variation so that a compact description of the variation within the data set is generated. Observations are assigned scores according to the variation measured by the principal component X, with those having similar scores clustering together. Where PCA proved inadequate to define clustering, a supervised approach was used.

PLS-DA is a classification technique that combines the properties of Partial Least Squares regression with the discrimination power of discriminant analysis[26]. The main advantage of PLS-DA models is that the main sources of variability in the data are modeled by the so-called latent variables (LVs) and, consequently, in their associated scores and loadings. This allows for the visualization and understanding of different patterns and relations in the data. PLS-DA is a supervised extension of PCA that is used to distinguish two or more classes by searching for variables (X matrix) that are correlated to class membership (Y matrix). In this approach, the axes are calculated to maximize class separation and can be used to examine separation that would otherwise be across three or more principal components[26]. In order to discriminate between samples associated with low weight and normal weight at birth, a PLS-DA model was devised. The PLS-DA model discriminating between controls and LBW was cross-validated by technical replication of the whole process on 25 random data splits. To validate the model, important quality parameters were calculated: R2X, R2Y, Q2 and RMSP. R2X and R2Y describe the variation in the X and Y variables, respectively. Q2 describes predictability. RMSP (Root mean squared error of prediction) gives a direct indication of the error in the predicted value.

Statistical analysis

Values were expressed as mean ± SD for each study group. The differences in the mean values of anthropometric parameters between the groups were assessed by analysis of variance (ANOVA) adjusted by sex. The normality of the data was verified using the Kolmogorov-Smirnov test. Choline, glutamine, alanine and glycogen in the control group do not have a normal distribution, and a Kruskal-Wallis test was used for these variables. An outlier analysis was performed, and those samples deviating from the mean by more than 2.5 times the standard deviation were excluded. A significant difference was considered present if p < 0.05. Pearson’s correlation coefficients were used to examine the relations between variables. Statistical analyses were performed using SPSS 15.0. (SPSS Inc, Chicago, Illinois, USA) and GraphPad Statmate 5.0 (GraphPad Software, La Jolla, California, USA) software graphs.


Clinical characteristics of mothers and newborns

The general characteristics of mothers and their newborns are shown in Table1. No differences were observed in maternal age, maternal BMI, pregnancy weight gain or type of delivery between the two groups. Although no significant differences were observed in heart rate and diastolic blood pressure between newborns groups (Table1), differences appeared in sex, height, head circumference and blood pressure. As expected, the LBW group had systolic values significantly lower than those for the control birth weight group[10]. All comparisons between newborns groups were adjusted by sex, and no changes were noted when comparing boys with girls.

Table 1 Anthropometric and clinical characteristics of mothers and newborns grouped by birth weight

Metabolomic profile in umbilical cord blood in low birth weight and control newborns

The standard 1D NMR spectrum is shown in Figure1A. PCA and PLS-DA scatter plot were performed on the pre-processed NMR spectra comparing plasma from LBW versus control newborns and were used to identify metabolite clusters. Although an unsupervised analysis by PCA does not show differences between groups, Figure1B, a supervised classification method (PLS-DA, 3 Latent Variables, see Additional file1: Figure S1) provides a differential global metabolic profile, Figure1C. The model had a R2X (cumulative) of 0.623, a R2Y (cumulative) of 0.871, a Q2 (cumulative) of 0.523 (a Q2 value superior to 0.5 is generally considered to be a good predictor) and a RMSP of 0.417 (medium predictive capacity). Spectral regions and peaks with the highest contribution to the loadings plot from this model were further quantified and analysed. Many metabolites may contribute to these loading plots and to the separation between classes. A total of thirty-four metabolites were tested in fifty-eight spectral regions, and the differences were evaluated by the T-Student test with the Kruskal-Wallis correction. However, only those that differed with p < 0.05 were taken into consideration.

Figure 1
figure 1

Umbilical cord blood profile. Superposed 1 H NMR spectra for the spectra of umbilical cord blood plasma (Panel A), Principal Component Analysis scores plot (PCA, panel B), and Projection to Latent Structures for Discriminant Analysis scores plot (PLS-DA, panel C) showing the global metabolic differences between low birth weight (open circles, blue spectra) and control birth weight (black triangles and red spectra) newborns. Although an unsupervised analysis by PCA does not show differences between groups, a supervised classification method (PLS-DA) provides a differential global metabolic profile. The cross-validated error percentage of the model for the classification of low weight at birth samples is 8%. Metabolites exhibiting largest differences between groups have been listed in Table2.

Of the 34 metabolic regions studied, 17 compounds were quantified according to the loadings of the multivariate analysis. Among the 17 metabolites, significant differences were observed for seven (proline, free choline, glutamine, alanine, glucose, phenylalanine and citrulline) between the LBW and the control group, while no differences were observed in the other seven (spectral region containing lipoproteins peaks, in carbohydrates and glycogen fragments, in ketone bodies like acetoacetate and hydroxybutyrate, in total lipids and in fatty acids both saturated and unsaturated) (data not shown).

Table2 shows median values ± SD for arbitrary units of intensity (AUIx10-3) from identified metabolites, and Figure2 provides the individual levels of 7 metabolites that display significant level differences between the two groups. In the LBW group, all metabolites have a normal distribution, and samples are more homogeneous than in the control group. In the control group, choline, glutamine and alanine do not have a normal distribution. An outlier analysis was carried out, and two subjects were excluded from the control group.

Table 2 List of identified metabolites in umbilical cord plasma and in maternal peripheral blood
Figure 2
figure 2

Metabolites distinguishing LBW and control newborns. Glucose, Proline, Glutamine, Choline, and Alanine levels were higher in control newborns. Significantly higher levels of citrulline and phenylalanine were detected in umbilical cord plasma from LBW newborns. Mean metabolite abundance is indicated by line. Each point represents a single newborn.

Five metabolites (proline, choline, glutamine, alanine and glucose) were reduced in the LBW group as compared to those for the controls. In the LBW group, proline values were 38.1% less (21.74 ± 9.80 AUI vs 13.00 ± 7.5 AUI; p-value = 0.002) while free choline was 31.4% less (3.76 ± 0.17 AU vs 2.58 ± 0.07 AU; p-value = 0.005). Glutamine (3.68 ± 0.17 vs 2.59 ± 0.07; p-value = 0.004), alanine (9.49 ± 4.1 vs 7.00 ± 2.2; p-value = 0.008) and glucose levels (2.15 ± 0.98 vs 1.59 ± 0.50; p-value = 0.023) were significantly lower in LBW newborns as well. In addition, LBW newborns exhibited a 15.7% increase in values of phenylalanine (1.07 ± 0.03 vs 1.27 ± 0.03 p-value = 0.018) and a 20.2% increase in values of citrulline (0.67 ± 0.02 vs 0.84 ± 0.002; p-value = 0.008). No difference in profiles between sexes was observed.

Maternal metabolic profile in venous peripheral blood

Nuclear Magnetic Resonance data from mothers were processed to give the initial PCA plot after a partial least squares-discriminate analysis (Figure3, PLS-DA, 3 Latent Variables, see Additional file1: Figure S2). The PLS-DA model had a R2X (cumulative) of 0.512, a R2Y (cumulative) of 0.611, a Q2 (cumulative) of 0.502 (a Q2 value superior to 0.5 is generally considered to be a good predictor) and a RMSP of 0.282 (low predictive capacity). The loadings plot showed 14 spectral regions with high relative weight belonging to 10 metabolites. Table2 shows median values ± SD for arbitrary units of intensity (AUIx10-3) from the identified metabolites. From a total of 10 metabolites, 6 were related to the amino acid group (proline, phenylalanine, alanine, glutamine, citrulline and free choline), 2 were related to the carbohydrate group (glucose and glycogen fragments), and 2 to the lipid group (spectral region containing large LDL + VLDL lipoproteins peaks and polyunsaturated fatty acids (PUFAs)). The results of the metabolomic profiling in maternal blood showed no significant differences between the two groups of mothers at either the PLS-DA score plot level and at the individual identified metabolite level (Table2, p-value M-C vs M-LBW).

Figure 3
figure 3

Spectra of maternal venous plasma. Principal Component Analysis (PCA, panel A) and Projection to Latent Structures for Discriminant Analysis (PLS-DA, panel B) scores plot for the spectra of maternal venous plasma showing the global metabolic differences between low birth weight (open circles) and control birth weight (black triangles ) newborns. Apparently, there are no global metabolic trends in the maternal blood associated with the weight of the newborns.

The feto-maternal ratio for carbohydrates (glucose and glycogen fragments), lipids (spectral region containing large LDL + VLDL lipoprotein peaks) and phenylalanine was comparable between the two groups (Table2, p-value Control vs LBW). In contrast, the feto-maternal ratio for proline, free choline, glutamine and alanine was significantly lower (p < 0.05) in the LBW group, whereas that for citrulline was significantly higher.

Relationship between metabolites and birth weight

Pearson correlations were performed for each of the 7 aforementioned metabolites versus birth weight. Out of the 7 metabolites, the analysis identified a significant positive correlation between birth weight and free choline, proline, and glutamine (free choline r = 0.50, p-value <0.01; proline r = 0.29, p-value < 0.05; glutamine r = 0.36, p-value < 0.01). A significant inverse association was found between citrulline and birth weight (r = −0.40, p-value <0.01) and between phenylalanine and birth weight (r = −0.34, p-value <0.05); i.e., phenylalanine and citrulline increase as birth weight decreases. No relationships were identified when correlating these metabolites with blood pressure. No significant associations were found between alanine and glucose with birth weight.


The present study is the first to utilize metabolomic analysis in umbilical cord blood samples from LBW children in order to identify metabolites that could be related to the abnormalities associated with the long-term development of the increased cardio-metabolic risk associated with LBW. The metabolomic profile of LBW newborns differed from that observed in a group of newborn controls. Higher values of phenylalanine and citrulline and lower levels of proline, choline, glutamine, alanine and glucose were observed. A significant relationship between some of these metabolites and birth weight was also present. Furthermore, the differences observed in the newborns were not observed in their mothers, all of whom were healthy and had similar weight gains during pregnancy. Although the significance of the observed metabolomic values is not well understood, interesting information can be derived from the present results since some of the observed data have been linked previously to the abnormal metabolic states associated with LBW.

Even though the impact of fetal metabolic programming on adult health is well documented, the underlying mechanisms are poorly understood. Animal models have been used extensively to investigate the mechanism by which the early-life environment induces persistent alterations in the metabolism of offspring[2730]. These studies support the hypothesis that nutritional imbalance during prenatal and early postnatal life can induce long term metabolic changes and increase susceptibility to metabolic diseases in later life[3133].

The absence of differences found for lipoproteins, carbohydrates, ketone bodies and fatty acids suggests that fatty acid oxidation and glucose metabolism remain unaltered. Changes, however, in the spectral region between 7.27 and 5.36 ppm, which has been assigned to glycogen fragments, may suggest alterations in carbohydrate storage. The large molecular size of glycogen makes it very difficult to observe its 1 H resonance signals. Longer NMR T2 relaxation times, which suggest smaller molecule sizes, produce the narrow signals observed for that spectral region. Perhaps some macromolecule degradation of glycogen into smaller molecular size fragments is the cause.

In the present study, the mismatch between the mother and the newborn in terms of the metabolomic profile is worth taking into consideration. While a different pattern was observed in the LBW newborns compared to controls, the mothers of both groups had a similar pattern. A trend towards reduced levels of amino acids in the umbilical cord was observed in LBW, something not observed in the mothers. The reason for the discrepancies is not well known, although suboptimal placental transport of amino acids could be involved. Impaired placental amino acid transport is often seen in association with IUGR, yet it is unclear whether the changes in amino acid transport are a cause or a consequence of IUGR[34, 35]. Experimental models in rats, however, have suggested a down-regulation of amino acid transporters as a cause rather than a consequence[36]. Consistent with impaired placental amino acid transport as a potential cause of IUGR in humans, umbilical cord blood (but not maternal blood) concentration of most essential amino acids is less in pregnancies with IUGR fetuses[34],[37]. This interesting finding should be addressed in future studies.

In contrast to the reduction of the majority of the amino acid detected are the increased values of phenylalanine, a neutral branched amino acid, in LBW newborn. Higher levels of amino acids, branched and/or aromatic, have been associated with insulin resistance[38, 39], and with the risk to develop type 2 diabetes in the Framingham offspring cohort[19]. The potential for amino acids to induce insulin resistance has been recognized for some time[40]. In a low-calorie environment, it is not surprising that large neutral amino acids would promote an anabolic state by inhibiting proteolysis and by directly stimulating protein synthesis[41]. That notwithstanding, the detection of significant spectral regions in the broad protein amide region (between 7.80 and 7.86 ppm) seems to support a potential difference in the anabolic rate between LBW and control newborns.

One of the greatest differences is the free choline, with low levels in the LBW group. The spectral region between 4.06 and 4.08 ppm, belonging to fragments of phosphoethanolamine[42], also shows statistically significant differences in the LBW group. During gestation there is a high demand for this essential nutrient[43]. Choline, via its metabolite betain, serves as a donor of methyl groups for the production of S-adenosylmethionine (SAM), a substrate of DNA and histone methyltransferases[44]. DNA-methylation is catalyzed by DNA methyltransferases that transfer methyl groups from SAM to cytosine located in CpG dinucleotides. Different studies have shown that the prenatal choline supply affects the expression of multiple genes whose expression is regulated via epigenetic marks[45],[46]. Maternal choline supply during pregnancy modifies fetal histone and DNA methylation in rat fetal liver and brain[47]. Conversely, animals fed with diets deficient in choline and in methionine have altered global and gene-specific DNA and histone methylation[48]. These studies have demonstrated that maternal choline alterations during pregnancy modify fetal histone and DNA methylation, suggesting that a concerted epigenomic mechanism contributes to the long-term developmental effects of varied choline intake in utero.

Finding that the lower levels of choline are associated with LBW is compatible with the epigenetic programming hypothesis. Interactions of nutrients with the epigenetic machinery lead to changes associated with the regulation of gene expression that underlies the developmental programming consequences in adult life[49]. The epigenetic programming of the fetus in utero is under the influence of the altered metabolic milieu of pregnancy and the types of nutrients available through the maternal diet[50]. Alterations in nutrients, above all those related to DNA methylation, can modify the levels of DNA methylation and fetal programming[51]. Disease propensity in LBW children can be epigenetically programmed by intrauterine exposures that have the capacity to modify fetal genes through alterations in DNA methylation[52]. Deregulation of the epigenome may explain changes that are maintained throughout adult life. Indeed, lower levels of choline can explain the link between altered epigenetic regulation and environmental influence.

The differences found in choline, proline, alanine, glutamine, glucose, citrulline and phenylalanine at birth show that metabolic alterations are present in LBW. Even though it will be interesting and useful to clarify the mechanisms by which these metabolic changes lead to LBW consequences later in life, they are difficult to establish at present. Metabolic profile may be useful, however, in detecting differential phenotypes and in identifying those populations with the greatest potential risk. The identification of these differences can facilitate the search for alterations in tracking studies. These metabolites could be promising candidates in the search for the molecular differences capable of explaining the increased risk that LBW subjects exhibit for developing cardio-metabolic diseases later in life. That notwithstanding, whether or not these abnormalities are the source of low birth weight is difficult to establish.

Our findings suggest that a substantial component of metabolic disease risk has a prenatal developmental basis. Finding discriminatory metabolites in umbilical cord blood plasma from LBW newborns with a higher risk to develop cardio-metabolic disease may lead to their being used as potential biomarkers for the early prediction of risk and with the concomitant clinical implications for follow-up.


Both the presence of the differential metabolic profile, as well as the relationship of these metabolites with birth weight demonstrate that metabolic alterations are present and detectable at birth in LBW subjects. Beginning at birth, metabolic abnormalities provide information on the impact of intrauterine life. Whether these early disturbances in metabolism contribute to the development of cardio-metabolic diseases in these subjects later in life needs to be assessed in prospective studies.


  1. Barker DJ, Gluckman PD, Godfrey KM, Harding JE, Owens JA, Robinson JS: Fetal nutrition and cardiovascular disease in adult life. Lancet. 1993, 341: 938-941. 10.1016/0140-6736(93)91224-A.

    Article  CAS  PubMed  Google Scholar 

  2. Burdge GC, Lillycrop KA: Nutrition, epigenetics, and developmental plasticity: implications for understanding human disease. Annu Rev Nutr. 2010, 30: 315-339. 10.1146/annurev.nutr.012809.104751.

    Article  CAS  PubMed  Google Scholar 

  3. Lubchenco L, Hansman C, Dressler M, Boyd E: Intrauterine growth as estimated from liveborn birth-weight data at 24 to 42 weeks of gestation. Pediatrics. 1963, 32: 793-800.

    CAS  PubMed  Google Scholar 

  4. Godfrey KM, Barker DJ: Fetal nutrition and adult disease. Am J Clin Nutr. 2000, 71 (Suppl 5): 1344S-1352S.

    CAS  PubMed  Google Scholar 

  5. Hales CN, Ozanne SE: The dangerous road of catch-up growth. J Physiol. 2003, 547 (Pt 1): 5-10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gluckman PD, Hanson MA, Cooper C, Thornburg KL: Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008, 359: 61-73. 10.1056/NEJMra0708473.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Singhal A, Lucas A: Early origins of cardiovascular disease: is there a unifying hypothesis?. Lancet. 2004, 363: 1642-1645. 10.1016/S0140-6736(04)16210-7.

    Article  PubMed  Google Scholar 

  8. Crispi F, Bijnens B, Figueras F, Bartrons J, Eixarch E, Le Noble F, Ahmed A, Gratacós E: Fetal growth restriction results in remodeled and less efficient hearts in children. Circulation. 2010, 121: 2427-2436. 10.1161/CIRCULATIONAHA.110.937995.

    Article  PubMed  Google Scholar 

  9. Ligi I, Grandvuillemin I, Andres V, Dignat-George F, Simeoni U: Low birth weight infants and the developmental programming of hypertension: a focus on vascular factors. Semin Perinatol. 2010, 34: 188-192. 10.1053/j.semperi.2010.02.002.

    Article  PubMed  Google Scholar 

  10. Lurbe E, Garcia-Vicent C, Torro I, Fayos JL, Aguilar F, de Llano JM, Fuertes G, Redón J: First-year blood pressure increase steepest in low birthweight newborns. J Hypertens. 2007, 25: 81-86. 10.1097/HJH.0b013e32801040ec.

    Article  CAS  PubMed  Google Scholar 

  11. de Martín LJJ, Fuertes G, Torró I, García Vicent C, Fayos JL, Lurbe E: Birth weight and characteristics of endothelial and smooth muscle cell cultures from human umbilical cord vessels. J Transl Med. 2009, 7: 30-10.1186/1479-5876-7-30.

    Article  Google Scholar 

  12. Kenny LC, Broadhurst DI, Dunn W, Brown M, North RA, McCowan L, Roberts C, Cooper GJ, Kell DB, Baker PN: Screening for Pregnancy Endpoints Consortium. Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension. 2010, 56: 741-749. 10.1161/HYPERTENSIONAHA.110.157297.

    Article  CAS  PubMed  Google Scholar 

  13. Suhre K, Meisinger C, Döring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabé de Angelis M, Wichmann HE, Kronenberg F, AdamsKi J, LLLiq T: Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010, 5: e13953-10.1371/journal.pone.0013953.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kim JY, Park JY, Kim OY, Ham BM, Kim HJ, Kwon DY, Jang Y, Lee JH: Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J Proteome Res. 2010, 9: 4368-4375. 10.1021/pr100101p.

    Article  CAS  PubMed  Google Scholar 

  15. Mutch DM, Fuhrmann JC, Rein D, Wiemer JC, Bouillot JL, Piotou C, Clément K: Metabolite profiling identifies candidate markers reflecting the clinical adaptations associated with Roux-en-Y gastric bypass surgery. PLoS One. 2009, 4: e7905-10.1371/journal.pone.0007905.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Romero R, Mazaki-Tovi S, Vaisbuch E, Kusanovic JP, Chaiworapongsa T, Gomez R, Nien JK, Yoon BH, Mazor M, Luo J, Banks D, Ryals J, Beecher C: Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery. J Matern Fetal Neonatal Med. 2010, 23: 1344-1359. 10.3109/14767058.2010.482618.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sabatine MS, Liu E, Morrow DA, Heller E, McCarroll R, Wiegand R, Berriz GF, Roth FP, Gerszten RE: Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation. 2005, 112: 3868-3875. 10.1161/CIRCULATIONAHA.105.569137.

    Article  CAS  PubMed  Google Scholar 

  18. Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK, Bethell HW, Clarke S, Schofield PM, McKilligin E, Mosedale DE, Grainger DJ: Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1 H-NMR-based metabonomics. Nat Med. 2002, 8: 1439-1444. 10.1038/nm1202-802.

    Article  CAS  PubMed  Google Scholar 

  19. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe MC, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE: Metabolite profiles and the risk of developing diabetes. Nat Med. 2011, 17: 448-453. 10.1038/nm.2307.

    Article  PubMed  PubMed Central  Google Scholar 

  20. De Meyer T, Sinnaeve D, Van Gasse B, Tsiporkova E, Rietzschel ER, De Buyzere ML, Gillebert TC, Bekaert S, Martins JC, Van Criekinge W: NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem. 2008, 80: 3783-3790. 10.1021/ac7025964.

    Article  CAS  PubMed  Google Scholar 

  21. Ballard JL, Novak KK, Driver M: A simplified score for assessment of fetal maturation of newly born infants. J Pediatr. 1979, 95: 769-774. 10.1016/S0022-3476(79)80734-9.

    Article  CAS  PubMed  Google Scholar 

  22. Battaglia FC, Lubchenco LO: A practical classification of newborn infants by weight and gestational age. J Pediatr. 1967, 71: 159-163. 10.1016/S0022-3476(67)80066-0.

    Article  CAS  PubMed  Google Scholar 

  23. Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC: 750 MHz 1 H and 1 H-13C NMR spectroscopy of human blood plasma. Anal Chem. 1995, 67: 793-811. 10.1021/ac00101a004.

    Article  CAS  PubMed  Google Scholar 

  24. Viant MR, Lyeth BG, Miller MG, Berman RF: An NMR metabolomic investigation of early metabolic disturbances following traumatic brain injury in a mammalian model. NMR Biomed. 2005, 18: 507-516. 10.1002/nbm.980.

    Article  CAS  PubMed  Google Scholar 

  25. Bertini I, Calabrò A, De Carli V, Luchinat C, Nepi S, Porfirio B, Renzi D, Saccenti E, Tenori L: The metabonomic signature of celiac disease. J Proteome Res. 2009, 8: 170-177. 10.1021/pr800548z.

    Article  CAS  PubMed  Google Scholar 

  26. Trygg J, Holmes E, Lundstedt T: Chemometrics in metabonomics. J Proteome Res. 2007, 6: 469-479. 10.1021/pr060594q.

    Article  CAS  PubMed  Google Scholar 

  27. Cleal JK, Poore KR, Newman JP, Noakes DE, Hanson MA, Green LR: The effect of maternal undernutrition in early gestation on gestation length and fetal and postnatal growth in sheep. Pediatr Res. 2007, 62: 422-427. 10.1203/PDR.0b013e31813cbe60.

    Article  PubMed  Google Scholar 

  28. Nissen PM, Nebel C, Oksbjerg N, Bertram HC: Metabolomics reveals relationship between plasma inositols and birth weight: possible markers for fetal programming of type 2 diabetes. J Biomed Biotechnol. 2011, 2011: 378268-

    Article  PubMed  PubMed Central  Google Scholar 

  29. García AP, Palou M, Sánchez J, Priego T, Palou A, Picó C: Moderate caloric restriction during gestation in rats alters adipose tissue sympathetic innervation and later adiposity in offspring. PLoS One. 2011, 6: e17313-10.1371/journal.pone.0017313.

    Article  PubMed  PubMed Central  Google Scholar 

  30. He Q, Ren P, Kong X, Xu W, Tang H, Yin Y, Wang Y: Intrauterine growth restriction alters the metabonome of the serum and jejunum in piglets. Mol Biosyst. 2011, 7: 2147-2155. 10.1039/c1mb05024a.

    Article  CAS  PubMed  Google Scholar 

  31. Godfrey K, Robinson S, Barker DJ, Osmond C, Cox V: Maternal nutrition in early and late pregnancy in relation to placental and fetal growth. BMJ. 1996, 312: 410-414. 10.1136/bmj.312.7028.410.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kwon H, Ford SP, Bazer FW, Spencer TE, Nathanielsz PW, Nijland MJ, Hess BW, Wu G: Maternal nutrient restriction reduces concentrations of amino acids and polyamines in ovine maternal and fetal plasma and fetal fluids. Biol Reprod. 2004, 71: 901-908. 10.1095/biolreprod.104.029645.

    Article  CAS  PubMed  Google Scholar 

  33. Tamashiro KL, Moran TH: Perinatal environment and its influences on metabolic programming of offspring. Physiol Behav. 2010, 100: 560-566. 10.1016/j.physbeh.2010.04.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cetin I, Ronzoni S, Marconi AM, Perugino G, Corbetta C, Battaglia FC, Pardi G: Maternal concentrations and fetal-maternal concentration differences of plasma amino acids in normal and intrauterine growth-restricted pregnancies. Am J Obstet Gynecol. 1996, 174: 1575-1583. 10.1016/S0002-9378(96)70609-9.

    Article  CAS  PubMed  Google Scholar 

  35. Cleal JK, Lewis RM: The mechanisms and regulation of placental amino acid transport to the human foetus. J Neuroendocrinol. 2008, 20: 419-426. 10.1111/j.1365-2826.2008.01662.x.

    Article  CAS  PubMed  Google Scholar 

  36. Jansson N, Pettersson J, Haafiz A, Ericsson A, Palmberg I, Tranberg M, Ganapathy V, Powell TL, Jansson T: Down-regulation of placental transport of amino acids precedes the development of intrauterine growth restriction in rats fed a low protein diet. J Physiol. 2006, 576: 935-946.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Bajoria R, Sooranna SR, Ward S, Hancock M: Placenta as a link between amino acids, insulin-IGF axis, and low birth weight: evidence from twin studies. J Clin Endocrinol Metab. 2002, 87: 308-315. 10.1210/jc.87.1.308.

    Article  CAS  PubMed  Google Scholar 

  38. Huffman KM, Shah SH, Stevens RD, Bain JR, Muehlbauer M, Slentz CA, Tanner CJ, Kuchibhatla M, Houmard JA, Newgard CB, Kraus WE: Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care. 2009, 32: 1678-1683. 10.2337/dc08-2075.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Llkayeva O, Wenner BR, Yancy WS, Heisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP: A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009, 9: 311-326. 10.1016/j.cmet.2009.02.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Felig P, Marliss E, Cahill GF: Plasma amino acid levels and insulin secretion in obesity. N Engl J Med. 1969, 281: 811-816. 10.1056/NEJM196910092811503.

    Article  CAS  PubMed  Google Scholar 

  41. Krebs M, Krssak M, Bernroider E, Anderwald C, Brehm A, Meyerspeer M, Nowotny P, Roth E, Waldhäusl W, Roden M: Mechanism of amino acid-induced skeletal muscle insulin resistance in humans. Diabetes. 2002, 51: 599-605. 10.2337/diabetes.51.3.599.

    Article  CAS  PubMed  Google Scholar 

  42. Monleón D, Morales JM, Gonzalez-Segura A, Gonzalez-Darder JM, Gil-Benso R, Cerdá-Nicolás M, López-Ginés C: Metabolic aggressiveness in benign meningiomas with chromosomal instabilities. Cancer Res. 2010, 70: 8426-8434. 10.1158/0008-5472.CAN-10-1498.

    Article  PubMed  Google Scholar 

  43. Zeisel SH: Choline: critical role during fetal development and dietary requirements in adults. Annu Rev Nutr. 2006, 26: 229-250. 10.1146/annurev.nutr.26.061505.111156.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mehedint MG, Niculescu MD, Craciunescu CN, Zeisel SH: Choline deficiency alters global histone methylation and epigenetic marking at the Re1 site of the calbindin 1 gene. FASEB J. 2010, 24: 184-195. 10.1096/fj.09-140145.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wolff GL, Kodell RL, Moore SR, Cooney CA: Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J. 1998, 11: 949-957.

    Google Scholar 

  46. Niculescu MD, Zeisel SH: Diet, methyl donors and DNA methylation: interactions between dietary folate, methionine and choline. J Nutr. 2002, 132 (Suppl 8): 2333S-2335S.

    CAS  PubMed  Google Scholar 

  47. Davison JM, Mellott TJ, Kovacheva VP, Blusztajn JK: Gestational choline supply regulates methylation of histone H3, expression of histone methyltransferases G9a (Kmt1c) and Suv39h1 (Kmt1a), and DNA methylation of their genes in rat fetal liver and brain. J Biol Chem. 2009, 284: 1982-1989.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Niculescu MD, Craciunescu CN, Zeisel SH: Dietary choline deficiency alters global and gene-specific DNA methylation in the developing hippocampus of mouse fetal brains. FASEB J. 2006, 20: 43-49. 10.1096/fj.05-4707com.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lillycrop KA, Slater-Jefferies JL, Hanson MA, Godfrey KM, Jackson AA, Burdge GC: Induction of altered epigenetic regulation of the hepatic glucocorticoid receptor in the offspring of rats fed a protein-restricted diet during pregnancy suggests that reduced DNA methyltransferase-1 expression is involved in impaired DNA methylation and changes in histone modifications. Br J Nutr. 2007, 97: 1064-1073. 10.1017/S000711450769196X.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Burdge GC, Hanson MA, Slater-Jefferies JL, Lillycrop KA: Epigenetic regulation of transcription: a mechanism for inducing variations in phenotype (fetal programming) by differences in nutrition during early life?. Br J Nutr. 2007, 97: 1036-1046. 10.1017/S0007114507682920.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Thompson RF, Fazzari MJ, Niu H, Barzilai N, Simmons RA, Greally JM: Experimental intrauterine growth restriction induces alterations in DNA methylation and gene expression in pancreatic islets of rats. J Biol Chem. 2010, 285: 15111-15118. 10.1074/jbc.M109.095133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Einstein F, Thompson RF, Bhagat TD, Fazzari MJ, Verma A, Barzilai N, Greally JM: Cytosine methylation dysregulation in neonates following intrauterine growth restriction. PLoS One. 2010, 5: e8887-10.1371/journal.pone.0008887.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


This work was supported by the Instituto de Salud Carlos III (Spain) Grant 543 PI08/1277), By the Minsiterio de Ciencia e Innovacion off Spain (Grant SAF2011-23029), by the Conselleria de Sanidad (Comunitat Valenciana. Grant 544 GE010/15), and by CIBEROBN and CIBERDEM (both are initiatives of the 545 Instituto de Salud Carlos III). CI is the recipient of a contract from the Sara 546 Borrell program (CD06/0111) and CIBEROBN. The authors would like to thank 547 Francisco Ponce Zanón, of the Laboratory of the Pediatric Cardiovascular Risk 548 Unit, Pediatric Department, for his technical assistance.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Empar Lurbe.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

EL and CI conceived the study, designed it and wrote the manuscript. CGV informed the parents of the objectives of the research project, took the anthropometric measurements at birth and obtained the UCB samples. FJC participated in the experimental design and biochemical interpretations. DM and JMM performed the NMR measurements, NMR data analysis and biochemical interpretation of metabolomic data. All authors have read and approved the final manuscript.

Electronic supplementary material


Additional file 1:Figure S1. Loadings plot of the PLS-DA model for discrimination between spectra of umbilical cord blood plasma from low vs normal weight babies at birth. Figure S2. Loadings plot of the PLS-DA model for discrimination between spectra of blood plasma from mothers of low vs normal weight babies at birth. (DOC 192 KB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Ivorra, C., García-Vicent, C., Chaves, F.J. et al. Metabolomic profiling in blood from umbilical cords of low birth weight newborns. J Transl Med 10, 142 (2012).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: